Competitive Energy Trading Framework for Demand-Side Management in Neighborhood Area Networks

被引:46
作者
Mediwaththe, Chathurika P. [1 ,2 ]
Stephens, Edward R. [1 ,3 ]
Smith, David B. [1 ,4 ]
Mahanti, Anirban [1 ,3 ]
机构
[1] CSIRO, NICTA Data61, Eveleigh, NSW 2015, Australia
[2] Univ New South Wales, Elect Engn & Telecommun, Sydney, NSW 2052, Australia
[3] Univ New South Wales, Sydney, NSW 2052, Australia
[4] Australian Natl Univ, Canberra, ACT 0200, Australia
关键词
Community energy storage; demand-side management; game theory; neighborhood area network; ELECTRICITY MARKETS; LOAD MANAGEMENT; GAME; STORAGE; GENERATION; OPTIMIZATION;
D O I
10.1109/TSG.2017.2654517
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully competitive CES operator in a non-cooperative Stackelberg game, a benevolent. CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully competitive CES model gives the best trade-off of operating environment between the CES operator and the users.
引用
收藏
页码:4313 / 4322
页数:10
相关论文
共 36 条
[11]  
Boyd S., 2004, Convex Optimization, P67, DOI DOI 10.1017/CB09780511804441
[12]  
Caron S, 2010, INT CONF SMART GRID, P391, DOI 10.1109/SMARTGRID.2010.5622073
[13]   A Game-Theoretic Analysis of Wind Generation Variability on Electricity Markets [J].
Chattopadhyay, Deb ;
Alpcan, Tansu .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2014, 29 (05) :2069-2077
[14]  
Chen C, 2011, INT CONF ACOUST SPEE, P5956
[15]   Sizing of Energy Storage for Microgrids [J].
Chen, S. X. ;
Gooi, H. B. ;
Wang, M. Q. .
IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (01) :142-151
[16]   A novel direct air-conditioning load control method [J].
Chu, Chi-Min ;
Jong, Tai-Lang .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2008, 23 (03) :1356-1363
[17]   Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications [J].
Kanchev, Hristiyan ;
Lu, Di ;
Colas, Frederic ;
Lazarov, Vladimir ;
Francois, Bruno .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (10) :4583-4592
[18]   Optimized Control of Price-Based Demand Response With Electric Storage Space Heating [J].
Kilkki, Olli ;
Alahaivala, Antti ;
Seilonen, Ilkka .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2015, 11 (01) :281-288
[19]   Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach [J].
Maharjan, Sabita ;
Zhu, Quanyan ;
Zhang, Yan ;
Gjessing, Stein ;
Basar, Tamer .
IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (01) :120-132
[20]   A Dynamic Game for Electricity Load Management in Neighborhood Area Networks [J].
Mediwaththe, Chathurika P. ;
Stephens, Edward R. ;
Smith, David B. ;
Mahanti, Anirban .
IEEE TRANSACTIONS ON SMART GRID, 2016, 7 (03) :1329-1336