Optimal Energy Management for Hydrogen and Electric Based Integration Based on Machine Learning and Demand Response Using Multi Objective Optimization Algorithm

The coordination between hydrogen and electricity within interconnected multi‐microgrids is challenging due to time‐varying renewable generation, electrical and hydrogen demand, and V2G availability, along with satisfying constraints associated with storage, network, market, and demand response across day‐ahead and intraday operation. This study introduces an integrated hydrogen electric multi‐microgrid energy management system based on RNN–LSTM forecasting, day‐ahead‐to‐intraday scheduling, incentive‐based demand response, and bi‐objective NSGA‐II optimization. The proposed framework is implemented on the IEEE 33‐bus radial distribution network with four interconnected microgrids incorporating PV and wind generation, BESS, EV/V2G, electrolyzers, hydrogen storage, an external hydrogen market, and hydrogen fueled vehicles. Five different scheduling scenarios are investigated to analyze the effect of using updated intraday information, DA‐to‐ID corrective scheduling, demand response, and hydrogen market participation. The proposed RNN–LSTM forecasting technique achieves R 2 values of 0.9765, 0.9623, 0.9590, 0.9830, and 0.9189 for hydrogen demand, V2G, PV generation, electrical demand, and wind generation, respectively. For the proposed Scenario S4, the knee‐point solution leads to an operating cost of 60,892.55 USD/day and 113,099.60 kg/day of grid‐related CO 2 emissions. Compared with Scenario S3, Scenario S4 reduces grid‐related CO 2 emissions by 1.52%, PV curtailment by 84.81%, wind curtailment by 36.90%, and network losses by 21.91%, while increasing operating cost by 2.88%. When hydrogen market participation is disabled in Scenario S5, the operating cost increases to 77,045.40 USD/day and the hydrogen service ratio decreases to 67.13%.