Optimisation of an energy system in Finland using NSGA-II evolutionary algorithm

Mikko Wahlroos, Jaakko Jaaskelainen, Janne Hirvonen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


Energy system optimisation often includes multiple objectives and their relative trade-offs, e.g. system costs, CO2 emissions and generation adequacy. Without a conversion factor between the objectives, result of the optimisation is a set of solutions instead of a single optimal solution, commonly known as a Pareto-optimal set. Instead of working with a single solution at a time, evolutionary algorithms work with a population of solutions, and can hence be used to find multiple optimal solutions in a single optimisation run. This paper uses a well-known multi-objective evolutionary algorithm, NSGA-II (nondominated sorting genetic algorithm II), to optimise an imaginary energy system in Finland with conflicting objectives. Furthermore, we analyse two optimal points in the far ends of the resulting Pareto-optimal set in generation 10,000. Our results indicate that evolutionary algorithms are not always the most accurate optimisation method, but they have potential to be applied more widely to energy system optimisation.

Original languageEnglish
Title of host publication15th International Conference on the European Energy Market, EEM 2018
ISBN (Electronic)9781538614884
Publication statusPublished - 20 Sep 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on the European Energy Market - Lodz, Poland
Duration: 27 Jun 201829 Jun 2018
Conference number: 15


ConferenceInternational Conference on the European Energy Market
Abbreviated titleEEM


  • Energy system
  • Evolutionary algorithm
  • Matlab
  • Optimisation


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