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Abstract
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 on both 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 IEEE 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over 5-hr receding horizon, takes 10 Pareto dominances in 24 hours.
Abstract
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 on both 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 IEEE 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over 5-hr receding horizon, takes 10 Pareto dominances in 24 hours.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Zhang, Jiahui
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
multi-energy virtual power plant (ME-VPP),economic cost (EC),power quality (PQ),bi-objective dispatch,long short-term memory (LSTM),multi-objective particle swarm optimization (MOPSO)
Data di discussione della Tesi
15 Marzo 2019
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Zhang, Jiahui
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
multi-energy virtual power plant (ME-VPP),economic cost (EC),power quality (PQ),bi-objective dispatch,long short-term memory (LSTM),multi-objective particle swarm optimization (MOPSO)
Data di discussione della Tesi
15 Marzo 2019
URI
Gestione del documento: