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

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Main Authors:	Jiahui Zhang, Zhiyu Xu, Weisheng Xu, Feiyu Zhu, Xiaoyu Lyu, Min Fu
Format:	Article
Language:	English
Published:	MDPI AG 2019-01-01
Series:	Applied Sciences
Subjects:	multi-energy virtual power plant economic cost power quality bi-objective dispatch long short-term memory multi-objective particle swarm optimization
Online Access:	http://www.mdpi.com/2076-3417/9/2/292

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record_format	Article
spelling	doaj-60045504e025460d9de1edc4ce1d3f222020-11-25T01:48:38ZengMDPI AGApplied Sciences2076-34172019-01-019229210.3390/app9020292app9020292Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm OptimizationJiahui Zhang0Zhiyu Xu1Weisheng Xu2Feiyu Zhu3Xiaoyu Lyu4Min Fu5College of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaThis 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.http://www.mdpi.com/2076-3417/9/2/292multi-energy virtual power planteconomic costpower qualitybi-objective dispatchlong short-term memorymulti-objective particle swarm optimization
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jiahui Zhang Zhiyu Xu Weisheng Xu Feiyu Zhu Xiaoyu Lyu Min Fu
spellingShingle	Jiahui Zhang Zhiyu Xu Weisheng Xu Feiyu Zhu Xiaoyu Lyu Min Fu Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization Applied Sciences multi-energy virtual power plant economic cost power quality bi-objective dispatch long short-term memory multi-objective particle swarm optimization
author_facet	Jiahui Zhang Zhiyu Xu Weisheng Xu Feiyu Zhu Xiaoyu Lyu Min Fu
author_sort	Jiahui Zhang
title	Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization
title_short	Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization
title_full	Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization
title_fullStr	Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization
title_full_unstemmed	Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization
title_sort	bi-objective dispatch of multi-energy virtual power plant: deep-learning-based prediction and particle swarm optimization
publisher	MDPI AG
series	Applied Sciences
issn	2076-3417
publishDate	2019-01-01
description	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.
topic	multi-energy virtual power plant economic cost power quality bi-objective dispatch long short-term memory multi-objective particle swarm optimization
url	http://www.mdpi.com/2076-3417/9/2/292
work_keys_str_mv	AT jiahuizhang biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization AT zhiyuxu biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization AT weishengxu biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization AT feiyuzhu biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization AT xiaoyulyu biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization AT minfu biobjectivedispatchofmultienergyvirtualpowerplantdeeplearningbasedpredictionandparticleswarmoptimization
_version_	1725011020785647616

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

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