A Robust Optimization Framework for Energy Management in Energy Hubs: Comparative Analysis of SMA, GA, and MILP with Demand Response Integration
الموضوعات : Journal of Computer & Robotics
Mohammad Reza Ohadi
1
,
Mahdi Hedayati
2
,
Reza Effatnejad
3
,
Abdolreza Dehghani Tafti
4
1 - گروه مهندسي برق،واحد كرج،دانشگاه آزاد اسلامي،كرج، ايران
2 - گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران
3 - گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران
4 - گروه مهندسي برق،واحد كرج،دانشگاه آزاد اسلامي،كرج، ايران
الکلمات المفتاحية: Energy management, Renewable energy sources, Demand response programs, Slime Mould Algorithm (SMA), Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA).,
ملخص المقالة :
The growing demand for sustainable development and the integration of renewable energy sources emphasize the critical role of energy hubs (EHs) in modern power systems. EHs synergize electrical, cooling, and heating equipment with demand response (DR) programs and renewable energy resources (RERs) to optimize energy management. This study proposes a combined energy system (CES) framework to manage uncertainties in energy sources and demands, including cooling, heating, wind speed, solar irradiation, and energy prices. The objective is to maximize EH profits by integrating DR programs and RERs across various scenarios. The study utilizes the Slime Mould Algorithm (SMA), the Genetic Algorithm (GA), and the Mixed-Integer Linear Programming (MILP) method to compare performance. The optimization focuses on minimizing operational costs while maximizing renewable energy utilization. Without DR integration, SMA generates 1500 kW from photovoltaic (PV) systems and 2000 kW from wind turbines at a cost of $500,000. Conversely, GA generates 1400 kW from PV and 1800 kW from wind turbines, costing $550,000, while MILP results in 1550 kW from PV and 1950 kW from wind turbines at $510,000. With DR integration, SMA reduces costs to $450,000, GA incurs $500,000, and MILP achieves $460,000. DR programs enhance load management, peak shaving, and load shifting, contributing to cost reduction and efficiency. Simulation results confirm SMA's effectiveness in managing energy resources within a microgrid, optimizing renewables, and significantly reducing costs. SMA consistently outperforms GA and MILP, demonstrating superior cost-effectiveness and resource optimization. This study underscores the importance of advanced optimization algorithms like SMA in modern energy systems, offering a reliable and cost-efficient solution for EHs and supporting sustainable energy management and environmental sustainability.
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