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Optimal Scheduling of a Multi-Energy Hub with Integrated Demand Response Programs

I tiakina i:
I whakaputaina i:Processes vol. 13, no. 9 (2025), p. 2879-2898
Kaituhi matua: Zubo Rana H. A.
Ētahi atu kaituhi: Onen, Patrick S, Mujtaba, Iqbal M, Geev, Mokryani, Abd-Alhameed Raed
I whakaputaina:
MDPI AG
Ngā marau:
Energy management
Linear programming
System reliability
Energy flow
Integer programming
Wind power
Energy storage
Clean energy
Sustainable yield
Prices
Case studies
Natural gas
Scheduling
Electricity
Renewable resources
Optimization
Flexibility
Electric power demand
Mixed integer
Alternative energy sources
Real time
Demand side management
Cogeneration
Operating costs
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Whakarāpopotonga:This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations.
ISSN:2227-9717
DOI:10.3390/pr13092879
Puna:Materials Science Database