As the penetration of intermittent renewable energy increases in microgrid systems, flexible power generation resources cause the power imbalance problem. To improve the absorption of renewable energy, this paper proposes a distributionally robust (DR) approximate framework considering conditional value-at-risk measures for energy management of microgrids based on cooperative scheduling energy storage and direct load control operations. First, combined with CVaR and DR theory, DR CVaR constraints that can quantitatively the power balance risk of microgrid is constructed. Then, to improve the efficiency of solving DR CVaR constraints, it is tractably approximated using Jensen's inequality, and the approximate error of DR CVaR constraint is analyzed by comparing two different Jensen's inequality gap expressions so that the decision maker can select the appropriate approximate DR CVaR constraint according to the actual needs. Finally, the appropriate DR CVaR framework of the microgrid is transformed into mixed-integer linear programming that can be directly solved by CPLEX. The approximate framework has the characteristics of risk aversion measurement, linear efficient solving, and flexible decision-making. And it is also verified on the IEEE 33-bus distribution system through a wide range of different tests.
机构:
Shanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Su, Lei
Li, Zhenkun
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Shanghai Univ Elect Power, Sch Elect Engn, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Li, Zhenkun
Zhang, Zhiquan
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Shanghai Univ Elect Power, Sch Elect Engn, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Zhang, Zhiquan
Du, Yang
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Shanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Du, Yang
Ge, Xiaolin
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Shanghai Univ Elect Power, Sch Elect Engn, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Ge, Xiaolin
Yang, Xingang
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Shanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R ChinaShanghai Elect Power Co, Shanghai Elect Sci Inst, Shanghai, Peoples R China
Yang, Xingang
2020 INTERNATIONAL CONFERENCE ON SMART GRIDS AND ENERGY SYSTEMS (SGES 2020),
2020,
: 718
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723
机构:
China Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
State Grid Suzhou City & Energy Res Inst, Suzhou, Peoples R ChinaChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
Zhai, Junyi
Wang, Sheng
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机构:
Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R ChinaChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
Wang, Sheng
Guo, Lei
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机构:
State Grid Suzhou City & Energy Res Inst, Suzhou, Peoples R China
State Grid Energy Res Inst, Beijing, Peoples R ChinaChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
Guo, Lei
Jiang, Yuning
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机构:
Ecole Polytech Fed Lausanne, Automat Control Lab, Lausanne, SwitzerlandChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
Jiang, Yuning
Kang, Zhongjian
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机构:
China Univ Petr East China, Coll New Energy, Qingdao, Peoples R ChinaChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China
Kang, Zhongjian
Jones, Colin N.
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机构:
Ecole Polytech Fed Lausanne, Automat Control Lab, Lausanne, SwitzerlandChina Univ Petr East China, Coll New Energy, Qingdao, Peoples R China