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

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Publicado en:International Journal of Energy Research vol. 2025 (2025)
Autor principal: Karimian, Shahriar
Otros Autores: Moazzami, Majid, Bahador Fani, Shahgholian, Ghazanfar
Publicado:
John Wiley & Sons, Inc.
Materias:
Hydrogen storage
Consumer behavior
Wind power
Systems stability
Energy storage
Multiple objective analysis
Energy resources
Uncertainty
Energy consumption
Natural gas
Expenditures
Storage systems
Electric vehicles
Renewable resources
Energy management
Optimization
Flexibility
Energy
Algorithms
Emissions
Alternative energy sources
Demand side management
Hydrogen
Optimization algorithms
Operating costs
Sorting algorithms
Electric vehicle charging
Photovoltaic cells
Adaptive algorithms
Probabilistic models
Photovoltaics
Consumers
Genetic algorithms
Artificial intelligence
Electricity
Planning
Probability theory
Energy management systems
Stochastic models
Wind turbines
Electric power demand
Cogeneration
Economic
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Resumen: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).
ISSN:0363-907X
1099-114X
DOI:10.1155/er/4644615
Fuente:Advanced Technologies & Aerospace Database