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Multi-Objective Virtual Power Plant Construction Model Based on Decision Area Division
Article

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

by 1,2, 1,*, 1,3, 1,2, 1 and 1
1
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
2
Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
3
Center of Bigdata and Informatization, Tongji University, Shanghai 20092, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(2), 292; https://doi.org/10.3390/app9020292
Received: 21 December 2018 / Revised: 8 January 2019 / Accepted: 10 January 2019 / Published: 15 January 2019
(This article belongs to the Special Issue Progress in Virtual Power Plant Design and Applications)
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. View Full-Text
Keywords: multi-energy virtual power plant; economic cost; power quality; bi-objective dispatch; long short-term memory; multi-objective particle swarm optimization multi-energy virtual power plant; economic cost; power quality; bi-objective dispatch; long short-term memory; multi-objective particle swarm optimization
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MDPI and ACS Style

Zhang, J.; Xu, Z.; Xu, W.; Zhu, F.; Lyu, X.; Fu, M. Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization. Appl. Sci. 2019, 9, 292. https://doi.org/10.3390/app9020292

AMA Style

Zhang J, Xu Z, Xu W, Zhu F, Lyu X, Fu M. Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization. Applied Sciences. 2019; 9(2):292. https://doi.org/10.3390/app9020292

Chicago/Turabian Style

Zhang, Jiahui, Zhiyu Xu, Weisheng Xu, Feiyu Zhu, Xiaoyu Lyu, and Min Fu. 2019. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization" Applied Sciences 9, no. 2: 292. https://doi.org/10.3390/app9020292

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