Capacity optimisation for an SAMS considering LCOE and reliability objectives

This study proposes a novel method to optimally allocate capacity for a stand-alone microgrid system (SAMS). An SAMS is usually found on offshore islands or in the areas, where electricity cannot be delivered by utility companies. With the continuing development of renewable energy, the photovoltaic...

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Bibliographic Details
Published in:	IET renewable power generation Vol. 12; no. 7; pp. 787 - 796
Main Authors:	Huang, Chao-Ming, Chen, Shin-Ju, Yang, Sung-Pei, Huang, Yann-Chang, Chen, Po-Yi
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 21.05.2018
Subjects:	battery energy storage system battery storage plants capacity optimisation cost reduction decision making demand side management differential evolution distributed power generation enhanced charged system search algorithm environmental damage mitigation environmental factors evolutionary computation fuzzy set theory generation cost reduction LCOE levelised cost of energy lower power curtailment minimisation Monte Carlo methods Monte Carlo simulation offshore installations offshore island optimal capacity allocation Pareto optimisation Pareto‐based fuzzy decision making method particle swarm optimisation particle swarm optimisation method photovoltaic plant photovoltaic power systems power distribution economics power distribution faults power distribution reliability power generation economics power generation reliability PV plant reliability objective renewable energy renewable generation Research Article SAMS search problems stand‐alone microgrid system Taiwan turbogenerators wind turbine generator wind turbines particle swarm optimisation fuzzy set theory power distribution faults distributed power generation capacity optimisation photovoltaic plant power generation economics stand-alone microgrid system renewable energy Monte Carlo methods SAMS lower power curtailment offshore island levelised cost of energy turbogenerators environmental damage mitigation enhanced charged system search algorithm search problems battery energy storage system cost reduction Pareto optimisation power generation reliability environmental factors optimal capacity allocation photovoltaic power systems power distribution economics offshore installations LCOE differential evolution evolutionary computation power distribution reliability generation cost reduction renewable generation Pareto-based fuzzy decision making method decision making Taiwan reliability objective particle swarm optimisation method battery storage plants wind turbine generator wind turbines demand side management minimisation PV plant Monte Carlo simulation
ISSN:	1752-1416, 1752-1424, 1752-1424
Online Access:	Get full text
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Summary:	This study proposes a novel method to optimally allocate capacity for a stand-alone microgrid system (SAMS). An SAMS is usually found on offshore islands or in the areas, where electricity cannot be delivered by utility companies. With the continuing development of renewable energy, the photovoltaic (PV) plant, wind turbine generator and battery energy storage system are integrated into an SAMS to reduce generation cost, mitigate environmental damage and increase generation efficiency. In terms of the uncertainty of renewable generation and to allow optimal capacity allocation for an SAMS, a combination of a Monte Carlo simulation, an enhanced charged system search algorithm and a Pareto-based fuzzy decision-making method is used in this study. The objectives considered are the minimisation of both the levelised cost of energy (LCOE) and the expected energy not supplied. An efficient scheme that reduces the convergence time is also developed. The proposed method is tested using an SAMS that is located on an offshore island of Taiwan. Testing results show that the proposed method produces more stable convergence, lower LCOE value, smaller PV capacity and lower power curtailment than those for the differential evolution and particle swarm optimisation methods.
ISSN:	1752-1416 1752-1424 1752-1424
DOI:	10.1049/iet-rpg.2017.0676