Integrated Hydrogen-Electrical microgrids comprehensive planning based on time division optimization of information gap decision theory method

• A comprehensive IHEM planning method is developed that integrates the site selection and configuration optimization of RES and HES, taking into account the dynamic characteristics of the HES and the uncertainties of RES output, ensuring both economic feasibility and practical application. • A novel TDIGDT method is proposed to address the uncertainty in RES output, reducing the conservatism inherent in traditional Information IGDT and eliminating subjective factors in decision-making, while retaining the non-probabilistic characteristics that make IGDT suitable for this type of planning. • The use of the NSGAII-Gurobi hybrid optimization algorithm significantly improves the solving efficiency for this complex multi-objective planning problem, enabling more effective solutions for large-scale systems. To reduce the construction costs of integrated hydrogen-electricity microgrids that are predominantly powered by renewable energy sources and to meet the rapidly growing hydrogen demand, this paper proposes a novel planning method. To more accurately reflect real-world scenarios and ensure simulation accuracy, a hydrogen energy system with dynamic characteristics is established. To address the limitations of existing IGDT and fully account for the uncertainties in RES output, TDIGDT is proposed. The overall IHEM planning model integrates the planning of RES and HES and involves a large number of nonlinear constraints, which presents significant optimization challenges. Therefore, an optimization framework combining NSGA-II and Gurobi is proposed for solution. Simulations on the IEEE 33-bus system demonstrate that the proposed planning method can reduce the comprehensive cost of IHEM planning by more than 5%; TDIGDT eliminates the influence of subjective factors in IGDT and enables the system to achieve better adaptability to uncertainty at the same cost; the proposed algorithm can improve solution efficiency by more than 75% by increasing the number of parallel processes.