Distributed Grid Control of Flexible Loads and DERs for Optimized - - PowerPoint PPT Presentation

Distributed Grid Control of Flexible Loads and DERs for Optimized Provision of Synthetic Regulating Reserves

ARPA-E NODES PROJECT DE-FOA-0001289 University of California-San Diego University of Illinois Urbana-Champaign Typhoon HIL Network Optimized Distributed Energy Systems (NODES) Annual Review Meeting April 26-28, 2017

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Project Summary

Objective Integrated control of flexible loads (FLs) and distributed energy resources (DERs) to provide regulation services to bulk power grid Technical development of coordination algorithms, software, and architectures Approach Grid-connected microgrids as entities to support frequency regulation Using existing microgrid controlling infrastructures to incorporate FLs and DERs Benefits arise from efficiently utilizing DERs and FLs instead of acting as constant powers when the microgrid is grid-connected

2 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Project Summary

Control Hierarchy for Frequency Regulation Non-profit organization RTO Aggregators (or DERPs) and resources with power flexibility DERs and FLs in microgrid

ISO/RTO

DERs DERs DERs Aggregators

Coordination

3 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Project Summary

Control Hierarchy for Frequency Regulation Non-profit organization RTO Aggregators (or DERPs) and resources with power flexibility DERs and FLs in microgrid Challenges

1

Optimally dispatch compensation of area control error to microgrids

2

Cooperation of DERs, FLs in microgrids to track regulation changing every 2-4 seconds

3

Hardware-in-the-loop (HIL) validation

4

Large-scale simulation to demonstrate positive benefit for the bulk grid

ISO/RTO

DERs DERs DERs Aggregators

Coordination

3 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Team - Senior Personnel

University of California-San Diego Sonia Mart´ ınez (scalable coordination, distributed opt) Jorge Cort´ es (network control, large-scale systems) Bill Torre (UCSD microgrid, renewable integration) Byron Washom (industrial outreach, tech transition) University of Illinois Urbana-Champaign Alejandro Dom´ ınguez-Garc´ ıa (modeling, control, and

• ptimization of electric power systems)

Peter Sauer (power system dynamics and stability,

• perational reliability, modeling of renewable resources)

Typhoon HIL Ivan Celanovic (HIL validation, tech to market) Plus 3 postdocs and 4 PhD students

4 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Team - Senior Personnel

University of California-San Diego Sonia Mart´ ınez (scalable coordination, distributed opt) Jorge Cort´ es (network control, large-scale systems) Bill Torre (UCSD microgrid, renewable integration) Byron Washom (industrial outreach, tech transition) University of Illinois Urbana-Champaign Alejandro Dom´ ınguez-Garc´ ıa (modeling, control, and

• ptimization of electric power systems)

Peter Sauer (power system dynamics and stability,

• perational reliability, modeling of renewable resources)

Typhoon HIL Ivan Celanovic (HIL validation, tech to market) Plus 3 postdocs and 4 PhD students

4 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Project Progress

M5.2.1 and M5.2.2: IAB Task 1: DER and FL abstractions to capture load flexibility Task 2: Provably-correct coordination algorithms Task 3: Partial distributed architectures Task 4: Testing the impact of DERs for control algorithms Task 5: Technology transition M5.1.1 Tech to market plan M1.1.1: Full-order model of one DER M2.1.1: Distributed RTO-DERP opt. algorithms M2.3.1: Distributed DERP-DER ratio consensus M4.1.1: Test scenario documentation Ongoing work (Q4-Q5) M4.1.2: Initial simulation platform layout for power distribution system&grid-connected microgrids w/ real&simulated data M5.1.2 Tech to market plan (1) Novel Capabilities (2) Pathways to adoption M3.1.1: Test scenarios for convergence rate M3.2.1: Coordination scheme for baseline M2.4.1: Distributed DERP-DER coordination with dedicated pricing M1.1.2: Validation of full-order models M4.3.1: Datasets for microgrid emulation

5 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Project Progress

1

Reduced-order models for inverters (T1.1)

2

Framework for optimal RTO-DERP coordination (T2.1)

3

Distributed algorithms for coordination of DERs and FLs (T2.3-4)

Robust to uncertainties and communication failures Fast convergence (≤ 40 iterations per node for convergence in 2 seconds)

4

Testing and validation (T4.2)

ISO/RTO

DERs DERs DERs Aggregators

Coordination

6 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Models for Inverters (T1.1)

Motivation Full-order model of inverters for DERs involve inner loops with high complexity ⇒ not suitable for real-time simulation & control Our work Identify full-order model of inverter control Develop reduced-order model for inverters to capture main characteristics Hardware-in-the-loop (HIL) simulation supports accuracy

7 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

RTO-DERP coordination (T2.1)

Motivation Current practice in frequency regulation market determines capacity and mileage of energy resources

capacity: upper bound on involvement of resource in regulation mileage: total absolute power change during regulation time period

Regulation signal set point Time Length of lines: Instructed mileage Power output sample Time Tracking error Capacity Power

RTO assigns regulation signal proportionally to procured mileage of each resource (redistributes overshoot power if any, again prop.) Our work Distributed coordination for efficient assignment of regulation signal:

• ptimized cost functions, respecting operational limits

8 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

RTO-DERP coordination (T2.1)

Motivation Current practice in frequency regulation market determines capacity and mileage of energy resources

capacity: upper bound on involvement of resource in regulation mileage: total absolute power change during regulation time period

Packet drops Time (sec) for entering 1% band Time (sec) for convergence to 1% band Chain Ring Ring with edge Chain Ring Ring with edge 0% 0.05 0.05 0.05 0.2 0.15 0.15 1% 0.16 0.15 0.15 0.48 0.3 0.29 5% 0.145 0.14 0.14 0.5 0.32 0.31 10% 0.15 0.15 0.15 0.55 0.35 0.32

RTO assigns regulation signal proportionally to procured mileage of each resource (redistributes overshoot power if any, again prop.) Our work Distributed coordination for efficient assignment of regulation signal:

• ptimized cost functions, respecting operational limits

8 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Distributed algorithms for DERs and FLs (T2.3-4)

Motivation: Coordination algorithms to coordinate DERs and FLs inside microgrid to track power reference varying every 2-4 seconds Challenges Nontrivial non-convex optimization, in principle with power flow equations (PFEs) for accurate tracking Tight convergence requirements (≤ 2 seconds) Must be robust to communication failures and uncertainty Ideally, want to solve optimal power flow problem every 2 seconds Our work Scheduled-asynchronous algorithm (with PFEs) Ratio-consensus algorithm (with relaxed PFEs)

9 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Distributed algorithms for DERs and FLs (T2.3-4)

Scheduled-asynchronous algorithm SDP relaxation exact for power networks of moderate size Distributed using insights from power flow equations, no global clock Converges to optimum with O(1/n) rate if topology is bipartite Robust to communication failures and handles load uncertainty

Illustration 5 buses with DERs and 9 buses with uncertain loads Tracking a 10 minutes RegD signal from PJM Number of iterations per node is 10

1 2 3 4 5 6 7 8 9 10 Time (minutes) 100 110 120 130 140 150

• Num. of iterations

1 2 3 4 5 6 7 8 9 10 Time (minutes) 4000 5000 6000 7000 8000 9000 Cost True Soln.

• Dist. Soln.

10 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Distributed algorithms for DERs and FLs (T2.3-4)

Scheduled-asynchronous algorithm SDP relaxation exact for power networks of moderate size Distributed using insights from power flow equations, no global clock Converges to optimum with O(1/n) rate if topology is bipartite Robust to communication failures and handles load uncertainty Ratio consensus algorithm Only incorporates resource box constraints – useful in scenarios where power flow constraints not significant inside microgrid Robust to communication failures Faster convergence than scheduled-asynchronous algorithm

10 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.2)

Test HIL system Ultra-high-fidelity model of microgrid connected to bulk power grid using Typhoon HIL (ultra-high-fidelity real-time power electronics emulator platform)

DERs in the microgrid model include

Microturbines Electrical energy storage devices Fuel cells Small rooftop photovoltaic systems Wind-based energy generators Flexible loads

All DERs controlled by local controllers developed at ILLINOIS

11 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.2)

Test HIL system (continued) Resource controllers connected to Typhoon HIL through industry standard serial comm. protocol (Modbus)

Each controller controls set-points of a group of DERs

Resource controllers communicate over wireless communication module (Xbee) Distributed optimization algorithms implemented on resulting network

12 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.2)

Test HIL system (continued) Resource controllers connected to Typhoon HIL through industry standard serial comm. protocol (Modbus)

Each controller controls set-points of a group of DERs

Resource controllers communicate over wireless communication module (Xbee) Distributed optimization algorithms implemented on resulting network Test plan Every 2-4 seconds, a regulation signal is sent to DERP, who forwards it to resource controllers over wireless communication network

Regulation signal is RegD test signal by PJM interconnection Microgrid contribution to the bulk power grid against regulation signal and response time, signal correlation compared to PJM specifications

Currently validated on 4 cyber-node network, with each controlling 1 HIL-simulated power node. Plan is to scale up to 100 (10x10)

12 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.2)

Demo: distributed microgrid controller via ratio-consensus algorithm

Demo at ARPA-e Innovation Summit, February 2017

13 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.3)

Microgrid Emulation Plan Open source power-flow based simulation platform to simulate loads and generators and electrical network based on actual distribution data Incorporate real data from UCSD microgrid, other databases (e.g. Pecan street) Quantify benefit of distributed coordination algorithms HIL Test Integrated with Microgrid Simulation Open source simulation platform implemented

• n Typhoon HIL to integrate with HIL system

and produce real-time executions Demonstrate scalability of distributed control regarding convergence and response time

UCSD microgrid circuit 14 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.4)

Large-Scale Simulation Plan Determine potential wide scale benefits of distributed control

Data from distribution and transmission system provided by utilities and grid operators Minimum 10K buses at the transmission level and 500 substations Aggregated DER with control response modeled at the substations Simulations to test impact of variations of aggregated load and DER

• n overall power grid

15 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Testing and Validation (T4.4)

Large-Scale Simulation Plan Determine potential wide scale benefits of distributed control

Data from distribution and transmission system provided by utilities and grid operators Minimum 10K buses at the transmission level and 500 substations Aggregated DER with control response modeled at the substations Simulations to test impact of variations of aggregated load and DER

• n overall power grid

Evaluate performance and scalability

Based on the developed hierarchical DER control and aggregation system using large-scale simulation testbed Ensure system response is adequate to meet requirements for synthetic regulating reserve service category

15 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Achievements

Presentations

1

• W. Torre, “Distributed grid control of flexible loads and DERs for optimized

provision of synthetic regulating reserves”, Schweitzer Engineering Las (SEL) workshop at UCSD, Aug. 2016

2

• J. Cort´

es, “Distributed algorithms for network optimization under non-sparse constraints”, Allerton Conference on Communication, Control, and Computing, Monticello, IL, Sep 2016

3

• J. Cort´

es, “Decentralized Nash equilibrium seeking by strategic generators for DC optimal power flow”, Conference on Information Science and Systems, Baltimore, MD, Mar 2017

4

• A. D. Dominguez-Garcia and P. Sauer, “Control and Coordination of

Distributed Energy Resources for Provision of Ancillary Services”, Exelon’s Corporate Strategy group, April 2017. 2017 Illinois Innovation Prize Finalist: Olaolu Ajala (Ph.D. student co-advised by

• Prof. Dom´

ınguez-Garc´ ıa and Sauer)

16 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Achievements: publications

1

Frequency regulation markets

C.-Y. Chang, S. Mart´ ınez, and J. Cort´

• es. Grid-connected microgrid participation in frequency-regulation markets via hierarchical
• coordination. In IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017. Submitted
• A. Cherukuri and J. Cort´
• es. Iterative bidding in electricity markets: rationality and robustness. IEEE Transactions on Control of

Network Systems, 2017. Submitted 2

Control framework for grid-connected microgrids

• H. Xu, S. C. Utomi, A. Dom´

ınguez-Garc´ ıa, and P. W. Sauer. Coordination of distributed energy resources in lossy networks for providing frequency regulation. In IREP Bulk Power System Dynamics and Control Symposium, Espinho, Portugal, 2017

• O. O. Ajala, M. Almeida, I. Celanovic, P. W. Sauer, and A. Dom´

ınguez-Garc´ ıa. A hierarchy of models for microgrids with grid-feeding

• inverters. In IREP Bulk Power System Dynamics and Control Symposium, Espinho, Portugal, 2017
• D. Fooladivanda, M. Zholbaryssov, and A. Dom´

ınguez-Garc´ ıa. Control of networked distributed energy resources in grid-connected AC micro grids. IEEE Transactions on Control of Network Systems, 2017. Under review, submitted in January 2017 C.-Y. Chang, S. Mart´ ınez, and J. Cort´

• es. Grid-connected microgrid participation in frequency-regulation markets via hierarchical
• coordination. In IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017. Submitted

3

Distributed coordination

• A. Cherukuri and J. Cort´
• es. Distributed algorithms for convex network optimization under non-sparse equality constraints. In Allerton
• Conf. on Communications, Control and Computing, pages 452–459, Monticello, IL, September 2016
• T. Anderson, C.-Y. Chang, and S. Mart´

ınez. Weighted design of distributed approximate newton algorithms for constrained

• ptimization. In IEEE Conference on Control Technology and Applications (CCTA), Kohala, Hawaii, 2017. Submitted

C.-Y. Chang, J. Cort´ es, and S. Mart´ ınez. A scheduled-asynchronous distributed optimization algorithm for the optimal power flow

• problem. In American Control Conference, Seattle, WA, May 2017. To appear

4

Reduced-order models

• O. O. Ajala, P. W. Sauer, and A. Dom´

ınguez-Garc´ ıa. A hierarchy of reduced-order models for inverter-based microgrids. In Energy Markets and Responsive Grids: Modeling, Control and Optimization, Berlin, Germany, 2017. To appear 17 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Technology to Market Path and IAB

Preliminary Tech to Market plan: main commercial vehicle is licensing Products and Markets Distributed microgrid controller targeting DoD microgrids, microgrids for developing countries (residential and neighborhood) Olaolu Ajala (Ph.D. student co-advised by Prof. Dom´ ınguez-Garc´ ıa and Sauer): 2017 Illinois Innovation Prize Finalist Robust distributed control and coordination, with accompanying software architecture targeting distribution system operators & DERPs Industrial Advisory Board (IAB) Rodney Hilburn (Ameren, micro-grid technologies) Mark G. Yao (IBM, smart energy) Simon Round (ABB, microgrid program) Darrell Massie (S&C/IPERC, microgrid technology) Laurence Abcede (SDGE, electric transmission and system engineering) Met with CAISO (Nov 16) to understand vision for regulation services & test project vision

18 / 19

Project Summary Team Project Progress Validation Plan Achievements Tech to Market Future Plans

Future Plans

Upcoming Activities Next intended achievements

Validation of reduced-order models for different types of DERs (microturbine, large electrical energy storage, fuel cells, and small rooftop PV and wind-based electricity generators) Abstract models capable of dynamically capturing capacity, mileage, and cost to meet regulation by individual microgrids Optimized architectures: understand role of communication topology in coordination algorithm properties Scale up HIL test, integrate with microgrid emulation

Tech to Market

Technology demonstration to IAB IAB engagement to refine Tech to Market Plan NSF I-Corps towards potential start-up

19 / 19