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

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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 http://hdl.handle.net/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


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