Low-carbon Industrial Park Energy Management Optimization in Dongguan Based on Adaptive Differential Evolution Algorithm

With the transformation of the global energy structure and the rise of the low-carbon economy, the energy management problem of low-carbon industrial parks has gradually become a research focus. Problems such as large fluctuations in energy demand, unstable energy supply, and difficulty in controlling carbon emissions in the park have seriously restricted the sustainable development of low-carbon parks. In order to effectively solve these problems, this paper proposes an energy management optimization method based on the adaptive differential evolution algorithm (ADE). The algorithm can adapt to the changes in the supply and demand of various energy sources in the park by dynamically adjusting control parameters (such as mutation factors and crossover probabilities) and improve the efficiency and accuracy of energy scheduling. The experimental part uses actual energy management data from the low-carbon industrial park in Dongguan City, combined with multiple energy scheduling and carbon emission control targets, to verify the effect of ADE in optimizing the energy management of the park.