Robust and Decentralized Operations for Managing Renewable Generation and Demand Response in Large-Scale Distribution Systems

Transcription

1 Robust and Decentralized Operations for Managing Renewable Generation and Demand Response in Large-Scale Distribution Systems Research Team Andy Sun, Georgia Tech Duncan Callaway, UC Berkeley PSERC Industry-University Meeting December 2-4, 2015

2 Industry Advisors Tongxin Zheng, ISO-NE Hong Chen, PJM Jim Price, CAISO Masoud Abbaszadeh, GE Research Bahman Darynian, GE Research Santosh S. Veda, GE Research Lei Fan, GE Energy Management Eduard Muljadi, NREL Mirrasoul J. Mousavi, ABB Harish Suryanarayana, ABB Evangelos Farantatos, EPRI Erik Ela, EPRI Jens Boemer, EPRI Xing Wang, Alstom Grid Ying Xiao, Alstom Grid Curtis Roe, ATC

3 Motivation Project Description Technical Approaches Outline Robust optimization for scheduling of demand response and distributed generators Fully decentralized optimal dispatch in distribution systems Potential Applications and Benefits Summary

4 Increasing Renewable Penetration

5 ERCOT 2015 Peak demand: 69,621MW (Aug 10, 2015) Wind Capacity: ~ 16,000MW Wind Generation record: 12,971MW (Nov 25, 2015) ~32% of load at that time

6 Demand Response Management ERCOT: 6.6 million advance meters 97% ERCOT load in competitive area settled with 15-min interval More than 2100 MW in demand response, including Load resources (mostly large industrial) ~ 1,390 MW Emergency response service (commercial & industrial) 850MW Utility load management programs ~220MW Demand response provider manages large portfolios of DR E.g. Enernoc 24-27GW peak load under management over sites DR resources can be highly uncertain

7 Project Description Develop Robust Scheduling Tools for managing uncertainty in demand response portfolios Robust operation of DG and DR in distribution systems with interface to transmission systems Fully decentralized optimal dispatch for active distribution systems Study flexibility and reliability performance of proposed models in distribution systems Develop Simulation platform for large-scale systems

8 TA 1: Robust Scheduling of DR A quick motivation on using robust optimization for power system management: Systematic and practical approach to model uncertainty in variable resources --- uncertainty set Dispatch decision is uncertainty-aware and adaptive Able to save cost and increase reliability comparing to deterministic approaches [BLSZZ, 2013][LS, 2015][TSX, 2014][LSLZ, 2015]

9 TA 1: Uncertainty Set A Primer Uncertainty set for renewable variation t hour This classic uncert set is Static We want to develop Dynamic uncertainty set

10 TA 1: The Need to Model Correlation Modeling temporal and spatial correlation of renewable resources is crucial for operations 39 km wind Kennewick Goodnoe Hill 146 km Vansycle Spatial and temporal correlation of 3 wind farms Wind and solar production are also correlated Source [Xie et. al. 2011]

11 TA 1: Dynamic Uncertainty Set A New Proposal A dynamic uncertainty set for wind speed: Residual Seasonal pattern Linear dynamics: Temporal & Spatial correlation Uncertainty in Estimation with Budget Constraints Dominates performance of static uncertainty set [LS 2015]

12 TA 1: Demand Response Uncertainty A demand response event: DR resource ramps up, holds reduction, ramps down DR aggregator/scheduler plans for DR events Final realization of DR performance can be quite different Uncertainty in realization depends on planning decision How to model such a correlation?

13 TA 1: A New Dynamic Uncertainty Set We propose to develop a new type of dynamic uncertainty sets that model this decision dependence: Scheduled DR reduction decision: Deviation in realized DR reduction: Final realized DR reduction: Uncertainty depends on decision Total variations controlled

14 TA 1: Robust Scheduling of DR Portfolio Now imagine a DR portfolio of hundreds of C&I DR resources Managing such a DR portfolio with uncertainty in DR performance is a challenging task No commercial software is available We propose the following robust scheduling model is a set of operational constraints on DR reduction decision

15 TA 1: Robust Scheduling of DR Portfolio Preliminary results Type A: Highest profit and highest uncertainty Type B: Medium profit and uncertainty Type C: Lowest profit and lowest uncertainty Type A: most favored in deterministic model Types B, C: favored in robust model, balance btw profitability and operation uncertainty

16 TA 2: Fully Decentralized Dispatch With thousands of distributed generators in the grid, centralized controlling is challenged Can we do decentralized control down to the device level? Yes?...!

17 TA 2: Fully Decentralized Dispatch Literature review: Parallelization of certain computation steps (matrix factorization) in centralized optimal power flow (OPF) algorithms [Huang,Ongsakul 94] [Lin, Ness 94] [Oyama et. al. 90] Market coordination: dividing high-voltage control area into a few sub-regions, each subregion solves a OPF [Kim, Baldick 97] [Baldick et. al. 99][Conejo et. al. 02][Ji, Tong, 15] Linearized approximation of OPF and decentralization to sub-systems [Biskas, Bakirtzis 06] Convex relaxation formulation of OPF and decentralization to cliques [Jabr 06] [Lavaei, Low 12] [Zhang, Tse 11,12] [Boyd et al. 12][Zhu et. al. 14] Mostly on linearized DC OPF, regional coordination, convexification etc. We want to do down to the nodal level decentralized control Full decomposition & AC OPF

18 TA 2: Fully Decentralized Dispatch Centralized AC OPF: Minimize cost or loss Power Flow Equations Nodal power/voltage bounds

19 TA 2: Fully Decentralized Dispatch Nodal decomposition: At each node i, a set of artificial variables are introduced: e j i, f j i,θ j i are node i s estimate of node j s variables i

20 TA 2: Fully Decentralized Dispatch Solve the following problem: Augmented Lagrangian: ADMM consists of three steps:

21 TA 2: Fully Decentralized Dispatch One generator at node 0, N loads at nodes 1 N Convergence to a stationary point Linear scaling of computation time vs N

22 Potential Benefits This research will provide the industry partners with a set of new tools for managing large-scale distribution systems with intrinsic uncertain and active resources, such as distributed renewable generation and complicated demand response portfolios. The new operational models and solution algorithms are anticipated to substantially increase the utilization of the DG and DR resources, therefore, encouraging their further adoption in the distribution system. The proposed models and algorithms will also provide new computational tools for the solution of fundamental operational problems in power systems, such as the multi-time scale optimal power flow problem in distribution systems. The proposed methodology is not limited to distribution system, but can be applicable to other power system analysis functions.

23 Expected Outcomes Robust scheduling tools for managing large DR portfolios under uncertainty of DR resources. Uncertainty modeling techniques for distributed DGs, DRs, and customer owned resources in the distribution system. A hierarchical and decentralized control scheme for solving multi-time scale optimal power flow problems in distribution systems. Software platform that implements the proposed modeling and operation tools with data management and processing functions. A comprehensive evaluation of the proposed methods and models in a real-world power system.

24 Potential Applications The proposed work can be used by utility companies and demand response aggregators for managing their operational portfolios and hedge against significant variations in DR resources and renewable generations. The decentralized control scheme provides a scalable approach for the distribution system operator to operate a large-scale distribution network with a significant number of active devices. If successful, the proposed models and algorithms can help distribution system operators to upgrade their operational scheme to allow much more accurate and robust control of the heterogeneous devices in the system and to improve the flexibility and reliability of the entire distribution system. The models and algorithms from this project can also be developed into commercial software packages.

25 Summary The distribution system is becoming more complex and active. Distribution system operators may face a portfolio of an extremely large number of devices including distributed generators (DG), demand response (DR) resources, storage devices, and emerging proactive customers with various resources (electric vehicles, smart appliances, rooftop PVs, TCLs). Many of these devices may exhibit stochastic supply or consumption patterns. The goal of this project is to develop new operational models and algorithms to efficiently operate such a large portfolio of controllable but uncertain resources in an active distribution system with the aim to increase flexibility and reliability of both distribution and transmission systems. The proposed models will provide the industry with computational tools to manage various types of uncertainties through robust optimization techniques and a mixture of centralized and decentralized control schemes in order to improve scalability of the operational algorithms. The project will also explore efficient solution methods for incorporating unbalanced multi-phase power flow models in the proposed scheduling algorithms in order to accurately model the distribution system.

26 Work Plan Number Task Year 1 Year 2 1 Develop robust optimization models and algorithms for the DR portfolio management problem 2 Develop robust operational models including renewable DGs, DRs, and storage 3 Develop decentralized operation model and algorithms for the multiphase AC OPF problem 4 Development of simulation platform with data processing functions 5 Comprehensive evaluation on real-world power systems 6 Project documentation

27 THANK YOU! References: D. Bertsimas, E. Litvinov, X. A. Sun, J. Zhao, T. Zheng, Adaptive robust optimization for the security constrained unit commitment problem, IEEE Trans. Power Syst., vol. 28, no. 1, pp , A. Lorca, X. A. Sun, Adaptive robust economic dispatch with dynamic uncertainty sets for significant wind, IEEE Trans. Power Syst., vol. 30, no. 4, pp , A. Lorca, X. A. Sun, E. Litvinov, T. Zheng, Multistage robust optimization for the unit commitment problem, accepted for publication at Operations Research, A. Thatte, X. A. Sun, L. Xie, Robust Optimization Based Economic Dispatch for Managing System Ramp Requirement, HICSS 2014.

28 Multi-period, Multi-phase AC OPF T min t=1 f t p t p t P t, R t p t p t+1 R t, t = 1,, T Time decoupling: Lagrangian relaxation of ramping constraints Resource decoupling: Ramping is resource specific Multiphase AC OPF N c P is = j=1 t=a G ijst e is e jt + f is f jt B ijst e is f jt