A new cloud-stochastic framework for optimized deployment of hydrogen storage in distribution network integrated with renewable energy considering hydrogen-based demand response

This study proposes a new cloud-stochastic framework for optimal placement and sizing of hydrogen storage-based fuel cell (FC) in radial distribution network (DN) alongside photovoltaic (PV) and wind turbine (WT) systems. The framework accounts for uncertainties in renewable energy generation and DN loading. The optimization problem is structured as a three-objective function aimed at minimizing the annual cost of energy losses (ACOEL), the annual cost of emission (ACOEM), and the annual cost of investment and operation (ACOIO). A novel Multi-objective Enhanced Exponential Distribution Optimizer (MOEEDO) is developed to determine the optimal sizing and placement of renewable energy resources and hydrogen storage-based FCs. The framework is applied to a 33-bus radial DN under various cases, incorporating PV, WT, hydrogen storage, and demand response (DR). Results highlight that integrating hydrogen storage with renewable resources significantly enhances DR capabilities. In a comprehensive case combining PV, WT, hydrogen storage, and DR, ACOEL and ACOEM were reduced by 43.47 % and 1.35 %, respectively, compared to the base network. Additionally, incorporating uncertainties through the cloud-stochastic framework led to increases in ACOEL, ACOEM, and ACOIO by 3.28 %, 0.028 %, and 6.46 %, respectively. Despite the additional costs, the framework provides operators with better decision-making and optimization tools under uncertain conditions, enhancing the efficiency of DNs.