Rodríguez-González, Ansel Y.Lezama, FernandoMartínez-López, YoanMadera, JulioSoares, JoãoVale, Zita2023-02-022023-02-022022http://hdl.handle.net/10400.22/22116Evolutionary computation is attracting attention in the energy domain as an alternative to tackle inherent mathematical complexity of some problems related to high-dimensionality, non-linearity, non-convexity, multimodality, or discontinuity of the search space. In this context, the research community launched the 2020 ”Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” and an associated simulation framework to evaluate the performance of state-of-the-art evolutionary algorithms solving real-world problems. The competition includes two testbeds: (1) Day-ahead energy resource management problem in smart grids under uncertain environments; and (2) Bi-level optimization of end-users’ bidding strategies in local energy markets. This paper describes the general framework of the competition, the two testbeds, and the evolutionary algorithms that participated. A special section is dedicated to the winner approach, CUMDANCauchy++, a cellular Estimation Distribution Algorithm (EDA). A thorough analysis of the results reveals that, led by CUMDANCauchy++, the top three algorithms form a block of approaches all based on cellular EDAs. Moreover, for testbed 2, in which CUMDANCauchy++ did not achieve the best performance, the winner approach is also based on EDAs. The outcomes of the competition show that CUMDANCauchy++ is an effective algorithm solving both testbeds, and EDAs emerge as an algorithm class with promising performance for solving smart grid applications.engEvolutionary algorithmsEstimation distribution algorithmsOptimizationSmart gridsStatistical analysisjournal article10.1016/j.asoc.2022.109162