Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS

•A data-driven framework is proposed to optimize the sizing of a hybrid energy system.•A modified NSGA-II based on reinforcement learning is utilized to obtain Pareto set.•CRITIC-TOPSIS is used to decide the weight of objectives and select the best solution.•A optimal system with LCOE of 0.226 $/kWh...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Energy conversion and management 2020-07, Vol.215, p.112892, Article 112892
Hauptverfasser:	Xu, Chuanbo, Ke, Yiming, Li, Yanbin, Chu, Han, Wu, Yunna
Format:	Artikel
Sprache:	eng
Schlagworte:	Abandonment Configuration optimization Configurations CRITIC Decision making Electricity consumption Electrolytic cells Energy conservation Fuel cells Fuel tanks Fuel technology Genetic algorithms Hydrogen Hydrogen storage Industrial parks Machine learning Multiple criterion NSGA-II Optimization Peak load Photovoltaic cells Reinforcement learning Sorting algorithms Storage tanks TOPSIS Turbines Wind power Wind turbines Wind/PV/hydrogen system
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!