Optimal Management of a Multi-Energy Hub with Efficient Utilization of Storage Systems
محورهای موضوعی : Electrical Engineering
Mohammad mehdi Mohammadi zaferani
1
,
Reza Ebrahimi
2
,
Mahmood Ghanbari
3
1 - Department of Electrical Engineering, Go.C., Islamic Azad University, Gorgan, Iran
2 - Department of Electrical Engineering, Go.C., Islamic Azad University, Gorgan, Iran
3 - Department of Electrical Engineering, Go.C., Islamic Azad University, Gorgan, Iran
کلید واژه: Energy Hub , Optimal Energy Management , Combined Heat and Power (CHP) , Operational Cost , Mixed-Integer Nonlinear Programming (MINLP),
چکیده مقاله :
This paper proposes an optimal management framework for a grid-connected multi-energy hub (MEH), integrating Combined Heat and Power (CHP), Electric Heat Pump (EHP), Electrical Energy Storage System (ESS), and Heat Storage System (HSS). The MEH serves as a critical node, converting and storing energy from electricity and natural gas networks to meet electrical, heating, and cooling demands efficiently. A novel mathematical model of the MEH is formulated, moving beyond traditional linear coupling matrices to incorporate practical operational constraints and the impact of equipment degradation on system economics. The core of the study is a Mixed-Integer Nonlinear Programming (MINLP) optimization problem, solved using the Particle Swarm Optimization (PSO) algorithm in MATLAB. The objective is to minimize total daily operational costs, which include expenses for purchasing electricity and natural gas, as well as a novel cost term representing the daily economic loss due to battery lifetime degradation within the ESS. This degradation is meticulously modeled by relating the Depth of Discharge (DOD) to the battery's cyclic lifespan. The model enforces comprehensive constraints, including energy balance, device capacities, storage dynamics, and the operational limits of all components. Simulation results, based on the 24-hour load profile of a university residence, demonstrate the superior performance of the proposed model. Compared to a conventional MEH model and a traditional separated energy system, the proposed framework achieves significantly lower operational costs. It enables intelligent scheduling: purchasing cheap grid electricity for storage, utilizing the CHP efficiently, and leveraging the EHP and storage systems to shave peak loads and shift consumption. A detailed comparative analysis shows the optimized system reduces electrical, cooling, and heating power generation peaks effectively. Furthermore, the study provides key insights into battery management, revealing that while the maximum allowed DOD has a minor impact on the calculated battery lifespan within the optimization horizon, it significantly influences the achievable operational cost. The optimized system also demonstrates a consistent 10-15% reduction in operational costs compared to a non-optimized baseline, validating the economic benefit of the integrated, lifespan-aware management strategy. In conclusion, this research presents a realistic and cost-effective MEH model that enhances energy flexibility, grid stability, and economic efficiency by co-optimizing energy dispatch and storage system longevity.
This paper proposes an optimal management framework for a grid-connected multi-energy hub (MEH), integrating Combined Heat and Power (CHP), Electric Heat Pump (EHP), Electrical Energy Storage System (ESS), and Heat Storage System (HSS). The MEH serves as a critical node, converting and storing energy from electricity and natural gas networks to meet electrical, heating, and cooling demands efficiently. A novel mathematical model of the MEH is formulated, moving beyond traditional linear coupling matrices to incorporate practical operational constraints and the impact of equipment degradation on system economics. The core of the study is a Mixed-Integer Nonlinear Programming (MINLP) optimization problem, solved using the Particle Swarm Optimization (PSO) algorithm in MATLAB. The objective is to minimize total daily operational costs, which include expenses for purchasing electricity and natural gas, as well as a novel cost term representing the daily economic loss due to battery lifetime degradation within the ESS. This degradation is meticulously modeled by relating the Depth of Discharge (DOD) to the battery's cyclic lifespan. The model enforces comprehensive constraints, including energy balance, device capacities, storage dynamics, and the operational limits of all components. Simulation results, based on the 24-hour load profile of a university residence, demonstrate the superior performance of the proposed model. Compared to a conventional MEH model and a traditional separated energy system, the proposed framework achieves significantly lower operational costs. It enables intelligent scheduling: purchasing cheap grid electricity for storage, utilizing the CHP efficiently, and leveraging the EHP and storage systems to shave peak loads and shift consumption. A detailed comparative analysis shows the optimized system reduces electrical, cooling, and heating power generation peaks effectively. Furthermore, the study provides key insights into battery management, revealing that while the maximum allowed DOD has a minor impact on the calculated battery lifespan within the optimization horizon, it significantly influences the achievable operational cost. The optimized system also demonstrates a consistent 10-15% reduction in operational costs compared to a non-optimized baseline, validating the economic benefit of the integrated, lifespan-aware management strategy. In conclusion, this research presents a realistic and cost-effective MEH model that enhances energy flexibility, grid stability, and economic efficiency by co-optimizing energy dispatch and storage system longevity.
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