Optimisation of an Energy System in Finland Using NSGA-II Evolutionary Algorithm

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.