"Dynamic Distribution System, a new Architecture for the Integrated Grid"

Transcription

1 1 "Dynamic Distribution System, a new Architecture for the Integrated Grid" Bruce Beihoff Tom Jahns Bob Lasseter University of Wisconsin Madison IEEE PES Panel July 29,2015

2 2 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.

3 3 Outline Background Dynamic Distribution Dynamic Distribution System Principles Architectural Approach to DDS Challenges Benefits The Path Forward..

4 4 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,...)

5 5 Background DDS To understand our proposition we will have to discuss two major converging themes: The Need for Dynamic Distribution System Dynamic Distributed Power System Concepts Architectural Concepts

6 A Changing Grid Environment 6

7 7 Pro s, Con s, Approaches Pros and Cons of Central and Distributed Power Ref: [1]

8 8 Solution, Move Some Resources to Distribution Grid... Ref: [1]

9 9 Dynamic Distribution Concept Centralized Least Autonomous Centralized Grid Dynamic Distribution System Decentralized Most Autonomous Personal Power Plants Best of Centralized Grid Best of Personal Power Plants Ref: [1] M-WERC DDS MG

10 10 What do the Icons Mean? Centralized Least Autonomous Most Autonomous Bulk Generation Transmission Centralized Grid Dynamic Distribution System Personal Power Plants Load Local Generation Distribution Price Signal Marketplace Controller Electricity Best of Centralized Grid Best of Personal Power Plants Communication Ref: [1] M-WERC DDS MG

11 11 Smart Feeders/Microgrids? Centralized Least Autonomous Centralized Grid Dynamic Distribution System Decentralized Most Autonomous Personal Power Plants Feeders (& u-grids) Feeders (& u-grids) Feeders (& u-grids) Best Path for Next Gen Distribution Architectures M-WERC DDS MG

12 12 Dynamic Distribution Concepts HSC(DER) High Scale Controller (Multiple Sub-Stations) Substation Midscale Controller Control Network Cluster 1 (Functional Feeder) SS-MSC Cluster n Cluster Controller Subst Substation Subst C/C C/C S/S Smart Switch G S G G S Distributed Storage L L L L L Distributed Generation L L Load S L Power Network (Feeder)

13 13 Dynamic Distribution Concepts Control System Functional Layers (Hierarchal Control) TSO Available Ancillary services Utility Request T/D T/D T/D DSO DSO DSO HSC Function Priority Combined Effects on larger diverse Networks Large Flow Optimization Market Interactions MSC Function Priority Mid-Scale Power Flows Combined Effects on diverse Networks Market Signals CC Function Priority Fast Dynamics Short Circuit ESD Resilience Intrinsic Security 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) t Mid-Scale Power Flow Multiple Cluster/Feeder Coordination Clusters Control (CC) & Autonomous Layer ( milliseconds) Track loads, regulates voltage, frequency, reactive power, and provide local stability Fastest Protection, Flow & Load Control Autonomous Resilience Midscale Grid following/formi ng Generation & Storage P on off interface Loads High Scale Controller Mid Scale Controller Cluster Cluster ( (Functional Feeder) Feeder) Controller Controller P, V on off interface Grid forming Generation & Storage Autonomous Merchant DER High Scale Controller Mid Scale Controller interface Static Switch, Feeder Automation Switch Gear state mode High Scale Controller Mid Scale Controller state mode interface Protection

14 Translating Layers to the Iconograph of DDS 14

15 15 Dynamic Distribution Concepts Distribution Grid Equipment Map T/D INTERFACE HSC (DER) SS-MSC SUBST 1;1 SUBST 1;2 SUBST 1;N SS-MSC SS-MSC... SS-MSC SUBST 2;1 SUBST 2;2 SUBST 2;3 SUBST 2;4... SS-MSC SS-MSC SS-MSC C/C C/C C/C C/C C/C C/C C/C C/C C/C... Source: Wiki Images FUNCTIONAL CLUSTERS (FEEDERS WITH CONTROLLERS) DDS Algorithm Family can run on Grid Automation Hardware Platforms

16 16 Dynamic Distribution Concepts Many Tests/Models Demonstrate Cluster Feeder Control Control Network Cluster Controller C/C G Distributed Generation L Load L Power Network (Feeder) Subst Substation S/S Smart Switch S L L Distributed Storage AUTONOMOUS DER SOURCE POWER BALANCING Ref: [1]

17 DDS and Hierarchal Control (Feasible Cooperation Model Predictive Control, Multi-Grid Formulation, Moving Horizon Prediction) Course Time Scale Long Predictive Horizon Reduction of required States for Optimum Control High Scale Control (HSC) Level 17 Mid Time Scale Mid Predictive Horizon High-Level Distribution (Areas) Multiple sub-stations Aggregated to Distribution Areas Mid-scale Control ( Substations) Short Time Scale Shortest Predictive Horizon Multiple Clusters Aggregated to Sub-Stations Cluster Control Level Functional Clusters (Basic Unit) Standard AGC for an MSC CC Group Large Disturbance Feasible Cooperation Model Predictive Control (FC-MPC ) Multigrid Formulation Ref: [4],[5], [9]

18 18 DDS Hierarchal Control And Fault Protection

19 DDS : Architecture Defined Function F1 F2 F3 F4 F5 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) Structure S1 S2 S3 S4 S5 Gen II Gen III

20 ARCHITECTURE AND THE GRID The Journey Conceptual Functions and Structures Primary Dynamic Network Microgrids Distribution Grid Transmission Grid Ref: WIKI Commons License Various Multi Doman Networks Domain Relations Standards Source: NIST Smart Grid Framework v Ref Source: Siemens Whitepaper 2013 Source: NIST Smart Grid Framework v

21 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...?

22 DDS Architecture : You don t have to start big... A Typical Design Process for DDS Applications You can begin the Architectural Evolution at key gating application cases...

23 23 Key Principles of DDS More 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 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

24 24 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...

25 25 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

26 26 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...

27 27 Contributions and Reference Many thanks to these contributors: Professor Thomas Jahns : University of Wisconsin- Madison Professor Emeritus Bob Lasseter: University of Wisconsin-Madison Dr. Victor Zavala : University of Wisconsin-Madison Professor Adel Nasiri: University of Wisconsin-Milwaukee References: [1}Nov PSERC ( Power System Engineering Research Center) Webinar [2] R. H. Lasseter, Smart Distribution: Coupled Microgrids, Proceedings of the IEEE, vol. 99, no. 6, pp , [3 ] Alegria, Lasseter, et al., CERTS µgrid Demo w/ Large-Scale Energy Storage and Renewable Gen., IEEE Trans. on Smart Grids, Mar [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, [5] Zavala, Victor M., and Lorenz T. Biegler. "The advanced-step NMPC controller: Optimality, stability and robustness." Automatica 45.1 (2009): [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, [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): [8] B. T. Stewart, Plantwide Cooperative Distributed Model Predictive Control, Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison, Madison, WI, [9] A.Venkat, Distributed Model Predictive Control : Theory and Applications, Ph.D. Dissertation, Dept. of Chemical Eng., UW-Madison, Madison, WI, 2006.