Multi-objective optimal design of hybrid renewable energy system under multiple scenarios

Rui Wang, Jian Xiong, Min-fan He, Liang Gao and Ling Wang

Renewable Energy, 2020, vol. 151, issue C, 226-237

Abstract: The design of hybrid renewable energy system (HRES) is crucial in terms of providing reliable power by renewable energies. So far, a number of studies have been conducted amongst which a single scenario based design is the mostly studied. However, we argue that handling multiple scenarios in the context of HRES design is more practical since operating conditions of a HRES (e.g., load demand) can be different periodically. For example, when designing HRES for a farmland, the busy season time and slack season time are two representative scenarios that correspond to substantially different load demand. Surprisingly, there is no adequate study of multi-scenario oriented multi-objective optimal HRES design. This study therefore fills in this research gap. A multi-scenario optimization based method is proposed for HRES design. Specifically, taking the PV(photovoltaic)–WT (wind turbines)–Bat(Battery)–DG (Diesel generator) as an example, a two-scenario bi-objective optimization model (minimization of system cost while maximization of system reliability) is proposed. In order to solve the model effectively, a scenario-dominance based multi-objective evolutionary algorithm (denoted as s-NSGA-II) is proposed. Lastly, a case study is shown to demonstrate the effectiveness of the proposed method, that is, s-NSGA-II is able to find well-balanced solutions for all scenarios, which therefore leads the s-NSGA-II to be a good alternative for dealing with the optimal design of HRES under multiple scenarios.

Keywords: Optimal design of HRES; NSGA-II; Multiple scenarios; Evolutionary algorithms; Multi-objective optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:151:y:2020:i:c:p:226-237

DOI: 10.1016/j.renene.2019.11.015

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