Designing a future regional energy system: multi-objective optimization of a regional energy system from a multi-actor perspective

Master thesis (2019)

Authors

P.J. Imhof Technology, Policy and Management

Contributors

B. Enserink Policy Analysis - TPM (supervisor 1)
P.W. Heijnen Energy and Industry (supervisor 2)
N. Wang Energy and Industry (coach)
Faculty
Technology, Policy and Management
Copyright: © 2019 Pieter Imhof
Renewable energy Multi-objective optimization Energy system optimization Socio-technical transition Multi-objective evolutionary algorithms Regional energy system Energy system Socio-technical system
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Published Date

23-09-2019

Language

English

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Faculty

Technology, Policy and Management

Abstract

The energy system is undergoing a grand transition. In 2019, the Dutch government presented the national climate accord stating that in 2030, 70% of all energy needs to come from a renewable source. In this accord, strong emphasis is put on a regional approach to the energy transition. Across the country, regions are picking up the gauntlet and 141 municipalities in The Netherlands have formulated ambitions to become energy neutral by 2050 or earlier. The road to reach these regional ambitions, however, is not always clear. One of the key issues in energy planning is defining the optimal mix of generation methods to fulfill the electricity demand. Historically, this challenge has been approached only from a least cost perspective. Different stakeholders, however, have a different view on what defines the ‘optimal’ situation and care about more than cost. It is found that minimizing land use and minimizing the visual impact of wind turbines are important objectives to consider when designing an energy system. This research presents a multi-objective optimization that employs a genetic algorithm (NSGA-II) to find the set of pareto-optimal solutions for an optimal generation mix for a regional energy system in The Netherlands minimizing costs, land use and visual impact. Three scenarios are investigated: reducing the total emissions by 70%, 90% and 98%. The results of the optimization are analyzed from a multi-actor perspective to provide insight into the most ideal solutions for different stakeholders. The results show that there are significant trade-offs to be made in designing an energy system: governments, investors and local residents all have a different view about the optimal generation mix. This research presents an average optimal solution: one that may work best for all actors. It shows that by finding a Pareto-optimal set, many optimal solutions can be compared on their desirability, leading to more insight into the functioning of the system and a more feasible design.