Energy Management for Smart Multi-Energy Complementary Micro-Grid in the Presence of Demand Response

Yongli Wang, Yujing Huang, Yudong Wang, Haiyang Yu, Ruiwen Li and Shanshan Song
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Yongli Wang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Yujing Huang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Yudong Wang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Haiyang Yu: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Ruiwen Li: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Shanshan Song: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

Energies, 2018, vol. 11, issue 4, 1-19

Abstract: With the application and the rapid advancement of smart grid technology, the practical application and operation status of multi-energy complementary microgrids have been widely investigated. In the paper presented, the optimal operation of a solar unit, a storage battery and combined cooling, heating and power is studied via an economic optimization model implemented in General Algebraic Modeling Systems (GAMS). The model represents an optimization strategy for the economic operation of a microgrid considering demand response programs in different scenarios, and it is intended for the targets of minimizing the operating cost of the microgrid and maximizing the efficiency of renewable energy utilization. In addition, a multi-time electricity price response model based on user behavior and satisfaction is established, and the core value of the model is to describe the mechanism and effect of participation in electricity price demand response. In order to verify the accuracy of the model proposed, we design the dispatch strategy of a microgrid under different states considering demand response, and use genetic algorithm to solve the optimization problems. On the other hand, the application of methodology to a real case study in Suzhou demonstrates the effectiveness of this model to solve the economic dispatch of the microgrid’s renewable energy park.

Keywords: multi-energy complementary; microgrid; demand response; operation optimization; electricity price (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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