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Final Report

Control System Research & Development Project

Microgrid optimized resource dispatch for public-purpose resiliencyand sustainability

U.S. DOE Award #: DE-OE0000734

Acknowledgments

Project Team:

Michael Burr John Camilleri David Lubkeman Jeff Hager

Michael Zimmer, Peter Douglass

Flynn O’Brien, Michael Sinclair, Daniel Evans, Paul Gregory, Steve Pullins, John Westerman

Qian Long, Yuhua Du, Ning Lu, Xiangqi Zhu, Jian Lu, Srdjan Lukic

Asif Anwar, Vincent Aulagnier, Slavko Vasilic, Keith Robertson, Evlyn Mark

Principal Investigator and Project Management Officer: Michael Burr

Primary Authors: Michael Burr, John Camilleri, David Lubkeman, Qian Long, Yuhua Du

Utility Partner: Pepco Holdings Inc. DOE Prime Contractor (DE-OE0000734): Burr Energy LLC dba Microgrid Institute

Report Permalink: http://www.microgridinstitute.org/olneymicrogrid.html

U.S. DOE Office of Scientific and Technical Information (OSTI) Citation:

Burr, Michael, Camilleri, John, Lubkeman, David, Long, Qian, and Du, Yuhua. Microgrid

optimized resource dispatch for public-purpose resiliency and sustainability. United States: N.

p., 2017. Web. https://www.osti.gov/biblio/1415998

U.S. DOE Acknowledgment: “This material is based upon work supported by the Department of Energy under Award Number(s) DEOE0000734.”

Disclaimer: “This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.”

Report Date: September 30, 2017

http://www.microgridinstitute.org/olneymicrogrid.html

https://www.osti.gov/biblio/1415998

Microgrid optimized resource dispatch for

public-purpose resiliency and sustainability

Final Report - Olney Town Center Microgrid Control System R&D Project

Table of Contents

Volume 1: Executive Summary and Feasibility Assessment

Volume 2: Technical Design

Appendix A: Specific Circuit Locations for Microgrid Resources

Appendix B: Resource Characteristics

Appendix C: Load Analysis Data Summary

Appendix D: References

Appendix E: Ritchie Station Design Summary

Appendix F: Black Start Procedure

Volume 3: Test Results & Analysis

Volume 4: Regulatory and Financial Structure

Appendix A: Maryland PSC BGE Microgrid Order – Implications for Maryland Public-Purpose

Microgrids

Appendix B: Regulatory and Financial Structure Options Matrix

Appendix C: County Priorities for Siting Public-Purpose Microgrid Pilot Projects

Annexes:

A. Statement of Project Objectives (PDF)

B. Olney Microgrid Oneline Diagram (AutoCad)

C. Olney Microgrid Oneline Diagram (PDF)

D. Olney Microgrid Test Plan (PDF)

E. Olney Microgrid Testing Guide (PDF)

F. Olney Microgrid Load Forecasting Analysis (PDF)

G. Olney Microgrid Test-Result Metrics (XLS)

H. Olney Microgrid Opal-RT Simulation Model (Simulink)

I. Olney Microgrid Opal-RT High-Resolution Time-Series Data (Matlab)

J. Olney Microgrid GreenBus High-Resolution Time-Series Data (CSV)

K. Olney Microgrid Short-Circuit Analysis (PDF)

Volume 1 – Executive Summary ………. Olney Town Center Microgrid Project – Final Report 1


public-purpose resiliency and sustainability


Vol. 1: Executive Summary and Feasibility Assessment

Sections:

Introduction .................................................................................................................................................. 1

A. Project Scope and Team ....................................................................................................................... 2

B. Microgrid Control System Summary ..................................................................................................... 3

C. Summary of Microgrid Test Procedures ............................................................................................... 6

D. Summary of Project Outcomes ............................................................................................................. 7

E. Lessons Learned .................................................................................................................................... 9

F. Feasibility Assessment ........................................................................................................................ 11

Introduction

Communities in Atlantic coastal regions have in recent years sought to improve the resiliency of their

critical infrastructure and public services, especially to protect against hurricanes and other events

capable of causing widespread damage and disruption. As the backbone of any community’s critical

functions, the electricity distribution system requires high resiliency in order to maintain local energy

delivery services. Against this backdrop, the Project sought to develop a resilient energy microgrid

control system capable of integrating distributed renewable energy resources, natural gas CHP units,

energy storage, and demand-side management technologies in near-real-time optimization schemes for

the community of Olney, Md.

The Montgomery County Planning Board in 2005 established the Olney Town Center area as a “civic

center/town commons,” in part because it serves as a key point of interaction in the community – but

also because it contains numerous vital community assets. With a total peak electrical load of about 8

MW (including Montgomery General Hospital with a 2.4 MW peak), the Project area is a business and

essential services area, directly serving a suburban population of more than 33,000 residents. It contains

a hospital, police station, two fire stations, two schools, grocery stores, and gas stations, and the

community’s water tower, among other things. Moreover, the location stands at the crossroads of two

state highways that represent major regional arteries for commerce and public safety in Montgomery

County.

These characteristics made the Project area an appropriate setting for considering microgrid

deployment. It presented a model of a typical Maryland suburban community, with geographic

dispersion of vital assets over a sizeable area, and a combination of overhead distribution lines and


underground cables serving those critical loads. Such a representative model helped to ensure the

solutions developed and the scenarios tested would be readily applicable to other communities in the

state and the region. Further, the Project’s outcomes and lessons provide insights to guide community

microgrid design and development in many locations.

To achieve Project objectives – including those established by the U.S. Department of Energy (DOE)

National Energy Technology Laboratory1 – the Project team researched, developed, and tested in

simulation a set of microgrid controls capable of maintaining electricity supplies for critical community

loads in the event of a regional utility outage lasting many days or even weeks.

Testing and analysis showed that the microgrid would be capable of maintaining electricity supply to

critical loads essentially indefinitely in most outage scenarios, while also substantially improving overall

reliability for microgrid customers. With targeted improvements in local utility distribution

infrastructure, test analysis showed that the microgrid would be capable of reducing annual electricity

outages for critical loads by 98%. Further, to help achieve environmental and efficiency policy goals

established by both the State of Maryland and the federal government, the team designed the system to

reduce the annual carbon footprint of served loads by 20%, and to improve system energy efficiency for

those loads by at least 20%. Testing showed that, as designed, the system is capable of meeting these

performance requirements, with potential for further improvements through more effective thermal

energy utilization.

This Final Report, comprised of four volumes and 11 annexes, presents the results of these project

efforts, including feasibility assessment (section F) and guidance for decision-makers considering

prospective deployment of public-purpose microgrid systems in Maryland communities.

A. Project Scope and Team

The Olney Town Center Microgrid Project from November 1, 2014 through May 31, 2017 focused on

performing the following six tasks in two phases, as defined under the Project Statement of Project

Objectives (SOPO) (Annex A):

PHASE I:

Task 1: Project Management & Planning

Task 2: Advanced Microgrid Controller Research and Development

Task 3: Plan and Design Microgrid System

Task 4: Prepare Control System Tests

PHASE II:

Task 5: Execute Control System Tests

Task 6: Analyze and Report Results

Phase I Task work began on Nov. 1, 2014, and was substantially completed by July 31, 2016. Phase II

Testing began in August 2016 was substantially completed by May 31, 2016. Task 6 and other

administrative activities continued until August 31, 2017.

1 DOE Funding Opportunity Announcement 0000997 (2014)


Tasks under this Project were performed by a multidisciplinary team of professionals, coordinated and

managed by the Project Prime Contractor, Burr Energy LLC dba Microgrid Institute. Task 2 and Task 3

efforts were performed primarily by Green Energy Corp., with inputs from Pepco Holdings, North

Carolina State University (NCSU) FREEDM Systems Center, Schneider Electric, and Microgrid Institute.

Task 4 and Task 5 work was performed primarily by Green Energy Corp. and NCSU FREEDM. In Task 6,

the Final Report was prepared primarily by Microgrid Institute, with inputs and analysis from Green

Energy Corp., NCSU FREEDM, and Schneider Electric.

B. Microgrid Control System Summary

In addition to the objectives established in the SOPO, the microgrid control system was developed to

accommodate key design factors affecting prospective microgrid deployment in the Project area (See

Figure B-1). Specifically:

- Dozens of community energy loads, including critical facilities and services, distributed over an urban/suburban area of approximately 1 square mile;

- Buried cable connecting many but not all parts of the Project area, with several overhead lines presenting vulnerabilities to physical damage; and

- Community policy priorities encouraging investments in renewable DERs and efficiency improvements.

As explained in Volume 2, these design factors required a control system capable of integrating DERs

and maintaining resilient energy supplies in multiple separately islanding zones, while also operating

system resources on a portfolio basis to optimize economic and environmental performance.

Figure B-1 illustrates the six distribution areas to be served as an islanding microgrid. This design

configuration was developed after the Project team reviewed and analyzed existing distribution system

infrastructure to identify optimal zonal configurations to serve critical community facilities (Volume 2,

Section G). Implementation of the design would require reconfiguration of the existing distribution

system to enable independently islanding microgrid zones to maintain critical loads essentially

indefinitely. Power supplies would be provided by a combination of solar PV, gas-fired CHP, and battery

energy storage systems of multiple sizes and configurations to suit various location constraints.


FIG. B-1: OLNEY TOWN CENTER MICROGRID OVERVIEW

Given these design requirements, the microgrid control system was developed on the Green Energy

Corp. GreenBus platform. The microgrid controller applied GreenBus support for interoperability and

application development in near real-time environments; low-latency messaging and secure transport to

communicate with clients on field devices and the data center; and data automation and control

features typical in the utility industry, and adaptable to many user scenarios. The GreenBus platform

also enabled development of a microgrid control architecture that includes multiple nodes or zones

capable of autonomous operation (See Figure B-2). This multi-zone microgrid control architecture – with

master microgrid control at the fleet and zone level, interfacing with DER device controllers –

accommodates the characteristics of a community with dispersed critical services and mixed overhead

and underground distribution systems. To accomplish this multi-zone microgrid control, the system

applied DNP3.0 level 2 compliant slave-and-master functionality for data acquisition and control, with a

traditional SCADA (supervisory control and data acquisition) system built on a service oriented

architecture. These capabilities enabled the microgrid control system to monitor and control devices

over a common bus architecture, regardless of the device type.


FIG. B-2: MULTI-ZONE MICROGRID CONTROL ARCHITECTURE

The control system was designed to operate and communicate with device controllers as well as

inverters and point-of-coupling systems. The microgrid model included more than 6.1 MW of generation

capacity (see Figure B-3), in the forms of natural gas-fired gensets, CHP units, photovoltaic (PV) systems,

and battery energy storage systems (ESS), located in the six zones shown in Figure B-1. The model also

provided for electricity imports during grid-connection operations.

FIG. B-3: MICROGRID DER RESOURCE SUMMARY

Type Units Capacity

Natural Gas Genset 4 150

Natural Gas CHP 9 3,451

Battery Energy Storage 11 728

Dispatchable Subtotal 21 4,329

PV 14 1,828

The microgrid model included inverters for DC sources based on a prototype advanced bidirectional

smart inverter provided by Schneider Electric for hardware-in-loop (HIL) testing and modeling at NCSU

FREEDM, and also four absorption chiller units, totaling 290 tons of capacity, to simulate CHP thermal

energy utilization.2 The model also enabled load-modulation capabilities through control of building

energy management and demand response systems.

2 In prospective Phase 3 deployment, CHP units would be located and sized to accommodate specific customer thermal loads. Because only aggregated customer data was available for load modeling, the Project team adopted a conservative thermal utilization profile with absorption chillers for cooling loads only, modeled to represent typical commercial and institutional cooling load profiles.


C. Summary of Microgrid Test Procedures

The Project team developed a simulation model of microgrid Zone 1 for real-time testing at NCSU

FREEDM. Figure C-1 illustrates the test environment setup. The FREEDM Microgrid Testbed was built on

the Opal-RT simulation platform. During real-time simulation, the testbed supported a DNP3 outstation

and communicated with the microgrid master controller (MMC). This allowed the testbed to

communicate system state information to the controller for monitoring and control purposes, and to

receive control commands generated by the MMC.

FIG. C-1: NCSU FREEDM MICROGRID TEST ENVIRONMENT DIAGRAM

In the testing platform, the network operations center (NOC) was hosted on an Amazon EC2 service. The

Zone 1 controller was deployed on a Lanner hardened PC, located in the NCSU FREEDM lab, and the

remaining zone controllers were hosted in the cloud and communicated with the NOC. The ODROID

units served as protocol gateways between the zone controllers and the Opal-RT simulator, translating

DNP3 to a standard messaging protocol for secure transport and communication. A local human-

machine interface (HMI) in the FREEDM lab gave the test team access the Zone 1 local controller, and an

HMI interface on the hosted system provided access to the NOC HMI to view all the microgrid zones and

their status, and enabled control over all microgrid controllers.

Two SEL relays were connected to the real-time simulator through the Opal-RT I/O card. One relay

represented the substation relaying and the other represented the PCC relaying. This HIL interface was


built to create a realistic simulation environment to demonstrate how a real relay unit would react to

the microgrid controller. The relays could be tripped locally or by the microgrid control system.

The test team performed a series of functional tests to demonstrate operational capabilities and gather

metrics on system performance (See Volume 3). The process simulated various day types, loading

conditions, and fault scenarios for a variety of use cases in the following categories:

- Energy Management – Grid-Connected

- Ancillary Services – Demand Response

- Energy Management – Islanded

- Intentional Islanding – Stability

- Unintentional Islanding – Stability

- Island-to-Grid Transition

- Black Start

- Cybersecurity

Post-test processing enabled analysis of efficiency, emissions, resilience, reliability, security, and

technical viability.

D. Summary of Project Outcomes

In addition to providing enhanced resilience against long-duration utility outages, the microgrid

controller and system design were shown capable of substantially improving performance on emissions,

efficiency, and outage metrics. Together with targeted improvements in local utility distribution

infrastructure, the microgrid would be capable of reducing annual electricity outages for critical loads by

98%, and the system would reduce the Town Center’s carbon footprint and improve its energy efficiency

by at least 20% (See Figure D-1).

Project design, testing, and analysis efforts yielded the following outcomes vis-à-vis the major project

objectives established in the SOPO:

1. Reliability: The SOPO called for reducing outage time of critical loads by at least 98%. The metric selected to measure this performance was System Average Interruption Duration Index (SAIDI).3 Baseline SAIDI for the test area was estimated at 175.3 minutes per customer per year. Improving that SAIDI performance by 98% for critical customers would require achieving a SAIDI measure of 3.5 min./cust./yr. The Project team’s analysis showed that without converting line segments to underground cables, the microgrid system alone would improve SAIDI performance for critical customers to 9.1 min./cust./yr. With targeted improvements in local distribution infrastructure (e.g., converting to underground cables overhead line segments totaling an estimated 3,800 feet), the revised microgrid would be expected to improve SAIDI to 3.3 min./year for critical loads.

3 The SAIDI metric was designed to measure overall utility transmission and distribution system reliability and not individual customer uptime. As discussed in Volume 2, SAIDI statistics specifically omit, for example, outages caused by weather events. Accordingly, the Project team considers baseline SAIDI statistics to be conservative estimates of service interruptions actually experienced by customers.


2. Emissions: The SOPO called for reducing the greenhouse gas footprint of loads served by the microgrid by at least 20%. Microgrid testing and analysis showed that the microgrid design would over-perform on this goal, reducing the area’s CO2 footprint by nearly 46%.

3. Efficiency: The SOPO called for increasing the system energy efficiency of served loads by 20% or more. Microgrid testing and analysis showed that the microgrid design would increase efficiency by 20% with conservative CHP loading and thermal utilization. Greater system efficiency would be attainable under scenarios with more effective thermal utilization than was assumed in the model.

4. Economics: As designed, the system would be capable of achieving the SOPO goal of meeting community-defined resiliency requirement at a cost lower than the baseline solution of uninterruptible power supply (UPS) units, backup generators, and automatic transfer switches. Specifically, over its lifetime the microgrid would be expected to serve electricity loads at an estimated levelized cost of $0.059/kWh, compared to the baseline of $0.071/kWh. Testing revealed opportunities to further minimize operating costs by utilizing microgrid energy storage capacity to reduce electricity imports during peak-price periods.

FIG. D-1: OLNEY TOWN CENTER MICROGRID PERFORMANCE

SOPO Objective Baseline Target Metric Microgrid

Reduce outage time of critical loads by 98%

175.3 min/cust/yr <3.5 min. 3.3 min. with ~3,800 ft. of undergrounding

9.1 min. with no undergrounding

Reduce CO2 footprint of served loads by 20%

1,651 Metric Tons CO2/year

1,321 MT 891 MT

Improve system efficiency of served loads by 20%

40% system efficiency

48% 49.02%

Costs comparable to baseline non-integrated UPS

$0.071/kWh levelized cost of energy (LCOE)

$0.071 or less per kWh

$0.059/kWh

Project outcomes also included tangible advances in technology R&D, regulatory and business model

development, and engagement among key stakeholders.

5. Technology R&D and Testing: The Project team developed microgrid control technologies capable of managing distributed energy resources to serve a variety of use cases. The microgrid model applied real-world distribution system data, and tested that system to serve actual customer loads in laboratory simulation. The Project yielded greater understanding of design and operations factors for multi-zone public-purpose microgrids.

6. Regulatory and Business Model Development: The Project team researched and analyzed factors affecting legal and regulatory treatment of utility investments in public-purpose microgrids, and developed and analyzed a series of regulatory models to enable microgrid investments and operations. As discussed in Volume 4, various possible approaches could be successful, depending on strategic choices to be made by the utility, and on related regulatory decisions by the Maryland


Public Service Commission (MPSC). The Project team’s recommendations call for a hybrid business structure, in which microgrid distribution and control systems would be owned and operated as regulated utility assets, and DERs would be owned by both the utility and non-utility entities, and operated under agreements with the utility. Utility investments would be recovered through a combination of general rates for all Pepco customers in Maryland (for costs equivalent to the utility’s expected business-as-usual (BAU) costs) and rate surcharges on microgrid customers to recover incremental costs above BAU. For demonstration and pilot initiatives such as the Project, some portion of incremental costs could be defined by MPSC as research and development costs subject to recovery from all ratepayers.

7. Stakeholder Engagement: The Project team worked closely with multiple key stakeholder groups during the course of the Project. In addition to working closely with the utility partner, Pepco Holdings Inc., the Team engaged and received guidance from the U.S. Department of Energy, Federal Energy Regulatory Commission, Maryland Energy Administration, Maryland Emergency Management Agency, Montgomery County Council, and Metropolitan Washington Council of Governments.

E. Lessons Learned

Project activities yielded noteworthy insights that can guide design and development of resilient local

energy systems.

i. Resource Size-Optimization: A key design factor for the Project is the desire to avoid over-building

generation and storage systems, and thereby to reduce the cost premium that typically accompanies

highly resilient energy systems. Accomplishing this required optimizing system sizing and operations.

Project tests showed the size-optimized design meets resiliency needs in almost all test cases, with

noteworthy exceptions during low solar production periods accompanied by high customer loads. The

resiliency of any system that relies on solar generation for battery charging will decline during extended

periods of low-solar irradiance. Ensuring stable operations during such periods would require additional

investment in dispatchable generation capacity, load control capabilities, or both.

ii. SAIDI and Overhead Lines: A key design factor for the Project is to avoid reliance on overhead lines,

while also minimizing requirements for installing new underground cable in the Town Center area.

Accordingly, the Project team focused on reconfiguring the existing utility distribution system to rely

primarily on underground lines, while continuing use of some overhead segments – including the main

feeder from the Norbeck Substation, which serves all microgrid zones. Field data showed critical points

of failure on overhead line segments, creating ongoing reliability challenges. Analysis showed that the

target 3.5 min./customer/year SAIDI performance in the tested zone could be achieved with design

revisions to replace vulnerable overhead line segments with underground cables. Underground cable

requirements for all four zones are estimated to total about 3,800 feet.

iii. Natural Gas Supply Constraints: For purposes of Project design and testing, natural gas was assumed

to be available, sufficient, and uninterrupted in all test cases. However, natural gas supplies can be

affected by short-term and long-term constraints that present a risk to resilience and project economics.

First, natural gas may not be available to all facilities within a microgrid area, or at some sites it may be

available only at volumes and pressures lower than those required to operate some CHP systems. At any

potential microgrid site, gas supply capacity – and not just gas availability – must be established before

implementation can occur. The Project team determined that in the Olney Town Center area,


substantial local supply line upgrades may be required to serve CHP units of the capacity specified in the

design. Second, while natural gas supply interruptions are rare, they do sometimes occur as a result of

regional supply shortages, or from damage to the gas transmission and distribution system. Accordingly,

a gas-fired microgrid may not obviate every other form of energy resilience, such as UPS and non-gas

backup generators for critical loads. Examples include life-sustaining medical equipment, as well as

emergency operations center, first-response, and telecommunications systems. Moreover, diversity of

energy supply increases microgrid resilience, favoring an approach that includes alternative fuel sources.


FIG. E-1: ELECTRIC DISTRIBUTION SYSTEM RE-CONFIGURATION

DETAIL FROM VOLUME 2, SECTION G

F. Feasibility Assessment

The Project team assessed the technical and operational feasibility of the microgrid, and also assessed

non-technical factors that would affect potential for Phase 3 deployment. These assessments are

summarized below, and explored in Volumes 2, 3, and 4.


i. Technical and Operational Feasibility: Testing and analysis demonstrated that the microgrid control

system is capable of meeting the Project objectives. The control system effectively managed DERs to

maintain resilient electricity supply for community critical loads, as well as meeting performance metrics

for efficiency and emissions. Accordingly, the Project team determined that the microgrid control

system is technically and operationally feasible. The Team also determined that the microgrid system as

designed could be installed using existing technology components, and its resources would be sufficient

to support stable operations in most use cases, with exceptions as discussed in section D, above.

ii. Regulatory and Financial Options: The Project team considered numerous options and provided

recommendations regarding regulatory and financing strategies. The regulatory and market context

changed substantially during the course of the Project, and those changes affect potential for

deployment of any public-purpose microgrid in Maryland. Specifically, the host utility merged with

another company (Exelon), and elections changed administrations in both the Maryland and U.S. federal

governments. Thus feasibility for deployment is affected by evolving policy priorities at utility, state, and

federal levels.

At this writing, Pepco was planning MPSC filings for two public-purpose microgrids, pursuant to the

terms of the Exelon-Pepco merger settlement agreement with MPSC. The outcome of such filings will

determine a potential site for deployment of a public-purpose microgrid, and which regulatory and

financial models will be most feasible to support deployment.

iii. Economics: Economic feasibility depends on factors to be resolved in prospective Phase 3 planning

and deployment, including, as noted, whether the utility would seek to recover any Project costs

through general rates, and whether substantial costs for new natural gas infrastructure would be

imposed on the Project. Additionally, economic feasibility would be affected by changes to federal tax

incentives for solar energy investments, and by any changes in design objectives. For example, different

targets for reliability or efficiency improvements could yield substantially different resource strategies,

and leading to different economic outcomes.

iv. Customer Adoption: The feasibility of Phase 3 deployment would depend on the supportive

participation of approximately 120 end-use customers in the Project area. For design and testing

purposes, Pepco provided the team with customer load-size information, as well as interval data for

customer electricity loads. This data was anonymized and aggregated to protect customer privacy, while

allowing simulation of actual system loads for design, testing, and analysis of system performance. Any

deployment of a public-purpose microgrid would have to include direct engagement with customers to

establish individual customer requirements, site conditions, total energy costs, and potential resiliency

benefits, and ultimately to execute operating agreements for customer participation. The results of

those steps would determine what loads would be served by the microgrid, by what resources, and

under what terms.

The Project team anticipates that customers in the Project area likely would favor Phase 3 deployment –

assuming reasonable cost impacts – and the Montgomery County Council generally supports efforts to

modernize energy infrastructure and to provide customers with greater resiliency, sustainability, and

energy independence.

In summary, Phase 3 deployment feasibility depends on multiple dynamic factors that require resolution

in future deployment phases. The Project demonstrated the technical feasibility of the microgrid control


system and microgrid design, and established the basis for potential adaptation and development in any

community with supportive conditions – namely, the need to ensure ongoing availability of critical

community services and facilities, while also achieving substantial improvements in system energy

efficiency, reliability, and environmental attributes.

Volume 2 – Technical Design ………. Olney Town Center Microgrid Project – Final Report 1

Microgrid optimized resource dispatch for public-purpose resiliency and sustainability


Vol. 2: Technical Design

Section:

A. Introduction .......................................................................................................................................... 1

B. Microgrid Design Objectives ................................................................................................................. 6

C. Microgrid Design Criteria ...................................................................................................................... 9

D. Design Assumptions ............................................................................................................................ 12

E. Handling of Fire Stations ..................................................................................................................... 13

F. Microgrid Structure ............................................................................................................................. 15

G. Reconfiguration of Circuits ................................................................................................................. 18

H. Selection of Use Cases ........................................................................................................................ 24

I. Load Analysis ....................................................................................................................................... 25

J. Energy Analysis including Resources .................................................................................................. 30

K. CEFM and Results ................................................................................................................................ 34

L. Modeling Assumptions ....................................................................................................................... 37

Appendix A: Specific Circuit Locations for Microgrid Resources (.kmz file images) ................................... 38

Appendix B: Resource Characteristics ........................................................................................................ 43

Appendix C: Load Analysis Data Summary .................................................................................................. 45

Appendix D: References .............................................................................................................................. 48

Appendix E: Ritchie Station Design Summary ............................................................................................. 49

Appendix F: Black Start Procedure .............................................................................................................. 53

A. Introduction

This volume summarizes the design basis and decisions supporting the Olney Town Center Microgrid control system research and development project. The Project’s primary objective is to develop a microgrid control system capable of providing highly resilient electricity services for critical facilities in Olney, Montgomery County, Md. The Project scope focuses on developing the microgrid controls required to actively manage a microgrid modeled on the Olney Town Center utility distribution area.

The conceptual design summarized in this report is a necessary precursor to subsequent Project Phases – including modeling and simulation of the microgrid test environment to validate microgrid controls


operations, and (prospectively) to provide a conceptual basis for deployment of the microgrid controls solution.

Community Microgrid Model

The Olney community in Maryland is committed to providing safe shelter and essential services during disasters. Pepco has taken many steps to harden the grid, but the company also recognizes the value of supplying power to critical loads locally during rare times when a grid outage is extended. These critical loads include essential services such as hospitals and clinics, fire departments, police, and emergency operations centers; vital commercial assets like pharmacies, grocery stores, gas stations, and building materials centers; and various school buildings for public shelter and for distributing food and disaster supplies. The Project team designed and modeled a highly resilient microgrid capable of supporting safety and security advantages by ensuring the community’s ability to provide emergency and essential services during and after an emergency or other outage-causing event, and to allow vulnerable residents to safely shelter in place within the community.

As illustrated in Figure A-1, evacuation from the Olney area in an emergency or extended grid outage is extremely difficult. The team’s discussions with the Maryland Emergency Management Agency (MEMA) indicated that the major roads and feeder streets likely would be clogged with traffic in a mass evacuation. This would lead to citizens trapped in vehicles, delays in emergency first response, and barriers preventing repair and fuel-supply vehicles from reaching critical sites for service restoration (critical infrastructure, emergency services, vulnerable populations, etc.). A shelter-in-place strategy, augmented by a community resiliency microgrid, is intended to maintain operations at critical services for local citizens – serving to accelerate recovery, reduce community impacts, and potentially save lives.

The Project site provided a general model of many suburban communities along the Atlantic coast – with substantial vital assets relatively co-located in a central area, but not immediately adjacent to each other. In this setting, a community resiliency microgrid model can inform public agencies and utilities about the technology potential of microgrids and factors affecting their development in Maryland.

The Project area, like many Atlantic coastal communities, is expected to experience significant growth in distributed photovoltaic (PV) systems and electric vehicle (EV) charging stations, as well as steady growth in residential neighborhoods around the Town Center area. The growth of residential communities will make mass evacuation even more challenging, thus increasing the need for community resiliency microgrids as contemplated at the Project site.

Figure A-2 shows the critical loads and overview of the circuits feeding the Project area. The substation (Norbeck), located approximately two miles southwest of the Town Center area, energizes five circuits in the microgrid area. Figure A-3 summarizes peak demand and average demand from the critical loads on each circuit.


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FIGURE A-3: OLNEY TOWN CENTER DEMAND AND ENERGY FOR CRITICAL LOADS

SHEET NUMBER

SHEET OF

CA

D D

WG

FIL

E:

DR

AW

N B

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BY

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: 510 k

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M: 0

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: 263 8

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Wp 7

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M: 3

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L: 2

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O: 4

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NC

: 224 5

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Ckt 1

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: 0 k

Wp 0

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0L: 0

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: 0 0

NC

: 0 0


B. Microgrid Design Objectives

The design objectives derive from the project objectives required under the Project SOPO:

• Design a system capable of improving the Olney Town Center System Average Duration Index (SAIDI) reliability measure by 98%.

• Maintain comparable life-cycle cost of energy to non-integrated baseline solutions.

• Indefinitely sustain critical services during major outage events.

• Reduce Olney Town Center CO2 footprint by at least 20%.

• Improve Olney Town Center average system energy efficiency factor by at least 20%.

Each of the objectives requires some description to properly apply to the design of the Olney Town Center microgrid.

Reliability Measure

SAIDI performance in U.S. utility networks varies from approximately 60 and 200 minutes (national average ~120) per customer per non-storm outage. Such statistics are misleading, however, because SAIDI (also known as the IEEE 1366 reliability index) is a transmission and distribution (T&D) network-level index that specifically omits outages related to storms and other outside “events” that cause unexpected outages. Notably, the IEEE 1366 consensus standard defines outages due to storms to be outside the utility’s responsibility to prevent. Because storms and other events result in a large share of outages, actual service availability experienced by customers can be substantially different from network reliability performance as measured by SAIDI.

Pepco reports a three-year (2011-2013) average SAIDI value of 176 minutes without major event outages. The DOE objective is a 98% improvement in the reliability as a measure of the improved resiliency. Given Pepco’s 2013 reported SAIDI index, a 98% improvement would yield an equivalent SAIDI reliability design objective of 3.5 minutes.

One of the design criteria specified for the DOE program requires comparing the cost of the microgrid solution to the cost of non-integrated baseline solutions (e.g., uninterruptible power supply (UPS) and backup diesel generators). The project team’s analysis is shown in Figure B-1. To establish a valid comparison, the team evaluated solutions using common parameters:

• Total cost of ownership over a 25-year period

• Microgrid operations 24/7 vs. sporadic UPS/diesel operations (200 hours)

• Levelized cost of energy (LCOE) measured in $/kWh


FIG. B-1: MICROGRID CEFM COST COMPARISON RESULTS

Description MW Rate (per MW) Total ($000) Present Value

UPS ($1M/MW) - Critical

7.639 $ 1,000,000 $ 7,639,000 $ 7,639,000

Generators ($200k/MW) - Non-critical

0.393 $ 200,000 $ 78,600 $ 78,600

ATS ($10k/MW) 7.639 $ 10,000 $ 76,390 $ 76,390

Total CapEx $ 7,793,990 $ 7,793,990

Installation and Commissioning

$ 590,000 $ 590,000

O&M Costing (annual) 8.032 $ 0.015 $ 120,480 $ 1,327,510

Total $ 9,711,500

Levelized Energy Production

$ 457,005,442

Levelized Cost of Energy ($/kWh)

$ 0.021

Current Utility Avoided Costs (average)

$ 0.050

Baseline LCOE $ 0.071

Microgrid LCOE $ 0.059

CO2 Footprint

Project objectives include reducing the CO2 footprint of the served area in Olney Town Center by greater than 20% from the current baseline. Since nearly all of the energy consumed in Olney Town Center is provided by the utility, Pepco, the current baseline for CO2 emissions will be the current CO2 emissions profile of the utility.

Pepco’s current baseline CO2 emissions are based on the utility’s filing with the District of Columbia Public Service Commission, “Environmental Information for Standard Offer Service Provided by Pepco” for calendar year 2013 (ref. 30906-1-0309):

• Pepco CO2 emissions = 1,112 pounds emitted per MWh of electricity generated

• One metric ton = 2,204.6 pounds

• The CO2 footprint is ~0.5 Metric Ton / MWh

To achieve a 20% reduction, the Olney Town Center community resiliency microgrid needs to result in a CO2 emissions footprint of less than 0.4 metric Tons/MWh. This reduction will be achieved by displacing energy supplies from the utility generation portfolio with microgrid distributed resources having greater amounts of high-efficiency clean generation (natural gas CHP) and non-emitting renewable energy sources. The preliminary analysis for microgrid resources serving all zones is summarized in Fig. B-2.


FIG. B-2: MICROGRID CEFM ESTIMATED EMISSIONS RESULTS

lbs./MWh Tons/MWh Total MWh Total CO2 (Tons)

Baseline Grid CO2 missions 1,112 0.504 37,466 18,898

Microgrid CO2 emissions

PV 0 0.000 2,887 - CHP - NG 1,003 0.455 27,567 12,542 Less Displaced Thermal

(5,245)

ESS 0 0.000 (93) - NG Generator - - Grid 1,112 0.504 7,105 3,584

Total Microgrid emissions

0.290 37,466 10,881

Emissions Reduction 8,017

Emissions Reduction Rate 42%

Average System Energy Efficiency

Project objectives include improving the Project area’s average system energy efficiency by 20% from the current electric distribution network system energy efficiency.

• The typical efficiency rate after accounting for generation, transmission, and distribution losses is under 40%

• A 20% improvement in average system energy efficiency for the microgrid = 40% * (1 + 20%) = 48%

The energy efficiency rates for each microgrid resource are calculated on an annual basis as follows:

• Natural gas engine combined-heat and power units – published heat rate: (65% efficiency assuming optimal utilization of thermal output; or 30% percent efficiency assuming under-loading and minimal use of thermal output)

• NG Generators – published efficiency rate: (34%)

• PV Systems – DC/AC round trip efficiency: (83%)

The weighted sum of all resources, including those derived from the grid, are used to estimate the “after microgrid” efficiency factor. The system design CEFM results are summarized in Figure B-3.1

FIG. B-3: MICROGRID CEFM ESTIMATED EFFICIENCY

Critical Load (kWh) Efficiency Factor Energy Input

Baseline Grid Efficiency 37,465,983 40% 93,664,958

Microgrid Efficiency

PV 2,886,538 83% 3,477,757

Energy Storage (92,753) 100% (92,753)

NG - 34% -

CHP - NG 27,566,732 65% 42,410,357

Grid 7,105,466 40% 17,763,664

1 Tested metrics for microgrid model are presented in Volume 3.


Average Microgrid System Efficiency 37,465,983 59% 63,559,025

Efficiency Improvement Rate

47%

Summary of System Design Objectives

• Resiliency objective: 98% improvement (SAIDI < 3.5 min/customer/year)

• CO2 Footprint objective: 20% reduction (0.4 Metric Tons/MWh)

• Average system energy efficiency objective: 20% improvement (to 48%)

C. Microgrid Design Criteria

As discussed in section B, the Olney community lacks safe evacuation options in the event of major storms and extended grid outages. The community therefore has a greater need for critical services during these disruptive events, with resilience requirements beyond simply providing energy to support the police, fire, and other emergency services.

A community’s requirements for services, and hence their criticality, varies over time. Police, fire, and emergency medical services are critical from time-zero in a major storm or extended outage, and other services become more critical with time. For example, access to groceries is not critical during the first few hours of an outage, but within 24 hours such services become critical to the health, safety, and vitality of the community. Similarly, the longer an outage lasts, the greater the community’s requirement for public shelters, pharmacies, gas stations, banks, and even places to charge cell phones.

One approach to maintaining resilient energy for critical community needs is to allow critical loads to be curtailed or reduced to a minimum at the beginning of an event, and to rely on mobilization of temporary generation to support local energy loads. This approach can complicate relief and recovery efforts, as it impairs essential services from the beginning of an event when they may be most critical. In addition, service restoration depends entirely upon access to an inventory of temporary generation and regular refueling. In a major event, hundreds of local communities likely will requisition portable generation and fuel supplies, both of which may be insufficient or inaccessible if roads are blocked by fallen trees, downed lines, and traffic jams.

Another approach focuses on serving the entire critical load at its typical level at the beginning of the event, enabling critical services to continue supporting community safety and health through the entire event duration. This reduces the load on such critical services as police, fire, and emergency services, as well as helping to keep streets clear for recovery efforts. This approach avoids these and other risks, and enables improvements in system efficiency and environmental attributes, and thus creates the basis for the Project’s resilient microgrid design.

Criticality of Load

The Olney Town Center area includes a high density of loads that are critical to the health, safety, and vitality of the Olney community. It also has substantial loads that provide convenience and other services that help improve community resiliency and the ability to shelter in place during long-duration outages, with a small percentage of loads that generally do not substantially support community resiliency.

The Project team considered potential customer relationship and perception issues involving how customer loads qualify to receive resilient microgrid service, both during project development phases and also during outage events. Because a primary objective of the microgrid project is to improve


resiliency of critical services, selection criteria prioritized criticality over geography, defining four levels of criticality: High, Medium, Low, and Optional.

• High (H) – important public safety and life-related services that must remain available from event time-zero and continue operations throughout the major storm and aftermath, and extended grid outages.

• Medium (M) – critical services that become important within 8 to 24 hours following event time-zero.

• Low (L) – critical services that become important within two days.

• Optional (O) – additional retail and other facilities, most notably restaurants that have significant perishable inventory and that can serve the community in the aftermath of major storms and extended grid outages.

From the criticality criteria, substantially all loads in the Olney Town Center area were evaluated. This evaluation showed the area has a total critical load of 7.6 MW peak and 4.1 MW average. Within the microgrid area, additional non-critical loads total 98 kW average (393 kW peak). The team evaluated options for addressing non-critical loads and determined the most cost-effective and feasible approach is to slightly increase the size of generation and storage resources to accommodate all loads within the microgrid boundary. However, in extended outages or contingency scenarios, some or all of the non-critical and optional loads served by the microgrid can be cycled using the utility’s existing advanced metering infrastructure (AMI).

Outage Influence on Microgrid Design

Overhead distribution lines impose vulnerability to storms and other threats. Thus, overhead distribution lines in a community tend affect how a microgrid is designed.

During Superstorm Sandy and its aftermath, approximately 30 microgrids continued to provide service to customers in areas affected by the storm. A common element among these successful microgrids was that none of them relied on overhead distribution lines. The lesson for community resiliency microgrids is to design systems that avoid using overhead distribution lines.

The Olney Town Center is served by portions of five Pepco circuits (See Figure A-3), all of which have some overhead feeders from the substation or distribution lines within the microgrid area. This reliance on overhead lines leaves the community vulnerable to storms and other outage risks, and therefore influences the design of a microgrid intended to increase community resiliency.

In addition, all five circuits are fed from a single substation. A common-mode failure at the substation will de-energize all five circuits to the Project area. This network configuration represents another vulnerability to outage risks. Figure C-1 below shows the area’s overhead distribution lines in magenta and underground distribution cables in blue.


FIG. C-1: OLNEY TOWN CENTER OVERHEAD LINES AND UNDERGROUND CABLES

Because the system configuration and major overhead distribution lines within Olney Town Center divide the area into separate parts, the microgrid design must accommodate these vulnerabilities.


Some of the high-criticality services to be served by in the microgrid are geographically remote from the Town Center area. Specifically, two fire stations (See section 6) and, to a lesser extent, the hospital complex, are separated from the Olney Town Center by overhead distribution lines. Accordingly, the microgrid design must consider this vulnerability for those facilities.

Figure C-2 illustrates outage information (from April 1, 2009 through Jan. 6, 2015) on the circuits that feed the Project area.

FIG. C-2: OLNEY CIRCUITS OUTAGE PROFILE

Circuit Total Circuit

Outages

Weather/Lighting Unknown All Other Types

Outages within Olney

Microgrid Area

15118 57 4 16 37 1

15119 191 13 20 158 62

15125 101 4 12 85 17

15126 123 3 20 100 28

15129 83 5 13 65 1

The area’s outage history suggests a need for reconfiguration to minimize the vulnerability associated with circuit outages. This reconfiguration can take two forms: (1) permanent reconfiguration, or (2) reconfiguration upon islanding from the grid.

Design Criteria Summary

In summary, the microgrid design criteria include:

• Provide critical services over time that support a safe shelter-in-place emergency strategy

• Provide critical services at near normal levels from time-zero of an event – e.g., continuous microgrid versus temporary backup

• Categorize loads into criticality groups of High, Medium, Low, and Optional

• The definition of critical services grows with duration of an outage event

• The project’s resiliency objectives require avoiding reliance on vulnerable overhead distribution lines, as well as avoiding reliance on a single substation that presents vulnerability to common-mode failure

• The microgrid must compete favorably, in LCOE terms, against non-integrated UPS or diesel backup systems

D. Design Assumptions

The Project area contains approximately 120 non-residential electric customers. Using the criticality criteria outlined in section C, critical loads were identified among a subset of the 120 electric customers. Specifically, Pepco defined 28 load groups from actual 2014 customer-load data, and these load groups were categorized according to criticality and circuit location to protect the privacy of individual customer data (Appendix C).


Two fire stations are included in the microgrid design. However, solutions for those facilities are not modeled and simulated because their small size results in a negligible effect on microgrid controls design and testing.

The estimated cost of implementing underground distribution power cable at 12kV-15kV in urban and suburban areas is $3.6 million per mile, according to Pepco.2

The configuration and scale of the existing underground distribution circuits allows connecting many critical loads in the project area, while avoiding overhead distribution lines. Exceptions occur when crossing two streets in the Project area (Georgia Avenue and Olney Sandy Spring Road).

Distributed siting of resources within the area means that some resources will be sited on public property and some on private property. The design assumes the value of the community resiliency microgrid to consumers justify securing available sites for generation, storage, and control systems, and therefore the costs of physical space will be minimal. It also assumes that the location of resources will not impact local businesses in ways that would create material costs.

The utility has deployed smart meters throughout the Project area, enabling the use of its AMI for many functions. One of the functions included in the Project area’s smart meters is a connect-disconnect collar, which allows the utility to remotely disconnect and re-connect an AMI-metered customer from the distribution circuit. The project design assumes substantially all unserved non-critical loads in Project area can be disconnected on loss of the distribution grid to support priority service for critical loads, especially in long-duration scenarios. The design anticipates the utility’s AMI disconnect routine will require minutes to implement, necessitating a microgrid capable of managing loads and resources to serve the area’s full load capacity.

Design Assumptions Summary

• Critical loads are aggregated into load groups to protect the privacy of individual customer data. The resulting aggregated load profile informs modeling and simulation, as well as microgrid controls design.

• Satellite systems for two fire stations are included in the microgrid, but are not modeled and simulated. (See section E).

• The cost of implementing underground distribution cable at 12kV+ is $3.6 million per mile.

• Within each zone of the microgrid, critical loads will be served to the greatest possible degree without relying on overhead distribution lines.

• Siting distributed resources will not adversely affect businesses and thus will not create additional project costs.

• Pepco will use its AMI system to disconnect substantially all non-critical loads in Olney Town Center on loss of the distribution grid in Olney. (Additional processes and protocols may be required to implement this requirement at some customer sites.)

E. Handling of Fire Stations

Two fire stations (Sandy Spring Volunteer Fire Department) are critical loads for the Project area, but they are located outside the primary microgrid footprint. One fire station is located about 1 mile south of the Olney Town Center on Georgia Avenue, and the other is located about 1.5 miles east of Olney Town Center off Olney Sandy Spring Road.

2 Internal PHI planning document: “10-Year Forecast Cost Estimating Data (2015-2024),” Rev. 7/29/2014


In Figure F-2, the fire station to the east is designated Microgrid Zone 4 and the fire station to the south is designated Microgrid Zone 6.

Each fire station is a small load in both continuous and emergency scenarios, yet each is highly critical. For purposes of developing the microgrid controls scheme, the fire stations are considered too small and too far away to factor into the microgrid model:

• The two fire stations represent a 71 kW peak electric load out of 7,638 kW peak for the Olney Town Center (less than 1% of total peak critical load);

• Neither fire station can rely on resources within the Olney Town Center area 1 to 1.5 miles away, and consequently each must have its own resilient energy solution; and

• The additional task effort to model the two fire station systems would yield no change in the results (reliability, CO2 reductions, or system energy efficiency) due to the fire stations’ small size and distance in relation to the Town Center area.

Therefore, while the fire stations are considered an essential part of the community resiliency microgrid design, they are not to be modeled for controls, simulation, and testing.

Fire stations summary:

• Resiliency solutions will be designed to meet project objectives for the fire stations.

• The fire stations will not be included in the overall envelope of microgrid controls.

• The fire stations will not be considered for microgrid zone testing.


F. Microgrid Structure

The microgrid design divides the Project area into six (6) zones for resiliency reasons – avoiding to the greatest possible degree the use of storm-vulnerable overhead distribution lines. In addition, as discussed above, the microgrid design will mitigate the vulnerability of all circuits coming from a single substation.

General Approach

The general approach to designing the microgrid is to structure a portfolio of resources that efficiently provides 24/7 service for critical loads in the Project area, with: (1) costs comparable to baseline alternatives; (2) local energy supplies with a lower emissions footprint than grid-sourced electricity supplies; and (3) a structure that improves reliable operations in grid-connected mode as well as the ability to safely transition to intentional island operation without loss of critical loads.

The team worked with the utility partner to identify and analyze several options for a microgrid design that meets project objectives in the Project area, which is divided by overhead distribution lines that create substantial vulnerabilities.

The team applied Green Energy Corp.’s converged energy and financial model (CEFM) to produce a viable microgrid comprised of several zones running semi-autonomously with a cloud-based supervisory overlay for further optimization. Design planning focused on interties among zones of the microgrid, enabling the team to identify various options and factors for analysis in consultation with Pepco:

• One Microgrid with extensive undergrounding – zones 1, 2, 3, and 5 combined; zones 4 and 6 still separate: This would require undergrounding of the overhead lines along Georgia Avenue and Olney Sandy Spring Road, at an estimated rough order of magnitude (ROM) cost of $9 million.

Technical View Economic View

kW

24 00 Hrs

$/W

20 00 Yrs

PV

Load

NG CHP

• Multi-resource fit • Hedging – reliability, economic

• Enables higher utilization and more renewables, thus lower emissions / MWh

CapEx OpEx + Fuel

• Portfolio Business Case is better than individual ones

• Hedging ability results

Portfolio BC

NG CHP

NG CHP

PV

PV

Energy Storag

e

FIG. F-1: MICROGRID RESOURCE PORTFOLIO CONCEPT


• Multi-Zone Microgrid – zones 1 through 6 treated as individual optimized microgrids with a cloud-level overall optimization and management: This would be the most straightforward approach to avoiding the overhead lines along Georgia Avenue and Olney Sandy Spring Road.

• Multi-Zone Microgrid with Interties – zones 1, 2, 3, and 5 combined with short underground interties only; zones 4 and 6 still separate: The ROM estimate for the underground interties is $3 million. The advantage would be the possibility of treating zones 1, 2, 3, and 5 as a single microgrid, with the added complexity of energy-transfer restrictions at the interties.

Selected Option and Reasoning

The team analyzed the typical layout of U.S. communities and power networks, and determined that developing the multi-zone microgrid structure would yield the greatest potential benefit by demonstrating cost-effective options for resilient microgrids to serve multiple critical and vital community assets that are widely dispersed and not conveniently concentrated within a given network segment. Consequently, the team decided to pursue the multi-zone microgrid configuration.

The reasoning that supports this approach is found in the resource portfolio concept for financially viable microgrids (See Figure F-1).

Natural gas fuel cells, microturbines, and engine-based combined heat and power (CHP) units are not designed to follow load. However, within constraints such units can be operated as load-following resources by de-rating their output to match reduced baseload requirements and then allow output to increase on demand. However, such resources generally are not designed to support the needed ramp rates, and routinely operating these units outside their design specifications causes equipment damage and reduced service life. In addition, emissions per MWh and maintenance costs are increased due to sub-optimal use. Finally, vendors may void warranties when base-load units are inappropriately operated as load-following units. These factors inform selection of dispatchable resources to ensure they are capable of safely operating as needed to support Project objectives.

To match the load profile, base-load natural-gas fired CHP are sized to operate at design output of at least 8,000 hours per year. This means that for approximately half of a given day, base-load requirements and generation capacity are matched. During the daytime, electricity load increases greatly and falls off in the late afternoon or evening – a load profile that generally is matched by solar PV output. Because energy storage is designed to change its output rapidly, it can be used to balance CHP, PV, and load throughout the day.

With respect to long-term operations, the portfolio enables the microgrid to use its resources within their design envelope, which helps keep maintenance and fuel costs at their minimum, making the total cost of ownership as low as possible.

Figure F-2 shows the resulting microgrid resource portfolio in each Zone of the microgrid.


FIG. F-2: OLNEY TOWN CENTER MICROGRID DESIGN OVERVIEW


G. Reconfiguration of Circuits

The outage history (discussed in section 4) on the Olney Town Center circuits supports the expectation of greater reliability and resiliency if the microgrid is treated separately from the remainder of the circuits. This takes two forms: (1) a permanent reconfiguration to simplify grid-connected operations and transitions between modes, and (2) an islanding reconfiguration that happens upon loss of the distribution grid.

Point of Common Coupling (PCC) and reconfiguration:

• PCC to be located along a section of the primary feeder at the microgrid boundary, avoiding voltage-drop issues

• Locations of head and tail breakers can help determine logical PCC locations

• The locations of switches are derived from a combination of inputs, including Pepco community utility network KMZ files, CYME models, microgrid design models, and refinements from Pepco’s engineering department

• Switch functionality requirements include the ability to trip fault current

Point of Common Coupling and Grid Reconfiguration

Figures G-1 through G-4 and explanatory notes below describe grid reconfiguration requirements affecting Zones 1, 2, 3, and 5.


FIG. G-1: OLNEY MICROGRID ZONE 1 (15119 AND 15126)

Zone 1 reconfiguration notes:

• Circuits 15126 and 15119 west of Georgia Avenue to be permanently connected together with tie switch 779473-930470 (normally closed).

• PCC head and tail breakers (780480-910400 and 780475-510770) isolate microgrid segments from the remainder of the circuits 15119 and 15126 to the south and north.


FIG. G-2: OLNEY MICROGRID ZONE 2 (15125)


• Tie switch 781480-390440 (normally closed) connects circuit 15118 east of Georgia Avenue to section of circuit 15125 in Zone 2 of the microgrid.

• Neighborhood conductors serve adult day-care center (15). • Upon islanding, PCC head and tail breakers to isolate Zone 2 from the remainder of the circuits

15118 and 15125 to the west, south, and north.


FIG. G-3: OLNEY MICROGRID ZONE 3 (15119, 15125, AND 15129)


• Circuit 15125 segment feeding the hospital complex (20) to reconfigured to be permanently connected to circuit 15119 in Zone 3.

• Upon islanding, PCC head breakers (783479-590720, -500490, and -210500) isolate Zone 3 circuits (15119, 15125, and 15129) from the remainder of the circuits 15119, 15125, and 15129 to the south and west.


FIG. G-4: OLNEY MICROGRID ZONE 5 (15119 AND 15125)


• Tie switches 781479-700750 and 781478-750980 (normally closed) connect Circuits 15125 and 15119 south of Olney Sandy Spring Road.

• Upon islanding, PCC head and tail breakers (780475-510770 and 780480-910400) isolate Zone 5 from the remainder of the circuits 15119 and 15125 to the south and north.

Zone 2 and 3 Design Alternative

Project objectives included utilizing existing underground conductors to the degree possible. Figure G-5 (below) illustrates an alternative system design that the team considered and ultimately rejected. In addition to using the current underground neighborhood system, the design that was selected (Figure G-6) would enable selective future expansion of Zone 2 to serve adjacent and interconnected loads, if doing so would support a more economical and cost-effective system and lower net costs to customers.

The encircled facility – an adult day-care center – presents a level of criticality somewhat lower than the emergency hospital located in Zone 3. As a consequence, its load may be easier to serve as part of the largely commercial Zone 2.

Finally, executing the simpler Figure G-5 design would incur somewhat higher costs, with new conductoring and switchgear, as well as expanded generation capacity to serve the encircled facility’s load as part of Zone 3. These factors led to the selected design, in which the Figure G-5 encircled area is energized instead by Zone 2’s existing underground cables.


FIG. G-5: OLNEY TOWN CENTER MICROGRID – ALTERNATIVE DESIGN (REJECTED)

FIG. G-6: OLNEY TOWN CENTER MICROGRID (PROJECT SELECTED DESIGN)


H. Selection of Use Cases

In the proposal for the project, the team committed to employ the microgrid use cases found on the EPRI Use Case Repository. These generic use cases provide the team with a starting point for developing the Olney Town Center microgrid control applications. The EPRI-derived use cases are summarized below, with the addition of a Security use case to ensure secure operations of high-value community energy assets.

1. Frequency control: In normal conditions, the microgrid is operated to prioritize service for critical loads, and will not have sufficient resources to affect frequency on the grid. In principle it could participate in the ancillary services markets by increasing output to support the frequency in the local grid, but total impact would be small. The microgrid controller will monitor system frequency, and DER controllers (including BESS inverters and CHP system controllers) will respond to frequency deviations in real time.

2. Voltage control: Similar to frequency control, in normal operations microgrid resources are dedicated to critical loads and in any case are insufficient to have an appreciable impact on grid voltage. The microgrid controller will monitor system voltage, and DER controllers (including BESS inverters and CHP system controllers) will respond to voltage deviations in real time.

3. Intentional islanding: For each microgrid zone the islanding process will be semi automatic so that a utility operator or local energy manager will be able to step through each step before opening the PCC. The utility operator will provide the appropriate permissions for opening the PCC. The local GreenBus MMC for each microgrid zone will be responsible for setting the voltage source and load-following resource.

4. Unintentional islanding: For each microgrid zone, the local GreenBus MMC will have the ability to enter island mode based on internal and external actions. The controller will manage the stability risk with a specific sequence of events for islanding.

5. Islanding to grid-connected transition: As with intentional islanding the utility operator will provide the appropriate permission to close in the PCC. The local GreenBus MMC will support the reconfiguration of each dispatchable resource.

6. Energy management: The EPRI use case takes a traditional energy management approach – economic dispatch, short-term dispatch, optimal power flow, and other processes typical in utility control room environments. For the portfolio of resources in the Olney Town Center deployment, energy management applications will manage controllable generation and load assets. Within that portfolio, the system will optimize the microgrid based on load forecast, ancillary services events, changes in configuration, outage of specific equipment, or any other kind of change to determine the optimal use of assets 48 hours ahead.

To protect data privacy Pepco provided aggregated information about groups of customer loads. Defining load-management actions, therefore, required models of representative customer loads and systems, including building energy management systems (BMS).

7. Microgrid protection: This GreenBus MMC will ensure two primary conditions. The first is that each protection device is properly configured for the current operating state of the microgrid, either islanded or grid connected. The second condition is that after a transition, the GreenBus MMC will switch settings or verify that settings have changed appropriately. If the controller


cannot establish either condition, it will perform a shutdown of each resource and generate the appropriate alarms.

8. Ancillary services: The primary focus of the ancillary services use case is to perform demand response and power export functions. Specifically, the utility operation will have the ability to request and or schedule balance up and balance down objectives for the microgrid, without impairing microgrid resiliency objectives. For single-zone testing purposes, ancillary services tests will demonstrate microgrid zone response to ancillary services requests. In a real-world multi-zone microgrid deployment, the cloud-based controller would take the responsibility to parcel out the objectives to each microgrid zone based on the available capacity. As noted above, available capacity is constrained by critical-load resiliency objectives.

9. Black start: The local GreenBus MMC provides a workflow process for restarting the system. Each microgrid zone will have a sequence of operations for use cases requiring starting the system following a complete shutdown. Meeting the reliability requirements will require operator inputs and lockout-tagout actions.

10. User interface and data management: Project implementation included designing a dashboard interface to autonomous real-time systems. The solution provides local controllers in each microgrid zone as well as a hosted controller that can operate each microgrid zone separately or collectively. The primary actors are the utility operator, local energy managers, maintenance personnel, and analyst. The user dashboard enables primary functions in the system around Operations, Stability, Ancillary Services, and Administration.

11. Security: The solution demonstrates a protected design and integration, including validated methodologies that confirm:

a. human and machine actors are authenticated and authorized, b. data in motion is protected, c. data at rest is managed, and d. systems are being monitored

Domain data are used to provide simple event processing for anomaly detection and a threat model of the system guides analysis of suspect operations.

I. Load Analysis

Load analysis task work required a set of end-user load data from Pepco, including hourly load data for one year for 28 load groups. The hourly data provided the basis for understanding the microgrid area’s load profiles, albeit aggregated within load groups. The load profile is important to load forecasting and expected responses to weather changes and market influences.

The peak load and average load were determined for each microgrid zone by criticality (See Figures I-1 and I-5). This provides the team with design and operations insights. For example, the peak-to-average ratio is 1.9, which is below average for PJM Interconnect territory, so the daytime load does follow the form described in Figure F-1.

The peak-to-average ratio for the High criticality load is 1.4, which suggests these loads have higher-than-average night loads. In contrast, the peak-to-average ratio for the Optional criticality load is 2.6, and for the Non-Critical load is 3.9. If there were a significant event that challenged the microgrid operations in islanded mode, a significant reduction in demand could be achieved by curtailing the Optional criticality and Non-Critical loads for a short period of time, while maintaining the higher criticality loads. Similar logic can be applied to the Low (ratio of 2.3) and Medium (ratio of 3.4) criticality loads in this zone of the microgrid. The microgrid controls can take action when the stability of the


microgrid is challenged beyond normal expectations by load modulation or curtailment within the load criticality framework, to protect the mission in the best manner possible.

FIG. I-1: MICROGRID PEAK AND AVERAGE ELECTRIC LOAD (ZONE 2)

kW (peak)

kW (avg)

High 892 632

Medium 192 56

Low 272 117

Optional 403 156

NC 224 58

Total 1,983 1,020

The high peak-to-average ratios in Optional, Low, and Medium suggest that some load can be modulated or curtailed without significant or long-lasting impact on those loads. For Optional, modulating 250 kW of load, for example would still allow average load (~150 kW) to continue.

In addition, zone 3, 4, and 6 have little capacity to curtail load within the criticality framework. Thus, load modulation – rather than curtailment – represents the primary tool for mitigating any problems within those microgrid zones. This exemplifies the importance of integrating microgrid controls with building management systems.

These and other such insights about load profiles support an operating strategy configurable within the microgrid controls. Figures I-2 through I-4 describe load groups in each zone of the microgrid.


FIG. I-2: OLNEY TOWN CENTER MICROGRID – LOAD CRITICALITY

Figure I-3 provides overall data for circuit capacities, demand, and load, as well as critical demand and load for the zones and load groups.


FIG. J-3: OLNEY TOWN CENTER CIRCUIT LOAD INFORMATION


Figure I-4 places the 28 critical load groups within the zones of the microgrid.

FIG. I-4: OLNEY TOWN CENTER MICROGRID LOAD GROUP INFORMATION


FIG. I-5: CRITICAL AND NON-CRITICAL LOADS IN OLNEY TOWN CENTER

Microgrid

Zone

Critical

Load

(kWpeak)

Critical

Load

(kWavg)

Non-

Critical

Load

(kWpeak)

Non-

Critical

Load

(kWavg)

Total

Load

(kWpeak)

Total

Load

(kWavg)

Non-

Critical %

of Total

(kWpeak)

Non-

Critical

% of

Total

(kWavg)

1 1,158 390 133 27 1,291 417 10% 6%

2 1,758 961 225 59 1,983 1,020 11% 6%

3 2,611 1,736 0 0 2,611 1,736 0% 0%

4 42 25 0 0 42 25 0% 0%

5 2,040 1,054 37 12 2,077 1,066 2% 1%

6 29 13 0 0 29 13 0% 0%

Total 7,639 4,179 393 98 8,033 4,277 5% 2%

Figure I-5 shows that the non-critical load within the microgrid area is a very small portion of the total microgrid load, as well as each zone of the microgrid. Because the complexity and cost of omitting these loads exceeds the incremental value of resources required to serve them, the team designed the microgrid and sized the resources to accommodate running non-critical loads alongside critical loads in grid-connected and islanded modes. If necessary in contingency scenarios, non-critical loads may be curtailed using the utility’s existing AMI infrastructure.

J. Energy Analysis including Resources

The energy analysis is conducted using the GEC converged energy and financial model (CEFM). The energy model portion seeks the following goals:

• Match the energy requirement of the multiple critical loads in the microgrid

• Support all critical load in an island indefinitely

• Maximize utilization of all assets through application of a right-sized portfolio of resources

• Support the peak demand where feasible, recognizing that meeting all peak demand may be uneconomical and potentially risks reliability

• Support use of flexible load as a resource

• Support use of utility grid energy supplies as a variable resource where economically beneficial

Fig. J-1 summarizes the total microgrid resources portfolio. Figure J-2 (a and b) shows the One-Line schematic for Zone 1. Figure J-3 shows the resources modeled in each zone of the microgrid as well as the distributed location of each resource.


FIG. J-1: OLNEY TOWN CENTER MICROGRID RESOURCE PORTFOLIO

Resources Total kW

capacity

Description

NG Engine-based

CHP

3,451 2 x 762 kW + 2 x 358 kW + 5 x 248 kW (MTU Onsite Energy) with 20T and 30T absorption chillers

Solar PV 1,828 Rooftop PV arrays of various sizes and parking lot 13.5 kWac Solar Trees

Battery Energy

Storage

728 Li-ion community energy storage units

NG Generator 150 2 x 50 kW + 1 x 30 + 1x 20 kW natural gas engine generators as base generation supplement to CHP units

Total 6,157

FIG. J-2: ONE-LINE DIAGRAM Fig. J-2a: Zone 1 – circuit 15126


Fig. J-2b: Zone 1 – circuit 15119


FIG. J-3: OLNEY TOWN CENTER MICROGRID RESOURCES


K. CEFM and Results

Green Energy Corp. developed the CEFM to assist in designing microgrids. The CEFM has been used in the design of several dozen microgrids to date. The premise across a spectrum of microgrid designs, and the results, bear out the value of the CEFM. A simplified block diagram is shown in Figure K-1.

FIGURE K-1: CONVERGED ENERGY AND FINANCIAL MODEL (CEFM)

The results of CEFM analysis are summarized below in Figures K-2, K-3a, and K-3b. Figure K-3a provides a financial summary of the base-case microgrid design with no additional costs for converting overhead lines to underground cables. As discussed in Volume 3, system testing revealed that some undergrounding would be required in order to meet the Project reliability objectives. Figure K-3b provides a financial summary of the revised microgrid design, including costs for converting approximately 0.72 miles of overhead lines to underground cables. Note that the levelized cost of energy for the revised design still meets the Project cost objective vs. non-integrated baseline solutions, as shown in Figure B-1.

Base Resources

Solar Wind

Bio

Objectives

Economics Reliability

Emissions

Renewable Resources

Peak Resources

Energy Storage

Energy Rates Demand Rates

Fixed Charges Rate Escalation

Energy Load Demand Requirement

Load Analysis Demand Analysis Resource Analysis Rates Analysis

Energy Output

Demand Output

Grid Buy / Sell

Financial Model

Incentives & Credits

Assumptions

Proposed Rates

Output

CapEx & OpEx Debt

IRR

DSCR

Performance Cost

Equity

Energy Model


FIG. K-2: OLNEY TOWN CENTER MICROGRID CEFM LOAD/RESOURCE PROFILE RESULTS


FIG. K-3: OLNEY TOWN CENTER MICROGRID CEFM FINANCIAL SUMMARY Fig. K-3a: Base design (no additional undergrounding)

Total

Financial Summary

Total kW-AC Capacity 6,157

Gross System Costs $15,255,512

Total Pretax Revenue (Year 1) $3,184,809

Total Annual Costs (Year 1) $(1,468,403)

Net Income (EBITDA) (Year 1) $1,716,406

Equity

Investment ($M) $15,255,512

Internal Rate of Return (IRR) 14.10%

Energy Pricing

Initial Average PPA rate (per kWh) $0.1000

Rate Escalator 2.25%

Power Supply Agreement Term 25

Production (kWh Year1) 31,559,432

Tax credits and Incentives

Federal Investment Tax Credit (ITC) $3,021,699

State Investment Tax Credit $ -

Employer Federal Income Tax Rate 28.00%

Employer State Income Tax Rate 5.50%

Levelized Costing

Levelized Cost of Energy (per kWh) $0.0568

Levelized PPA pricing (per kWh) $0.0819


Fig. K-3b: Revised design (Undergrounding in four zones (0.72 mi.))

Totals

Total kW-AC Capacity 6,157

Gross System Costs $17,925,272

Total Pretax Revenue (Year 1) $3,184,809

Total Annual Costs (Year 1) $(1,468,403)

Net Income (EBITDA) (Year 1) $1,716,406

Equity

Investment ($M) $17,925,272

Internal Rate of Return (IRR) 12.49%

Energy Pricing

Initial Average PPA rate (per kWh) $0.1000

Rate Escalator 2.25%

Power Supply Agreement Term 25

Production (kWh Year1) 31,559,432

Tax credits and Incentives

Federal Investment Tax Credit (ITC) $3,807,522

State Investment Tax Credit $ -

Employer Federal Income Tax Rate 28.00%

Employer State Income Tax Rate 5.50%

Levelized Costing

Levelized Cost of Energy (per kWh) $0.0594

Levelized PPA pricing (per kWh) $0.0819

L. Modeling Assumptions

Analysis and iterative development yielded several baseline assumptions governing microgrid design:

• Distributed siting of resources within Olney Town Center means that some resources will be on public property and some will be on private property.

• Critical loads can be properly represented by load groups to protect the privacy of individual customer data. This assumption permits appropriate modeling and simulation, as well as microgrid controls design, without accessing identifiable individual customer information.

• Within each zone of the microgrid, critical loads will be powered from underground distribution cables to the greatest possible degree.

• The cost of implementing underground distribution cable at 12 kV+ is $3.6 million per mile, per Pepco estimates.

• Siting distributed resources will not adversely affect property owners or businesses, and consequently siting costs will be immaterial.

• The two fire stations are excluded from advanced simulations, because their energy loads and systems are too small to justify model simulation and integration costs.

• Non-critical loads may be curtailed during island-mode operations as needed using existing utility AMI systems.


Appendix A: Specific Circuit Locations for Microgrid Resources (.kmz file images)

Circuit 15129


Circuit 15126


Circuit 15125


Circuit 15119


Circuit 15118


Appendix B: Resource Characteristics

1. CHP specifications used in CEFM modeling


2. PV performance data derived from PV Watts


Appendix C: Load Analysis Data Summary

Olney Customer List (available data) Criticality = High, Medium, Low, Optional

Customer Type Ckt. Criticality s.f. Demand kW Annual kWh

Retail-Grocery 15125 H 15,000 462.86 2,980,031.07

Retail-Banking 15125 M 1,000 25.84 54,554.87

Retail-Banking M 1,500

Retail-Food Service 15125 O 2,000 34.13 133,541.41


Retail-General 15125 L 15,000 135.39 467,971.03

Retail-General 15125 L 1,000 20.04 57,331.46


Retail-Food Service O 2,000




Olney Town Center (North)

Retail-Grocery and Pharmacy

H 35,000


Retail-Auto Service

L 2,500 22.81 55,633.14

Retail-Food Service 15125 L 2,000 76.57 351,485.30


Retail-Food Service

O 500 47.49 188,448.11

Retail-Food Service 15125 O 3,000

Retail-Banking 15125 M 1,500

Retail-Banking 15125 M 1,500

Retail-General L 1,000



Retail-Auto Fuel H 1,500 22.62 16,216.77

Retail-Food Service 15119 L 1,000 24.10 96,706.21

Retail-Banking 15125 M 2,000 16.10 47,126.64

Retail-Pharmacy 15119 H 10,000 64.04 347,067.74



15119 H 50,000 400.44 2,420,443.59


15119 H 70,000 361.46 2,319,537.14

Retail-Banking 15119 M 20,000 191.54 1,030,880.71

Retail-Banking 15119 M 2,000 50.26 117,804.87

Retail-Auto Repair 15119 L 1,500 18.86 80,635.31

Retail-Banking 15119 M 2,000 35.30 12,169.87



Retail-Food Service 15125 O 1,500

Healthcare-Physician Services

H 2,000

Retail-Auto Fuel 15119 H 1,000 58.61 344,160.08

Olney Shopping Center




Retail-Pharmacy 15126 H 5,000 6.04 19,890.06






Retail-Food Service 15119 O 300 16.94 42,804.46

Retail-Banking 15119 M 1,000 38.06 75,395.07


Retail-Convenience 15126 H 1,500 44.48 245,951.62




Retail-Auto Repair L 8,000


Other Key Sites

Public Shelter-Library 15126 H 30,000 127.76 415,206.49

Healthcare-Hospital 15119 H 552,200

Public Shelter-School 15126 H 60,000 244.14 538,032.58

Public Shelter-School 15126 H 10,000 40.84 53,077.25


15126 H 4,000 39.91 78,677.77

Public Water Services H 0


15126 H 4,000 6.61 5,324.71


15119 M 2,000 102.68 37,539.80


15119 H 15,000 188.24 667,219.68


15119 H

Healthcare-Elder Care

15125 M 20,000 53.49 155,486.02



15125 H 3,000 23.12 66,735.88

First Response-Police H 300

Healthcare-Veterinary Services

15129 L 2,000 54.18 80,740.48

Fire Stations

First Response-Fire Station

15254 M 7,000 29.36 130,632.14


15125 H 10,000 41.65 217,956.37


15119 H 3,000 29.20 119,727.86

Notes: Customer locations derived from Google Earth and openstreetmap.org na = not available


Appendix D: References

District of Columbia Public Service Commission, “Environmental Information for Standard Offer Service

Provided by Pepco” for calendar year 2013 (ref. 30906-1-0309) http://www.pepco.com/uploadedFiles/wwwpepcocom/Content/Page_Content/my-

home/Pay_Your_Bill/Pepco%20Fuel%20Mix%20DC%20Insert%2011.14%20FINAL.pdf

“10-Year Forecast Cost Estimating Data (2015-2024),” Pepco Holdings Inc., Rev. 7/29/2014 (Internal planning document)

EPRI Use Case Repository http://smartgrid.epri.com/Repository/Repository.aspx

http://smartgrid.epri.com/Repository/Repository.aspx


Appendix E: Ritchie Station Design Summary

Project Description: Examined as a conceptual project, a microgrid was considered to provide resilient energy supplies for the Ritchie Station Marketplace in Prince Georges County, Md., a mixed-use retail property with around 1 million square feet of floor space plus outdoor parking and substantial cleared space for future site expansion. In addition to maintaining energy services for numerous retail locations that serve the community, the project envisioned resilient electric vehicle (EV) charging and sites for charging phones, computers, and other mobile devices, as well as wi-fi hotspots for communication and Internet access. The concept also envisioned temporary facilities for emergency management services.

Resiliency Justification: Ritchie Station Marketplace is located off the Capital Beltway (I-495). It can serve as a temporary evacuation site during storm outages, and also can host temporary facilities and staging areas for emergency management personnel and vehicles. The location includes retail, food service, and auto-fuel businesses that become increasingly critical during long-duration outages.

Operational Objectives:

- Provide economical electric service, and also space heating and cooling for larger stores

- Support highly reliable 24/7 operations

- Seamless transitioning between island and grid-connected operations

- Capability to support priority loads during long-duration outages (1 day or longer)

- Integrate onsite PV generation with natural gas-fired CHP and stand-alone gensets

- Provide ancillary services to utility local distribution system

- Provide EV charging facilities (Levels 2 and 3)

Proposed DER Systems: Multiple onsite energy systems provide resilient electricity and thermal energy production for the microgrid, including CHP, PV and battery energy storage systems (ESS) supplying about 85% of the electricity used by 11 store sites at Ritchie Station Marketplace.

FIG. A: RITCHIE STATION MICROGRID DERS

DER Source Phase 1 Phase 1 and 2

Sites

served

kW Sites served kW

Parking Lot PV 4 350 4, 7, 9 1,700

NG Engine CHP (248 kW) 11 248 11 248

NG Engine CHP (358 kW) 1 358 1, 2, 10 1,074

Energy Storage (12 hour) 2, 6 153 2, 6, 8, 10,

11

642

NG Engine (358 kW) 2 358 3 716

NG Engine (248 kW)

11 248


FIG. B: RITCHIE STATION MICROGRID LOADS Phase 1 Phase 1 and 2

Peak Load (kW) 1,700 4,882

Peak Microgrid Capacity (kW) 1,470 4,990

FIG. C: RITCHIE STATION EV CHARGERS

EV Chargers Phase 1 Phase 1 and 2

Sites

served

kW Sites served kW

Level 2 EV Chargers 6 10 6, 7, 8, 9 24

Level 3 EV Chargers 6 2 6, 7, 8, 9 6

FIG. D: RITCHIE STATION DISTRIBUTION FEEDERS

Local Distribution

Feeders

Phase 1 Phase 1 and 2

Sites

served

kW Sites served kW

13.8kV/480V 500kVA 9 500 9, 10 1,000

13.8kV/480V 1,000kVA

3, 7 2,000


FIG. E: RITCHIE STATION MICROGRID - ONELINE DIAGRAM:


FIG. F: RITCHIE STATION MICROGRID SITE PLAN


Appendix F: Black Start Procedure

Black start procedures for the microgrid system address process steps and coordination requirements. Functional steps address requirements for a microgrid that connects geographically dispersed resources and loads, and multiple end-use equipment and DER types. Each step assumes successful completion of prior steps, and continued system stability. FIG. A: MAJOR FUNCTIONAL STEPS IN THE BLACK-START PROCEDURE

Function Step Description

Verification Verify state of electrical generation Verify status of protection Verify state of communication Verify state of environmental systems and conditions Verify any equipment on UPS power

Preparation Deploy resources to various locations Communicate with third-party DER owners Communicate to customers Set electrical topology Enable load shedding Enable load modulation Enable emergency shedding

BESS Startup Operation Start BESS

Base Generation Startup Operation Start or re-engage CHP unit(s)

PV Inverter Startup Operation Start PV inverters

Load Restoration Restore emergency shedding

MMC Operations Engage stability operations of the MMC

Post Verification Verify protection settings Verify electrical topology Verify generation (expected, normal operations) Verify any equipment on UPS power


Figure B diagrams the verification steps. The primary goal of the procedure is to establish and verify the state of the system. FIG. B: BLACK START VERIFICATION FLOW DIAGRAM

Figure C describes black-start preparation – e.g., the basic steps for preparing the microgrid for energization. Note that black-start depends on uninterruptible power supply (UPS) for auxiliary equipment, with sufficient capacity to support the entire black-start procedure. Human operators are required to make visual inspection and in some cases to manually set switch position. FIG. C: BLACK-START PREPARATION FLOW DIAGRAM


Load reduction is accomplished by several methods. The first is load shedding, which can apply to entire services or sections of circuits. Load modulation occurs at service locations with the capability to interface with building management systems, residential water heaters, or other controllable systems. Both load shedding and modulation are normally enabled during islanding mode. Loads and load groups also may be shed using utility AMI systems. Emergency loads are prioritized and protected with UPS systems. Figure D describes the steps in energizing the microgrid to enter normal operations. The goal is to deploy sufficient generation to support restoration of additional loads, starting with emergency loads. Once engaged, the MMC will attempt to stabilize the battery SoC. Both generation and load are restored gradually, in increments.


FIG. D: BLACK-START OPERATIONS FLOW DIAGRAM

Volume 3 – Test Results and Analysis … Olney Town Center Microgrid Project – Final Report 1


public-purpose resiliency and sustainability Final Report - Olney Town Center Microgrid Control System R&D Project

Vol. 3: Test Results and Analysis Sections: Introduction: ................................................................................................................................................. 1

A. Energy Management – Grid-Connected ................................................................................................... 2

B. Ancillary Service – Demand Response .................................................................................................... 19

C. Energy Management – Islanded ............................................................................................................. 26

D. Intentional Islanding - Stability ............................................................................................................... 38

E. Unintentional Islanding – Stability .......................................................................................................... 44

F. Island-to-Grid Transition ......................................................................................................................... 50

G. Reliability Evaluation .............................................................................................................................. 53

H. Microgrid Blackout/Black Start Procedure ............................................................................................. 61

H. Cyber Security – Grid Connected ............................................................................................................ 65

Introduction: The Project Team executed a series of tests and post-processing analysis to demonstrate and validate the core functional requirements and overall operating schemes of the microgrid controller to support the Project performance objectives. The Project use cases were used to define the triggers, actors, and functions required by the microgrid system and specifically the microgrid controller. Tests were performed in real-time simulation modeling using the Opal-RT simulation test environment at NCSU-FREEDM as described in the Test Plan (Annex D). The following notes highlight key outcomes from the testing process:

1. A key design factor for the Project is the desire to avoid over-building generation and storage systems, and thereby to reduce the cost premium that typically accompanies highly resilient energy systems. Accomplishing this required optimizing system sizing, design, and operations to serve critical load requirements on a priority basis. Project tests showed the size-optimized design meets resiliency needs in almost all test cases, with noteworthy exceptions during low solar production periods accompanied by high customer loads (Section D).

2. Microgrid testing and analysis showed that the microgrid design would over-perform on the 20% emissions-reduction goal established in the Statement of Project Objectives (SOPO; Annex A), reducing the area’s CO2 footprint by nearly 46% (Section A).


3. Microgrid testing and analysis showed that the microgrid design would meet the SOPO goal to increase efficiency by 20% with conservative CHP loading (Section A). Additional efficiency improvements would be achievable with increased CHP thermal energy utilization.

4. The design philosophy of the microgrid controller is to leverage local high-speed control functions at the device while allowing slower control and optimization to occur centrally. Accordingly, local DER controllers automatically regulate islanded microgrid frequency and voltage. Testing demonstrated this distributed control approach is effective for maintaining stable island-mode operations (Sections D, E and F).

5. To avoid reliance on vulnerable overhead distribution lines, the Project Team focused design efforts on reconfiguring the existing utility distribution system to rely primarily on underground lines, while continuing use of some overhead segments. Testing and analysis demonstrated that overhead lines are critical points of failure, creating ongoing reliability challenges. Analysis showed that the target 3.5 min./customer/year SAIDI performance could be achieved only with design revisions to replace two vulnerable overhead line segments, totaling about 0.18 mi. in length, with underground cables (Section G).

6. The utility partner specified that the microgrid should seek to minimize power exports to the local electric distribution system during grid-connected operations. Grid-connected energy management tests demonstrated that the control system effectively avoided power exports in most scenarios, with exceptions during periods of high PV irradiance and low loading conditions (Section A). Accordingly, the project’s economic analysis (Section A) excludes export revenues even though generation owners may receive compensation. This valuation approach is consistent with the resource-optimization outcomes noted in 1., above. However, it also limits potential to support ancillary services for the utility distribution system.

Each section describes the test runs and results relevant to the Project use-case performance analysis. More than 1,380 hours of scheduled tests were performed to generate the results. To optimize productive use of test-environment and processing time, duplicative test runs were avoided. TABLE 16: TEST CASE GUIDE

Test Case Report Section

Energy Management – Grid Connected A. Energy Management – Grid Connected

Energy Management – Islanded B. Energy Management – Islanded

Energy Management – Resiliency B. Energy Management – Islanded G. Reliability Evaluation

Ancillary Services – Demand Response C. Ancillary Services Ancillary Services – Power Management

Intentional Islanding – Grid Operator D. Intentional Islanding – Stability

Islanded – Stability

Unintentional Islanding - External E. Unintentional Islanding – Stability

Unintentional Islanding - Internal

Island-to-Grid Transition F. Island-to-Grid Transition

Microgrid Blackout H. Microgrid Black-Start Procedure

Cyber Security – Grid Connected I. Cyber-Security – Grid-Connected

A. Energy Management – Grid-Connected


Overview: The original test plan calls for running a set of grid-connected energy management scenarios during various conditions. Below is the basic test description and results narrative. Description: The purpose of this test group is to validate the ability of microgrid controller to optimally dispatch microgrid resources. This test plan focuses on grid-connected operation, based on a multi-objective optimization scheme that accounts for reliability, efficiency, economics and environmental impacts. This test group accounts for microgrid resource operating characteristics, resource status, forecasts and external pricing signals.

FIG. A-1: ENERGY MANAGEMENT – GRID-CONNECTED TEST PROCEDURES

Initialization Steps Run initialization script with test variation inputs, including varied optimization weight factors

Triggers Grid-connected operation Top-of-the-hour start computation

Actions The controller will generate a new forecast every 60 minutes and produce a new dispatch schedule

Expected States Resources are dispatched per dispatch schedule Electricity exports to adjacent systems are to be avoided

Metrics Amount of emissions (tons of CO2) saved during test Average microgrid system efficiency Cost ($) of operation and savings vs. grid baseline

Data Collected Customer load PCC import/export CHP generation during test PV generation during test BESS charge/discharge during test Cost of electricity from the wholesale market BESS SOC

Pass/ Fail Each Run Microgrid controller dispatches resources per schedule Capture cost savings per run Total of Runs Emission improvement meets the target Efficiency improvement meets the target Cost savings vs. grid baseline run without microgrid operation

Post Processing Each test produces a 24-hour run based on specific days. In post-processing analysis, test results of these days will be extrapolated to create one-year metrics. The full-year metrics will be used to determine the pass/fail criteria. Factors are based on wholesale PJM prices and the cost of natural gas. This will be averaged to show the cost per kWh.


Execution Notes: CHP electrical efficiency follows the function of output level. It operates with 30% efficiency at half load and 35% efficiency at full load. Local thermal loads (absorption chillers only) are modeled so that a conservative utilization of waste heat from CHP is captured during the simulation. Results Narrative: All the grid-connected energy management cases start with 20% BESS SOC, and seek an economically optimal solution to avoid (but not prohibit) exports of electric power to the utility distribution system. To coordinate a PCC zero-export objective and CHP maximum utilization, CHP is set to half load condition constantly throughout the day. The controller is tuned to minimize power exports. The controller dispatches energy storage proactively based on a multi-objective optimization method. The test pass/fail criteria are defined in Figure A-1. The following narrative and figures provide details on analysis from relevant test runs, as well as annual evaluation using linear-regression techniques. Test: Fall Minimum-kWh day / Sunny PV day (A8B1) The 24-hour dispatch results are shown in Figure A-2. Under a fall minimum-kWh day, load profile (blue) is relatively flat, varying between 250 kW and 350 kW. PV (green) peak power happens during the 13:00 hour. PJM wholesale prices are shown in black, reaching the lowest price of $23.60 at 3:00 and the highest price of $43.30 at 19:00. The battery (yellow) is set idle at night and most of the morning until 10:00. When PV output is above 400 kW, the battery is charged to 80% SOC for discharging later in the evening. Discharge begins at 18:00 to limit power imports during peak wholesale price periods. Figure A-3 shows the BESS SOC is managed to hold the same value as the initial SOC at the end of the day, indicating a sustainable operation of energy storage. Note that the system exceeded zero power export significantly at around noon due to high PV output and low load levels.


FIG. A-2: MICROGRID RESOURCES AND LOADS DISPATCH SCHEDULE FOR FALL MINIMUM KWH DAY TEST

FIG. A-3: BESS SOC FOR FALL MINIMUM KWH DAY TEST

Performance metrics are summarized in Figure A-4, including efficiency, emissions, and cost savings. Efficiency is calculated to include system energy losses in the utility T&D system, local distribution circuits, and generation. It is assumed that utility T&D system is 40% efficient while PV systems are 100% efficient. Losses of energy storage and CHP are considered during modeling phases. Under this test run, microgrid system efficiency meets the target by operating at 52.64% efficiency. It indicates a 31.61% efficiency improvement compared to the baseline.


CO2 emission is calculated based on the assumption that PV and BESS are both zero emission resources. Average emission factors for utility and CHP are 0.504 kg/kWh and 0.455 kg/kWh, respectively. CO2 emissions are reduced in microgrid scenario by 36.79%, meeting the target of 20% improvement. The cost savings are obtained by subtracting microgrid operation cost from the cost of only buying power from the utility. Microgrid operation cost consists of two parts: cost of CHP fuel input and cost of purchasing power from the utility. Under the scenario, the microgrid can save around $84.70 for the day, on average saving $3.50 per hour. FIG. A-4: ENERGY MANAGEMENT - GRID-CONNECTED PERFORMANCE SUMMARY FOR FALL MINIMUM KWH DAY TEST

Metrics Baseline Microgrid Improvement Rate (%)

Efficiency (%) 40.00 52.64 31.61

Total CO2 Emissions (kg) 3724.6 2354.2 36.79

Average Emission Factor (kg/kWh) 0.504 0.319 36.79

Microgrid Total Cost Savings ($) $84.73

Microgrid Average Hourly Cost Savings ($) $3.53

Test: Summer Maximum-kWh day / Sunny PV day The 24-hour dispatch results are shown in Figure A-5. On a summer maximum-kWh day, load profile (blue) increases in the morning and decreases in the afternoon. Peak load occurs at 13:00, coincident with PV (green) peak power. PJM wholesale prices are shown in black, having lowest price $30.80 at 4:00 and highest price $137.80 at 16:00. The battery (yellow) is charged at night during a low-price period to 80% SOC, so the BESS has enough capacity for later discharge. It is intensively discharged from 14:00 to 17:00 in order to limit power import from the utility during peak wholesale price period. Figure A-6 shows BESS SOC is managed to hold the same value as the initial SOC at the end of the day, indicating a sustainable operation of energy storage. It is worth noting that due to high load level, zero power export constraint is held for almost the whole day. FIG. A-5: MICROGRID RESOURCES AND LOADS DISPATCH SCHEDULE FOR SUMMER MAXIMUM-KWH DAY / SUNNY PV

DAY TEST


FIG. A-6: BESS SOC FOR SUMMER MAXIMUM-KWH DAY / SUNNY PV DAY TEST

Performance metrics are summarized in Table A-7, including efficiency, emissions and cost savings. Under this test run, microgrid system efficiency meets the target by operating at 48.81% efficiency. It indicates a 22.02% efficiency improvement, compared to the baseline. CO2 emissions are reduced in the microgrid scenario by 32.35%, meeting the target of 20% improvement. Under the scenario, the microgrid can save around $471.60 for the day, or $19.70 per hour on average. FIG. A-7: ENERGY MANAGEMENT - GRID-CONNECTED PERFORMANCE SUMMARY FOR SUMMER MAXIMUM-KWH DAY /

SUNNY PV DAY TEST



Efficiency (%) 40.00 48.81 22.02





Test #5 Summer Average kWh day / Sunny PV day (A5B1) The 24-hour dispatch results are shown in Figure A-8. On a summer average-kWh day, the load profile (blue) increases in the morning and reaches its first peak load of 531 kW at 12:00, followed by a second peak of 455 kW at 16:00. PJM wholesale prices are shown in black, having lowest price $17.30 at 3:00 and highest price $51.70 at 16:00. The battery (yellow) is charged at night during a low-price period to 80% SOC, so the BESS has enough capacity for later discharge. Then it is intensively discharged from 15:00 to 17:00 in order to limit power import from the utility during peak wholesale price period. Figure A-9 shows BESS SOC is managed to hold almost the same value as the initial SOC at the end of the day, indicating a sustainable operation of energy storage.


FIG. A-8: MICROGRID RESOURCES AND LOADS DISPATCH SCHEDULE FOR SUMMER MAXIMUM-KWH DAY / SUNNY PV

DAY TEST

FIG. A-9: BESS SOC FOR SUMMER MAXIMUM-KWH DAY / SUNNY PV DAY TEST

Performance metrics are summarized in Figure A-10, including efficiency, emissions, and cost savings. Under this test run, microgrid system efficiency meets the target by operating at 51.91% efficiency. It indicates a 29.78% efficiency improvement compared to the baseline. CO2 emissions are reduced in microgrid scenario by 36.90%, meeting the target of 20% improvement. Under the scenario, the microgrid can save around $108.70 for the day, or $4.50 per hour on average.


FIG. A-10: ENERGY MANAGEMENT - GRID-CONNECTED PERFORMANCE SUMMARY FOR SUMMER MAXIMUM-KWH DAY

/ SUNNY PV DAY TEST


Efficiency (%) 40.00 51.91 29.78





Test: Winter Maximum-kWh day / Sunny PV day The 24-hour dispatch results are shown in Figure A-11. On a winter maximum-kWh day, the load profile (blue) increases in the morning and reaches its first peak load of 551 kW at 11:00, followed by a second peak of 538 kW at 16:00. PJM wholesale prices are shown in black, having a price spike of $95.30 at 17:00. The battery (yellow) is charged at night during a low-price period until 80% SOC is achieved. Then it is intensively discharged from 7:00 to 10:00. The battery repeats its charging/discharging cycle from 12:00 to 20:00. The discharge behavior happens during the price peak period to reduce imports from the grid and in turn to achieve more economic benefits. Figure A-12 shows BESS SOC is managed to hold almost the same value as the initial SOC at the end of the day, indicating a sustainable operation of energy storage. FIG. A-11: MICROGRID RESOURCES AND LOADS DISPATCH SCHEDULE FOR MAX WINTER KWH / SUNNY DAY TEST


FIG. A-12: BESS SOC FOR MAX WINTER KWH / SUNNY DAY TEST

Performance metrics are summarized in Figure A-13, including efficiency, emissions, and cost savings. Under this test run, a sunny winter maximum-kWh day, the microgrid system operates at 46.91% efficiency, indicating a 17.27% efficiency improvement compared to the baseline. This test run is the only one among 12 test runs in which the system falls short of the targeted 20% efficiency improvement. The reason for a relatively low efficiency is that the test day is the winter maximum-kWh day with low thermal energy utilization under the model’s conservative CHP loading assumptions (absorption chillers only). CO2 emissions are reduced in the microgrid scenario by 38.06%, meeting the target of 20% improvement. Under the scenario, the microgrid can save around $149.10 for the day, $6.20 per hour on average. FIG. A-13: ENERGY MANAGEMENT - GRID-CONNECTED PERFORMANCE SUMMARY FOR MAX WINTER KWH / SUNNY

DAY TEST


Efficiency (%) 40.00 46.91 17.27





Test: Winter Minimum-kWh day / Sunny PV day The 24-hour dispatch results are shown in Figure A-14. Under winter minimum-kWh day, load profile (blue) increases in the morning and has its peak at 18:00. Like the fall minimum-kWh day, the profile (blue) is relatively flat. PJM wholesale prices are shown in black, having the first price peak of $33.50 in the morning from 8:00 to 10:00 and the second price peak of $37.10 at 17:00. The battery (yellow) is charged at night during low price period until 80% SOC is achieved. Then it has been intensively discharged from 7:00 to 9:00 to avoid a large power purchase from the grid. The battery repeats its charging/discharging cycle from 12:00 to 20:00. Its charge behavior corresponds to the second price valley, and discharge behavior happens during the second price peak. Figure A-15 shows BESS SOC is managed to hold almost the same value as the initial SOC at the end of the day, indicating a sustainable


operation of energy storage. It can be seen from PCC power (gray) that in this scenario, the microgrid has net energy flow from microgrid to the utility at the end of the day. FIG. A-14: MICROGRID RESOURCES AND LOADS DISPATCH SCHEDULE FOR WINTER MIN / SUNNY PV DAY TEST

FIG. A-15: BESS SOC FOR WINTER MIN / SUNNY PV DAY TEST

Performance metrics are summarized in Figure A-16, including efficiency, emissions and cost savings. Under this test run, microgrid system efficiency meets the target by operating at 49.16% efficiency, indicating a 22.89% efficiency improvement compared to the baseline. CO2 emissions are reduced in


microgrid scenario by 32.01%, meeting the target of 20% emission reduction. Under the scenario, microgrid can totally save around $61.40 for that day and an average saving of $2.60 per hour. FIG. A-16: ENERGY MANAGEMENT - GRID-CONNECTED PERFORMANCE SUMMARY FOR WINTER MIN / SUNNY PV DAY

TEST


Efficiency (%) 40.00 49.16 22.89





Annual Evaluation Test results from multiple tests are analyzed to support full-year analysis. Annual efficiency and emission metrics are calculated using linear regression techniques. A detailed description of data extrapolation is broken into two parts: efficiency and emissions. Efficiency A summary of microgrid efficiency metrics from 12 test runs is shown in Figure A-17. Test runs that meet the target are marked in green. The microgrid system meets or exceeds the 48% operating efficiency target on all test days except Dec. 02. FIG. A-17: ENERGY MANAGEMENT - GRID-CONNECTED EFFICIENCY PERFORMANCE SUMMARY

Test Day 0501 0504 0513 0527 0702 0718 0902 0914 0922 1202 1224 1225

Efficiency 48.7% 50.6% 50.1% 50.8% 48.8% 51.9% 50.0% 52.6% 48.5% 46.9% 48.4% 49.2%

Efficiency for each test run is calculated from a set of measurements, including load consumption (both electrical and thermal), energy production from PV, CHP, and BESS, and PCC energy import/export. Microgrid efficiency for the full year assumes the same PV profile as is used for all the grid-connected tests, and conservatively assumes 50% loading of CHP units. On average, BESS energy production/consumption represent a small portion in the daily energy portfolio. However, the BESS is a vital asset in the system, sometimes performing two full charge and discharge cycles in a single day. PCC energy import/export is estimated using a linear regression (LR) model. The two first-order features in the LR model are electrical and thermal load consumption, as plotted in Figure A-18. Blue dots represent test results.


FIG. A-18: LINEAR REGRESSION MODEL FOR ESTIMATING PCC ENERGY IMPORT/EXPORT

Microgrid efficiency for the full year is calculated based on multiple inputs as shown as A-21 below. In A-19, the high-efficient days are marked as blue circles while days with lower-than-48% efficiency are all marked as circles containing a red x. It’s worth noting that this LR model is validated against test results and it proves to be highly accurate. FIG. A-19: EFFICIENCY METRICS FOR YEAR 2014


Figure A-20 shows the percentage of days that exceed or fall short of the efficiency criteria. In Year 2014, the microgrid operates with 48% or higher efficiency on 73% of days. All the kWh values are annual data based on day-to-day analysis. FIG. A-20: PERCENTAGE OF DAYS IN YEAR 2014 THAT MEET THE TARGET

FIG. A-21: ANNUAL EFFICIENCY EVALUATION

Baseline Case Energy Production (kWh)

Input Energy (kWh)

Losses (kWh) Efficiency (%)

Grid 3,276,500 8,191,250 4,914,750 40.00%

Microgrid Case Energy Production (kWh)

Input Energy (kWh)

Losses (kWh) Efficiency (%)

Grid 778,610 1,946,525 1,167,915 40.00%

PV 1,458,175 1,458,175 0 100.00%

ESS NA NA 16,304 86.46%

CHP Electric 1,096,825 3,656,083 2,419,188 30.00%

CHP Thermal 140,070 NA

CHP Net 1,236,895 3,656,083 2,419,188 33.83%

Total Microgrid 3,473,680 7,060,783 3,603,407 49.02%

Efficiency Improvement Rate

22.55%


In Figure A-21, annual ESS losses are calculated as a factor of battery charging and discharging cycles, and positive and negative changes in battery state of charge, for seasonal average-kWh days, using the formulas:

Daily: 11 1

_

lossesEff

kWh cap

Yearly:

365

12 1

_ 365

i

i

losses

EffkWh cap

where losses are all daily losses and kWh_cap represents the kWh capacity of the ESS. Emissions Under the analysis on CO2 emissions, average emission factors (AEFs) are the metrics that are extrapolated to the full year for performance evaluation. A summary of microgrid emission metrics from 12 test runs is shown in Figure A-22. Test runs that meet the target are marked in green, and they all substantially outperform on 0.40 AEF target. FIG. A-22: ENERGY MANAGEMENT - GRID-CONNECTED EMISSION PERFORMANCE SUMMARY

Test Day 0501 0504 0513 0527 0702 0718 0902 0914 0922 1202 1224 1225

Emissions 0.297 0.314 0.321 0.316 0.341 0.318 0.333 0.319 0.304 0.312 0.321 0.343

Slightly different from efficiency analysis, emissions are evaluated based on hourly emission curves. Figure A-23 shows an example of hourly emission curve for test run on 05/01/2014. Emissions in the daytime are lower than nighttime because of the contribution of zero emission daytime PV output. All the data points in hourly emission curves are fed to a three-order polynomial linear regression algorithm as data input, with the LR model addressing hour number and PCC energy import/export. Figure A-24 shows the LR model, which is used to obtain hourly emission curves for the rest of the year. FIG. A-23: SAMPLE HOURLY EMISSION CURVE


FIG. A-24: LINEAR REGRESSION MODEL FOR ESTIMATING EMISSIONS

As shown in Figure A-25, emission performance of the microgrid for each hour of the year is plotted on two curves: maximum (orange) and minimum (gray) emissions. The blue curve is the average, showing an AEF factor less than 0.4 from 8:00 am to 8:00 pm. Fig. A-26 summarizes the evaluation of total annual emissions performance. All the kWh values are annual data according to the previous day-to-day analysis. The annual microgrid AEF based on microgrid design and operation is estimated to be around 0.267, which overperforms on the annual emission target of 0.400. FIG. A-25: MAX/MIN/AVERAGE HOURLY EMISSION CURVES


FIG. A-26: ANNUAL EMISSIONS EVALUATION

Baseline Case Average Emission Factor (kg/kWh)

Energy Production (kWh) Total CO2 Emissions (kg)

Grid 0.504 3,276,500 1,651,356.000

Microgrid Case Average Emission Factor (kg/kWh)

Energy Production (kWh) Total CO2 Emissions (kg)

Grid 0.504 778,610 392,419.440

PV 0 1,458,175 0.000

ESS 0 0 0.000

CHP 0.455 1,096,825 499,055.375

Total Microgrid 0.267 3,333,610 891,474.815

Emission Reduction Rate

46.94%

Data set links: All the recorded raw data can be found in the Excel workbook titled: A-Energy Management-Grid Connected.xlsx Conclusions: FIG. A-27: ENERGY MANAGEMENT- GRID-CONNECTED CONCLUSIONS

Test Case Information

Test Case Name

Duration (Hour)

PV Type Load Type Trigger Event

Grid-connected Cases

24 Sunny Considering various seasons and load types

N/A

Performance Evaluation

Test Date

Efficiency

Emissions

Controller Status Pass/Fail Note

05/01/14 48.70% 0.297 Successfully schedule resources

Pass


Pass


Pass


Pass


Pass


Pass


Pass


Pass



Pass


Pass Daily efficiency lower than average target


Pass


Pass

Annual 49.20% 0.267 Successfully schedule resources

Pass

B. Ancillary Services Overview: The test plan calls for demonstrating ancillary service actions, including a. demand response and b. power management-export1, under various operating conditions. Below is the test description and results narrative. Description: The test will validate the controller’s ability to optimally dispatch microgrid resources while grid-connected, in response to a demand response or power management request from the ESP. The microgrid controller will optimize operations to achieve the ancillary service goals of both a) reduced microgrid demand and b) net exports to the utility distribution system.

FIG. B-1: ANCILLARY SERVICES TEST PROCEDURES

Initialization Steps Run initialization script with test-variation inputs.

Triggers Grid-connected. Top-of-hour start computation

Demand Response Actions

Controller will generate a new forecast every 15 minutes and produce a new dispatch schedule. The controller will respond to requests from the ESP to a. reduce load at the PCC or b. export electric power to the grid, for a period of one hour.

Power Export Actions

Time Resolution 100-microsecond simulation and DER control modeling 1-second Opal-RT data logging

Expected States Equipment should be dispatched according to dispatch schedule

Metrics Establish MMC response to an ancillary services request for: a) Demand Response or b) Power Export

Data Collected Customer load CHP generation during test PV generation during test

Pass / Fail Controller responds to ESP with relevant response to a. reduce load or b. export power.

Post Processing Generation output before the ancillary services call will be calculated. Generation output after the call will be computed. The difference and duration will be logged to determine if the call was honored. Load changes will be taken into account. After the ancillary services call is complete, the

1 The MMC settings and test conditions are identical for both ancillary services use cases tested – a) demand response and b) power export. The test results capture metrics for both types of ancillary services calls.


generation output will be noted to determine when to return to normal operating mode. Load management actions will be logged and accounted.

Results Narrative: All the following tests start at 5:33 a.m., with the microgrid operating in grid-connected mode. MMC operation mode is set to ancillary services response after tests begin. This is triggered by an external command to the system which the MMC uses to reschedule at the top of the hour. MMC Ancillary Services function allows the user to determine the start and duration of operation. When enabled the MMC attempts to generate more using the resources in the microgrid. The optimization addresses a 48-hour period to determine how much ESS is used during the ancillary services call period. Test: Spring Peak kW / Sunny PV Day In the case of spring peak-power load profile and sunny day PV generation profile, a one-hour test is performed to validate that the MMC is able to dispatch the BESS and CHP based on an ancillary services scheme under various system operation conditions. The simulation results are presented in the following plots (Figures B-2 through B-6), where positive power means power generation while negative power means power consumption: FIG. B-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR SPRING PEAK KW / SUNNY PV DAY

FIG. B-3: RECORDED PV GENERATION FOR SPRING MAX KW / SUNNY PV DAY

-500.00

-400.00

-300.00

-200.00

-100.00

0.00

Tim

e (

s)

5:45

AM

6:0

0 A

M

6:1

5 A

M

6:30

AM

Customer Load (KW)

0.00

0.50

1.00

1.50

2.00

Tim

e (s

)

5:4

5 A

M

6:00

AM

6:15

AM

6:30

AM

PV Generation (KW)


FIG. B-4: RECORDED BESS GENERATION FOR SPRING PEAK KW / SUNNY PV DAY

FIG. B-5: RECORDED CHP GENERATION FOR SPRING PEAK KW / SUNNY PV DAY

FIG. B-6: RECORDED PCC IMPORT/EXPORT POWER FOR SPRING PEAK KW / SUNNY PV DAY

In this case, BESS and CHP operate under dispatch mode. The MMC dispatches both BESS and CHP every 15 minutes based on its ancillary services scheme. Test: Fall Peak kWh / Sunny PV Day In the case of a fall peak-power consumption load profile and sunny day PV generation profile, a one-hour test is performed to validate that the MMC is able to dispatch the BESS and CHP based on the ancillary services scheme under various system operation conditions.

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

Tim

e (s

)

5:45

AM

6:00

AM

6:15

AM

6:30

AM

BESS Generation (KW)

0.0050.00

100.00150.00200.00250.00300.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM

CHP Generation (KW)

0.00

50.00

100.00

150.00

200.00

250.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM

PCC Import/Export (KW)


The simulation results are presented in the following plots (Figures B-7 through B-11), where positive

power means power generation while negative power means power consumption:

FIG. B-7: RECORDED CUSTOMER LOAD CONSUMPTION FOR FALL PEAK KW / SUNNY PV DAY TEST

FIG. B-8: RECORDED PV GENERATION FOR FALL PEAK KW / SUNNY PV DAY TEST

FIG. B-9: RECORDED BESS GENERATION FOR FALL PEAK KW / SUNNY PV DAY TEST

-600.00-500.00-400.00-300.00-200.00-100.00

0.00

Tim

e (

s)

5:45

AM

6:0

0 A

M

6:1

5 A

M

6:30

AM

Customer Load (KW)

0.00

0.50

1.00

1.50

2.00

Tim

e (s

)

5:4

5 A

M

6:00

AM

6:15

AM

6:30

AM

PV Generation (KW)

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

Tim

e (s

)

5:45

AM

6:0

0 A

M

6:15

AM

6:3

0 A

M



FIG. B-10: RECORDED CHP GENERATION FOR FALL MAX KW / SUNNY PV DAY TEST

FIG. B-11: RECORDED PCC IMPORT/EXPORT POWER FOR FALL PEAK KW / SUNNY PV DAY TEST

In this case, BESS and CHP operate under dispatch mode. The MMC dispatches both BESS and CHP every 15 minutes based on its demand-response scheme. Test: Spring Max kW / Sunny PV Day In the case of a spring maximum energy consumption load profile and sunny day PV generation profile, a one-hour test is performed to validate that the MMC is able to dispatch BESS and CHP based on ancillary services scheme under various system operation conditions. The simulation results are presented in the following plots (Figures B-12 through B-16), where positive power means power generation while negative power means power consumption:

0.0050.00

100.00150.00200.00250.00300.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM

CHP Generation (KW)

0.00

100.00

200.00

300.00

400.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM



FIG. B-12: RECORDED CUSTOMER LOAD CONSUMPTION FOR SPRING MAX KW / SUNNY PV DAY

FIG. B-13: RECORDED PV GENERATION FOR SPRING MAX KW / SUNNY PV DAY

FIG. B-14: RECORDED BESS GENERATION FOR SPRING MAX KW / SUNNY PV DAY

-500.00

-400.00

-300.00

-200.00

-100.00

0.00T

ime

(s)

5:45

AM

6:0

0 A

M

6:1

5 A

M

6:30

AM

Customer Load (KW)

0.00

0.50

1.00

1.50

2.00

Tim

e (s

)

5:4

5 A

M

6:00

AM

6:15

AM

6:30

AM

PV Generation (KW)

-20.000.00

20.0040.0060.0080.00

100.00120.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM



FIG. B-15: RECORDED CHP GENERATION FOR SPRING MAX KW / SUNNY PV DAY

FIG. B-16: RECORDED PCC IMPORT/EXPORT POWER FOR SPRING MAX KW / SUNNY PV DAY

In this case, BESS and CHP operate under dispatch mode. The MMC dispatches both BESS and CHP every 15 minutes based on its demand-response scheme. Data set links: All the recorded raw data can be found in the Excel workbook titled: B-Ancillary Service.xlsx Conclusions: FIG. B-17: ANCILLARY SERVICES TEST CONCLUSIONS

Duration (Hour)

PV Type

Load Type Trigger Event SoC Maintained Successfully?

Test Run Passes the Design Metrics?

Notes

1 Sunny Summer Max kW

a. Demand Response

N/A a. Pass

b. Power Export b. Pass

1 Sunny Fall Max kW

a. Demand Response

N/A a. Pass

0.0050.00

100.00150.00200.00250.00300.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM

CHP Generation (KW)

0.00

50.00

100.00

150.00

200.00

250.00

Tim

e (s

)

5:45

AM

6:00

AM

6:1

5 A

M

6:30

AM




1 Sunny Spring Max kW

a. Demand Response

N/A a. Pass


C. Energy Management – Islanded Overview: The test plan calls for running islanded energy management scenarios under various operation conditions. Below is the test description and results narrative. Description: The test will validate the controller’s ability to optimally dispatch microgrid resources in islanding mode. Island-mode operations are based on a multi-objective optimization scheme that accounts for reliability, economics, and environmental impact. In these tests, reliability will be the highest-weighted priority objective. Island-mode tests will account for microgrid resource operating characteristics, resource status, load forecasts, estimated outage duration, and generation and load balancing with sufficient reserve. Optimization algorithms will factor economics and environmental considerations when calculating generation-dispatch options. Energy Management – Islanded tests also will support analysis of microgrid system resiliency. Resiliency performance will be calculated by analyzing critical loads served for multiple days with low PV outputs.

FIG. C-1: ENERGY MANAGEMENT- ISLANDED TEST PROCEDURES

Initialization Steps

Run initialization script with test-variation inputs.

Triggers Islanded

Time Resolution 100-microsecond simulation and DER control modeling 1-second Opal-RT data logging

Expected States Equipment should be dispatched according to dispatch schedule

Metrics Minimum/maximum of available generation for critical load in microgrid A resilience run of multiple days will be computed based on low PV output.

Data Collected Customer load CHP generation during test PV generation during test SOC initialized each day at the appropriate level based on previous day type

Pass / Fail System dispatched resources according to schedule Load shed and modulation actions are appropriate

Post Processing Test produces a 24-hour run based on specific days. The total year will be used to determine the pass/fail criteria. Factors are based on wholesale PJM prices and the cost of natural gas. This will be averaged to show the cost per kWh. Resiliency performance will be calculated by analyzing critical loads served for multiple days with low PV outputs.


Execution Notes: Several techniques were applied to ensure stable islanded microgrid operation. To enable a stable transfer from grid-connected mode to islanded mode, an intentional island procedure is implemented in the MMC to minimize power exchange at the PCC before opening the breaker. Such control over PCC power flow is achieved by load shedding and changing the power set point in the BESS. The breaker is opened once the PCC power flow is minimized to a configurable limit of plus or minus 1 kW. After islanding, the BESS operation mode is switched from dispatch mode to stabilizing mode. The BESS inverter in stabilizing mode operates as a voltage source converter under voltage control mode (VSC-VCM). Local controllers are implemented in the BESS to automatically regulate islanded microgrid frequency and voltage. When the microgrid operates under islanded mode, the SoC of BESS is kept at a predefined value (Rated SoC) for resiliency of microgrid operation. Such SoC control is achieved by PV curtailment and CHP dispatching. Results Narrative: All the following tests start at 5:33 a.m. The microgrid operates under grid-connected mode initially. The intentional island procedure is implemented after the test begins and the main breaker opens. The amount of time it takes for the main breaker to open depends on system operation conditions. Test #2 Summer Max kW day / Sunny PV day (A2B1) In the case of a summer peak-power consumption load profile and sunny PV generation profile, a 24-hour test is performed to validate system resiliency under the test-case load conditions. Rated SoC is set to 65% and initial SoC is set to 80%. The simulation results are presented in the following plots (Figures C-2 through C-6). Positive power indicates power generation while negative power indicates power consumption: FIG. C-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR TEST #2

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Customer Load (KW)


FIG. C-3: RECORDED PV GENERATION FOR TEST #2

FIG. C-4: RECORDED BESS GENERATION FOR TEST #2

FIG. C-5: RECORDED CHP GENERATION FOR TEST #2

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CHP Generation (KW)


FIG. C-6: RECORDED SOC CHANGE FOR TEST #2

In this case, the microgrid is able to operate under islanded mode through the entire test. SoC of the BESS is successfully kept around 65%. After islanding, CHP operates at rated power output and the BESS is used to track load changes. As PV generation increases, PV curtailment is first initiated at around 11:06 a.m. to maintain SoC. As load consumption decreases, PV is curtailed again at around 03:16 p.m. to maintain SoC. Load consumption continues decreasing and PV is almost fully curtailed, until the MMC starts to dispatch CHP at around 06:53 p.m. to maintain SoC.

Test #3 Fall Max kW day/ Sunny PV day (A3B1) In the case of a fall peak power-consumption load profile and sunny PV generation profile, a 24-hour test is performed to validate system resiliency under the test-case conditions. Rated SoC is set to 65% and initial SoC is set to 80%. The simulation results are presented in the following plots (Figures C-7 through C-11), where positive power means power generation and negative power means power consumption: FIG. C-7: RECORDED CUSTOMER LOAD CONSUMPTION FOR TEST #3

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Customer Load (KW)





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PV Generation (KW)

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CHP Generation (KW)



In this case, the microgrid is able to operate under islanded mode through the entire test. The BESS SoC is successfully kept around 65%. The CHP units operate at rated power output and the BESS is used to track load changes. As PV generation increases, PV curtailment is initiated at around 11:40 a.m. to maintain SoC. As load consumption decreases, PV is curtailed again at around 03:50 p.m. to maintain SoC. As load consumption continues decreasing and PV has been almost fully curtailed, the MMC starts to dispatch CHP at around 04:33 p.m. to maintain SoC. After 09:23 p.m., as load consumption becomes stable and PV generation nears zero, CHP generation is able to support most of the electric load, and there are only minor system frequency/voltage deviations requiring regulation by the BESS. SoC changes little, and CHP is dispatched to output constant power. Test #4 Winter Max kW day/ Sunny PV day (A4B1) In the case of a winter peak-power consumption load profile and sunny PV generation profile, a 24-hour test is performed to validate system resiliency under the test-case conditions. Rated SoC is set to 80% and initial SoC is set to 60%. The simulation results are presented in the following plots (Figures C-12 through C-16), where positive power means power generation and negative power means power consumption:

FIG. C-12: RECORDED CUSTOMER LOAD CONSUMPTION FOR TEST #4

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In this case, the microgrid is able to operate under islanded mode through the entire test. The BESS SoC is successfully kept around 80%. After islanding, load consumption is relatively small and CHP generation is able to support most of the electric load. PV curtailment and CHP dispatch are implemented to maintain SoC. After 05:13 p.m., as load consumption becomes stable and PV generation nears zero, CHP generation is able to support most electric loads and there are only minor system frequency/voltage deviations requiring regulation by BESS. SoC changes little and CHP is dispatched to output constant power. Test #17 Summer Max kW day / Rainy PV day (A2B4) In the case of a summer peak power consumption load profile and rainy-day PV generation profile, an 8-hour test is performed to validate system resiliency in low-PV generation conditions. Rated SoC is set to 80% and initial SoC is set to 65%. The simulation results are presented in the following plots (Figures C-17 through C-21), where positive power means power generation and negative power means power consumption:


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CHP Generation (KW)



In this case, the microgrid is not able to operate under islanded mode through the whole test. SoC of BESS is not successfully kept around 65%. After islanding, CHP continues generating at rated power. Because of low PV generation, the BESS is heavily dispatched to track the high energy load. At around 08:13 a.m., the SoC reaches zero and BESS is no longer available. Under these conditions, critical load cannot be fully supported within the islanded microgrid. The microgrid will collapse at this point when the BESS can no longer support the load and/or the safety functions of the BESS cause it to shut down. The test results indicate need to expand generation or storage capacity within the microgrid to manage high-load, low-PV conditions during long-duration utility outages. Other options include manual intervention to reduce load beyond the pre-programmed amounts. The simulation keeps running until the end of the test time for the purpose of demonstration. Test #19 Summer Max kW day / PV Trip (A2C2) In the case of a summer peak power consumption load profile and sunny PV generation profile, a 2-hour test is performed to validate system resiliency after PV tripping. Rated SoC is set to 65% and initial SoC is set to 80%. (See Figures C-22 through C-26).


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In the test case, the microgrid is able to operate under islanded mode through the entire test. However, the BESS SoC declines below the target of 65%. After islanding, CHP operates at rated power output and the BESS is used to track load change. At 06:40 a.m. the PV trips off line. The microgrid is able to maintain stable island-mode operation after PV tripping. CHP operates at rated power output and BESS is used to track load change. Because the BESS is heavily loaded, the SoC declines to less than 30% when the two-hour test is complete. Resiliency: A specific long running test was conducted to understand the resiliency of the system design and controller. The test was run in excess of 60 hours and the system was maintained. Simulation challenges prevented the tests from continuing beyond 60 hours. Data set links: All the recorded raw data can be found in the Excel workbook titled: C-Energy Management-Islanded.xlsx

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Conclusions: FIG. C-27: ENERGY MANAGEMENT- ISLANDED TEST CONCLUSIONS

Duration (Hour)

PV Type

Load Type

Trigger Event

SoC Maintained Successfully?


Notes

24 Sunny Summer Peak KW

N/A Yes Pass

24 Sunny Fall Peak KW

N/A Yes Pass

24 Sunny Winter Peak KW

N/A Yes Pass

8 Rainy Summer Peak KW

N/A No Fail System fails because of insufficient energy generation

2 Sunny Summer Peak KW

PV Trip No Fail SoC maintenance fails because of insufficient power generation

D. Intentional Islanding - Stability Overview: The test plan calls for running intentional islanding scenarios under various operation conditions. Below is the test description and results narrative. Description: The purpose of this test group is to validate the microgrid system’s ability to manage an island transition, and provide an active solution that maintains the stability of the island. FIG. D-1: INTENTIONAL ISLANDING - STABILITY TEST PROCEDURES



Triggers Manual triggering of an island.

Actions The MMC Controller will trip the PCC, set settings of the CHP and BESS, reduce load, and enable a SoC resiliency strategy.

Expected States Default voltage and frequency tripping tolerances on DER units; may be modified if required to prevent over-voltage and over-frequency conditions.

Time Resolution 100-microsecond simulation and DER control modeling 200-microsecond Opal-RT data logging 500-ms MMC data capture

Metrics Dynamic Stability Min / max of available generation for critical load in microgrid


Data Collected Customer load (various load scenarios) CHP generation during test PV generation during test (various generation scenarios) Millisecond transition times from the simulator

Pass / Fail Islanding is successful based on each test variation Timing limits for islanding honored (less than 160 ms) Load shedding and modulation reduction is appropriate given generation capability and forecasts.

Post Processing Data captured by Opal-RT will be reviewed to evaluate the transitions during the time of islanding. The generation output will be captured from the controller prior to the islanding and after. For each trigger following the islanding event the controller changes will be logged along with the Opal-RT data to observe the effects.

Execution Notes: Several techniques were applied to ensure stable islanded microgrid operation. To enable a stable transfer from grid-connected mode to island mode, an intentional island procedure is implemented in the MMC to minimize the power exchange at PCC before opening the breaker. Such control on PCC power flow is achieved by load shedding and changing the power set point in the BESS. The breaker opens once the PCC power flow is minimized to a configurable limit of plus or minus 1 kW. Once the microgrid begins island-mode operations, the operation mode of the BESS is switched from dispatch mode to stabilizing mode. The BESS inverter in stabilizing mode operates as voltage source converter under voltage control mode (VSC-VCM). Local controllers are implemented in BESS to automatically regulate islanded microgrid frequency and voltage. Results Narrative: All the following tests start at 5:33 am. The microgrid operates under grid-connected mode initially. The intentional island procedure is implemented after the test starts and the main breaker opens once the procedure is done. The amount of time it takes for the main breaker to open depends on system operating conditions. Test #1 Summer Average kWh day / Rainy day / Large Motor Startup (A5B4E1) In this case, summer average energy consumption load profile and rainy-day PV generation profile are adopted. This is a 1-hour test and the purpose is to validate system resiliency with startup of a large motor. The simulation results are presented in the following plots (Figures D-2 through D-7), where positive power means power generation and negative power means power consumption:


FIG. D-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR TEST #1

FIG. D-3: RECORDED PV GENERATION FOR TEST #1

FIG. D-4: RECORDED BESS GENERATION FOR TEST #1

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FIG. D-5: RECORDED CHP GENERATION FOR TEST #1

FIG. D-6: RECORDED SYSTEM FREQUENCY FOR TEST #1

FIG. D-7: RECORDED SYSTEM PCC VOLTAGE FOR TEST #1

In this case, the microgrid is able to operate under islanded mode through the entire test. After islanding, CHP generation is dispatched to support the bulk of the load and there are only minor system frequency/voltage deviations that need to be regulated by the BESS. At around 06:10 a.m., an extra 50 kW motor load is introduced. Local controllers implemented in the BESS are able to stabilize system frequency and voltage by increasing power output at the instant of load switching. After the transient, CHP is dispatched to operate at rated power output and the BESS is used to track load change.

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Test #3 Summer Average kWh day / Rainy day / CHP Trip (A5B4C1) In this case, summer average energy consumption load profile and rainy day PV generation profile are adopted. This is a 1-hour test and the purpose is to validate the system resiliency with a CHP trip. The simulation results are presented in the following plots (Figures D-8 through D-13), where positive power means power generation while negative power means power consumption: FIG. D-8: RECORDED CUSTOMER LOAD CONSUMPTION FOR TEST #3

FIG. D-9: RECORDED PV GENERATION FOR TEST #3

FIG. D-10: RECORDED BESS GENERATION FOR TEST #3

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FIG. D-11: RECORDED CHP GENERATION FOR TEST #3

FIG. D-12: RECORDED SYSTEM FREQUENCY FOR TEST #3

FIG. D-13: RECORDED SYSTEM VOLTAGE FOR TEST #3

In this case, the microgrid is able to operate under islanded mode throughout the entire test. After islanding, CHP generation is dispatched to support the bulk of the load and there are only minor system frequency/voltage deviations that need to be regulated by BESS. At around 06:08 a.m., the CHP trips off line. Local controllers implemented in BESS are able to stabilize system frequency and voltage by increasing power output at the instant of load change. After the transient, the BESS is used to track load change. Data set links: All the recorded raw data can be found in the Excel workbook titled: D-Intentional Islanding-Stability.xlsx

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59.8859.9

59.9259.9459.9659.98

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Conclusions: FIG. D-14: ENERGY MANAGEMENT- ISLANDED TEST CONCLUSIONS

Duration (Hour)

PV Type

Load Type Trigger Event SoC Maintained Successfully?

Test Run Passes Design Metrics?

Notes

1 Rainy Summer Avg. kWh

Motor Load Startup

N/A Pass

1 Rainy Summer Avg. kWh

CHP Trip N/A Pass

E. Unintentional Islanding – Stability Overview: The test plan calls for running islanding scenarios under various operation conditions with unintentional loss of utility power. Below is the test description and results narrative. Description: The purpose of this test group is to validate the microgrid’s ability to enter island mode based on external actions. Without a specific sequence of events for islanding, the stability of the microgrid is at greater risk. The controller will attempt to manage the risk. FIG. E-1: UNINTENTIONAL ISLANDING - STABILITY TEST PROCEDURES



Triggers PCC(s) open due to external protection issue

Actions Controller will set island mode and re-optimize system Controller sheds load to maintain system

Expected States Island mode enabled. Equipment dispatched according to dispatch schedule


Metrics Min / max of available generation for critical load in microgrid


Pass / Fail Controller transitions to island mode Controller develops new schedule and dispatches Load shedding and modulation reduction is appropriate given generation capability and forecasts.

Post Processing The data captured by Opal-RT will be reviewed to evaluate the transitions during the time of islanding based on the external fault. The generation output will be captured from the controller prior to the islanding and after. During the islanding event the controller changes will be logged along with the Opal-RT data to observe the effects.


Execution Notes: Several techniques were applied to ensure stable islanded microgrid operation. After islanding, the BESS operation mode is switched from dispatch mode to stabilizing mode. The BESS inverter in stabilizing mode operates as a voltage source converter under voltage control mode (VSC-VCM). Local controllers are implemented in the BESS to automatically regulate islanded microgrid frequency and voltage. Results Narrative: All the following tests start at 5:33 a.m. The microgrid operates under grid-connected mode initially. The microgrid breaker opens due to an unintentional external event. Consequently the intentional islanding procedure is not implemented. Test: Winter Average kW day / Sunny day / Loss of Grid In the case of a winter average energy consumption load profile and sunny day PV generation profile, a 1-hour test is performed to validate system resiliency when there is loss of the LDC system. The simulation results are presented in the following plots (Figures E-2 through E-7), where positive power means power generation while negative power means power consumption: FIG. E-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

FIG. E-3: RECORDED PV GENERATION FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

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FIG. E-4: RECORDED BESS GENERATION FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

FIG. E-5: RECORDED CHP GENERATION FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

FIG. E-6: RECORDED SYSTEM FREQUENCY FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

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FIG. E-7: RECORDED SYSTEM PCC VOLTAGE FOR WINTER AVERAGE KW / SUNNY DAY / LOSS OF GRID TEST

In this case, the microgrid is able to transfer from grid-connected mode to islanded mode upon the unintentional loss of the LDC system, and to operate under islanded mode throughout the entire test. The microgrid operates under grid-connected mode initially. At around 06:10 a.m., the PCC breaker opens unintentionally. The BESS inverter controls are able to stabilize system frequency and voltage by adjusting power output at the instant of switching. After the transient event, load-modulation and load-shedding protocols are applied in accordance with generation capability and forecast for each test. CHP generation is able to support most of the load, and only minor system frequency/voltage deviations need to be regulated by the BESS. Test: Summer Average kW / Sunny Day / External Fault In the case of a summer average energy consumption load profile and sunny day PV generation profile, a 1-hour test is performed to validate system resiliency when an external fault occurs. The simulation results are presented in the following plots (Figures E-8 through E-13), where positive power means power generation and negative power means power consumption: FIG. E-8: RECORDED CUSTOMER LOAD CONSUMPTION FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT

TEST

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FIG. E-9: RECORDED PV GENERATION FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT TEST

FIG. E-10: RECORDED BESS GENERATION FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT TEST

FIG. E-11: RECORDED CHP GENERATION FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT TEST

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FIG. E-12: RECORDED SYSTEM FREQUENCY FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT TEST

FIG. E-13: RECORDED SYSTEM VOLTAGE FOR SUMMER AVERAGE KW / SUNNY DAY / EXTERNAL FAULT TEST

In this test case, the microgrid is able to transfer from grid-connected mode to islanded mode in an unintentional loss of the LDC system, and to operate under island mode throughout the entire test. At around 06:10 a.m., the PCC breaker opens because of the unintentional external fault, and consequently the intentional islanding procedure cannot be performed. Local controllers implemented in the BESS are able to stabilize system frequency and voltage by adjusting power output at the instant of switching. After the transient event, load-modulation and load-shedding protocols are applied in accordance with generation capability and forecast. CHP generation is able to support most of the load and only minor system frequency/voltage deviations need to be regulated by the BESS. At around 06:13 a.m., as PV generation begins, PV curtailment is implemented by the MMC to balance the system and maintain the BESS SoC. CHP is re-dispatched. At 06:22 a.m., as PV generation increases, PV curtailment is implemented again and CHP is re-dispatched. Data set links: All the recorded raw data can be found in the Excel workbook titled: E-Unintentional Islanding-Stability.xlsx

59.99659.99759.99859.999

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Conclusions: FIG. E-14: UNINTENTIONAL ISLANDING - STABILITY TEST CONCLUSIONS

Duration (Hour)

PV Type

Load Type Trigger Event

SoC Maintained Successfully?

Passes Design Metrics?

Notes

1 Sunny Winter Average kWh

Loss of Grid Supply

N/A Pass

1 Sunny Summer Average kWh

External Fault

N/A Pass

F. Island-to-Grid Transition Overview: The test plan calls for running reconnection of an islanded microgrid to the LDC system under various operating conditions. Below is the test description and results narrative. Description: The purpose of this test group is to validate the microgrid’s ability to achieve seamless resynchronization back to the utility grid at the points of common coupling. This is a planned event and initiated at the utility control center.

FIG. F-1: ISLAND-TO-GRID TRANSITION TEST PROCEDURES

Initialization Steps Run initialization script with test-variation inputs.

Triggers Utility provides permission to resynchronize.

Actions Controller will prepare to issue control to PCC and change modes on CHP and/or ESS.

Expected States Equipment dispatched according to dispatch schedule


Metrics Successfully transition back to grid


Pass / Fail Reconnection within 2 minutes Operates appropriate switches for grid tied mode

Post Processing For each test both the controller generation and actions will be logged along with the Opal-RT data during the transitions. Timing for reconnection will be measured by the both the Opal-RT system and the Microgrid controller. Dynamic adjustments will be monitored during the enhanced test to speed up the reconnection process.

Execution Notes: The following steps document the islanding process as preparation for reconnection. Several steps were applied to manage stable islanded microgrid operation. To enable a stable transfer from grid-connected mode to islanded mode, an intentional-island procedure is implemented in the MMC to minimize the


power exchange at PCC before opening the breaker. Such control on PCC power flow is achieved by load shedding and changing the power set point of the BESS. After islanding, the operation mode of the BESS is switched from dispatch mode to stabilizing mode. The BESS inverter in stabilizing mode operates as a voltage source converter under voltage control mode (VSC-VCM). Local controllers are implemented in the BESS to automatically regulate islanded microgrid frequency and voltage. Results Narrative: All the following tests start at 5:33 am. The microgrid operates under grid-connected mode initially. When the test begins, the intentional island procedure is implemented for initialization. Test: Spring Min kW / Partial Cloudy Day In the case of a spring minimum energy consumption load profile and partial cloudy day PV generation profile, a 1-hour test is performed to validate the system resiliency during the transition from islanded mode to grid-connected mode. The simulation results are presented in the following plots (Figures F-2 through F-7), where positive power means power generation while negative power means power consumption: FIG. F-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR SPRING MIN KW / PARTIAL CLOUDY DAY

FIG. F-3: RECORDED PV GENERATION FOR SPRING MIN KW / PARTIAL CLOUDY DAY

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FIG. F-4: RECORDED BESS GENERATION FOR SPRING MIN KW / PARTIAL CLOUDY DAY

FIG. F-5: RECORDED CHP GENERATION FOR SPRING MIN KW / PARTIAL CLOUDY DAY

FIG. F-6: RECORDED SYSTEM FREQUENCY FOR SPRING MIN KW / PARTIAL CLOUDY DAY

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FIG. F-7: RECORDED SYSTEM PCC VOLTAGE FOR SPRING MIN KW / PARTIAL CLOUDY DAY

In this test case, the microgrid is able to perform seamless transfer from island mode to grid-connected mode. After islanding, the CHP operates at constant power output and the BESS is used to track load change. At around 06:09 a.m., the reconnection procedure is issued by the MMC. Local controllers implemented in the BESS ensure frequency and voltage are regulated all the time. After the breaker closes, the BESS is switched back to dispatch mode and islanded load shedding is disabled. The islanded microgrid is able to seamlessly reconnect to the main grid as system frequency and voltage remain stable. Data set links: All the recorded raw data can be found in the Excel sheet under name: F-Island-to-Grid Transition.xlsx Conclusions: FIG. F-8: ISLAND-TO-GRID TRANSITION TEST CONCLUSIONS

Duration (Hour)

PV Type

Load Type

Trigger Event SoC Maintained Successfully?


Notes

1 Partial Cloudy

Spring Min kWh

Reconnection N/A Pass

G. Reliability Evaluation Overview: The reliability evaluation follows the approach described in Olney Town Center Microgrid Test Plan (See Annex D). The baseline performance analysis is shown in Fig. G-1. In total, there are four types of fault locations that we consider in our testing: one is a fault external to microgrid zone 1, while the other three are internal faults within zone 1. Fault locations are indicated with red Xs on Figure G-12, “Microgrid Zone 1 One-Line Diagram for Reliability Analysis.” (Magenta lines represent overhead lines and blue lines represents underground cables.) All the fault locations are chosen based on a 2009 through 2015 Olney outage map that indicated there was a small chance that faults would happen at any other locations within zone 1. The relevant test category is mainly Unintentional Islanding – Stability.

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FIG. G-1: BASELINE RELIABILITY METRICS SUMMARY

Fault Location

Number of Events (counts/year)

MTTP (hrs)

Number of Customers Served

Number of Customers Not Served

Weighted SAIDI (min/customer/year)

1 0.652 4 0 50 156.5

2 0.054 4 0 50 13.0

3 0.017 4 0 50 4.1

4 0.027 4 35 15 1.9

SAIDI (min/customer/year) 175.5

Description: Four scenarios are tested against microgrid zone 1. For each scenario, test results are analyzed to evaluate post-fault conditions, including whether the microgrid can still supply a partial group of customers and sustain their loads for the entire repair period, or how many customers are interrupted after the fault happens. The distribution of customer counts is shown in Figure G-2. Also, the reliability of distribution components is shown in Figure G-3. FIG. G-2: CUSTOMERS DISTRIBUTION INFORMATION

Load Group Number

Load Criticality

Number of Locations

Number of Customers at Each Location

Total Number of Customers

Group 1 High 2 3 6

Group 3 High 3 8 24

Group 4 High 2 1 2

Group 6 Optional 1 7 7



Total 50

FIG. G-3: RELIABILITY OF DISTRIBUTION COMPONENTS

Distribution Line Types

Failure Rate (counts/mile)

MTTP (hrs)

Overhead Main Lines

0.36 4

Overhead Laterals 0.36 4

UG Cables 0.05 10

Test: Fault Location 1 – Upstream of Utility Zone 1 Circuit In the baseline scenario without microgrid installation, all critical load groups are interrupted and must be shut down when an upstream fault happens. In the microgrid scenario, the upstream fault case test is performed on a winter average-kWh demand day. Based on test results from winter average-kWh day (A6D2), Figure G-4 shows the microgrid is able to transfer from grid-connected mode to islanded mode in an unintentional outage while keeping voltage and frequency stabilized. However, there is a load drop at 6:05 a.m. due to load shedding and modulation control from the microgrid controller as shown in Figure G-5. All the medium, low, and optional priority loads have been shed since this point of time. Figure G-6 shows the microgrid controller is able to manage battery SOC within a certain limit, indicating a sustainable and resilient microgrid


operation. With reserve kWh in the BESS, all the high critical loads can survive the 4-hour repair period until the microgrid reconnects back to the grid. As shown in Figure G-5, the rest of microgrid loads are kept for one hour but shut down for three hours to maintain operational reliability. Figure G-7 shows load-shedding results and reliability metrics with respect to an upstream circuit fault. FIG. G-4: (A) SYSTEM FREQUENCY (A6D2) (B) SYSTEM VOLTAGE (A6D2) FIG. G-4A

FIG. G-4B

FIGURE G-5: RECORDED CUSTOMER LOAD CONSUMPTION (A6D2)

FIGURE G-6: BESS SOC AFTER MICROGRID OPERATION (A6D2)

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FIGURE G-7: MICROGRID RELIABILITY TEST RESULTS

Fault Location


Shutdown Period (hrs)



Weighted SAIDI (min/cust/yr)

1 0.652 3 32 18 42.2

Load Group No. Load Shedding (Yes/No/Partial)

1 No

3 No

4 No

6 Yes

7 Yes

9 Yes

Test: Fault Location 2 – Upstream Overhead Primary of Internal Recloser Similar to the baseline scenario without microgrid installation, all the critical load groups are interrupted and must be shut down when the fault happens at location 2. In the microgrid scenario, the internal fault case is applied on a peak-demand day. After the fault, the internal recloser is tripped and a microgrid subzone can form to supply the rest of the load. Post-test feasibility analysis shows whether a microgrid subzone has sufficient resources in order to survive the repair period (Figure G-8). FIG. G-8: MICROGRID FEASIBILITY TEST (A) CRITICAL LOAD PEAK (B) DER CAPACITY

High Critical Loads Load Group1 Load Group2 Load Group3 Load Group4 Total

Zone 1 Peak 46 47 361 41 495

Sub-zone Peak 46 47 120 41 254

Generation PV Storage NG-CHP Total

Zone 1 Capacity 500 165 248 913

Sub-zone Capacity 400 165 248 813

Based on test results, microgrid subzone is able to transfer from grid-connected mode to islanded mode in an unintentional outage while keeping voltage and frequency stabilized. It can be seen from Figure G-8 that the subzone has a peak critical load of 255 kW, slightly higher than CHP kW capacity. The microgrid subzone can operate self-sufficiently when drawing upon the capacity of CHP, BESS, and PV. Under this condition, all the high critical loads are supplied, except two thirds of load group 3, which are at the faulted section. Figure G-9 shows load-shedding results and reliability metrics with respect to fault location 2.


FIG. G-9: MICROGRID RELIABILITY TEST RESULTS

Fault Location






2 0.054 4 16 34 8.8


1 No

3 Partial

4 No

6 Yes

7 Yes

9 Yes

Test: Fault Location 3 – Section Between Z1_26_0 and Z1_26_2 on Lateral 1 Fault location 3 is on the same section as fault location 2, so the post-fault operation is the same. The microgrid subzone is able to transfer from grid-connected mode to islanded mode in an unintentional outage while keeping voltage and frequency stabilized. Two thirds of load group 3 at the faulted location are disconnected as well as all the medium, low, and optional loads in the subzone. Figure G-10 contains information on how many customers are served/not served as well as reliability metrics with respect to location 3. FIG. G-10: MICROGRID RELIABILITY TEST RESULTS

Fault Location






3 0.017 4 16 34 2.8


1 No

3 Partial

4 No

6 Yes

7 Yes

9 Yes

Test: Fault Location 4 – Lateral of Load 1&6&7 Between Z1_26_5 and Z1_26_6 on Lateral 2 Fault case 4 directly impacts load group 6 and 7, plus part of group 1. Based on test results, the closest fuse upstream of the fault opens and isolates the fault. Instead of going into microgrid mode, the distribution feeder still operates radially and supplies the rest of the loads. No additional load modulation or shedding is performed in this scenario. Figure G-11 shows post-fault reliability index results with respect to fault location 4.


FIG. G-11: MICROGRID RELIABILITY TEST RESULTS

Fault Location






4 0.027 4 35 15 1.9


1 Partial

3 No

4 No

6 Yes

7 Yes

9 No

Conclusions: Figure G-13 summarizes reliability performance based on four fault scenarios. As designed, making use of some existing utility overhead infrastructure, the microgrid would be expected to improve SAIDI outage metrics for critical loads from 175.3 min/customer/year to 9.1 min/customer/year. Improving SAIDI further – for both critical and non-critical customer loads – would require design revisions to extend BESS capacity, or investments in underground cable. Specifically, upgrading the 0.03-mile overhead lateral between Z1_26_0 and Z1_26_2 and the 0.15-mile overhead main line between Z1_26_0 and the internal recloser produced a substantial reliability improvement (Figure G-15). After circuit upgrading, the number of events per year is greatly reduced, although the shutdown period is increased from 4 hours to 10 hours due to an increase in mean time to repair (MTTP) for underground cables. These design revisions improve SAIDI performance of 3.3 minutes/customer/year, meeting the reliability design objective. The test conclusion is shown in Figure G-16.


FIGURE G12: MICROGRID ZONE 1 ONE LINE DIAGRAM FOR RELIABILITY ANALYSIS Magenta lines represent overhead lines, blue lines represent underground cables, and red Xs indicate test-case fault locations.


FIG. G-13: MICROGRID RELIABILITY METRICS SUMMARY

Fault Location






1 0.652 3 32 18 42.2

2 0.054 4 16 34 8.8

3 0.017 4 16 34 2.8

4 0.027 4 35 15 1.9


FIG. G-14: MICROGRID RELIABILITY METRICS SUMMARY FOR CRITICAL LOADS

Fault Location



Number of High Critical Customers Served

Number of High Critical Customers Not Served


1 0.652 3 32 0 0

2 0.054 4 16 16 6.5

3 0.017 4 16 16 2.0

4 0.027 4 29 3 0.6


FIG. G-15: MICROGRID RELIABILITY METRICS SUMMARY FOR CRITICAL LOADS (WITH SYSTEM REVISIONS)

Fault Location



Number of High Critical Customers Served

Number of High Critical Customers Not Served


1 0.652 3 32 0 0

2 0.0075 10 16 16 2.25

3 0.0015 10 16 16 0.45

4 0.027 4 29 3 0.6


FIG. G-16: RELIABILITY TEST CONCLUSION

Baseline SAIDI (min/customer/year) 175.3

Microgrid SAIDI (min/customer/year) 55.7

Microgrid SAIDI for Critical Loads (min/customer/year) 9.1

Microgrid SAIDI for Critical Loads (With Design Revisions) 3.3

Target SAIDI (min/customer/year) 3.5

Whether test cases pass or fail design metrics Pass


H. Microgrid Black-Start Procedure Test Overview: The test plan calls for running a microgrid black start, which is assumed to be a post-fault

scenario. Below is the basic test description and results narrative.

Description: The purpose of this test group is to validate the microgrid’s resiliency upon a failure of

seamless transfer from grid-connected to islanded mode. In the event of a seamless transfer failure, the

microgrid controller should be able to support a black start procedure (Volume 2, Appendix F).

FIG. H-1: MICROGRID BLACK START TEST PROCEDURES

Initialization Steps Run initialization script with test variation inputs

Triggers Startup ready

Actions The controller will cycle up generation and load

Expected States Generation and loads will be started and stepped up in a predefined sequence


Metrics Generation steps and durations Load steps and durations

Data Collected Customer load PV generation BESS charge/discharge BESS SOC System voltage System frequency

Pass/ Fail Controller starts up generation and engages load to produce a stable microgrid

Post Processing The data from the controller will be logged to show what and how much generation is being added in combination of the load. OPAL-RT data will be reviewed to evaluate the transients during each operation to ensure they are within limits. Any ride-through settings will be logged

Execution Notes:

Microgrid design objectives included enabling seamless transfer to minimize any loss of critical load. This

design called for the battery energy storage system (BESS) and not the CHP unit to serve the primary

role of grid forming. Drivers for this design decision included the superior flexibility of BESS compared to

CHP, which would have imposed warmup procedures and minimum load constraints, and would be

unable to support very fast response to establish and maintain a balanced system in island mode.

Therefore the BESS model was developed to include capabilities to adjust its terminal voltage magnitude

and frequency in its role as a grid-forming device, and support black-start functionality. In future project

phases, additional modeling would enable the CHP unit to support grid forming, or to synchronize once

BESS-supported black-start procedure is complete.


Results Narrative:

The black-start procedure includes manual intervention to visually confirm system states, communicate

with personnel, and enable load-curtailment actions required in a real-world environment. These steps

are defined to be complete before the functional black-start test procedures are performed:

1. Start BESS with no load at t=11:32 AM

2. Reconnect the first 50 kW load increment at t=11:33 AM

3. Reconnect the second 50 kW load increment at t=11:36 AM

4. Reconnect the third 50 kW load increment at t=11:39 AM

5. Reconnect PV with full curtailment at t=11:42 AM

6. Reconnect the fourth 50 kW load increment at t=11:45 AM

The black-start procedure is completed within 16 minutes. Load profile is shown in Figure H-2, following

a predefined sequence. Figure H-3 shows that, after PV is reconnected, the curtailment is reduced and

PV output is ramped up with 10 kW/min ramp rate. The BESS output is shown in Figure H-4. The BESS

starts in a no-load condition and then its output follows the load step-up pattern, operating in

frequency-droop mode. Once PV is ramped up at t=11:42 AM, the BESS proactively reduces its active

power output to regulate frequency. Black-start procedures are concluded with operator verification of

protection settings, electrical topology, and other manual logs required to enter island-mode control

operations as tested in Sections D and E.

FIG. H-2: RECORDED CUSTOMER LOAD CONSUMPTION FOR BLACK START


FIG. H-3: RECORDED PV OUTPUT FOR BLACK START

FIG. H-4: RECORDED BESS OUTPUT FOR BLACK START

During the black-start procedure, the BESS, as the grid-forming device, regulates frequency and voltage

to ensure the microgrid is stable. The frequency is regulated around 60 Hz and small frequency glitches

happen for four load reconnections as shown in Figure H-5. Figure H-6 shows BESS voltage is regulated

around nominal voltage (277V). Similar to frequency, four load reconnections also cause small voltage

deviation, which is eventually regulated back to 277V.

FIG H-5: RECORDED MICROGRID FREQUENCY FOR BLACK START


FIG. H-6: RECORDED BESS VOLTAGE (1Φ) FOR BLACK START

Conclusions:

FIG. H-7: MICROGRID BLACK START TEST CONCLUSIONS

Test Run

Duration (Hour)

PV Type

Load Type

Trigger Event SoC Maintained Successfully

Test Run Passes the Design Metrics

Black-

start

0.25 Partial

Cloudy

Fall Min

kWh

Post-fault and

full microgrid

black out

N/A Pass


I. Cyber Security – Grid Connected Overview: The test plan calls for a grid-connected energy management scenario during a fall low-load day that is mildly cloudy. During the test, the communication between the local controller and the hosted controller is interrupted. Below is the test description and results narrative. Description: The purpose of this test group is to validate the microgrid’s security controls and ability to respond to potential cyber-attacks. Testing will be based on a threat model to determine the vulnerabilities, threats, and risks to the microgrid system. FIG. H-1: CYBER-SECURITY – GRID-CONNECTED TEST PROCEDURES


Run initialization script with test variation inputs.

Triggers Security events, equipment failures

Actions The controller will manage the appropriate alert and action for various events.

Expected States Grid Connected

Metrics Microgrid access logs Detail logs of message failures and rejections System logs during planned communication failures

Data Collected Security: The solution will demonstrate a trust-based design and integration. This will show the following: a) How human and machine actors are authenticated and authorized; b) How data in motion is protected; c) How data at rest is managed; and d) How system monitoring is accomplished. Internal communication failure logs Tampering – Internal system rejection logs Repudiation – Dispatching variations Information Disclosure - No specific test Denial of Service – Lack of communication Elevation of Privilege – Human and machine actors access failure logs

Pass/ Fail Controllers able to maintain system stability during various attacks

Post Processing Each test generates controller events and system events that show the attack. For each event, logs will be captured as evidence of the attack. In addition, the control events at the NOC and local microgrid controllers will show the impact of each attack and the response. In post-processing the tester will review and evaluate responses to notifications to the users and/or controller actions to standby and resume based of the attack type.

Execution Notes: Several techniques were applied to verify security configurations.


Results Narrative: The test passed as defined above. The following narrative provides details on the configuration and testing approach. By design communication loss between the local system and the hosted system will immediately impact the operation of the microgrid. The local controller runs all the services to support grid optimization while connected. The operations center loses visibility of the local controller and is alerted by event messages for the hosted system. The local controller continues but will have a degraded load forecast over time since the weather updates will not be available. NOTE: For testing purposes, the historical weather for 2014 was downloaded to the local controller. Use of a real-time weather service like darksky.io was tested and shown effective. Logs from the hosted system show communication failure with the local system. There is no specific log on the local controller other than a service failure event/alarm, since the local controller is intended to not have a human console (headless). Information was collected on events, and the system configuration was analyzed to address basic security threats. The communication failure log is created by an authenticated service on the hosted system. Events cannot be injected into the system without authentication and authorization. This limits spoofing and tampering. Communication between the hosted system and local system used TLS 1.2. The wire protocol was AMQP, an OASIS standard. TLS 1.2 was implemented with a local Certificate Authority that supports expiration dates. Revocations are not automatic but require effort to replace a certificate on the local systems for this configuration. Wireshark was used to verify traffic encryption. Communication from the local controller to the Opal-RT system over DNP3 was unencrypted, creating a security vulnerability. The vulnerability would need to be addressed in actual deployment, but was accepted as the standard for testing purposes. Events are stored locally on a PostgreSQL database, which could be vulnerable to tampering since the database lacks encryption. This vulnerability can be addressed by routinely performing audits that compare database records to hosted system logs. A denial-of-service (DoS) attack on the hosted service port for SSL/AMQP was simulated by dropping the communication between the local and hosted system. In actual deployment, DoS attacks on the hosted system would be deterred with router-logging and syslog server monitoring tools available to system administrators. Mitigating elevation of privilege was addressed several ways. First, the local GreenBus MMC Controller was installed under a non-privileged account and group on the Linux server. This account was unable to become a super user. Second, user accounts were created for services and human to access the system. Authorization was configured as appropriate to the user, and the configuration was verified in a side-by-side system, since the testing system was reconfigured for each test.

Vol. 4 – Regulatory and Finance Structure … Olney Town Center Microgrid Project – Final Report1

Microgrid optimized resource dispatch for public-purpose resiliency and sustainability Final Report - Olney Town Center Microgrid Control System R&D Project Vol. 4: Regulatory and Finance Structure Sections: Introduction .................................................................................................................................................. 1

A. Regulatory History and Context ............................................................................................................ 2

B. Ownership Model ................................................................................................................................. 3

C. Options for Recovery of Microgrid Costs: ............................................................................................. 5

D. Factors Affecting LDC-Owned Microgrid Business and Regulatory Model ......................................... 11

Appendix A: Maryland PSC BGE Microgrid Order – Implications for Public-Purpose Microgrids .............. 18

Appendix B: Regulatory and Financial Structure Options Matrix ............................................................... 20

Appendix C: County Working Priorities for Siting Public-Purpose Microgrid Pilot Projects ....................... 22

Introduction Consistent with the requirements of the Project SOPO, the Project Team sought to identify and analyze structural options for community microgrids in Pepco’s Maryland service territory. The purpose of this analysis is to achieve three primary objectives:

a. Define and analyze the regulatory and business assumptions required for inputs into the microgrid design model and control system operating parameters;

b. Describe a viable structure to enable development of a pilot microgrid project in Pepco’s Maryland service territory; and

c. Establish structural options for application and replication at numerous locations in Maryland, and indeed in any jurisdiction with similar utility regulatory and business conditions.

Two test cases were analyzed, for public-purpose microgrids at a suburban Town Center (Olney Town Center) and a suburban Commercial Campus (Ritchie Station Marketplace). The Town Center scenario includes multiple customers, located on multiple parcels of land, separated by one or more public rights of way. The Commercial Campus scenario includes multiple tenants on one or more parcels of land owned by a single landlord, with no public rights of way separating any customers within the campus. To be considered a viable structure, each option was subjected by the Project Team to the following screens:

a. Legal Viability: The structure legally can be established and maintained in Pepco’s Maryland service territory without requiring substantial changes in existing regulations, laws, or ordinances, or major waivers, variances, or alternative regulatory treatment.


b. Financial Viability: The structure enables assets to be financed for installation and commissioning, and allows ongoing recovery of operations, maintenance, and financing costs.

In addition to these viability screens, the Project Team applied certain key assumptions intended to enable analysis of various options, independent of specific cases. These assumptions included the following:

c. Positive benefit-cost attributes: The test case produces net positive economic outcomes for affected Maryland residents, compared to business-as-usual (BAU) as well as baseline backup generation/uninterruptible power supply (UPS) options;

d. Utility support: The local distribution company (LDC) would support a prospective project based on the test case; and

e. Regulatory and market stability: Deployment would not require substantial changes in regulatory requirements, incentive programs, legal provisions, or market conditions (e.g., available fuels, market prices for energy, economic conditions, market structures, etc.).1

A. Regulatory History and Context The State of Maryland, through a task force of the Maryland Energy Administration (MEA), defined community microgrids like those studied in the test cases as “public purpose microgrids.”2 Specifically:

[P]ublic purpose microgrids serve critical community assets across multiple properties. Critical community assets include resources that provide important community functions, such as community centers, commercial hubs, and emergency service complexes. Facilities that contribute to quality of life during an extended power outage could also be included in a public purpose microgrid. A public purpose microgrid may be owned in whole or in part by either an electric distribution company or a third party entity, and must provide services to multiple customers across multiple property lines.

The Project Team relied upon the MEA definition in performing the structural analysis as set forth herein. Additionally, the Team considered current and recent Maryland legislative, statutory, and regulatory conditions, most notably including Maryland Public Service Commission (MPSC) orders and proceedings, for guidance in structuring public-purpose microgrids. The Project Team included analysis on a MPSC July 19, 2016 order3 rejecting a Baltimore Gas & Electric (BG&E) proposal to build two natural gas-fired microgrids in Edmonson Village in Baltimore and Kings Contrivance in Howard County (see Appendix A, “Maryland PSC BGE Microgrid Order – Implications for Project Test Case Microgrids”). Finally, the Project Team engaged policy staff at Montgomery County to understand and clarify the County’s objectives and considerations for public-purpose microgrids. In particular, the Project Team analyzed Montgomery County’s application of a 2016 settlement agreement between Pepco and MPSC

1 Regulatory conditions changed during the course of the project, regarding Montgomery County ordinances, as well as the laws of the State of Maryland and the U.S. federal government. The Project Team sought to apply the most current regulatory conditions at the time of the analysis. Foreseen future changes and uncertainties are noted in the analysis.

2 Maryland Resiliency through Microgrids Task Force Report, 2014, infra. p.i.

3 Maryland PSC Order No. 87669, Case 9416 (July 19, 2016).


allowing Pepco’s merger with Exelon Corp. to proceed. The settlement in part required Pepco to support development of a public-purpose microgrid in Montgomery County, and the Project Team engaged Montgomery County to ascertain its priorities for siting and development of such a microgrid (See Appendix C). B. Ownership Model The Project Team’s engagement with stakeholders and team members has yielded the following general ownership model for Maryland public-purpose microgrids. The public-purpose microgrid will be developed, owned, and operated as a form of public-private partnership (P3), with multiple entities owning and operating various assets that contribute to microgrid operation. The local distribution company (LDC) will own and operate all above-the-the meter distribution assets designated as part of the microgrid, including microgrid control and distribution systems that cross public rights of way to serve different customers. Multiple parties (LDC and non-LDC, and public and private) will own and operate energy supply, building energy management/demand side management (DSM), distribution, and control systems located inside and outside of the microgrid boundary (see Fig. B-1). The Project Team determined that this ownership model is appropriate for public-purpose microgrids in both test cases, and that it enables comparison and contrast of factors affecting regulatory and financial viability in multiple scenarios. FIGURE B-1: TOWN CENTER MICROGRID BUSINESS MODEL – ASSET OWNERSHIP

Under this approach, the LDC’s revenue requirement associated with investing in microgrid assets will be subject to the regulatory jurisdiction of the Maryland Public Service Commission (MPSC), and will be


recovered through LDC proposals filed with the Commission for rate review.4 Concurrently, non-LDC investments and costs for energy supply and services will be recovered through competitive market mechanisms, and in some cases may be regulated and administrated by the Federal Energy Regulatory Commission (FERC) through the regional transmission organization (RTO), the PJM Interconnection. Generation assets above the meter may be owned by the LDC or a third-party, potentially including a municipality or government unit. The LDC will provide for interconnection of this generation into its distribution system under requirements adopted by the MPSC.5 The LDC-owned distributed generation may include solar, fuel cells, or battery storage if the LDC chooses to own generation within the microgrid boundary. Any combined heat power (CHP) will be owned by a third-party as a non-LDC asset.6 A non-LDC entity also may own other forms of generation as well as battery storage, and may participate in energy efficiency and demand-side management programs. Under this business model, the LDC will have the ability to dispatch or constrain all above-the-meter generation assets to balance loads on a feeder line or to address safety, reliability, or system-operating problems. Non-LDC owners also may own behind-the-meter assets, including generation, storage, DSM or related facilities. These types of assets will be treated under current LDC tariff provisions7 in the same manner as behind-the-meter generation assets located outside of a microgrid. (See Figure C-2.) Under certain conditions – such as if behind-the-meter generation operations are resulting in system reliability concerns, high-voltage problems, or otherwise destabilizing the local grid – the LDC will have the option to implement a reduction in generation as currently provided by standard interconnection agreements. Both LDC and non-LDC owners of microgrid distribution and generation assets are responsible for the maintenance and operation of all resources and services associated with such assets. The LDC may choose to offer operating agreements for non-LDC generation assets with these agreements filed and approved by the MPSC. Such operating agreements would allow the LDC to use non-LDC generation assets as primary generation sources to serve microgrid loads. Additionally, to ensure that all customers can take advantage of competitive-market energy supplies, and to support least-cost outcomes, customers served by the microgrid will obtain their energy supply through an aggregated supply contract, awarded through competitive procurement. This new aggregated energy supply contract will succeed any currently effective supply contracts with microgrid customers upon termination of those contracts, or will supersede currently effective contracts that include provisions enabling customers to terminate the contract to participate in an alternative energy supply arrangement. This aggregated supply contract is envisioned specifically for customers to be served by the microgrid – combining their competitive supply procurement to simplify servicing of microgrid customers. However, Pepco could offer other community members the opportunity to opt-in to this aggregated supply arrangement, thereby expanding the aggregated buying pool and improving cost outcomes for all customers supplied through the contract.

4 Public Utilities subtitle, MD Pub. Util. Code §7-504.

5 Maryland Standard Small Generators Interconnection Rule, http://webapp.psc.state.md.us.intranet/electricinfo/home_new.cfm

6 Pepco advised the Project Team that it does not anticipate investing in CHP assets. In principle, an LDC could invest in CHP assets, but doing so may raise questions about LDC ownership of behind-the-meter systems whose economic utility may depend on continued thermal energy demand by specific customers.

7 MD Pub Util Code §7-505 (c); 7-510 (c) (6)

http://webapp.psc.state.md.us.intranet/electricinfo/home_new.cfm


The Project Team determined that, in general, this business model is applicable for both the Town Center and Commercial Campus test cases. Notably, Maryland law8 specifically allows owners of commercial buildings and shopping centers to sub-meter energy services for specific rental tenants, and exempts them from regulation as a public utility. In the Commercial Campus test case, behind-the-meter assets owned and operated by a non-LDC entity may include microgrid control systems and some or all microgrid distribution systems; by contrast, in the Town Center test case, these assets would be located above the meter and owned by the LDC. Under this business model, each Town Center microgrid plan for development will be submitted to the MPSC and approved by the commission prior to installation of such microgrid. Proposals for such projects shall include the project objectives, a statement of the benefits to the community and the local grid, and the types of assets and estimated cost of the assets to be recovered through LDC rates and charges prospectively to be approved by the MPSC. Commercial Campus microgrid plans for development may be subject to MPSC approval to the degree they require LDC rate-base recovery of microgrid costs. C. Options for Recovery of Microgrid Costs: The Project Team considered several options for LDC recovery of its costs associated with construction and operation of the public-purpose microgrid. These options include: 1. General rate recovery; 2. microgrid customer surcharge and shared savings; 3. microgrid community surcharge and shared savings; 4. LDC research and development (R&D) investment rider; 5. on-bill financing and repayment; 6. private financing; and 7. some combination of options. These options are described and analyzed in sections C-1. though C-7., below, and the Project Team’s recommendations are provided in section C-8. Appendix B provides a matrix comparing the regulatory and financial structure options. Additionally, Volume 1 addresses the feasibility of the public-purpose microgrid test cases. Fig. C-1 illustrates value streams in a public-purpose microgrid, and Fig. C-2 illustrates various transaction models for recovering costs associated with microgrid value streams. Currently, the LDC provides standard bundled retail service to all of its customers in Maryland. This basic service (value stream B) would not change under any of the studied cost-recovery options; all customers connected to the microgrid would continue to pay the distribution rates provided in tariffs that are attributable to their rate class, based on the total energy delivered to each such customer. The other value streams are variously addressed by cost-recovery options 1 through 7.

8 MD Pub Util Code §7-303 (g)


FIG. C-1: LDC MICROGRID VALUE STREAMS

FIG. C-2: LDC MICROGRID – TRANSACTION MODELS

Transaction Model Buyers Sellers Regulation

• Standard bundled retail rates • Onsite service contracts

End users LDC, non-LDC operators

State PUC

• Net metering rate • Operating agreements

LDC Non-LDC operators State PUC

• Wholesale capacity • Wholesale energy (forward

and spot contracts) • Wholesale ancillary services

ISO, LDC, and non-LDC operators

LDC and non-LDC operators

ISO, FERC

• Aggregated competitive supply contracts

End users Non-LDC energy service companies

State PUC

1. General rate recovery: Microgrid costs incurred by the LDC would be included in the general rate base for cost recovery by all Pepco customers in all applicable rate classes (value stream C). This approach treats the public-purpose microgrid investment as equivalent to other T&D system investments, such as substations and feeder undergrounding. To the degree MPSC would allow the LDC to include costs for such investments in its general rates, the LDC could consider microgrid technologies as a capital investment option in any appropriate situation. General rate recovery for such investments would be subject to review and approval by MPSC.


Analysis: General rate recovery customarily is used for costs incurred to provide standard service to all customers in a given rate class on an equivalent basis. By contrast, a microgrid is designed to provide unique services (primarily value streams E, F and G) that generally are not delivered by traditional T&D and generation technologies, and that are not required by all customers. Accordingly, to obtain MPSC approval for general rate recovery of public-purpose microgrid costs, the LDC would need to show that its investment provides equivalent service on a least-cost basis, or that it is necessary to meet some other mandate or regulatory requirement. Such requirements could be established by such public agencies as the State of Maryland or the applicable County or municipal government. Notably, provisions of the MPSC Pepco-Exelon merger settlement agreement require Pepco to develop and deploy public-purpose microgrids in both Montgomery County and Prince George’s County. Montgomery County developed criteria to assess potential sites for deployment of a public-purpose microgrid. LDC investments in microgrids to serve public-purpose requirements established in accordance with such criteria could qualify for general rate-recovery treatment – especially because those investments are made to comply with a State order. However, to the degree such investments would produce benefits for customers in one community and not another, then costs that are borne by customers in the second community could be deemed a cross-subsidy. Accordingly, a general rate-recovery approach might be more viable as part of a system-wide approach to grid modernization, in which the LDC seeks rate recovery for numerous investments that produce equivalent benefits for most or all of its Maryland customers. This approach also might be viable for a given microgrid designed as a pilot project intended to prove the concept toward future widespread deployment. (See option 4., below.) 2. Microgrid customer surcharge and shared savings: In this option, incremental costs (or savings) produced by the microgrid would be calculated by analyzing the utility’s de-averaged BAU costs to serve customers connected to the microgrid, and recovering any differences through surcharges (or credits) reflected on microgrid customers’ monthly invoices according to formulas approved by the MPSC. Specifically, microgrid costs incurred by the LDC that are comparable to its BAU distribution capital and operating costs would be recovered through general rates applied to all Maryland Pepco customers (value stream C). BAU costs include but are not limited to operations, maintenance (O&M), and replacement expenses for current LDC assets within the microgrid footprint, as well as capital costs and associated O&M and replacement costs for system upgrades and expansions that would be incurred over the 25-year useful operating life span of the microgrid. The LDC’s microgrid costs in excess of the LDC’s BAU distribution costs (i.e., the LDC’s “incremental microgrid costs”) would be recovered through a surcharge paid only by microgrid customers (value stream E). In this way, those customers who directly benefit from microgrid investments would directly bear all the LDC’s incremental costs of those investments, and other general rate-paying customers would bear no incremental costs. Similarly, to the degree microgrid assets would enable a net deferral or reduction in the LDC’s costs (e.g., deferment of substation replacement or expansion), then the incremental cost savings (as compared to the BAU case) would be divided among microgrid customers and the general rate base using a formula approved by the MPSC. In this way, local microgrid customers would enjoy the benefits of the LDC’s cost savings associated with local microgrid systems – whose benefits would accrue in part from their investments in distributed energy resources (DER) – and general rate base customers also


would enjoy savings associated with economical distribution system investments made on their behalf, just as they would for other T&D investments that produced net cost savings. Analysis: Option 2 prevents potential cost subsidies financed by customers outside the community where the microgrid is located. However, it ascribes no social value to the microgrid’s public purpose, and thus option 2 might create a subsidy financed by microgrid customers to fulfill that public purpose for the broader community. Moreover, to the degree the LDC identifies substantial incremental costs for the microgrid, the proposed surcharges might be prohibitively high for some or all microgrid customers to bear. This effectively would prevent construction of the microgrid, leaving its public purpose unfulfilled. Accordingly, this option might be viable only for microgrid investments that reduce the LDC’s costs to serve microgrid customers, or that produce only incremental costs that may be justified by microgrid customers on the basis of benefits derived. 3. Microgrid community shared costs and savings: The shared-costs/savings option begins with the same proposition as option 2, recovering the LDC’s BAU-equivalent costs through standard rates applied to all Pepco customers in Maryland. It differs from option 2, however, in that it accounts for the public purpose of the microgrid and seeks to allocate the associated costs and benefits accordingly. At least two mechanisms could be used to recover incremental microgrid costs from a larger community of customers who benefit from the public-purpose microgrid:

1) LDC rate surcharges apportioned to microgrid host community customers in proportion to the benefits they may derive from the microgrid; and

2) Taxes or fees imposed by local municipalities. Under the first mechanism, the LDC’s incremental costs would be recovered from customers in the microgrid host community through fees and surcharges on their monthly invoices. Microgrid customers would pay a monthly microgrid-connection surcharge, and community-benefit surcharges would be assigned according to an MPSC-approved formula. The rate design would distribute costs in a way that is proportional to the benefits customers derive from their location near the microgrid (value stream D). For example, the surcharge could decline in proportion to the customer’s proximity to the microgrid center. The second mechanism would involve cooperation among the utility and units of local government that would enable recovering incremental microgrid costs through local tax-based mechanisms. The County or municipality could pay for a portion of the LDC’s incremental costs for the microgrid, and could then recover those costs through its municipal bonding and taxing authority – via real estate taxes, use taxes, or sales taxes. Additionally, the County could make certain microgrid investments eligible for Maryland Property Assessed Clean Energy (PACE) financing, with costs to be repaid through property tax assessments. Funds from PACE bonds could be used to pay costs associated with clean-energy investments that are consistent with the County’s PACE program.9

9 At this writing, Montgomery County was implementing a PACE program for commercial properties, pursuant to a resolution adopted in 2015. See: Council Bill 6-15.


Finally, the Montgomery County Green Bank, established in 2015, could support some microgrid-related investments, either through direct grants and low-interest loans, or through credit enhancements including investment guarantees.10 Analysis: Option 3 avoids cross-subsidy concerns arising either from costs borne by customers who do not benefit at all from the microgrid, or from costs borne by microgrid customers to fulfill the microgrid’s public purpose for the benefit of a broader community. Such an approach could be viable if the charges are reasonable and if the system fulfills a clear public purpose defined in partnership with community stakeholders. PACE financing is new in Maryland, and thus its application for clean-energy microgrid investments has not been tested. A County could define certain microgrid costs as eligible for PACE financing, with cost recovery through property tax assessments. This approach likely would be viable and advantageous for financing property owners’ investments in microgrid-related assets, but may not be viable or advantageous for LDC incremental costs. Finally, although the Montgomery County Green Bank has not yet released details about its investment programs or criteria, the County’s enabling legislation11 defines its mission broadly enough that its programs likely will be able to support microgrid investments that yield improvements in energy efficiency, environmental performance, and renewable energy generation. The enabling legislation specifically provides for Green Bank support of investments that are ancillary to renewable and energy efficiency measures, which suggests that in addition to supporting renewable DERs and efficiency improvements, the Green Bank will be able to support costs for microgrid systems that are required to integrate and operate clean energy assets and resources. 4. LDC R&D rider: The LDC can seek to recover through general rates its research and development (R&D) costs for a public-purpose microgrid. Microgrid capital costs and investments in design, engineering, and network reconfiguration would be consistent with the LDC’s prudent grid-modernization efforts, and with the interests of state and local governments in establishing technical platforms that enable deployment and optimization of DERs including microgrids. Analysis: In principle, the MPSC could approve cost recovery through general rates for investments that demonstrate and test the efficacy of public-purpose microgrids. Accordingly, an R&D rider applied to all of the LDC’s customer invoices could be a viable rate recovery methodology for pilot public-purpose microgrid projects in Maryland. However, such a rider likely would not be viable for large-scale deployment of microgrids throughout the LDC’s service territory. 5. On-bill repayment or financing: On-bill repayment or financing mechanisms could provide convenient ways to recover costs for specific investments made to serve individual customers or groups of customers. Specifically, the LDC could recover incremental microgrid costs for certain facility-specific

10 At this writing, the Montgomery County Green Bank was working to develop its program offering and criteria for the first $3 million tranche of funding made available through its settlement agreement in the Pepco-Exelon merger.

11 Bill 18-15, enacted June 30, 2015: http://www.montgomerycountymd.gov/COUNCIL/Resources/Files/bill/2015/20150630_18-15.pdf

http://www.montgomerycountymd.gov/COUNCIL/Resources/Files/bill/2015/20150630_18-15.pdf


assets, such as energy efficiency improvements and building energy-management systems, through on-bill repayment. On-bill financing mechanisms could allow the LDC to provide financing services for customers’ on-site energy investments. In this way, costs associated with specific facilities would be borne by the owners of those facilities, and microgrid customers that separately invest in efficiency or energy management upgrades that support the public-purpose microgrid would not have to bear additional costs for such upgrades at other microgrid customers’ facilities. Analysis: The State of Maryland has not adopted an on-bill repayment or on-bill financing policy for LDCs in the state. Bills have been proposed during previous legislative sessions to create mandatory on-bill financing programs for energy efficiency and geothermal heating and cooling systems, but such legislation has not been enacted. Nevertheless, an LDC could propose a voluntary on-bill mechanism to enable customer repayment of facility-specific investments associated with a microgrid. The Project Team anticipates that such an independent utility initiative would be welcomed by the MPSC and local and county officials as it would facilitate customer investments in systems that support a public purpose. However, on-bill repayment or financing would not be a viable mechanism for recovering the LDC’s incremental microgrid costs. 6. Private financing: Some costs in a public-purpose microgrid could be recovered through private non-utility financing mechanisms, such as tax-equity investment and non-recourse debt. Such financing mechanisms may be appropriate for those investments that produce risk-adjusted rates of return commensurate with similar assets in the market – e.g., renewable generation systems and energy efficiency measures that produce predictable cost savings. Such assets would need to be supported by long-term contracts with creditworthy counterparties. Analysis: For the Commercial Campus test case, private non-utility financing may be applicable for a large portion of costs incurred for DERs and other on-campus systems. Incremental LDC costs for such a campus microgrid would be minimal, which would limit the applicability of rate-based cost recovery. Moreover, if a Commercial Campus microgrid provides service to a single creditworthy energy customer – e.g., the campus owner – then private non-utility financing may be readily available on cost-effective terms. Private financing also might be appropriate for some investments in the Town Center test case, provided those investments are scaled and structured to access traditional project financing sources. However, private financing will be less accessible to the degree assets involve multiple energy customers, customers with mixed or impaired credit, or capital investments that are small or have complex value streams that increase repayment risk. For example, in a Commercial Campus environment with numerous LDC revenue meters for unrelated customers, multiple energy service agreements likely would be required, and offtake risks would reflect the creditworthiness of a mixed group of customers. Such factors increase the complexity of a microgrid and make private non-utility financing more scarce and costly. 7. Combined approaches: Cost-recovery methodologies are not mutually exclusive. For example, the LDC could propose an R&D rate rider to recover incremental microgrid engineering, development, and capital costs for a pilot public-purpose microgrid, and also could propose a surcharge on microgrid customers for O&M costs. Or the LDC could propose a public-private partnership (P3) approach, wherein the County or municipality supports local investments directly or through the Montgomery County Green Bank, and the LDC imposes connection fees for microgrid customers, along with a general rate rider to recover incremental costs for a system-wide microgrid deployment.


Analysis: Combined cost-recovery approaches likely will be necessary, because a single approach may be unable to address all cost categories (for both LDC and non-LDC entities) in a public-purpose microgrid. 8. Recommendations: The Project Team anticipates that for microgrids that the LDC determines will either produce net savings or costs substantially equivalent to the LDC’s BAU costs to serve customers connected to the microgrid, option 2 will be most viable. This approach – which recovers BAU-equivalent costs through general rates, recovers the LDC’s incremental microgrid costs from microgrid customers, and shares any savings with microgrid customers and the general rate base – would be the simplest approach to minimizing potential cross subsidies and enabling microgrid customers to capture benefits from local DER investments. However, option 2 will be less viable for public-purpose microgrids that produce substantial incremental LDC costs. This is especially problematic for a pilot microgrid project, which may be categorized as an R&D investment to demonstrate new technology and business concepts. Imposing all such R&D costs onto microgrid customers may be deemed a cross-subsidy for the benefit of non-microgrid customers. Moreover, least-cost planning and service-equivalency standards may not be appropriate for assessing R&D costs prudently incurred for the benefit of all customers or a given rate class. The Project Team anticipates that a combined approach will be most successful at providing workable cost recovery to enable a pilot microgrid while also establishing a structure that will be viable for wider-scale deployment of public-purpose microgrids. In this way, representatives of the local community may determine the best way for community members to bear their share of costs for fulfilling a local public purpose defined by that community, and general ratepayers will bear their share of the LDC’s R&D costs incurred on their behalf. (Recoverable R&D costs for microgrids would be expected to diminish to zero with subsequent deployments, leaving any incremental costs the responsibility of the community that most directly benefits from public-purpose microgrid investments.) Additionally, PACE financing and utility on-bill financing or repayment structures offer useful mechanisms to enable customer investments in DERs that support the public-purpose microgrid. Such structures would be important tools to encourage competitive and local investments in such assets, and thereby reduce the LDC’s incremental microgrid costs subject to cost recovery through other mechanisms. Accordingly, the Project Team recommends working with the County to define microgrid clean-energy investments that are eligible for PACE financing, and also considering potential on-bill financing or repayment mechanisms to facilitate customer investments that may not qualify for PACE financing but that would support microgrid operations. D. Factors Affecting LDC-Owned Microgrid Business and Regulatory Model 1. Need for Cost-Benefit Analysis: Regulators are facing a range of new cost drivers and uses of the distribution system. Uncertainty is increasing regarding network uses and the efficient cost of network investments and maintenance into the future. Regulatory commissions have confronted their ratemaking responsibility using various approaches in balancing trade-offs between incentivizing utility management to pursue cost savings while minimizing economic rents collected by the utility from ratepayers.12

12 See Cossent, 2013; Joskow, 2013


In the Project Team’s research of regulatory issues and business models for microgrids, a critical question to address involves whether microgrids can be deployed to serve public purposes (e.g., provide resilient energy supplies for vital community infrastructure) solely on the basis of a cost-benefit analysis. Factors affecting this question include the incremental costs of electrical, communication, IT, and control systems for the microgrid (See Volume 2). Different microgrid deployment cases present different cost-benefit factors and results. The Project Team’s analysis shows that the cost-effectiveness of a microgrid will improve when the system can economically operate on a more frequent or continuous basis, with generation and storage resources in close proximity to customer loads (behind or near the meter). A microgrid designed solely as backup generation and operated only in the event of emergencies will produce higher life-cycle costs and fewer benefits, and will face greater economic challenges for ultimate construction and deployment. Pepco has provided the project team with highly useful information regarding on-site characteristics that affect microgrid development potential. Such information enables identification of additional benefit streams that may enhance the efficacy and economics of the microgrid option. This cooperative approach ideally yields greater advantages to the developer, the utility system, its regional transmission operator (PJM, in this instance), and the local and regional community. 2. Shared Generation and Backup Power Systems: Frequently, local governments and customers focus on the details of sharing resilient generation capacity with other facilities. In an interconnected microgrid network, the fear arises that the microgrid could affect individual facility operations by introducing instability into the system. Moreover, some critical facilities already may have invested in their own substantial backup generation systems. This can affect perceived needs for resilience, even though most backup power systems are critically dependent upon the availability of fuel, which for liquid-fueled systems becomes a significant risk factor during a prolonged outage. In the Project Team’s experience, facilities typically store only enough fuel on-site to operate their backup generators for a maximum period of up to four days – and often substantially less. Private contracts are executed with outside vendors to refill supplies, but refueling capacity is overburdened during long-duration regional outages, and delivery routes may be rendered impassable. LDCs – with their knowledge of the T&D system, and working with other parties including generation owners – can help spearhead analysis to define critical risks and solutions regarding shared capacity and backup generation limitations. Utility guidance on the resilience of various options for resilience can help clarify the added benefits of resilient microgrids serving vital community facilities. 3. Regulatory Structure: The current legal and regulatory environment in Maryland can support the development of public-purpose P3 microgrids.13 The State in its 2014 study conducted a comprehensive review of rules and regulations pertaining to microgrids in Maryland.14 Future analysis might identify best practices within Maryland and nationwide; investigate the valuation of costs and benefits of microgrids to be reflected in rates, tariffs and contracting; review and update interconnection rules; and examine statutory ambiguities and desirable interpretations to remove uncertainty for microgrid development for LDCs, customers, and sponsors of such projects.

13 Maryland Resiliency through Microgrids Task Force Report, 2014, infra. p.26

14 Op cit., Pp. 26-37


Better coordination between gas and electric utilities also may be fostered to ensure security of supply to enhance the resilience objectives of public-purpose microgrids. 4. Interconnection: The microgrid also will raise technical and regulatory considerations for its point-of-coupling (POC) interconnection systems. An updated review of microgrid interconnections, as well as evolving industry standards such as IEEE 1547a and P2030.7, may induce optimization of future microgrid designs and control systems that could create value or reduce costs in use of grid-supplied or distributed energy. Moreover, interconnected DERs can provide additional benefits to the utility transmission and distribution system, such as ancillary services, capacity congestion relief, and avoided T&D costs in the future. To best inform existing utility interconnection requirements, microgrids may require specialized protection equipment. Microgrid design and control system requirements may vary depending upon tolerance levels for short-duration outages when switching to island mode, compared with demands for service that require seamless non-interruptible power for resiliency and emergency service purposes. In any event, the microgrid may create cost responsibility for rate-based expenses associated with upgrades to electric infrastructure that are required to integrate the microgrid into the utility distribution system. 5. Public Funding Constraints: In Maryland as well as nationwide, economic constraints for state, county, or municipal projects likely will limit implementation of microgrids without additional funding support from the federal or State government or other County/local entities. The U.S. Department of Energy recognized this critical issue in its most recent quadrennial review. However, the current federal policy environment seems unlikely to yield substantial support for local sustainability initiatives in the foreseeable future. As a result, responsibility for securing resilient energy supplies for critical infrastructure rests with state and local agencies. Policy support from Maryland and other State and local governments will help public-purpose microgrid projects to raise capital and finance resiliency investments. Maryland could assist the process by disseminating substantive and objective information and tools, and by providing project evaluation methodologies and other resources for technical assistance to encourage better market-based project assessment and development. Additionally, the State of Maryland could support public-purpose microgrid deployment through the proposed Maryland Green Bank.15 If the State established and funded a green bank, that institution could provide credit enhancement and low-cost financing for clean-energy assets. Such financing options would most notably mobilize private capital for non-utility investments in microgrid assets, and potentially also could support LDC investments in public-purpose microgrids. 6. Microgrid Asset Investments and Risk Management: Business structures and financing options will be critical for future microgrid development in Maryland. Effective funding mechanisms and tools will be essential to facilitate the deployment of microgrids within the state. A P3 approach would enable public-purpose microgrids such as those described in the test cases to access financing sources that are appropriate to the ownership structures and risk profiles of particular assets. In that regard, the utility rate base approach may be preferable for distribution and control system costs associated with public-purpose microgrids in the current market and regulatory environment. Additionally, LDC rate-base capital, including local surcharges, may be most efficient to finance above-

15 See Blueprint for Building the Energy Economy in Maryland: Green Bank Preliminary Findings Report, Maryland Clean Energy Center (Dec. 1, 2014). http://msa.maryland.gov/megafile/msa/speccol/sc5300/sc5339/000113/020000/020980/unrestricted/20150433e.pdf

http://msa.maryland.gov/megafile/msa/speccol/sc5300/sc5339/000113/020000/020980/unrestricted/20150433e.pdf


the-meter generation systems required to serve microgrid loads, as well as costs for energy and services that the LDC procures through operating agreements. Non-utility investments may be most efficient for behind-the-meter systems and other assets owned by customers and third parties, whose value streams can be monetized with durable contracts and supportive regulatory mechanisms. Public investments with tax-based cost recovery may be required for incremental microgrid costs incurred to enable the microgrid to fulfill its public purposes. An ability to recognize and quantify long-term revenue streams is essential in assuring adequate cost recovery in the level of investment, particularly by the LDC and third-party investors. Some microgrid revenue streams cannot be captured by third-party owners. There are presently no mechanisms in Maryland (or in most other states) to compensate microgrid or distributed generation owners for distribution-level ancillary services or for avoided T&D investment and costs. This could impact the economic performance and financial assessments of customer- and third-party owned assets and their ability to attract capital. The MPSC in September 2016 initiated the PC44 proceedings16 to examine issues involving grid transformation and modernization, including mechanisms to value and monetize DER contributions to T&D operations. The commission issued a notice17 in January 2017 that established the scope and focus of the PC44 proceedings. The MPSC identified rate-design issues as the first of five areas requiring focus. Specifically, the notice states that “it is appropriate to begin assessing whether more can be done to ensure that rate structures account for evolving technology. In particular, we want to explore whether timevarying (sic) rates that value the benefits and costs of distributed solar could both empower customers and provide appropriate market signals, helping customers, utilities and all other stakeholders.” Among other things, the MPSC envisions potentially implementing a pilot program with time-varying rates for customers with distributed solar generation. Also, regarding the PC44 topic area focused on energy storage, the MPSC states, “we want to learn more about energy storage and specifically explore considerations for energy storage as a resource for individual customers and as a distribution grid asset.” Potential actions include changes in the way energy storage is classified and treated for interconnection standards and other rules, tariffs, and policies, and consideration of criteria for evaluating utility investments in energy storage as a grid asset, and associated cost recovery. Because the outcome of the PC44 proceedings will not be known until mid-2018, their implications for the regulatory viability of the test cases remains unknown. However, the MPSC’s guiding principles in PC44 establish a regulatory direction that is consistent with that of several other states in the Northeast – e.g., toward utility tariffs and other cost-recovery mechanisms that seek to monetize the location- and time-specific contributions of advanced DER technologies like microgrids. To the degree these principles represent guidance from the MPSC, then LDC cost-recovery proposals that seek appropriately to monetize DER services as part of a public-purpose microgrid likely will meet with favorable reception at the commission.

16 Public Conference 44 or “PC44.”

17 “In the Matter of Transforming Maryland’s Electric Distribution Systems to Ensure that Electric Service is Customer-Centered, Affordable, Reliable and Environmentally Sustainable in Maryland,” MPSC PC44 (Jan. 31, 2017). MPSC intends the work of this proceeding to fulfill a condition of the Pepco-Exelon merger, requiring Pepco to provide $500,000 in non-ratepayer funds to support grid modernization studies.


The Project Team anticipates that microgrid services will be supported by generation, storage, and load-management systems owned by multiple parties, including the LDC, microgrid customers, and third parties. Customer- and third-party owned DERs would be financed, installed, and operated through competitive market mechanisms, and the utility would install and own additional resources necessary to meet microgrid load requirements during island-mode operations. In each case, local DERs would reduce grid-supplied generation. As Project design and testing has shown, increased use of non-emitting renewable DERs and high-efficiency CHP systems supporting thermal and electric loads will increase net system efficiencies, reduce environmental impacts, and minimize customer costs. Such benefits accrue from DERs under any ownership structure, but are maximized when system operations are optimized to minimize reliance on grid supply. (See Volume 3, Section A.) Montgomery County has expressed its preference for an approach that facilitates integration of competitive and customer-owned DERs, and the MPSC, in its ruling denying BG&E’s application for microgrid cost recovery, noted that the Maryland Electric Choice and Competition Act of 1999 “envisions a competitive market for energy generation and services,” and that “third-party generation owners could reasonably assume a portion of the risks associated with microgrids.”18 Accordingly, the Project Team anticipates that successful cost-recovery proposals for public-purpose microgrids will include mechanisms to facilitate customer- and third-party investments in DERs that support public-purpose microgrids in Maryland. Specifically, the proposed microgrid will include mechanisms that support behind-the-meter energy services involving onsite generation, storage, efficiency improvements, and load management, as well as above-the-meter generation and energy storage systems. One such mechanism may be a unified menu of turnkey solutions, with installation and O&M performed by third-party service providers under long-term agreements with customers. This menu of solutions and services would be competitively procured and administrated by an independent special purpose entity (SPE) established in partnership with the County or other unit of local government. In addition to providing pre-certified and competitively procured turnkey systems to DER customers, this SPE also could be responsible for pre-screening alternative systems seeking to provide services to the microgrid. In this way, microgrid customers would be afforded multiple options, including a bundled option that would provide access to greater scale economies, akin to an aggregated service contract. And the LDC would be assured that customer systems installed through this SPE would comply with pre-established technical and operating standards. Per the cost-recovery option discussed in section C-5 above, the LDC could further facilitate customer DER investments with a voluntary on-bill financing program. Such an approach would be administratively efficient, combining billing for retail LDC services, aggregated wholesale energy supplies, and DER services in a single invoice. Additionally, it would enable access to favorable financing terms for systems that are pre-certified for microgrid operations. To the degree customer- and third-party owned DER systems provide services to the LDC, those services would be handled through standard net-metering tariffs or operating agreements with the LDC. Any



wholesale market transactions for both LDC and non-LDC resources located above the meter19 would be handled through the local distribution company under current law.20 Operating agreements between the LDC and non-LDC owners of microgrid DERs may present financial risks equivalent to debt liabilities for payment under such agreements. Any LDC reliance on DERs through operating agreements may be conditioned upon a satisfactory resolution of the debt treatment for accounting purposes of operating agreements on the utility’s financial statements. Additionally, operating agreements should include standard risk-management provisions and warranties protecting LDC ratepayers from liquidated damages arising from nonperformance on counterparty obligations. The LDC’s prudently incurred costs for managing such financial risks may be considered necessary to support a public-purpose microgrid in a way that is consistent with State and local policy goals, as well as the spirit of the Electric Customer Choice and Competition Act. Accordingly, these costs should be considered for MPSC-approved cost recovery along with other microgrid costs. 7. Cost-of-Service Regulation: Cost-of-service regulation would effectively serve as a method of revenue requirement collection under any of the cost-recovery options discussed above in Section C. The LDC would set allowed revenues to equal realized costs for the microgrid plus an allowance for a regulated rate of return. Such regulated rates are reviewed and adjusted before the MPSC and would better mitigate the impact of uncertainty regarding the microgrid performance as a pilot facility for R&D purposes. Cost-of-service regulation will readily ensure that the project remains achievable and can maximize allocative efficiency. Moreover, the complete range of benefits that a microgrid provides may not be able to be monetized by third parties, for reasons discussed above. In the absence of regulatory changes and new tariff structures, the electric utility is in a stronger position to capture and manage those benefits at the distribution level and in its responsibilities to manage its interface for T&D planning with the RTO. The Project Team’s analysis and testing activities suggest that in many instances, the sum of the social and private benefits of the microgrid will exceed the total costs of the project. If third-party owners or investors cannot monetize such benefits, however, they will not make equity and debt investments in microgrid facilities. Under this market circumstance, the LDC may be the preferable recipient and entity to recognize and quantify microgrid benefits, and to use its exclusive position as distribution system operator and electric delivery company to enable and facilitate competitive investments in DERs. This would occur as part of the LDC’s efforts to provide modernized service to its customers, and to facilitate competitive choice under Maryland law. 8. Assessment of New Technologies: The test-case microgrid that the Project Team designed and tested for potential deployment – focusing on a suburban Town Center environment – supports the LDC’s efforts to advance its understanding of the potential for DERs to be deployed and managed to produce various benefits. In particular, the microgrid control system, applications, and architectural

19 Financial analysis indicates that PJM market prices likely are too low to support investments in DER capacity. In any case, for testing purposes the utility partner specified a requirement to minimize exports from the microgrid to the minimize potential effects on the local distribution system. (See Volume 3, Section A.)

20 16 USC §§ 824 (b), 824d (c), 824e (a) (1997)


design advanced through this project yield unique opportunities to examine and refine strategies for managing DERs to serve vital loads in any representative Maryland community. An LDC-sponsored public-purpose microgrid using the control technologies and architectural approaches that were developed and tested in this project will allow the LDC, the County, and the State of Maryland to assess the increasing penetration of DERs – including distributed generation, energy storage, demand response, energy efficiency, and electric vehicles – and to understand how such new technologies can be leveraged and managed to support modernized utility services. The growth of DERs is coupled with changing smart-grid technologies using advanced power electronics and information and communication systems along with active distribution management systems, which among other things enable LDC management of multi-directional energy flows on the distribution system. Power increasingly is flowing in multiple directions across distribution networks, and important experience can be gained from deploying a Town Center microgrid. This experience will support Maryland LDCs in planning for the future, assessing new opportunities for decentralized power, and creating new electricity services with customer value within the utility cost-of-service regulatory framework. Distributed storage also introduces important changes in the real-time operation of electric power systems. Cost-effective electrical or thermal storage at scale would offer an important alternative and a buffer between system supply and demand for the utility. Further, pursuant to the MPSC’s current PC44 proceeding, demand response, time-variable rates, and advanced metering infrastructure also will make electricity loads more responsive to economic and operational signals. A more dynamic grid, with growing DER sources and customer uses of the distribution system, will require increased visibility and monitoring of DERs, customer loads, and their impact on network components. Wider adoption of electric vehicles – also supported by the PC44 proceeding – constitute an important new class of electric system users and loads. A public-purpose Town Center microgrid based on the project test case offers the opportunity to analyze such technologies in a controlled environment to determine value, benefits, and impact for all stakeholders. For the LDC’s purposes, deployment of a pilot Town Center microgrid will inform understanding about the level of future investments required to enable the system to deal with bidirectional power flows, increased volatility in peak demand, and varying DG-related system peaks at various voltage levels. This will enhance prioritization of future T&D investment to create better customer value and service. The proposed project will help clarify understanding of delivery mechanisms for electricity services that reduce end users’ dependence on the grid and provide alternatives that have operating-system value, environmental benefit, resiliency, or congestion-management benefit. Using LDC general rates to address uncertainty of cost drivers of grid modernization and DERs may be appropriate as the market and technologies are evolving. Cost-recovery approaches can be re-examined in the future by the MPSC with the benefit of better-defined cost parameters and conditions. As a result, the pilot Town Center microgrid will enable the MPSC to provide clearer and more stable guidance for LDC recovery of costs associated with future public-purpose microgrids. This approach will help to prove technical and performance efficacy for the benefit of the utility’s shareholders and its customers served.


Appendix A: Maryland PSC BGE Microgrid Order21 – Implications for Public-Purpose Microgrids BGE Proposal: Two natural gas-fired microgrids to support “critical service” establishments in Edmonson Village in Baltimore (3 MW, $9.2 million) and Kings Contrivance in Howard County (2 MW, $7 million). BGE would own and operate the facilities first as a pilot study and then as part of a wide program. BGE proposed a new rate rider on all ratepayers to fund the microgrid pilot, totaling approximately $1.56 a year per customer. The microgrids were not designed to provide 24/7/365 service, but rather to operate as backup systems dispatched during a utility outage (and also to supply peaking power resources, helping to offset microgrid costs). FIG. 1: APPLYING PSC ORDER 21

BG&E proposal issue identified by PSC Project solution

Proposal doesn’t contemplate renewable energy options, CHP, or energy storage; can’t “capture the full breadth of potential benefits” that microgrids could offer,” and cannot “test whether these elements could work in Maryland and be replicated.”

Includes CHP, gas-fired generators, PV, and storage designed for modularity and scalability

Sole reliance on natural gas fuel “casts doubt on the resilience of the microgrid itself.” Proposal lacks discussion about emergency or reliability testing or maintenance.

Design includes multiple fuel and storage sources; Simulation testing at NCSU FREEDM Systems Center included testing of performance during emergency scenarios.

Fails to capture such potential benefits as reduce emissions, greater efficiencies, and customer load-management opportunities.

Addressed in detailed design and operations plan; tested in simulation; metrics reported and analyzed.

“[O]verlooks the opportunity to explore sophisticated integration of microgrid resources in any smart grid or grid modernization design…”

DERs, controls, and interconnection schemes simulated and tested in multiple operating scenarios.

Overlooks “partnerships with third parties, integration of customer owned generation with storage, and demand response capabilities.”

Study contemplates integration of customer generation, storage, and demand-response capabilities; considers partnerships with energy service providers and independent SPEs.

Does not explain how microgrids relate to BGE’s long-term distribution plan.

LDC to support utility transformation proceeding and install public-purpose microgrid as conditions of merger.

Lacks tangible metrics to support the assertion that it’s a pilot program intended to yield lessons learned for the greater advancement of public purpose microgrid development in Maryland.

Test development efforts yielded metrics that provide benchmarks for many operational characteristics, customer cost-benefit outcomes, and qualitative factors; Town Center and Commercial Campus models defined and analyzed for replicability.

Did not engage or solicit input from local officials or customers when selecting sites,

Project team engaged many local and state stakeholders, including Montgomery County,



calling into question whether the sites were appropriate and would yield benefits commensurate with costs.

which is working with Pepco to select sites for public-purpose microgrids. Phase 3 deployment would require detailed engagement with all stakeholders.

PSC staff specifically questioned whether SAIFI and SAIDI data BGE cited from 2010-2012 reflect current feeder performance, whether BGE accounted for current reliability projects, and whether BGE’s scoring methodology was valid.

Project team used more current SAIFI and SAIDI data for modeled feeders. More detailed study of individual facility loads and costs will quantify the need for resilience improvements among customers.

Proposal lacks discussion of coordination and communication with state and local emergency personnel. (PSC noted illustratively that Baltimore assistant deputy mayor testified that hospitals, police, fire department buildings, and other critical functions already have backup power systems.)

Phase 3 deployment process must include substantial engagement with local stakeholders and customers, and also development of operational communication and coordination protocols.

Does not contemplate third-party participation or service provider options, which the PSC had specifically asked BGE to address in a Jan. 15, 2016 notice. Cites the “spirit” of the Electric Consumer Choice and Competitive Act, notes the Proposal requires microgrid customers to adopt BGE Standard Offer Service, and states “The competitive element is lacking in this Proposal …”

Enables customers to select competitive providers for onsite generation and services; establishes an aggregated competitive procurement model for customers’ grid-supplied power.

Creates a class of customers who will have little to no access to retail choice in microgrid services during islanded operation … lends additional credence to our conclusion that the Proposal is deficient as filed.”


Appendix B: Regulatory and Financial Structure Options Matrix

Options for Recovery of Microgrid

Costs

LDC’s Role / Cost Distribution Other Options/Features

Disadvantages

1. General Rate Recovery

LDC cost included in all rate classes (value stream C)

Maryland PACE financing may be used for non-LDC incremental costs

incurred by taxable customers.

Montgomery County Green Bank can support some microgrid related

investments through grants and

loans or credit enhancements

Treats public purpose microgrid investment as equivalent to T&D system investments

2. Customer surcharge and shared savings

LDC cost included in all rate classes (value stream C), but LDC’s incremental microgrid costs would be recovered through a surcharge paid only by MG customers

Applicable primarily for projects that produce net cost savings or minimal incremental costs

3. Microgrid community shared costs and savings

LDC cost included in all rate classes (value stream C), but also:

• allocate costs and benefits from LDC rate surcharges apportioned to microgrid host community in proportion to the benefits; and

• 2. Taxes and fees imposed by local municipalities

Avoids cross-subsidy concerns arising either from costs borne by customers who do not benefit from the investment, or from costs borne by microgrid customers to fulfill the microgrid’s public purpose for the benefit of a broader community.

4. LDC R&D rider

Cost recovery through general rates for investments that demonstrate and test the efficacy of public-purpose microgrids. Accordingly, an R&D rider applied to all ratepayer invoices could be a viable rate recovery methodology for pilot public-purpose microgrid projects in Maryland

Applicable only for R&D costs approved by MPSC, including potential pilot projects; not applicable for systemwide deployment of public-purpose microgrids.

5. On-bill repayment or financing

The LDC could recover incremental microgrid costs for certain facility-specific assets, such as energy efficiency improvements and building energy-management systems, through on-bill repayment.

Could allow LDC to provide financing services for customers’ on-site energy investments. Costs associated with specific facilities would be borne by the owners of those

Applicable only for customer-specific microgrid costs (e.g., onsite DERs, building energy management systems, efficiency improvements, etc.).


facilities, with reduced financing burdens

6. Private financing

Costs in a public-purpose microgrid could be recovered through private non-utility financing mechanisms, such as tax-equity investment and non-recourse debt.

Financing mechanisms may be appropriate for those investments that produce risk-adjusted rates of return commensurate with similar assets in the market such as renewable generation systems that produce predictable cost savings.

Increased complexity

7. Combined Approaches

Combined cost-recovery approaches likely will be necessary, because a single approach may be unable to address all cost categories (for both LDC and non-LDC entities) in a public-purpose microgrid

Increased complexity


Appendix C: County Working Priorities for Siting Public-Purpose Microgrid Pilot Projects 1. Maximize Public purpose benefits to Microgrid connected Facilities and Surrounding Communities:

• Support science, technology, engineering and math (STEM) industries that may be sensitive to power quality or reliability.

• Support current or future commercial hubs and urban corridors.

• Foster opportunities for economic development

• Ensure operation of key public amenities such as grocery stores, pharmacies, financial services and gas stations. Especially facilities near major transportation or public transit corridors.

• Reduce the need for independent generators for mixed-use and commercial real estate.

• Minimize Impact on Low, Limited, and Fixed Income Residents.

2. Ensure Continuous Operation of Critical and Emergency Services

• Support critical public facilities and services, including fire stations, shelters, police stations, schools and transportation corridors.

• Ensure continuous operation of medical, senior housing and other healthcare assets.

3. Minimize Environmental and Social Impact:

• Minimize environmental impact from energy generation by maximizing renewables and storage.

• Maximize cost savings opportunities for residential and commercial customers and minimize impact on low, moderate and fixed income customers.

4. Scale

• Maximize scale to enable the broadest overall benefits, and if necessary spread costs over the largest possible group of beneficiaries.

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