A cloud-metaheuristic-based framework for stochastic optimization of a hybrid wind/hydrogen based-Fuel cell system in distribution network considering uncertainty - Ali S. Alghamdi (International Journal of Electrical Power & Energy Systems)
This study introduces an innovative cloud-metaheuristic framework for the stochastic optimization of a hybrid wind/hydrogen-based fuel cell system in a distribution network. The aim is minimizing the annual costs related to energy loss, reliability, and the hybrid renewable energy system (HRES). To effectively manage uncertainties in wind power production and network loading, the cloud model theory is employed, providing a more robust approach to handling complex stochastic variations in renewable energy optimization. An improved Fire Hawks Optimization (IFHO) algorithm is utilized in solving the optimization problem by determining the optimal installation locations and sizes of HRES components. Enhancing the traditional Fire Hawks Optimization (FHO), the IFHO incorporates a piecewise linear chaotic map, improving search efficiency and preventing local optima entrapment. The proposed methodology is evaluated on 33–, real 59-, and 118-bus distribution networks. The results confirm that integrating the hybrid wind/hydrogen-based fuel cell system with hydrogen energy management significantly enhances network performance. In the first scenario for the 33-bus network, optimal allocation and scheduling of HRES, combined with hydrogen-based fuel cell reserve power, led to reductions of 38.79% in annual energy loss cost and 56.68% in ENS cost in comparison with the base network. In the second scenario, applying the cloud-IFHO framework resulted in cost increases of 6.32% for energy losses, 10.79% for ENS, 14.53% for HRES, and 10.66% for total system cost compared to the deterministic scenario. Additionally, network customer reliability was identified as the most sensitive objective to uncertainties. Furthermore, implementing the proposed stochastic methodology on real 59-bus and 118-bus networks demonstrated reductions in energy loss cost by 27.00% and 36.09%, respectively, and ENS cost by 48.80% and 54.16%, respectively, relative to the baseline network. Scenario analysis highlights the cloud model’s capability to account for both randomness and fuzziness, offering a more detailed and insightful approach to uncertainty modeling.