Advanced day-ahead scheduling and management of renewable energy systems integration with hydrogen storage in the power grid

Abstract

The current state of the power system is significantly influenced by various factors, including price fluctuations, which have an uncertain impact on efficiency. Therefore, employing uncertainty modeling is crucial. This study investigates the application of the Improved Manta Ray Foraging Optimization strategy for intelligently managing electric vehicle parking spots, taking into account the uncertainties associated with changes in primary power grid pricing throughout the demand response program (DRP). By alternating between peak and light-load periods, the proposed strategy effectively reduces daily costs. Key components of the proposed scheme include a non-dominated arrangement model, variable discovery, a memory-based method for selection, and fuzzy logic to identify the optimal Pareto front. The suggested method demonstrates a rapid response time in reaching final solutions and exhibits a high potential for achieving global optima. However, Hydrogen Storage Systems present several significant constraints that must be considered during modeling. The most critical limitations involve the electrolyzer's capacities, the fuel cell boundaries, and the storage tank's capacity. The efficacy of the proposed algorithm is validated within a system that incorporates parking and multiple uncertain resources. Results confirm the exceptional ability of the proposed method to manage uncertainty effectively. Consequently, the cost fluctuations of the system power load have been reduced by up to 41%. Additionally, when DRP is included, the average SPL cost increases by 4.92%, while the variation in SPL expenses decreases by 47.01%.