DEVELOPMENT OF ENERGY MANAGEMENT ALGORITHM FOR BATTERY ENERGY STORAGE SYSTEM SCHEDULING WITH MODEL PREDICTIVE CONTROL STRATEGY
The integration of renewable energy sources (RES) into microgrids (MGs) presents various challenges due to their intermittent nature. Consequently, an effective and efficient energy management system (EMS) is required to address these challenges. This research focuses on developing an EMS algorithm...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83644 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The integration of renewable energy sources (RES) into microgrids (MGs) presents various challenges due to their intermittent nature. Consequently, an effective and efficient energy management system (EMS) is required to address these challenges. This research focuses on developing an EMS algorithm that schedules a battery energy storage system (BESS) using a model predictive control (MPC) strategy. The primary objective is to optimize the scheduling of the BESS to balance energy supply and demand within the MG, considering both normal and dynamic energy pricing conditions. The proposed EMS algorithm is designed to minimize operational costs, maximize the utilization of RES, and optimize the use of the BESS. The algorithm minimizes a cost function over the scheduling horizon by considering load demand forecasts and photovoltaic (PV) power generation predictions, utilizing IBM's CPLEX library with Python programming.
The performance of the proposed algorithm is evaluated through simulations using key metrics such as renewable fraction (RF), battery utilization (BU), and daily operational costs. The proposed EMS algorithm was successfully developed using Python programming. Performance comparison with an existing decision tree (DT) algorithm shows advantages for the proposed algorithm. The results indicate that both algorithms achieve similar performance metrics under normal energy pricing conditions. However, under dynamic energy pricing conditions, the proposed algorithm significantly reduces operational costs, demonstrating its robustness and adaptability to varying energy prices. These findings indicate the potential of optimization-based algorithms in EMS to achieve sustainable and efficient energy management in MGs.
Keywords: Microgrid, algorithm, battery energy storage system, scheduling, model predictive control?
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