dynamic distribution system, a new architecture for the integrated grid … · · 2015-09-21new...
TRANSCRIPT
"Dynamic Distribution System, a new Architecture for the
Integrated Grid"Bruce Beihoff
Tom JahnsBob Lasseter
University of Wisconsin – MadisonIEEE PES 2015 - Panel
July 29,2015
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Panel Presentation Abstract"Dynamic Distribution System, a new Architecture for the Integrated Grid"Abstract:For the first time in decades the Electrical Grid is undergoing great change. The advent of distributed energy resource systems (DERS) and large scale improvements in electrical power conversion have combined to become an engine of this change that extends far beyond the growing and measurable effects of just today. Almost universally this engine has driven us towards a rethinking of the Distribution Grid, a part of the Electrical Network that had remained for the most part constant for 70 years. In this talk we will walk down this path a bit ahead of the vision we see in today's technical journals towards a proposition we call Dynamic Distribution System. This architectural approach promises to help us rethink the grid "from the middle out". It holds out the possibility of grid evolution that increases speed fast enough to become a grid revolution. It holds out the possibility of an architecture that creates the best combination of the central , the distributed, the old, and the new in power systems. It holds out the further promise of forming new integrated value architecture with the fuel, water, and resource grids that have always been intertwined with the Electrical Grid and the society that counts on it. It is the second great network challenge of the next industrial revolution.
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Outline
• Background Dynamic Distribution• Dynamic Distribution System Principles• Architectural Approach to DDS• Challenges• Benefits• The Path Forward ..
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Background DDS
• Dynamic Distribution System is a Electrical Distribution Power System Architecture utilizing the best attributes of distributed and centralized power topologies.
• DDS utilizes a combination of the best capabilities of autonomous DER control (e.g. CERT’s
droop based control) and Hierarchal Control (Multi-grid, Model Predictive, Moving Horizon, ...)
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Background DDS
• To understand our proposition we will have todiscuss two major converging themes:– The Need for Dynamic Distribution System– Dynamic Distributed Power System Concepts– Architectural Concepts
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Dynamic Distribution Concept
M-WERC DDS MG2 9.11.2014.7
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Personal Power PlantsDynamic Distribution SystemCentralized Grid
Centralized Decentralized
Least Autonomous Most Autonomous
Best of Centralized Grid Best of Personal Power PlantsRef: [1]
What do the Icons Mean ?
M-WERC DDS MG2 9.11.2014.7
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Centralized
Least Autonomous Most Autonomous
Personal Power PlantsDynamic Distribution SystemCentralized Grid
Best of Centralized Grid Best of Personal Power Plants
Bulk Generation
Transmission
Load
Local Generation
Distribution
Price Signal
Marketplace
Controller
Electricity
CommunicationRef: [1]
Smart Feeders/Microgrids ?
M-WERC DDS MG2 9.11.2014.7
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Personal Power PlantsDynamic Distribution System
Centralized Grid
Centralized Decentralized
Least Autonomous Most Autonomous
Feeders (& u-grids)
Feeders (& u-grids)
Feeders (&
u-grids)
Best Path for Next Gen Distribution Architectures
Dynamic Distribution Concepts12
GL
SL
S/S
L
C/C
L
Subst
LLLG
GC/C
S
Subst
L
S
Cluster Controller Distributed
Storage
Cluster 1(Functional
Feeder)
Cluster n
Load
Substation SmartSwitch
Distributed GenerationSS-MSC
Substation Midscale Controller
Power Network(Feeder)
Control Network
HSC(DER)High Scale Controller (Multiple
Sub-Stations)
Cluster (Functional Feeder) Controller
Dynamic Distribution Concepts13
t
interface
Switch Gear
Protection
interface
“Grid forming”Generation & Storage Autonomous Merchant DER
interface
Static Switch,Feeder
Automation
interface
High Scale Control (HSC) (minutes- hours)• Large Power Flows & Protection Load Tracking
Voltage /Frequency• Volatility Minimization• Areas CHP ,Renewables• Performance/Price Optimization/Market Models
Mid-Scale Control (MSC) (Sub Station)(seconds-minutes) •Mid-Scale Power Flow•Multiple Cluster/Feeder Coordination
High Scale Controller
Utility RequestAvailable Ancillary services
“Midscale Grid following/forming” Generation & Storage
CC Function Priority• Fast Dynamics• Short Circuit• ESD Resilience• Intrinsic Security
MSC Function Priority• Mid-Scale Power Flows• Combined Effects
on diverse Networks• Market Signals
HSC Function Priority• Combined Effects
on larger diverseNetworks
• Large Flow Optimization• Market Interactions
Mid Scale Controller
DSODSO
TSO
DSO
T/D T/D T/D
Control System Functional Layers(Hierarchal Control)
Loads
Clusters Control (CC) & Autonomous Layer (10-1000 milliseconds)• Track loads, regulates voltage, frequency,
reactive power, and provide local stability• Fastest Protection, Flow & Load Control• Autonomous Resilience
Cluster ( Functional Feeder) Controller
Pon off
P, Von off
statemode
statemode
Mid Scale Controller
Mid Scale Controller
High Scale Controller
High Scale Controller
Dynamic Distribution Concepts15
T/D INTERFACE
SUBST 1;1 SUBST 1;2 SUBST 1;N
SUBST 2;1
SUBST 2;2
SUBST 2;3
SUBST 2;4
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C/C[ ] [ ]
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SS-MSC SS-MSC SS-MSC
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HSC (DER)
...
FUNCTIONAL CLUSTERS (FEEDERS WITH CONTROLLERS)
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SS-MSCSS-MSCSS-MSC
Distribution Grid Equipment Map
Source: Wiki Images DDS Algorithm Family can run on Grid Automation Hardware Platforms
SL
GL
S/S
L
C/C
L
Subst
Cluster Controller Distributed
GenerationLoad
Substation SmartSwitch
Distributed Storage
Power Network(Feeder)
Control Network
Dynamic Distribution Concepts16
Many Tests/Models Demonstrate ClusterFeeder Control
AUTONOMOUS DER SOURCEPOWER BALANCING
Ref: [1]
DDS and Hierarchal Control(Feasible Cooperation – Model Predictive Control, Multi-Grid Formulation, Moving Horizon
Prediction)
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Functional Clusters (Basic Unit)
Cluster Control Level
Mid-scale Control ( Substations)
High Scale Control (HSC) Level
High-Level Distribution (Areas)Multiple sub-stations
Aggregated to Distribution Areas
Multiple Clusters Aggregated to Sub-Stations
Course Time ScaleLong Predictive Horizon
Mid Time ScaleMid Predictive Horizon
Short Time ScaleShortest Predictive Horizon
“Reduction of required States forOptimum Control”
Feasible Cooperation Model Predictive Control(FC-MPC ) Multigrid Formulation
Standard AGC for an MSC –CC GroupLarge Disturbance
Ref: [4],[5], [9]
DDS : Architecture Defined A logical description of present and future interactions between structure and
function .... (Natures View)
A logical description of interactions between structure and function to meet present and future objectives.. (Designers View)
A set of principles that enable interactions between structure and function to meet present and future objectives.. ( Framework View)
S1 S2 S3 S4 S5
F1
F2
F3
F4
F5
Structure
Func
tion
Gen II Gen III
ARCHITECTURE AND THE GRIDConceptual Functions and Structures Primary Dynamic Network
Multi Doman Networks Domain Relations Standards
The Journey
Microgrids
Distribution Grid
Transmission Grid
Ref Source: Siemens Whitepaper 2013
Ref: WIKI Commons License Various
Source: NIST Smart Grid Framework v1.0 2010
Source: NIST Smart Grid Framework v1.0 2010
The Grid may be the Biggest System
Electrical Grid
Fuel Grid
Water Grid
Atmosphere Grid
Economic Grid
• Coupling is increasing between these grids ....• The Largest Man Made Systems interacting with the Largest Terrestrial Systems• Could we hope to improve our grids without Architecture ...?
DDS Architecture : You don’t have to start big ....
• You can begin the Architectural Evolution at key gating application cases .....
A Typical Design Processfor DDS Applications
Key Principles of DDSMore reliable/efficient systems using 1000’s of DER near loads• Increase efficiencies and reduced emissions through use of waste heat• Reduced transmission losses• More resilient system using local generation, microgrids& network reconfiguration
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Economic efficiencies via distribution-based marketplace• Utility Linked and Independent Distribution System Operators• Distributed and Local balancing authority • Distributed and Local marketplace
Simplify the central generation planning and operation• Handle distribution system’s dynamics locally (minimize volatility at the T-D interface)• Improve efficiencies by increasing base load operation. • Constant/contracted wholesale energy transactions.• Minimize CO2content
DDS Challenges
• Control Architecture Effectiveness Across all Distribution Configurations.
• Control and Interoperability standards that allow true “plug and play”
• Evolution of Grid Economic Models, Policies, Regulations
• Gaining acceptance of a new architectural approach for the grid ....
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DDS Benefits• Highly Scalable and Upgradable Architecture• Intrinsic High Efficiency and Reliability (CHP , Autonomous Modes)
• Stable and Controllable – Handles Reserves, Voltage, Current Support Locally ( Close to
the Source of the disturbance)– Enables High Penetration of Renewables
• Supports shorter life cycle economics: Promises a better cost to performance model
• DDS improves the Bulk Grid ; it does not replace it...• DDS can support a better evolution for the grid
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A Path Forward• The DDS Team Recommends
– Accelerate the growing research in the Electrical Distribution Grid ...as a System... as an Architecture
– Gather the excellent resources working in different parts of the vineyard and consider a new model of Grid Architectural Development ....
– Take on the tough challenges of Economic Models, Regulations, Policies, and Standards as part of the R&D
– Find that new grid value proposition that pays off the cost of transition .....
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Contributions and Reference
• Many thanks to these contributors:Professor Thomas Jahns : University of Wisconsin- MadisonProfessor Emeritus Bob Lasseter: University of Wisconsin-MadisonDr . Victor Zavala : University of Wisconsin-MadisonProfessor Adel Nasiri: University of Wisconsin-Milwaukee
• References:– [1}Nov. 2014 PSERC ( Power System Engineering Research Center) Webinar– [2] R. H. Lasseter, “Smart Distribution: Coupled Microgrids,” Proceedings of the IEEE, vol. 99, no. 6, pp. 1074–1082, 2011.– [3 ] Alegria, Lasseter, et al., “CERTS µGrid Demo w/ Large-Scale Energy Storage and Renewable Gen.”, IEEE Trans. on Smart Grids, Mar. 2014.– [4] Magni, Lalo, and Riccardo Scattolini. "Robustness and robust design of MPC for nonlinear discrete-time systems." Assessment and future
directions of nonlinear model predictive control. Springer Berlin Heidelberg, 2007. 239-254.– [5] Zavala, Victor M., and Lorenz T. Biegler. "The advanced-step NMPC controller: Optimality, stability and robustness." Automatica 45.1
(2009): 86-93.– [6] Zavala, Victor M., and Lorenz T. Biegler. "Nonlinear programming strategies for state estimation and model predictive control." Nonlinear
model predictive control. Springer Berlin Heidelberg, 2009. 419-432.– [7] Haseltine, Eric L., and James B. Rawlings. "Critical evaluation of extended Kalman filtering and moving-horizon estimation." Industrial &
engineering chemistry research 44.8 (2005): 2451-2460.– [8] B. T. Stewart, “Plantwide Cooperative Distributed Model Predictive Control,” Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison,
Madison, WI, 2010.– [9] A.Venkat, “Distributed Model Predictive Control : Theory and Applications”, Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison,
Madison, WI, 2006.
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