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Intelligent Cost Optimization in Hybrid Electricity-Gas Microgrids Considering Distributed Generation and Energy Storage
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Shahryar Behnia   , Saeed Kharrati * , Farshad Khosravi , Abdollah Rastgou |
| Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran |
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Abstract: (44 Views) |
This paper presents a comprehensive framework for smart cost optimization in integrated electricity-gas microgrids considering distributed generation units, energy storage systems, and market price uncertainties. The primary objective is to minimize operational costs while enhancing the performance of microgrids under realistic and uncertain conditions. To this end, a hybrid optimization model based on Mixed-Integer Linear Programming (MILP) and Distributionally Robust Chance Constraints (DRCC) is developed and implemented in GAMS software using the CPLEX solver. The proposed model is formulated within a two-stage market structure, including the day-ahead and real-time markets, to ensure efficient planning and adaptive corrections under uncertainty. To evaluate the performance of the model, a typical hybrid microgrid comprising photovoltaic (PV), wind turbine (WT), microturbines, fuel cells, and battery energy storage systems (BESS) along with variable and interruptible loads is simulated. Numerical results indicate that the proposed framework leads to an 18% reduction in total operational cost, a 27% reduction in load curtailment, and over 95% utilization rate of renewable energy sources. Moreover, the deviation from market schedules is reduced by 55%, indicating improved robustness and resilience. The flexibility of the proposed model also enables its extension to other energy carriers such as heating and cooling, making it a promising solution for future multi-energy systems. |
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| Keywords: Hybrid microgrid, Energy hub, Robust planning, Cost optimization, Energy storage, DRCC, Renewable energy sources |
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Full-Text [PDF 842 kb]
(20 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2025/12/26 | Accepted: 2025/02/26 | Published: 2025/02/28
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