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A Comparison of Different Many-Objective Optimization Algorithms for Energy System Optimization

Tobias Rodemann, "A Comparison of Different Many-Objective Optimization Algorithms for Energy System Optimization", Applications of Evolutionary Computation, pp. 1-16, 2019.


The usage of renewable energy sources, storage devices, and flexible loads has the potential to greatly improve the overall efficiency of a building complex or factory. However, one needs to consider a multitude of upgrade options and several performance criteria. We therefore formulated this task as a many-objective optimization problem with 10 design parameters and 5 objectives (investment cost, yearly energy costs, \CO emissions, system resilience, and battery lifetime). We investigated if different optimization algorithms might produce different results, we therefore tested several different many-objective optimization algorithms in terms of their hypervolume performance and the practical relevance of their results. We found substantial performance differences between the algorithms, both in terms of hypervolume and in the basic distribution of solutions in objective space. We have also used the concept of desirabilities to better visualize and assess the quality of solutions found.

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