Multiobjective Probabilistic Planning of Energy Hub With Hydrogen Storage Technologies Considering Demand Response Programs

Abstract

This paper develops a stochastic bi-objective energy management system (EMS) for an integrated energy hub (EH) comprising photovoltaic (PV) arrays, wind turbines (WTs), a dual-fuel boiler, combined heat and power (CHP) generation, electric vehicle (EV) charging infrastructure, and hydrogen storage systems, interconnected with the main grid. The proposed EMS framework simultaneously minimizes operational expenditures (OPEX) and carbon emissions while addressing uncertainties in renewable generation and load demand through probabilistic modeling and demand response programs (DRPs). A novel modified multi-objective grasshopper optimization algorithm (MMOGOA) with adaptive mutation operators is introduced to solve this complex optimization problem, demonstrating superior convergence characteristics and 7.2% lower OPEX compared to conventional MOEAs (Non-dominated Sorting Genetic Algorithm [NSGA-II] and MOPSO) in baseline scenarios. Comprehensive simulations reveal that demand response program (DRP) implementation achieves significant reductions (18.87% in costs and 14.62% in emissions), while uncertainty incorporation increases costs by 10% and emissions by 4.38%, with MMOGOA consistently maintaining performance dominance across all operational regimes. The results quantitatively highlight the importance of optimizing DRP participation and managing uncertainties to improve the efficiency and sustainability of energy management systems (EMSs).