A Two-Stage Robust Energy Sharing Management for Prosumer Microgrid

被引:99
作者
Cui, Shichang [1 ,2 ]
Wang, Yan-Wu [1 ,2 ,3 ]
Xiao, Jiang-Wen [1 ,2 ]
Liu, Nian [4 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Key Lab Image Proc & Intelligent Control, Minist Educ, Wuhan 430074, Hubei, Peoples R China
[3] China Three Gorges Univ, Hubei Prov Collaborat Innovat Ctr New Energy Micr, Yichang 443003, Peoples R China
[4] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China
基金
中国国家自然科学基金;
关键词
Bi-level optimization; demand response; energy sharing; online optimization; prosumers; robust optimization; MODEL; OPTIMIZATION; ALGORITHM; PRICE;
D O I
10.1109/TII.2018.2867878
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The paper proposes a two-stage energy sharing framework for a new prosumermicrogrid with renewable energy generation, multiple storage units, and load shifting. In the first stage, a robust bilevel energy sharing model is formulated to provide a robust energy sharing schedule for prosumers and retailer overcoming the impact of the uncertainties of market prices and renewable energy. Through proper linearization techniques, the bilevel optimization problem is transformed into a single-level mixed integer linear programming problem that is practically solvable. In the second stage, an online optimization model is formulated for each prosumer to continually optimize its energy schedule at each hour according to the latest system state, and the proposed punishment mechanism is embedded for prosumers adjusting their previous energy sharing schedules. The simulation cases show the benefits of the energy sharing management framework.
引用
收藏
页码:2741 / 2752
页数:12
相关论文
共 35 条
[1]  
[Anonymous], 2012, ELECT J
[2]  
[Anonymous], 2010, 1 INT ICST C E EN
[3]  
[Anonymous], 2015, EUR COUNC ENERGY EFF
[4]  
[Anonymous], 2011, P CIB W78 W102 INT C
[5]   The price of robustness [J].
Bertsimas, D ;
Sim, M .
OPERATIONS RESEARCH, 2004, 52 (01) :35-53
[6]   Online 24-h solar power forecasting based on weather type classification using artificial neural network [J].
Chen, Changsong ;
Duan, Shanxu ;
Cai, Tao ;
Liu, Bangyin .
SOLAR ENERGY, 2011, 85 (11) :2856-2870
[7]   Real-Time Implementation of Multiagent-Based Game Theory Reverse Auction Model for Microgrid Market Operation [J].
Cintuglu, Mehmet Hazar ;
Martin, Harold ;
Mohammed, Osama A. .
IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (02) :1064-1072
[8]   A trust-region method for nonlinear bilevel programming: Algorithm and computational experience [J].
Colson, B ;
Marcotte, P ;
Savard, G .
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2005, 30 (03) :211-227
[9]   Data-Driven Energy Management in a Home Microgrid Based on Bayesian Optimal Algorithm [J].
Dong, Guangzhong ;
Chen, Zonghai .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (02) :869-877
[10]   Multi-Agent System for Distributed Management of Microgrids [J].
Eddy, Y. S. Foo. ;
Gooi, H. B. ;
Chen, S. X. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2015, 30 (01) :24-34