Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization

作者：Zhang, Jiahui; Xu, Zhiyu^*; Xu, Weisheng; Zhu, Feiyu; Lyu, Xiaoyu; FU, Min

来源：Applied Sciences-Basel, 2019, 9(2): 292.

DOI：10.3390/app9020292

• multi-energy virtual power plant
• economic cost
• power quality
• bi-objective dispatch
• long short-term memory
• multi-objective particle swarm optimization

This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, "improvisational" and "foresighted", are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that "foresighted" strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.

• 出版日期2019-1-2
• 单位同济大学