Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators

Taimur Al Shidhani, Anastasia Ioannou and Gioia Falcone
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Taimur Al Shidhani: School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Anastasia Ioannou: School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Gioia Falcone: School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Energies, 2020, vol. 13, issue 9, 1-32

Abstract: The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares, except for hydropower and onshore wind technologies. Accordingly, to investigate the trade-offs among the objective functions, Pareto front candidates for each pair of objective functions were generated, indicating a strong correlation between the minimisation of carbon emissions and the social opposition. Limited trade-offs were also observed between the minimisation of costs and land use. Integrating the objective functions in the multi-objective model resulted in various non-dominated solutions. This tool aims to enable decision-makers identify the trade-offs when optimising the power system under different objectives and determine the most suitable electricity generation mix.

Keywords: multi-objective optimisation; genetic algorithm; electricity; sustainability; power system expansion planning; environmental; social; financial (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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