Optimal energy management of microgrid using advanced multi-objective particle swarm optimization. Issue 6 (7th February 2020)

• Record Type: Journal Article
• Authors: Anh, Ho Pham Huy; Kien, Cao Van
• Is Part Of: Engineering computations. Volume 37:Issue 6(2020)
• Page Start: 2085
• Page End: 2110
• Publication Date: 2020-02-07
• Subjects: Optimal energy management -- Hybrid microgrid -- Multi-objective genetic algorithm -- Multi-objective particle swarm optimization algorithm -- 24-hour optimal energy management simulations -- Bio-inspired optimization methods; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-05-2019-0194
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach: Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings: Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users' selection as to satisfy their power requirement. Originality/value: This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.