Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables

Katsigiannis, YA; Georgilakis, PS; Karapidakis, ES

dc.contributor.author	Katsigiannis, YA	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Karapidakis, ES	en
dc.date.accessioned	2014-03-01T01:33:46Z
dc.date.available	2014-03-01T01:33:46Z
dc.date.issued	2010	en
dc.identifier.issn	1752-1416	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20587
dc.subject	Environmental Performance	en
dc.subject	multiobjective genetic algorithm	en
dc.subject	Power System	en
dc.subject.other	Conventional power	en
dc.subject.other	Cost of energies	en
dc.subject.other	Economic and environmental performance	en
dc.subject.other	Environmental objectives	en
dc.subject.other	GHG emission	en
dc.subject.other	Hybrid power systems	en
dc.subject.other	Life cycle analysis	en
dc.subject.other	Life cycle emissions	en
dc.subject.other	Local search	en
dc.subject.other	Materials extraction	en
dc.subject.other	Multi-objective genetic algorithm	en
dc.subject.other	Multiobjective optimisation	en
dc.subject.other	Non-dominated sorting genetic algorithms	en
dc.subject.other	Non-linear	en
dc.subject.other	Nondominated solutions	en
dc.subject.other	NSGA-II	en
dc.subject.other	Optimal solutions	en
dc.subject.other	Renewables	en
dc.subject.other	Environmental management	en
dc.subject.other	Extraction	en
dc.subject.other	Gas emissions	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Global warming	en
dc.subject.other	Greenhouse gases	en
dc.subject.other	Hydrogen storage	en
dc.subject.other	Life cycle	en
dc.subject.other	Multiobjective optimization	en
dc.title	Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables	en
heal.type	journalArticle	en
heal.identifier.primary	10.1049/iet-rpg.2009.0076	en
heal.identifier.secondary	http://dx.doi.org/10.1049/iet-rpg.2009.0076	en
heal.identifier.secondary	ISETCN000004000005000404000001	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria. © 2010 The Institution of Engineering and Technology.	en
heal.publisher	INST ENGINEERING TECHNOLOGY-IET	en
heal.journalName	IET Renewable Power Generation	en
dc.identifier.doi	10.1049/iet-rpg.2009.0076	en
dc.identifier.isi	ISI:000285460800002	en
dc.identifier.volume	4	en
dc.identifier.issue	5	en
dc.identifier.spage	404	en
dc.identifier.epage	419	en