2014 TECHNICAL REPORT

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Analysis of the Costs and Benefits of Model-Based Grid Modernization for Orange and Rockland Utilities

Analysis of the Costs and Benefits of Model-Based Grid Modernization for Orange and Rockland Utilities Final Report, October 2014

EPRI Project Manager J. Roark

ELECTRIC POWER RESEARCH INSTITUTE 3420 Hillview Avenue, Palo Alto, California 94304-1338 ▪ PO Box 10412, Palo Alto, California 94303-0813 ▪ USA

800.313.3774 ▪ 650.855.2121 ▪ askepri@epri.com ▪ www.epri.com

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THE ELECTRIC POWER RESEARCH INSTITUTE (EPRI) PREPARED THIS REPORT.

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Electric Power Research Institute, EPRI, and TOGETHER…SHAPING THE FUTURE OF ELECTRICITY are registered service marks of the Electric Power Research Institute, Inc.

Copyright © 2014 Electric Power Research Institute, Inc. All rights reserved.

ACKNOWLEDGMENTS

The Electric Power Research Institute (EPRI) prepared this report.

Principal Investigator J. Roark

This report describes research sponsored by EPRI.

This publication is a corporate document that should be cited in the literature in the following manner:

Analysis of the Costs and Benefits of Model-Based Grid Modernization for Orange and Rockland Utilities. EPRI, Palo Alto, CA: 2014.

iii

CONTENTS

1 INTRODUCTION AND BACKGROUND ...............................................................................1-1

2 COST/BENEFIT ANALYSIS (CBA) FOR GRID MODERNIZATION PROJECTS .................2-1

Benefits of Smart Grid Investments .....................................................................................2-1

“Hard-Dollar” Benefits ..........................................................................................................2-2

Staging of “CBA Questions”.................................................................................................2-2

Considerations in a Model-Based Analysis ..........................................................................2-2

3 IMPROVEMENTS CONSIDERED IN THE STUDY ...............................................................3-1

Phase Balancing .................................................................................................................3-1

Capacitor Design .................................................................................................................3-1

Automated Control of Capacitors and Voltage .....................................................................3-2

Automated Control of Switches and Reclosers ....................................................................3-2

4 O&R’S ISM AND USE OF DEW ALGORITHMS ..................................................................4-1

DEW ....................................................................................................................................4-1

5 STUDY RESULTS ................................................................................................................5-1

Automation for Efficiency and Voltage .................................................................................5-1

Assumptions ...................................................................................................................5-1

Base Case ......................................................................................................................5-2

Phase Balancing Case....................................................................................................5-2

Capacitor Design Case ...................................................................................................5-4

Coordinated-Control Case ..............................................................................................5-7

Reduction in Peak Demand and Increased Feeder Capacity ...............................................5-9

Summary: Capacitor Design and Coordinated Voltage Control ...........................................5-9

Automation for Reliability and Flexibility ............................................................................ 5-10

Storm Response ........................................................................................................... 5-10

Monte Carlo Storm Analysis.......................................................................................... 5-11

v

Planning Response ....................................................................................................... 5-12

“Soft”-Dollar Benefits .................................................................................................... 5-14

Reduced Cost of Interruptions .................................................................................. 5-14

Reduction of Greenhouse Gas Emissions ......................................................................... 5-15

6 SUMMARY AND CONCLUSIONS ........................................................................................6-1

7 LESSONS LEARNED ...........................................................................................................7-1

A ORU’S INTEGRATED SYSTEM MODEL ............................................................................ A-1

B INTERRUPTION COST ESTIMATE CALCULATOR .......................................................... B-1

C REFERENCES .................................................................................................................... C-1

vi

LIST OF FIGURES

Figure 1-1 14-Feeder System in Upstate Orange and Rockland Territory ................................1-2

Figure 3-1 “Stacking Order” of Efficiency/Voltage-Control Improvements and CBA Questions .........................................................................................................................3-2

Figure 5-1 Example showing how automation allows 8-year deferral of capital equipment upgrade while maintaining reliability performance .......................................................... 5-13

Figure 5-2 Simple Analysis of Present-Value of Capital Costs for a conventional plan compared with a Smart Grid plan that allows an upgrade deferral from 2021 to 2029 .... 5-14

Figure A-1 ORU Distribution System ISM ................................................................................ A-1

Figure B-1 Home page for the ICE Calculator ......................................................................... B-3

vii

LIST OF TABLES

Table 5-1 Load and Loss Characteristics of the Base Case for 14 Feeders .............................5-2

Table 5-2 Load and Loss Characteristics of the Phase Balanced System ................................5-3

Table 5-3 Phase-Balancing Change from Base Case ..............................................................5-3

Table 5-4 Loads and Losses for the Capacitor Design Case ....................................................5-5

Table 5-5 Capacitor-Design Changes from the Phase-Balance Case ......................................5-5

Table 5-6 Load and Loss Changes from the Base Case to Capacitor-Design Case .................5-6

Table 5-7 Load and Loss Characteristics for the Coordinated Control Case ............................5-7

Table 5-8 Load and Loss Changes, Coordinated Control relative to Capacitor Design .............5-8

Table 5-9 Load and Loss Changes, Coordinated Control relative to Base Case ......................5-8

Table 5-10 Ten-year Storm Frequency in O&R Territory ........................................................ 5-11

Table 5-11 Estimation of System Wide Savings from Reductions in Crew Switching Time .... 5-12

Table 5-12 Improvement in Reliability Indices with Automated 14-feeder System .................. 5-15

Table 5-13 Avoided Greenhouse Gas Emissions from Load and Loss Reductions Based on EPA eGrid Marginal Emissions Rates........................................................................ 5-16

Table 6-1 Summary Results from Staging Analysis of Coordinated Control with CVR ..............6-1

Table 6-2 Storm Response, Asset Deferral, and Interruption Cost Estimates ...........................6-1

ix

1 INTRODUCTION AND BACKGROUND

Orange and Rockland Utilities (O&R) will be installing automation on a portion of its distribution system in upstate New York consisting of 14 feeders which represents approximately 5% of its total distribution system. The automation includes real-time model-based state estimation, automated reconfiguration for restoration, and volt/var control with conservation voltage reduction. The system, when functional, will be represented in near real time by a comprehensive model known as an Integrated System Model (ISM). The ISM is used to calculate alternative operating conditions and provide control or control alternatives under “blue sky” conditions and also extreme or abnormal operating conditions. This evaluation will examine the costs and benefits that result from this automation project. The ISM runs in the Distributed Engineering Workstation (DEW), and provides a one-to-one representation of O&R’s electrical system corresponding to its geospatial orientation in O&R’s service territory. DEW directly calculates power flows using the ISM and system measurements, including time-varying customer loads and SCADA measurements, and can then calculate the cost of losses and customer energy usage using time-varying cost of energy.

By installing volt/var control with conservation voltage reduction, O&R will reduce losses and kWh consumption by customers, and reduce peak demand as well. By installing automated switching and fault-location logic, O&R will reduce the cost of responding to both large events (e.g., storms) and small events (random blue-sky faults). The time to find and repair simple faults will be reduced, and the system will return as many customers to service as is possible even before the fault is repaired, reducing the average duration of customer interruptions, a characteristic represented by the reliability index SAIDI1.

1 System Average Interruption Duration Index, as specified in IEEE Standard 1366-2003.

1-1

Introduction and Background

Figure 1-1 14-Feeder System in Upstate Orange and Rockland Territory

In this evaluation the economic impacts associated with these investments will be estimated using analysis of physical system models provided by the ISM and DEW. In particular, O&R’s ISM can be used to calculate losses on a circuit in lines and transformers, and consequently can estimate the changes in losses that result from changes in system configuration or operation of capacitors for power factor correction. In addition to loss calculations, DEW is used to project changes in reliability indices that improve following the installation of automatic switching devices on the circuits. A system of some size was necessary to properly test the ability of O&R’s ISM and control system to reconfigure circuits. The study concentrates on 14 feeders (about 5% of O&R’s total distribution system) that were modeled in comprehensive detail under a variety of configurations before and after various stages of modifications. In addition, the reliability response of O&R’s ISM was tested in a Monte Carlo analysis reflecting the incidence of various levels of storms that are typically experienced in the area, based on 10-years of historical data. This system analyzed consisted of 14 feeders, serving 22,000 customers with 14,000 transformers, switches, reclosers, capacitors, and sensors.

1-2

2 COST/BENEFIT ANALYSIS (CBA) FOR GRID MODERNIZATION PROJECTS

This Cost/Benefit Analysis will generally follow the framework jointly developed by EPRI and DOE, described in Methodological Approach for Estimating the Costs and Benefits of Smart Grid Demonstration Projects. 2 While some of the methodology deals with measurement and verification issues dealing with physical demonstration equipment deployed in the field, the Cost/Benefit Analysis portions of the methodology are clearly applicable in a model-based study, such as this O&R evaluation.

The Cost/Benefit Analysis methodology itself does not stray from long-standing principles of economic analysis as it is usually applied in utility planning. An important characteristic of conventional utility planning analysis is that its revenue-requirement approach takes a customer point of view of costs and benefits. In the narrow traditional sense, utility planning analysis views utility decisions in terms of changes in customer cost of service over time, sometimes trading off near-term costs for long-term benefits, or even vice versa. Such analysis proceeds under the assumption that the utility recovers its cost but only its cost, where cost includes return of and on capital.

Benefits of Smart Grid Investments EPRI’s Guidebook for Cost/Benefit Analysis of Smart Grid Demonstration Projects3 lists six general categories in which impacts and benefits of Smart Grid investments are typically found:

• Reliability (frequency and duration of customer interruptions)

• Utility Operations (people and how they do their jobs: non-fuel O&M, non-production assets, public and employee safety)

• System Operations (the power system and how efficiently it runs: losses, combustion, dispatch optimization, emissions)

• Utility Assets (production assets required to provide service)

• Power Quality (harmonics, sags/swells, voltage violations)

• Customer (customer-borne costs, changes in service amount or value)

The project being examined here addresses both efficiency and reliability under a single, coordinated, model-based control scheme. We will find physical impacts, costs, and benefits that

2 EPRI, Palo Alto, CA: 2010. Product ID 1020342 3 EPRI, Palo Alto, CA: 2012. Product ID 1025734

2-1

Cost/Benefit Analysis (CBA) for Grid Modernization Projects

touch most of these domains of electric service. If viewed broadly, we can observe that these domains interact.

In this study, investments in assets will improve reliability and efficiency, and will change how the utility does its work. Furthermore, for the particular system analyzed in this study, investments in “smart” assets will delay the need for large investments in conventional assets, reducing overall cost.

“Hard-Dollar” Benefits The cost/benefit analysis for this set of investments will include only the “hard-dollar” benefits of the various improvements. Impacts such as reduced losses or reduced repair time produce hard-dollar cost reductions that appear as reductions in the present-value cost of electricity for utility customers. “Soft-dollar” benefits that do not appear in reduced electricity cost, such as reduction in customer cost of interruptions or reduction in carbon emissions, are noted but not included as part of the cost/benefit balance.

Staging of “CBA Questions” A cost/benefit analysis can be structured as a series of economic questions that address the subject in stages or decision points. Rather than providing a single lump-sum benefit/cost evaluation for a complex project, this staging of economic analysis provides information about the incremental costs versus the incremental benefits of each stage. This is important because a staged or layered analysis can expose uneconomic stages that may be imbedded in a project that provides overall benefits. A step that is uneconomic in its own right may be necessary to reach economic benefits in a subsequent stage, but uneconomic steps that are not necessary can be eliminated or deferred until the economics improve. In this study the procedure for outfitting a feeder is itself a series of stages, which naturally suggests a structure for the subsequent cost/benefit analysis.

Considerations in a Model-Based Analysis A cost/benefit analysis compares at least two alternatives, though one of them is often implicit. Quantities associated with an investment scenario must be compared to the same quantity in the baseline scenario, a story line that must be at least as long as the study period detailing what would have happened but for the investments or changes under study. A baseline scenario can be difficult to construct without an explicit model, but this model-based study has the advantage of an explicit, high-quality, baseline scenario that includes modifications to maintain reliability over time as load grows. This capability will be used to determine an explicit asset deferral resulting from technology applications.

Another feature of a model-based analysis is the ability to perform Monte Carlo analysis to estimate the impacts of uncertain events such as the location of faults and their type (e.g., 3-phase-to-ground, single-phase-to-ground, etc.), as well as the location and extent of storm damage. Monte Carlo analysis can be used where analytic methods would be difficult to develop or would be computationally burdensome. A Monte Carlo analysis uses probabilities to create a number of realistic scenarios (“draws”) that can be analyzed separately. The results of the

2-2

Cost/Benefit Analysis (CBA) for Grid Modernization Projects

analyses are then characterized with statistical quantities such as means and standard deviations that represent the uncertainty in the results.4

Among the details included in the feeder model is a factor for the sensitivity of load to voltage. Tests on many feeders in the U.S. and abroad have confirmed that reducing delivery voltage reduces loads and losses, though results vary. In this modeling study we have assumed that for every 1% change in voltage at a delivery point, load changes by 1% in the same direction. That is, a 1% voltage reduction at a load point produces a 1% reduction in the load at that point, and vice versa. The assumption of some voltage sensitivity is realistic, but this adds some complexity to the cost/benefit analysis, especially for evaluating the project in stages.

Time series analysis using hourly loads and hourly cost of energy are used in this study as opposed to using average values, such as load factors and average costs of energy. This is a more accurate approach to calculating energy losses and costs.

4 For example, Monte Carlo can be employed to analyze a simple throw of any number of dice, where each draw assigns a randomly selected score between 1 and 6 for each die. A typical Monte Carlo analysis would simulate many throws of the dice. The scores for the draws establish a random sampling of results for a throw of the dice. The mean and standard deviation of the sample characterizes the expectation and uncertainty for a throw of the dice.

2-3

3 IMPROVEMENTS CONSIDERED IN THE STUDY

In this study a group of 14 feeders will be viewed in four steps that take the feeders from their baseline conditions to being modernized feeders with distribution automation devices, switchable capacitors, and centralized coordinated controls. The improvements will be considered in steps, illustrated in Figure 1-1 as a “stacking order” of incremental improvements and CBA questions:

Phase Balancing In this first step of the project load is balanced among the phases of each feeder by physically disconnecting various transformers or taps from one phase and reconnecting to another phase. The choices in this activity were identified by using O&R’s ISM in conjunction with DEW algorithms, which optimized the phase balancing over the time varying load rather than just at peak hours. Because losses are non-linear with respect to power flow, balancing the load among phases will produce a net reduction in losses across the year, saving energy and reducing emissions. The CBA question (CBA1) for this phase is whether the benefits of phase balancing outweigh its cost, although we expect this to be affirmative since phase balancing is not expensive.

Capacitor Design In this second phase each feeder receives a new optimized system of capacitors for voltage control and power-factor correction. O&R’s ISM is used in conjunction with DEW algorithms to determine the optimum size and location of the capacitors, which act as distributed generators of magnetizing current for transformers, motor loads, and other devices on the circuits. When magnetizing current is provided close to the loads, it no longer has to flow from upstream generators or sources, so losses are reduced and line capacity is made available for delivery of real power. However, in this stage the capacitors operate only on local controls. That is, they respond to local measurements only and are not coordinated or otherwise optimized. The CBA question (CBA2) for this step is whether capacitors operating on local controls provide sufficient benefits to overcome their costs.

3-1

Improvements Considered in the Study

Figure 3-1 “Stacking Order” of Efficiency/Voltage-Control Improvements and CBA Questions

Automated Control of Capacitors and Voltage In the third phase, the DEW model controls and optimizes voltage levels, altering schemes according to sensed conditions. The control scheme allows voltage reduction, which chronically reduces both losses and consumer loads. The CBA question (CBA4) for this final stage is whether the controls for voltage optimization overcome the cost of the control systems.

Automated Control of Switches and Reclosers In addition to capacitors, the 14 feeders were outfitted with automatic switches and reclosers that affected reliability. The impacts of these devices were examined in two separate ways:

• Storm response: The system responds to faults and system damage in a variety of storm conditions, examined through a Monte Carlo analysis of typical storm damage.

• Planning response: By improving reliability and flexibility of the feeders through automated switching, ORU is able to meet its reliability planning criteria while deferring an otherwise-needed substation transformer addition.

This portion of the analysis is separate from the voltage-control system and not illustrated in Figure 3-1. Although it uses the same model as voltage control, the field devices to automate switching and reconfiguration are fault sensors and automated switches and reclosers that impact reliability rather than voltage and efficiency. The CBA for this portion poses the limited question of whether the benefits of the investments in these field devices are outweighed by the benefits. The hard-dollar benefits for this step are recognized in terms of storm response cost and in deferral of conventional upgrade investments (e.g., new substations).

3-2

4 O&R’S ISM AND USE OF DEW ALGORITHMS

DEW The DEW analysis involved design calculations that address time-varying load patterns and quasi-steady state power flow analysis. Using DEW automation, many millions of power flow calculations over annual, hourly-varying load curves were performed. The DEW analysis eliminated simplifying assumptions normally made in economic evaluations, thus underpinning the economic results reported with more rigorous analysis than is typically used in economic evaluations.

Smart grid automation investments are evaluated using the following considerations:

• Better use of existing distribution system capacity

• Improved efficiency

• Maintenance of required reliability at lower cost than classical design approaches

• Reduced customer costs.

For example, in this work when a smart grid design results in a reduction in loss when the same load is supplied, then the loss reduction is counted as an energy savings and is considered in the hard dollar economic evaluations.

The Integrated System Model used in the DEW analysis models the physical system with a one-to-one correspondence, thus supporting the accuracy of the calculations performed. With an Integrated System Model, as use of the model increases and experts throughout the organization contribute to the model, its accuracy improves.

At O&R the design for the distribution automation and the control calculations that run as part of the distribution automation all use the same root model, the ISM. This is referred to as a model-centric approach to distribution automation. With the model-centric approach the same root ISM is used across all functions – planning, design, economic evaluation, training, real-time analysis, and real-time control. ORU is among the first utilities in the US to take this approach.

4-1

5 STUDY RESULTS

Automation for Efficiency and Voltage The four cases in this section are those involving voltage control with capacitors and tap-changing transformers, staged in steps to be examined separately.

Assumptions The hourly marginal cost of energy was assumed to be the hourly Location Based Marginal Prices (LBMP) for the Hudson Valley load zone in New York, escalated with the price of gas as forecasted by EIA in the Annual Energy Outlook 2011.

Capacity prices in New York’s market are currently quite low, substantially lower than the all-in cost of new capacity. This is consistent with the leveling off of electricity consumption in recent years, along with the participation of demand response in the capacity market.

When loads and losses on the 14 feeders are reduced, there is a corresponding reduction on the transmission system which further reduces losses back to the source. The LBMP energy prices in NY include marginal losses from the source to the point of delivery; otherwise this study will not evaluate losses above the distribution level, even while acknowledging that there is some reduction. The value is represented to some extent in the locational price of energy.

The load forecast for the 14 feeders was developed from O&R’s forecasting methodology which is based on historical energy usage and known block load additions.

5-1

Study Results

Base Case The base case for the 14-feeder system5 is characterized in Table 5-1 below:

Table 5-1 Load and Loss Characteristics of the Base Case for 14 Feeders

Load Energy Feeder Losses Peak Load MWh $ (000s) $/MWh MWh % $ (000s) $/MWh kW

2013 657,531 27,766 42.2 11,644 1.8% 503 43.2 350,410 2014 664,982 30,281 45.5 11,853 1.8% 554 46.3 354,134 2015 672,433 32,797 48.8 12,063 1.8% 605 49.4 357,857 2016 679,884 35,313 51.9 12,272 1.8% 656 52.5 361,580 2017 687,335 37,829 55.0 12,482 1.8% 707 55.6 365,304 2018 694,786 40,345 58.1 12,691 1.8% 759 58.8 369,027 2019 702,238 42,861 61.0 12,901 1.8% 810 61.9 372,750 2020 709,689 45,377 63.9 13,111 1.8% 861 65.0 376,474 2021 717,140 47,893 66.8 13,320 1.9% 912 68.1 380,197 2022 724,591 50,409 69.6 13,530 1.9% 964 71.2 383,920

Energy losses are ultimately dissipated into the atmosphere as heat, but the most immediate impact is their cost. Losses are purchased from the market along with energy to serve loads, with the marginal value being that of the local market price. The cost of losses for the fourteen feeders is over $500,000 per year in hard dollars, with a present value for the 10-year period at about $5.2 million. Even a small reduction in losses in the short term can amount to a substantial savings over a longer period of time.

Phase Balancing Case As described previously, balancing load among phases reduces load on heavily loaded phases and increases it on lightly loaded phases. Because line losses vary non-linearly with load, the total losses are lower after the loadings are balanced. This activity precedes optimization of capacitor placement and improves the optimization that can be achieved with capacitors. Phase balancing also slightly increases the total capacity of the feeder, but total capacity is even further increased with the placement of capacitors in the next step.

Phase balancing is an inexpensive activity to perform, assuming there exists a sufficiently accurate model to guide the process. Not every feeder has potential for great improvement, and the balance of phases may change throughout the year. The phase balancing process used O&R’s ISM, which optimized the phase balancing to reduce losses over the time varying load pattern

5 A 10-year study period was analyzed in detail for a number of feeders. However, detailed review of the analysis showed that a few of the feeders analyzed at each stage changed from one analysis stage to the next. For the results to discern the slight differences between stages, each stage shown is an interpolation of detailed analysis done for the first and last years for a consistent set of feeders.

5-2

Study Results

rather than just at peak. The phase balancing of the 14 feeders was undertaken at a cost of about $163,000, consisting of mainly crew time to make the changes.

Table 5-2 Load and Loss Characteristics of the Phase Balanced System

Load Energy Feeder Losses Peak Load MWh $ (000s) $/MWh MWh % $ (000s) $/MWh kW

2013 657,536 27,768 42.2 11,439 1.7% 494 43.2 350,896 2014 664,962 30,282 45.5 11,643 1.8% 544 46.3 354,618 2015 672,388 32,796 48.8 11,848 1.8% 594 49.4 358,341 2016 679,814 35,311 51.9 12,052 1.8% 644 52.5 362,063 2017 687,240 37,825 55.0 12,256 1.8% 695 55.6 365,785 2018 694,666 40,339 58.1 12,461 1.8% 745 58.8 369,507 2019 702,092 42,854 61.0 12,665 1.8% 795 61.9 373,230 2020 709,518 45,368 63.9 12,870 1.8% 845 65.0 376,952 2021 716,945 47,882 66.8 13,074 1.8% 895 68.1 380,674 2022 724,371 50,397 69.6 13,278 1.8% 946 71.2 384,397

Table 5-3 shows that the changes introduced to balance load among phases reduced losses by about 1.8% per year, thereby reducing the estimated cost of losses by about $94,000 present-value dollars over 10 years. The load balancing changes also slightly reduced consumption through the CVR effect, bringing the total PV savings to $125,000. While these present-value savings are less than the total cost of phase balancing, this stage is preliminary to the subsequent voltage-control stages, providing the maximum reduction in average voltage on each feeder, where a much larger cost benefit accrues.

Table 5-3 Phase-Balancing Change from Base Case

Change in Load Change in Losses Change in Peak MWh ∆% $ (000s) MWh ∆% $ (000s) kW ∆%

2013 + 5 + 0.00% $ 2 - 205 - 1.8% $ (9) + 486 + 0.1% 2014 - 20 - 0.00% $ 0 - 210 - 1.8% $ (10) + 485 + 0.1% 2015 - 45 - 0.01% $ (1) - 215 - 1.8% $ (11) + 484 + 0.1% 2016 - 70 - 0.01% $ (3) - 220 - 1.8% $ (12) + 483 + 0.1% 2017 - 95 - 0.01% $ (4) - 225 - 1.8% $ (13) + 482 + 0.1% 2018 - 120 - 0.02% $ (6) - 231 - 1.8% $ (14) + 480 + 0.1% 2019 - 145 - 0.02% $ (7) - 236 - 1.8% $ (15) + 479 + 0.1% 2020 - 170 - 0.02% $ (9) - 241 - 1.8% $ (16) + 478 + 0.1% 2021 - 195 - 0.03% $ (10) - 246 - 1.8% $ (17) + 477 + 0.1% 2022 - 220 - 0.03% $ (12) - 251 - 1.9% $ (18) + 476 + 0.1% PV $ (29) $ (94)

5-3

Study Results

The CVR effect is not normally represented in distribution studies, but its presence in O&R’s ISM offers a chance to reflect on how changes on the system can impact voltage and customer cost. While losses were reduced by 1.8%, loads were reduced by only a few hundredths of a percent. However, because loads are so much larger than losses, the energy impact of this reduction was considerable, and the reduction in present-value load cost was almost a third of the reduction in cost of losses. This points to benefits that will be gained in subsequent steps of this study, but it also points to a need to evaluate the impact of voltage changes in routine distribution planning studies. The ultimate goal is not just to reduce losses in the distribution lines and transformers, but to control and optimize voltage, which will be completed only in the final step.

Interestingly, peak load increased by 0.1% from phase balancing. This may seem to be a negative impact, but this will be reversed in subsequent steps. And this serves as a reminder that the phase balancing was optimized across the entire year to minimize annual losses, and not to reduce losses only at peak. Optimizing over the entire year brings greater reduction in losses than if the optimization had focused only on the peak.

Capacitor Design Case In the capacitor design phase, capacitors are re-introduced onto the feeder models (having been removed for phase balancing) so that they can generate magnetizing current (reactive power) required by many of the various devices on the system, such as transformers and motors. This reduces the current required to flow from the substation, in turn reducing losses and voltage drop. Of course, the amount of magnetizing current required by the loads varies throughout the day.

Fixed capacitors are added to cover daily minimum requirements, and remotely switchable capacitors are added to cover the peak loads. In addition, some single-phase capacitors are used where the power factors on the phases diverge. Capacitors can be set to switch themselves on when voltage falls to certain levels, but the ultimate use of these switchable capacitors will be to control them remotely with the model-centric system. However, in this study case, which is about the placement of capacitors and their impact on losses, the capacitors are modeled as operating on their local settings, so as to isolate the impact of the controls in a subsequent case. The basic results of the case are shown in Table 5-4.

As a first look at the impact of the capacitor design, we can compare with the phase balanced case, as shown in Table 5-5. The addition of capacitors substantially reduces losses in every year. However, load is also lower than in the phase-balance case, indicating that the average voltage is reduced in this case. Normally, adding capacitors will increase voltage, flattening the voltage-drop profile from substation to load. However, in this case the voltage at the substation was reduced by 2 volts to take advantage of the flatter profile, thereby reducing loads through the CVR effect. In present-value terms, the benefit of a 0.8% reduction in load is 10 times the benefit of the loss reduction, again emphasizing the importance of monitoring voltage level year round.

5-4

Study Results

Table 5-4 Loads and Losses for the Capacitor Design Case

Load Energy Feeder Losses Peak Load

MWh $ (000s) $/MWh MWh % $ (000s) $/MWh kW

2013 652,164 27,541 42.2 10,962 1.7% 472 43.2 347,357

2014 659,540 30,035 45.5 11,156 1.7% 520 46.3 351,040

2015 666,916 32,530 48.8 11,351 1.7% 568 49.4 354,724

2016 674,292 35,024 51.9 11,545 1.7% 615 52.5 358,408

2017 681,667 37,518 55.0 11,739 1.7% 663 55.6 362,091

2018 689,043 40,013 58.1 11,934 1.7% 711 58.8 365,775

2019 696,419 42,507 61.0 12,128 1.7% 759 61.9 369,459

2020 703,795 45,002 63.9 12,322 1.8% 807 65.0 373,142

2021 711,171 47,496 66.8 12,516 1.8% 855 68.1 376,826

2022 718,547 49,991 69.6 12,711 1.8% 903 71.2 380,510

Table 5-5 Capacitor-Design Changes from the Phase-Balance Case

Change in Load Change in Losses Change in Peak

MWh ∆% $ (000s) MWh ∆% $ (000s) kW ∆%

2013 - 5,372 - 0.82% $ (227) - 477 - 4.2% $ (22) - 3,539 - 1.0%

2014 - 5,422 - 0.82% $ (247) - 487 - 4.2% $ (24) - 3,578 - 1.0%

2015 - 5,472 - 0.81% $ (267) - 497 - 4.2% $ (27) - 3,616 - 1.0%

2016 - 5,522 - 0.81% $ (287) - 507 - 4.2% $ (29) - 3,655 - 1.0%

2017 - 5,573 - 0.81% $ (306) - 517 - 4.2% $ (31) - 3,694 - 1.0%

2018 - 5,623 - 0.81% $ (326) - 527 - 4.2% $ (33) - 3,732 - 1.0%

2019 - 5,673 - 0.81% $ (346) - 537 - 4.2% $ (36) - 3,771 - 1.0%

2020 - 5,723 - 0.81% $ (366) - 548 - 4.3% $ (38) - 3,810 - 1.0%

2021 - 5,773 - 0.81% $ (386) - 558 - 4.3% $ (40) - 3,848 - 1.0%

2022 - 5,823 - 0.80% $ (406) - 568 - 4.3% $ (43) - 3,887 - 1.0%

PV $ (2,234) $ (227)

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Study Results

The cost of capacitors and installation for this scenario is $564,300. The CBA2 question for this stage is whether the incremental cost is outweighed by the benefits, and if we look at the benefits of loss reduction alone, the benefits do not outweigh the costs, as the additional loss savings amount to only $227,000. However, in this case the average voltage was lowered at the substation to take advantage of flatter voltage profiles afforded by the addition of capacitors, and the subsequent reduction in load provided savings of over $2.2 million.

It is important to keep in mind that these changes are relative to the phase-balanced case, and the changes are additive with those of the phase balancing case. To span the first two steps, as represented by CBA3 in Figure 3-1, the loads and losses from the base case are subtracted from those of the capacitor design case, as detailed in Table 5-6. Here the total change in losses from the base case is almost 6% in every year, saving a total of over 7 GWh for the 10-year period, saving $321 thousand present-value dollars.

Table 5-6 Load and Loss Changes from the Base Case to Capacitor-Design Case

Change in Load Change in Losses Change in Peak

MWh ∆% $ (000s) MWh ∆% $ (000s) kW ∆%

2013 - 5,367 - 0.82% $ (225) - 682 - 5.9% $ (31) - 3,053 - 0.9%

2014 - 5,442 - 0.82% $ (246) - 697 - 5.9% $ (34) - 3,093 - 0.9%

2015 - 5,518 - 0.82% $ (268) - 712 - 5.9% $ (37) - 3,133 - 0.9%

2016 - 5,593 - 0.82% $ (289) - 727 - 5.9% $ (41) - 3,172 - 0.9%

2017 - 5,668 - 0.82% $ (311) - 743 - 5.9% $ (44) - 3,212 - 0.9%

2018 - 5,743 - 0.83% $ (332) - 758 - 6.0% $ (47) - 3,252 - 0.9%

2019 - 5,818 - 0.83% $ (353) - 773 - 6.0% $ (51) - 3,292 - 0.9%

2020 - 5,893 - 0.83% $ (375) - 788 - 6.0% $ (54) - 3,331 - 0.9%

2021 - 5,969 - 0.83% $ (396) - 804 - 6.0% $ (57) - 3,371 - 0.9%

2022 - 6,044 - 0.83% $ (418) - 819 - 6.1% $ (61) - 3,411 - 0.9%

PV $ (2,263) $ (321)

The question CBA3 is whether the combined costs of phase balancing and capacitor design are outweighed by the combined benefits of the first two stages. On the basis of loss savings alone, the benefits of phase balancing and capacitor design present-valued over a 10-year period do not outweigh the costs of implementation to this point, a total of $727,300. However, by lowering the average delivery voltage of the feeders, taking advantage of the reduced voltage drops from the substation to the ends of the lines, customer consumption is reduced on the feeders, saving over $2.2 million present value dollars in addition to the value of loss reduction of $321,000. The net present value of energy savings and implementation cost is more than $1.8 million.

In addition to the energy savings associated with load reduction, the peak load for the 14-feeder system was reduced by about 1%, over 3.5 MW in each year. As mentioned above, capacity values in NY are currently quite low compared with the cost of new entry capacity. At a low

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Study Results

value of $.50/kW-month, 3.5 MW is worth more than $21,000 per year. At a value of $5/kW-month, the value of the capacity reduction is more than $210,000 per year.

Coordinated-Control Case In the Coordinated-Control case, shown in Table 5-7, voltage is lowered to take full advantage of the flatter voltage profiles afforded by the prior steps.

Table 5-7 Load and Loss Characteristics for the Coordinated Control Case

Load Energy Feeder Losses Peak Load

MWh $ (000s) $/MWh MWh % $ (000s) $/MWh kW

2013 646,943 27,316 42.2 11,045 1.7% 478 43.3 343,578

2014 654,347 29,796 45.5 11,253 1.7% 527 46.9 347,221

2015 661,752 32,276 48.8 11,461 1.7% 577 50.3 350,864

2016 669,156 34,756 51.9 11,669 1.7% 626 53.7 354,507

2017 676,561 37,236 55.0 11,877 1.8% 675 56.9 358,150

2018 683,965 39,716 58.1 12,085 1.8% 725 60.0 361,792

2019 691,370 42,195 61.0 12,293 1.8% 774 63.0 365,435

2020 698,775 44,675 63.9 12,501 1.8% 824 65.9 369,078

2021 706,179 47,155 66.8 12,708 1.8% 873 68.7 372,721

2022 713,584 49,635 69.6 12,916 1.8% 922 71.4 376,364

The coordinated-control step alters the capacitor-design case in an interesting way, as shown in differences from in Table 5-8. Coordinated control is intended to optimize the feeder performance from the customer perspective. Relative to the capacitor design case, the added control scheme further reduces load by almost as much as the capacitor-design case did, at the cost of only a minor increase in losses (relative to the capacitor-design case).

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Study Results

Table 5-8 Load and Loss Changes, Coordinated Control relative to Capacitor Design

Change in Load Change in Losses Change in Peak

MWh ∆% $ (000s) MWh ∆% $ (000s) kW ∆%

2013 - 5,221 - 0.8% $ (224) + 83 + 0.8% $ 6 - 3,779 - 1.1%

2014 - 5,193 - 0.8% $ (239) + 96 + 0.9% $ 8 - 3,819 - 1.1%

2015 - 5,164 - 0.8% $ (254) + 110 + 1.0% $ 9 - 3,860 - 1.1%

2016 - 5,135 - 0.8% $ (268) + 124 + 1.1% $ 11 - 3,901 - 1.1%

2017 - 5,107 - 0.7% $ (283) + 137 + 1.2% $ 12 - 3,942 - 1.1%

2018 - 5,078 - 0.7% $ (297) + 151 + 1.3% $ 14 - 3,983 - 1.1%

2019 - 5,049 - 0.7% $ (312) + 165 + 1.4% $ 15 - 4,023 - 1.1%

2020 - 5,021 - 0.7% $ (327) + 178 + 1.4% $ 16 - 4,064 - 1.1%

2021 - 4,992 - 0.7% $ (341) + 192 + 1.5% $ 18 - 4,105 - 1.1%

2022 - 4,963 - 0.7% $ (356) + 206 + 1.6% $ 19 - 4,146 - 1.1%

PV $ (2,064) $ 88

When measured against the base-case starting point, as shown in Table 5-9, the three steps are shown to combine to produce $4.6 million in present-value benefits through loss reduction and load reduction, from the combination of local provision of magnetizing current and the careful control of voltage along the feeders.

Table 5-9 Load and Loss Changes, Coordinated Control relative to Base Case

Change in Load Change in Losses Change in Peak

MWh ∆% $ (000s) MWh ∆% $ (000s) kW ∆%

2013 - 10,588 - 1.6% $ (449) - 599 - 5.1% $ (25) - 6,832 - 1.9%

2014 - 10,635 - 1.6% $ (485) - 601 - 5.1% $ (26) - 6,912 - 2.0%

2015 - 10,681 - 1.6% $ (521) - 602 - 5.0% $ (28) - 6,993 - 2.0%

2016 - 10,728 - 1.6% $ (557) - 604 - 4.9% $ (30) - 7,074 - 2.0%

2017 - 10,774 - 1.6% $ (593) - 605 - 4.8% $ (32) - 7,154 - 2.0%

2018 - 10,821 - 1.6% $ (629) - 607 - 4.8% $ (34) - 7,235 - 2.0%

2019 - 10,868 - 1.5% $ (665) - 608 - 4.7% $ (36) - 7,315 - 2.0%

2020 - 10,914 - 1.5% $ (701) - 610 - 4.7% $ (38) - 7,396 - 2.0%

2021 - 10,961 - 1.5% $ (738) - 612 - 4.6% $ (39) - 7,476 - 2.0%

2022 - 11,007 - 1.5% $ (774) - 613 - 4.5% $ (41) - 7,557 - 2.0%

PV $(4,328) $ (233)

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Study Results

Reduction in Peak Demand and Increased Feeder Capacity Reducing peak demand produces value from several sources. In particular, coincident-peak-demand reduction reduces the amount of capacity that must be built, whether built by the utility or by someone else in the market. In the case of O&R, reducing coincident peak reduces the amount of capacity that must be purchased from the market. A range of values for this reduction was described above. However, a second source of value is the freeing up of capacity in distribution lines.

The capacity of a feeder must be sufficient to accommodate its peak flow of current, which is composed of real and reactive power demands of all devices on the feeder. Reducing real-power load and losses through voltage optimization reduces both energy and peak demand. In addition, providing reactive power along the feeder with capacitors reduces the flow of reactive current from the substation, contributing to the reduction in losses, but also freeing capacity in the line.

When the peak current of a feeder exceeds the capacity threshold of the feeder, various means can be used to resolve the issue, such as switching portions of the feeder over to another source. Ultimately a feeder may be reconductored, or a new substation may be added to serve portions of several feeders.

The added capacitors, controls, and automated switching in this study also help to forestall these expensive measures in addition to their basic functions. Reducing the peak current extends the amount of time before load grows enough to require conventional upgrades. However, the additional headroom also allows greater flexibility for switching sections of feeders during the year to reduce loadings and optimize losses. The value of this flexibility appears to customers in “hard dollar” form when these capacity-driven upgrades would have been needed in the base case, but such upgrades were not avoided during the 10-year study period and are not evaluated here. Capacity-driven upgrades are separate from the reliability-driven upgrades, one of which is evaluated in the following section on reliability.

Summary: Capacitor Design and Coordinated Voltage Control This model-based study illustrates several important points:

First, there are benefits to be gained at low cost from maintaining a good balance of loads among phases. However, phase balancing can be done effectively only if an accurate picture of phase loading can be constructed, and optimizing phase balancing over time requires a model that can provide a view of every time step. In this study, an hourly model was used, and the balancing was done to optimize across the time varying load pattern, not just at peak conditions

Next, capacitors can be added to circuits to reduce losses on transmission and distribution lines by locally providing magnetizing current for the motors, transformers, and other magnetic devices on the circuit that require it. It is in essence distributed generation of reactive power. This gain in efficiency on the circuit itself is good from a variety of perspectives (energy savings, capacity savings, emissions, etc.), but it is important to note that the economic gains from loss reduction remain small relative to the gains available from closely controlling voltage. Comparing coordinated control with the base case, the reductions in consumption from voltage reduction are almost 18 times greater than that from loss reduction. The gains from voltage reduction would still be large relative to loss savings even if the load sensitivity to voltage were

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Study Results

half of the amount assumed. However, the other side of this coin is that if power factor is corrected to minimize power-system losses without looking at its impact on voltage and load, then it could improve efficiency on the utility side of the meter while simultaneously erasing or overwhelming the benefits with added costs on the customer side. That is, simply correcting power factor can reduce losses on a feeder, but the rise in voltage that it may bring can increase customer consumption by a greater amount.

Automation for Reliability and Flexibility O&R’s ISM and DEW algorithms together can provide for model-centric control of O&R’s system operations, directing response to events such as faults or storm damage. As with the voltage optimization and control systems described above, O&R’s ISM is able to model its response to hypothetical events in order to analyze its impacts on the system for a 10-year study period.

The automated controls modeled on the 14 feeders include automatic reclosers and switches that are able to reconfigure circuits following persistent faults. In the past, when a fault occurred on a feeder, the entire feeder might be out of service until a truck crew could be dispatched, travel to the area, find the problem, and repair it. Modern smart grid reclosers and remotely controlled switches break these feeders into segments that can be reconfigured within minutes when trouble strikes. Frequently, only the faulted segment must wait for the truck to arrive to restore service; segments that are not faulted may be switched over to be served from other circuits.

The benefits from distribution automation appear in a variety of ways. Customers who enjoy improved reliability realize economic gains from fewer costly interruptions, especially commercial and industrial customers. In this study we have not quantified these benefits, which we have characterized as “soft dollars.” However, automation of switching also allows deferral of conventional assets such as substations and transformer upgrades, while maintaining reliability within planning guidelines. Deferral of such upgrades result in present-value “hard dollar” savings.

Two avenues for examining the reliability impacts of smart grid automation were explored in this study:

• Storm Response: Evaluating how automation reduces crew time required for repairs following storms of various levels of severity.

• Planning Response: Evaluating how automation affects future system upgrades required to maintain reliability.

Storm Response O&R’s ISM, which will ultimately direct the automated response to fault conditions, can simulate what it would do in response to typical storm damage. Storms cause faults on lines and damage equipment, sometimes destroying parts of the system of overhead lines and all of the various devices they hold. As described above, a fault on a circuit may take down the entire circuit, locking out the breaker at the substation. If the circuit has switches, and if segments of the circuit can be switched to alternate sources, then undamaged unfaulted segments of the circuit can be restored to service by judiciously operating the switches. In the past the operation

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Study Results

of switches was fully manual, and required a manual process of verifying the state of the circuit segments before going through the process of actually throwing switches. During this manual process of searching and verifying, customers are in the dark, even those who may be on circuits that are not damaged or faulted.

Modern smart grid devices are able to communicate their state to the control system. O&R’s ISM will assess the system state and determine which segments are viable and which are faulted. Using calculated results based upon field device measurements, faults can be located faster by ground crews, while cleared circuit segments can be restored quickly, if not automatically. Customers on unfaulted circuit segments receive power quickly, an impact we can quantify by counting customer-hours of interruption (CHI), even if we don’t quantify the monetary savings associated with it.

Automation allows ground crews to concentrate on finding and repairing the actual fault, spending far less time determining the state of the system than under full manual response. Storm damage often requires crew overtime, including crews from other service territories in the worst cases, and the savings in crew expense is a true “hard dollar” benefit.

For the baseline of this analysis, manual switching for each fault is assumed to take one hour for the crew to be ready to operate the first switch, and 15 minutes for each switching action required thereafter until the fault is cleared and the circuit is returned to normal operating condition.

Monte Carlo Storm Analysis Storm force is randomly distributed within a power system, but statistics are kept with regard to the distribution of faults and damage for various types of storms that occur in the O&R service territory, and for the frequency of the various storm types, including both winter storms and summer storms. Over a recent 10-year period 82 storms were experienced in the ORU service territory and are classified as shown in Table 5-10.

Table 5-10 Ten-year Storm Frequency in O&R Territory

Ten-year Storm Counts by Storm Type Wind (≤ 20 mph)

Strong Wind

(> 20 mph)

Low Temperature (< 32° F) 7 10

Moderate Temperature (32° ≤ Temp ≤ 80° F) 12 23

High Temperature (> 80° F) 13 17

Each of these storm types was simulated in repeated “draws” by estimating damage and/or faults at locations selected probabilistically in accordance with historical frequency for each storm type. The system restoration response was calculated for the system with and without automation, assuming typical response rates experienced in history with the manual system. Results were averaged over the many draws to estimate an expected amount of time involved in switching for the manual system versus the automated system.

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Study Results

Automation reduces the amount of time crews spend in restoration activities, reducing restoration costs as well as restoring service to customers more rapidly. Reduction in restoration costs estimated from the Monte Carlo analysis are shown in Table 5-11.

Table 5-11 Estimation of System Wide Savings from Reductions in Crew Switching Time

Storm Type Storm

Restoration Cost ($/hr)

Number of Crews

Cost per Crew-Hour

Crew Hours

Saved per Storm

Number of Storms (10

years) 10-year Savings

High Temperature $70,000 100 $700 40 13 $364,000

Moderate Temperature $70,000 100 $700 60 12 $504,000

High Temp/Strong Wind

$100,000 142 $704 213 17 $2,550,000

Moderate Temp/Strong Wind

$100,000 142 $704 168 23 $2,721,127

Low Temperature $120,000 171 $702 127 7 $623,860

Low Temp/ Strong Wind $120,000 171 $702 403 10 $2,828,070

10-year Present Value $7,645,868

Planning Response Distribution Automation can provide benefits in several ways. Some benefits are related to improved reliability performance and some benefits arise from deferrals of capital equipment upgrades that would have otherwise been required to maintain reliability within planning criteria. This second category is referred to here as “Planning Response,” which lowers the cost of providing reliable electric service to customers. Planning response produces “hard-dollar” savings over a period of time, beginning when upgrades are deferred from the base case.

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Study Results

Figure 5-1 Example showing how automation allows 8-year deferral of capital equipment upgrade while maintaining reliability performance

Distribution planners study the distribution system to determine when upgrades are needed, such as new transformers, new substations, or new lines. As depicted in Figure 5-1, reliability performance commonly declines (i.e., customer-hours of interruption increases) as more customers and devices are added to feeders over time and the load grows. Future upgrades are needed to prevent reliability performance from dropping below distribution planning criteria which support regulatory mandated system reliability criteria.

Within the study area, automated switching was observed to allow planners to defer a planned substation upgrade from 2021 to 2029, as illustrated in Figure 5-1. A simplistic analysis6 of present value of capital costs is given in Figure 5-2.

6 A detailed analysis would evaluate the investments over their life cycles with their various lifetimes, including replacements and taxes. These details would not upend the conclusion of hard-dollar present-value savings. Note that the customer value of the reduced interruptions is not included here, although it is not disputed that such value exists.

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Study Results

Figure 5-2 Simple Analysis of Present-Value of Capital Costs for a conventional plan compared with a Smart Grid plan that allows an upgrade deferral from 2021 to 2029

The analysis in Figure 5-2 shows a conventional plan for upgrades in 2021, needed to bring reliability performance within the planning criterion. The total investment in 2021 is estimated at just over $46 million (allowing for modest inflation from the initial cost estimate in 2012 dollars). The alternative Smart Grid plan involves much lower short-term investments in automated switches, reclosers, and sensors, amounting to less than $2 million through 2014. These investments improve reliability performance of the relevant circuits to such an extent that the upgrades reflected in the conventional plan are not needed until 2029. Though inflation increases the investment cost in nominal terms, the present value savings for the Smart Grid plan are just over $7 million. Of course, deferred investments may be further deferred into the future as new technologies and techniques for reliability support are developed. Economic deferral of conventional investments preserves the option of taking advantage of new technologies as they arise.

“Soft”-Dollar Benefits

Reduced Cost of Interruptions

For completeness we calculated an estimate of the customer savings from reducing interruptions associated with the kinds of faults that are included in the calculations of the usual reliability indices, SAIDI, CAIDI, and SAIFI.7 The analysis estimated the changes in the indices based on

7 SAIFI = System Average Interruption Frequency Index

CAIDI = Customer Average Interruption Duration Index

Both indices are specified in IEEE Standard 1366-2003.

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Study Results

where the faults were typically located and how the new system would respond to them. The indices for the improved and the unimproved state at the beginning of the project are shown in Table 5-12.

Table 5-12 Improvement in Reliability Indices with Automated 14-feeder System

Unimproved Improved % Change

SAIFI 0.634 0.331 -48%

CAIDI 74.45 87.83 18%

SAIDI 47.22 26.60 -44%

Primarily, distribution automation reduces the average duration of sustained interruptions by returning many customers to service who would have otherwise had to wait to be manually switched back on or wait for repairs to be completed. The switching now takes place automatically within minutes, so the customers that were switchable are no longer included in the counts of sustained interruptions (affecting SAIFI) and reducing the duration of the interruptions that remain (affecting SAIDI). The average time of repair is represented by CAIDI, and this number actually increases because the shorter interruptions associated with the switchable customers are eliminated from the calculation. That is, formerly, the average “repair” time was an average of the durations of customers returned by switching and the customers who had to wait for completion of repairs. In the improved state, only those customers on faulted segments actually endure a sustained interruption, so the average “repair” time more accurately reflects the time it takes to make repairs.

A calculator provided by DOE, called the Interruption Cost Estimate Calculator (See Appendix B), was used to estimate the reduction in cost to customers for power interruptions occurring on the 14-feeder system based on the improvements shown in Table 5-12. The calculator estimates over $1.2 million reduction in interruption costs for the 14-feeder feeder system for a single year. These benefits were largely estimated to occur in the commercial and industrial customer classes. The amount seems large until we consider the number of customers affected. The impact of interruptions on industrial and commercial customers varies widely according to the type of business and the processes interrupted. The customer-average savings was about $650/customer for large customers and $230/customer for small customers. The estimated reduction in interruption costs for residential customers was estimated to be just over $27,000, or about $1.40 per customer.

Reduction of Greenhouse Gas Emissions Reducing energy consumption generally reduces associated carbon emissions from power plants, but mostly from those generating units that are “on the margin,” i.e., those fueled units that dispatchers call on to respond to variation in total demand.8 The resources that are adjusted

8 Not all power plants emit CO2, but many that do not are nuclear or renewable resources that generally do not reduce output in response to load reduction. Hydroelectric output is flexible, and its timing is optimized relative to other alternatives, but the total amount of hydro generation over time does not fluctuate with load levels.

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Study Results

continually in economic dispatch are fueled resources that burn coal, natural gas, or oil.9 In New York’s market, gas-fired units set the marginal price most of the time, and coal-fired generators provide only a small fraction of total energy in the state.

Table 5-13 Avoided Greenhouse Gas Emissions from Load and Loss Reductions Based on EPA eGrid Marginal Emissions Rates

Carbon emissions from gas-fired generators are related to the efficiency of the plant, which determines how much fuel must be burned to produce a kilowatt-hour of energy. Combined-cycle gas-fired generators may have an efficiency in the 45% range, which corresponds to about 1,000 pounds of CO2 per MWh consumed at the distribution level. A 30%-efficient gas-fired generator, typical of older boiler units, corresponds to about 1,500 pounds of CO2 per MWh consumed at the distribution level. Among gas-fired generators, most of the energy in NY is generated with efficient combined-cycle units, with a lesser amount of energy coming from older, less-efficient gas-fired boiler units. The amount of energy from these older units should decline over time as they are retired and/or replaced with renewables and combined-cycle generators. So the current rate of CO2 emissions reduction that can be associated with reduction

9 Economic dispatch optimizes the dispatch of units based on the costs or prices offered by the various units in its sphere of influence, and these prices may or may not include a price for carbon emissions, so shifting generation to a lower-price unit may increase carbon emissions, especially if the shift is from gas to coal.

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Study Results

in load and losses should be between 1,000 and 1,500 pounds per MWh, but should trend toward the 1,000 pound level over time.

The EPA’s eGrid program10 provides an estimate of marginal greenhouse gas emissions rates for various regions of the country based on current dispatch levels, and provides an emissions rate of 1,347 lbs of CO2/MWh for Upstate NY. On the same basis, the eGrid database provides emissions rates for Methane (CH4) and Nitrous Oxide (N2O). Emissions reductions based on the eGrid rates are shown in Table 5-13. These reductions were calculated based upon the improved feeder efficiencies and reduced customer energy usage due to Conservation Voltage Reduction.

Based on the estimated range of 1,000 to 1,500 lbs/MWh, the reduction in CO2 emissions from improved feeder efficiency (see Tables 5-1 through 5-6) and customer energy reduction due to CVR (see Tables 5-7 through 5-9) would range from 57,000 to 85,000 tons. This reduction in CO2 emissions is due directly to the improved feeder efficiency and the reduction in customer energy usage.

10 “The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States.” Quoted figures are from the 9th edition of eGrid, based on 2010 dispatch. http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html

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6 SUMMARY AND CONCLUSIONS

This study has shown significant benefits associated with installation of automation consisting of automated switches, reclosers, capacitors, and sensors, as well as with a coordinated control system that monitors conditions and provides optimization instructions under a variety of conditions.

Table 6-1 Summary Results from Staging Analysis of Coordinated Control with CVR

The results show that phase balancing alone may not be worth the cost just to reduce the energy cost of losses. However, phase balancing is a preliminary step leading to optimization of power factor correction, voltage control, and conservation voltage reduction, which provide significant savings over the 10-year study period.

In addition to load and loss savings, the automated remote control of switching was shown through a Monte Carlo storm-response simulation to provide over $7,500,000 present-value savings in crew time in storm response over a 10-year period. Further, deferral of a single substation upgrade from 2021 to 2029, allowed by estimated reliability improvement brought about by distribution automation, produced a present-value savings of over $7,000,000. Finally, the estimated improvement in reliability from distribution automation is estimated to save customers about $1,200,000 per year in economic losses for the 14-feeder system studied.

Table 6-2 Storm Response, Asset Deferral, and Interruption Cost Estimates

Analysis Components Savings k$ PV Basis of Savings Calculation

Storm Response > $7,500 Total system

Asset Deferral > $7,000 One substation deferral

Interruption Cost ~ $1,200/year 14 feeders, 1st year

6-1

7 LESSONS LEARNED

Circuit optimization (i.e., phase balancing, optimal capacitor sizing and placement) needs to be reviewed prior to evaluating coordinated volt/var control. Circuit optimization improves the ability of a coordinated control system to optimize volt/var support and also helps restoration during circuit reconfigurations, resulting in cost savings to the customer.

Automated switching allows rapid access to backup capacity, which can defer larger, conventional, capital investments, resulting in cost savings to the customer.

Automated switching minimizes customers interrupted during emergency conditions (e.g., storm damage), and reduces the crew hours needed for restoration, resulting in cost savings to the customer.

Blue sky day reliability is significantly improved due to the addition of automated switching, resulting in cost savings to the customer.

Based on calculations performed for this report, it was found that approaches to economic evaluations that use averages (e.g., load factors and average cost of energy), as opposed to time series data, result in large inaccuracies. [See Reference 2]

Based on calculations performed for this report, designs based on the time varying load improve the analysis accuracy, resulting in better decisions, resulting in increased savings to the customer. [See Reference 2]

More work is to better understand load-voltage dependencies on a more granular level, such as feeder-by-feeder and as a function of time. Having more accurate load-voltage dependency models will permit tighter control over Conservation Voltage Reduction, which will result in increased savings to the customer.

Very detailed models - where customers; customer classes; time varying loads; and time varying cost of energy are included in the model - are needed to insure the accuracy of the analysis.

7-1

A ORU’S INTEGRATED SYSTEM MODEL

Standard practice for utilities is to deploy many different models, where each model organizes certain data to help solve certain functional problems. In contrast to this standard modeling practice, an Integrated System Model (ISM) provides an environment in which the same model can be reused from planning to design to testing to training to operation to control. Furthermore, an ISM can incorporate all types of measurements - such as SCADA measurements, EMS measurements, customer load, or weather measurements- and make these measurements available to calculations that run on the ISM. This provides an architecture for data integration. Thus an ISM can change the paradigm of maintaining many custom data interfaces that “push data to algorithms” to a paradigm of maintaining one data interface and “pushing algorithms to data.”

Figure A-1 ORU Distribution System ISM

The ISM for the ORU distribution system is illustrated in Figure A-1. ORU also models the transmission system in its ISM (not shown in Figure B-1). As illustrated in the zoom windows shown in Figure A-1, all substation equipment and all sectionalizing devices are modeled, including isolation switches and bypass switches. In building the ISM equivalent models are avoided. That is, “the best equivalent is no equivalent.” This provides the ability to simulate all scenarios that can occur in the field.

The ORU ISM is reused from planning to economic analysis to lab testing to training to real-time analysis and control. DOE has termed this approach “model-centric smart grid.” The ORU ISM integrates data from GIS, CAD, and transmission system models together into the single analysis model, relating customer load, customer load research statistics, SCADA measurements, EMS measurements, weather (historical and forecast) measurements, outage data, solar generation, and other data to appropriate equipment modeled in the ISM.

A-1

ORU’s Integrated System Model

ORU’s ISM is maintained in memory 24x7 on a computer Model Server, and the Model Server can serve up any portion or all of the ISM to analysis requests. ORU’s ISM creates a collaborative analysis environment that allows different groups within the organization to couple their individual calculations into larger calculations, and provides a basis for automating calculations, such as the calculation of the annual system loss. ORU’s ISM is providing a path for managing emerging problems, such as the growth of solar generation.

A-2

B INTERRUPTION COST ESTIMATE CALCULATOR

When a utility invests in assets that improve reliability over historical norms, its delivery customers receive value in the form of reduced interruption costs. These are not costs that are paid to anyone in cash. They are economic costs of lost time, lost production, lost business, perhaps lost profits and spoilage. These costs may or may not be tallied or known by most customers, but some industrial customers know well the cost of an inopportune interruption in the middle of a production run, and the presence of backup generation at many places of business is indicative of the value of continuous service.

Residential customers may think of reliability in terms of losing air conditioning on an especially hot day, losing power to run heating equipment during a winter storm, food spoilage, or increased living expenses due to eating out or renting hotel rooms. There are also mundane interruptions that occur at random times throughout the year. Even a momentary interruption may cause frustration and lost time as a resident resets clocks on various devices or recreates work lost on a computer project. Reliability has value, but it is not simple to put a dollar figure on it in every case.

Because of the variety of customer times, the cost of interruptions varies by customer and is spread widely over all of the customers that experience them. Customer losses range from thousands of dollars in some industrial and commercial customers to minor irritations to residential customers. Investments to reduce the frequency and duration of interruptions of service reduce these losses of value, which are sometimes termed "soft-dollar savings," even though to some customers the losses are clearly monetary. The value proposition for reliability improvement is clear and well-documented, with the proviso that the costs of providing higher reliability appear in the utility's cost accounting, while the value gained is widely dispersed and perhaps in certain circumstances barely noticed.

Utilities needing to determine an economic level of generation reserves surveyed customers who were asked about their willingness to pay to avoid service interruptions at various times of day and season. These surveys were the basis of estimates of a constant often known as the Value of Lost Load, or VOLL. The VOLL has been used for a variety of purposes, sometimes as the optimal value for an increment of capacity in generation reliability studies, elsewhere in market systems as a maximum bid price for generators in times of scarcity. Values have varied from $1,000/MWh to $15,000/MWh and higher. While such values may seem outrageous as market prices for power, they are generally not sustainable in wholesale markets unless there is deep scarcity, so when they occur they generally have very short duration of only a few hours. In regulated vertically integrated systems with optimized reserve levels, the expected cost of the last increment of capacity is in the range of VOLL, if not equal, because its cost is spread over very

B-1

Interruption Cost Estimate Calculator

few hours.11 So the VOLL, which is very high relative to average retail prices, is commonly encountered in utility systems.

The VOLL is usually stated as an average value for all customer classes, and past surveys have determined that the value of service reliability varies strongly by customer class, and by the type of business. Residential customers value reliability, but far less than most commercial and industrial customers. The VOLL also is not typical of the value of reliability across all seasons, but rather an estimate of the value at the peak day and hour. The VOLL, as it is often quoted as a single number, is therefore not appropriate for evaluation of reliability improvement at the distribution level. For that use a more varied set of estimates of interruption costs are needed, by time of day and season, and by type of customer. In addition, the valuation should recognize differences in the duration of interruptions, determining whether the cost of interruption increases linearly with duration or whether it saturates.

Recognizing the need for evaluation of interruption cost (and the value of interruption reduction) for distribution-level, the U.S. Department of Energy commissioned a study to quantify the cost of interruptions characterized over a variety of conditions so as to be appropriate for evaluation of distribution interruptions distributed at random across the year. The end result of this study is a publicly available calculator known as the Interruption Cost Estimator (ICE). The ICE model, available for running online at icecalculator.com, provides an estimate of interruption costs for a feeder, area, or company service territory based on input data that describes the types of loads and the reliability indices that characterize the reliability of the system evaluated.

11 As a simple order-of-magnitude example, if an increment of peaking capacity costs $60/kW-year, and it is spread over only 10 hours, then its cost is $6/kWh or $6,000/MWh. The last increment of capacity should be expected to run rarely.

B-2

Interruption Cost Estimate Calculator

Figure B-1 Home page for the ICE Calculator

The basis for the interruption costs imbedded in the ICE model are the 28 surveys that were taken by utilities over several decades. The process of converting these survey results into a consistent set of "damage functions" involved many compromises and estimates, but the results are documented and freely available on the website under the “Relevant Reports” link. As with the VOLL, the cost rates are high for some classes of customers, suggesting a high value for reliability improvement.

The ICE calculator requires detailed information concerning the composition of loads on the subject area or feeder. The data reflect all of the various dimensions across which the cost of interruptions have been estimated, and while some utilities have this information for their service territories, it may be unusual for a utility to have such information at the feeder or substation level. To facilitate running the ICE model, census data for each state is included that characterizes the total makeup of load at the state level, so that the user can select a state and obtain a run with only a few bits of unsupplied data.

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C REFERENCES

1. “Automation Effects on Reliability and Operation Costs in Storm Restoration,” Danling Cheng, Ahmet Onen, Dan Zhu, David Kleppinger, Reza Arghandeh, Robert P. Broadwater, Charlie Scirbona, accepted for publication in Electric Power Components and Systems.

2. “Time-varying Cost of Loss Evaluation in Distribution Networks Using Market Marginal Price,” International Journal of Electrical Power and Energy Systems, Ahmet Onen, Jeremy Woyak, Reza Arghandeh, Jaesung Jung, Charlie Scirbona, Robert P. Broadwater, June 2014, pp. 712-717 .

3. “Model Centric Approach for Monte Carlo Assessment of Storm Restoration and Smart Grid Automation,” Danling Cheng, Ahmet Onen, Reza Arghandeh, Jaesung Jung, Robert Broadwater, Charlie Scirbona, Proceedings of the ASME 2014 Power Conference, July 28-31, Baltimore, Maryland, USA.

4. " Model Based Coordinated Control Based on Feeder Losses, Energy Consumption, and Voltage Violations," Ahmet Onen, Danling Cheng, Reza Arghandeh, Jaesung Jung, Jeremy Woyak, Murat Dilek, Robert P. Broadwater, Electric Power Component and Systems, Vol. 41, Issue 16, Oct. 23, 2013.

5. “Advantages of Integrated System Model-Based Control for Electrical Distribution System Automation,” Josh Hambrick, Robert Broadwater, Proceedings of 18th IFAC World Congress, Aug. 28 – Sept. 2, 2011, Milano, Italy.

6. “Configurable, Hierarchical, Model-based Control of Electrical Distribution Circuits,” Josh Hambrick, Robert Broadwater, IEEE Transactions on Power Systems, Pages Volume: 26, Number: 3, August 2011, Pages 1072-1079.

7. “Generic Reconfiguration for Restoration,” D. Kleppinger, R. Broadwater, Electric Power Systems Research Journal, Volume 80, Issue 3, March 2010, Pages 287-295.

8. “A Graph Trace Based Reliability Analysis of Electric Power Systems with Time Varying Loads and Dependent Failures,” D. Cheng, D. Zhu, R. Broadwater, S. Lee, Electric Power Systems Research Journal, Volume 79, Issue 9, September 2009, Pages 1321-1328.

9. “Storm Modeling for Prediction of Power Distribution System Outages,“ Dan Zhu, Danling Cheng, Robert Broadwater, Charlie Scirbona, Electric Power Systems Research, Vol 77, pp 973-979, 2007.

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