Differential Evolution Based on Light-Weight-Surrogate for Solving High-Dimensional Energy Management Problem
Chixin Xiao, Maoxin He, Dechen Jiang, Yiwei Zhang, Yuxin Tang, Zhenyu Ling · Proceedings of the Genetic and Evolutionary Computation Conference · 2024
In modern power engineering, given the high penetration of distributed energy resources, a risk-based and day-ahead management problem can often be formulated as a high-dimensional problem. In addition, the mathematical mapping from the input control vector to the quantiles of the system output becomes too complex due to the nested relationship between the different systems. Hence, this type of problem is often approached as a black box model, i.e., the decision maker can only observe the input and output ends. However, as a practical engineering problem, it often requires the corresponding optimization in limited time or evaluations, which poses a challenge to conventional differential evolution algorithms. This paper proposes a novel differential evolution algorithm with a compatible surrogate operator that provides dynamic rational exploration during the iterations to achieve reliable performance in limited time and space complexity. The surrogate also facilitates faster exploration of the optimal solution region compared with the state-of-the-art under the same configurations and conditions. A GECCO competition testbed is utilized to evaluate the performance, where the simulation results demonstrate that the proposed algorithm is more effective than most DE, PSO (and their typical variants).