TITLE:
Green Energy Microgrid Dispatching: A Hybrid Framework of Improved Genetic Algorithm and Reinforcement Learning
AUTHORS:
Shengke Zhuo
KEYWORDS:
Hybrid Optimization Framework, Improved Genetic Algorithm, Reinforcement Learning, Microgrid Scheduling, Renewable Energy Penetration Rate, Dynamic Mutation Strategy, Robust Optimization
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.14 No.9,
September
29,
2026
ABSTRACT: High renewable penetration turns microgrid dispatching from an optimization problem into an uncertainty management one. Solar and wind outputs flicker; load demand wavers without rhythm. Both economic efficiency and operational stability come under threat. Conventional methods fall into two camps: model-based approaches, brittle against parameter errors, and search-heavy algorithms, too sluggish for real-time shifts. Neither manages to reconcile global exploration with instantaneous responsiveness—the very balance that dynamic environments demand. To address these issues, this paper proposes a hybrid intelligent optimization framework that integrates an improved genetic algorithm and deep reinforcement learning, aiming to achieve real-time economic dispatching of microgrids with a high proportion of renewable energy. At the algorithmic level, an adaptive hybrid encoding strategy is designed to unify the representation of continuous control variables and discrete equipment states; further, a dynamic mutation mechanism based on deep reinforcement learning is introduced, enabling the algorithm to adjust the mutation probability in real time according to the degree of load disturbance, enhancing the system’s robustness in sudden conditions. At the optimization mechanism level, a DRL-driven fitness evaluation system is constructed: the deep reinforcement learning agent obtains real-time reward signals through interaction with the environment and uses these signals to dynamically modify the fitness function of the genetic algorithm, thereby achieving collaborative optimization of global exploration and local fine search. Simulation results based on the NREL OpenEI Commercial Building and Microgrid Reference Datasets (2018-2022) and the IEEE 33-node system show that, compared with traditional genetic algorithms, Deep Q-Network (DQN), and particle swarm algorithms, the proposed method performs exceptionally well in multiple key indicators: compared to traditional genetic algorithms, the average daily comprehensive operating cost is reduced by 16.3%; compared to particle swarm algorithms, the penetration rate of renewable energy increases by 8.7%; the convergence speed is increased by 20% (based on the genetic algorithm as the benchmark). Moreover, in load disturbance scenarios, the dynamic mutation strategy reduces the cost fluctuation amplitude by 25.0%, demonstrating excellent dynamic adaptability and robustness.