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Event-driven Automatic Demand Response Strategy for Residential Multi-microgrid System
Author:
Affiliation:

1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China;2.State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China;3.State Grid Huzhou Power Supply Company, Huzhou 313099, China

Abstract:

This paper proposes an automatic demand response strategy based on event-driven mechanism, considering great challenges brought by the diversity and richness of residential loads and random electricity consumption behaviors to residential multi-microgrid system. The operation statuses of residential microgrids are divided into different events by using event-driven mechanism. The residential load is guided to fulfil consumption optimization considering price incentive and schedulable potential with the event-driven signal, so as to achieve automatic demand response. On this basis, the concept of energy level is introduced to the energy sharing model. By calculating the energy levels of each sub-microgrid in multi-microgrid system, the interactive power among microgrids is determined. The decoupling between inter-network and intra-network can decrease the complexity of energy sharing model. The numerical simulation results of a residential multi-microgrid system show that the proposed algorithm can effectively reduce operation cost of residential multi-microgrid system and improve optimization efficiency.

Keywords:

Foundation:

This work is supported by National Natural Science Foundation of China (No. 51777193) and National Postdoctoral Program for Innovative Talents of China (No. BX20190126).

Get Citation
[1]WU Hangfei, YANG Xiaodong, ZHANG Youbing, et al. Event-driven Automatic Demand Response Strategy for Residential Multi-microgrid System[J]. Automation of Electric Power Systems,2020,44(9):119-127. DOI:10.7500/AEPS20190521001
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History
  • Received:May 21,2019
  • Revised:September 27,2019
  • Adopted:
  • Online: May 10,2020
  • Published: