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.

Highlights

  • With increasing concerns regarding the impact of climate change, energy demand growth, and resource depletion, careful consideration must be given to power expansion planning and the shift from fossil fuels to more sustainable resources [2].International energy agreements and policies have driven this shift, including the Kyoto Protocol [3], Paris’ Agreement [4], and the European Union 2030 Strategy [5].Determining the optimal combination of power generation technologies at country level can be formulated as a mathematical programming problem, using the optimisation of a Key PerformanceIndicator (KPI) as the objective function, for example, the total power system cost [6,7,8]

  • This study investigates the impact of sustainability factors towards defining optimal power generation mixes in medium-to-long-term national or regional energy expansion planning

  • This paper highlighted the importance of incorporating sustainability criteria into long-term power system planning by means of a Multi-objective Optimisation (MO) approach

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Summary

Introduction

With increasing concerns regarding the impact of climate change, energy demand growth (electricity demand expanded by 4% in 2018 [1]), and resource depletion, careful consideration must be given to power expansion planning and the shift from fossil fuels to more sustainable resources [2]. In Reference [16], authors developed a mixed integer linear, two-stage multi-objective model to solve electricity-planning problems, minimising three objective functions: (i) cost, (ii) CO2 , and (iii) NOx emissions. The paper addressed mainly environmental factors using the cost and vulnerability as objective functions, while other sustainability aspects, such as social indicators, were not considered. In Reference [21], authors constructed a long-term dual-stage MO approach to solve Croatia’s energy expansion planning problem. EPLANopt generated a set of Pareto-optimal solutions with objective functions the minimisation of: (i) total annual costs, (ii) CO2 emissions per person, and (iii) non-renewable contribution. This paper proposes a multi-objective multi-period optimisation model, which derives the optimum power generation expansion mix of a country integrating sustainability indicators.

Overview of Sustainability Indicators in Energy Systems
Multi-Objective Genetic Algorithm Process
Problem Statement
Nomenclature
Model Structure
Section 3. For
Pareto fronts for varying initial populationand and Pareto
Assumptions
Minimising Discounted Total Costs
Minimising Land Use
Minimising Social Opposition
Additional Indicators
Constraints
Required Capacity and Demand
Energy Diversity
Import Dependency
Construction Limit
Land Use Constraint
Indonesian Context
Energy Situation and Prospects in Indonesia
Biomass
Technology Data
Single-Objective Optimisation Results
Multi-Objective Optimisation Results
Conclusions
Full Text
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