Multi-objective optimisation for hybrid electric system setup in a remote island

This paper assesses the optimum configuration of a hybrid electric system, incorporating different forms of marine renewable energy. The efficiency of three multi-objective optimisation approaches—Multi-objective Pareto Search, Multi-Objective Genetic Algorithm (MOGA) and a hybrid MOGA is assessed a...

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Bibliographic Details
Published in:Journal of ocean engineering and marine energy Vol. 11; no. 4; pp. 885 - 908
Main Authors: Ramos-Marin, Sara, Guedes Soares, C.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.11.2025
Springer Nature B.V
Subjects:
Alternative energy
Coastal Sciences
Configurations
Cost effectiveness
Electric power generation
Electricity
Energy resources
Energy sources
Engineering
Engineering Fluid Dynamics
Environmental impact
Genetic algorithms
Marine energy
Mechanical Engineering
Multiple objective analysis
Oceanography
Offshore
Offshore Engineering
Pareto optimization
Photovoltaics
Power plants
Renewable and Green Energy
Renewable energy
Renewable resources
Tradeoffs
Wind
Pareto search
Marine energy
Hybrid renewable electric system
Genetic algorithm
Multi-objective optimisation
ISSN:2198-6444, 2198-6452
Online Access:Get full text
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Summary:This paper assesses the optimum configuration of a hybrid electric system, incorporating different forms of marine renewable energy. The efficiency of three multi-objective optimisation approaches—Multi-objective Pareto Search, Multi-Objective Genetic Algorithm (MOGA) and a hybrid MOGA is assessed and compared when utilised to define the Pareto front of optimal trade-off configurations, balancing low environmental impact with cost-effectiveness. This method is implemented in the electricity market of the Portuguese island of Porto Santo, where current conventional and renewable energy power plants (such as thermal, onshore wind, and photovoltaic) are analysed alongside other marine energy sources (wave and offshore wind). Various linear and non-linear constraints restrict the optimisation. Results point to a system dimensioned with a 63% share of renewable energies (Res), the optimal configuration with the cheapest electric generation cost (3.7 M€/year), in a trade-off of the highest environmental impact. The lowest environmental impact is linked to a system wholly composed of RE sources, although it is also associated with the most expensive cost of electricity (4.6 M€/year). Overall, configurations in the Pareto optima present higher installed capacities of offshore wind technology, followed sequentially by lower penetrations of onshore wind, thermoelectric, wave and solar photovoltaic energies.
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ISSN:2198-6444
2198-6452
DOI:10.1007/s40722-025-00410-z