研究目的
Investigating the optimal scheduling of generators and Battery Energy Storage Systems (BESS) using forecasting in power systems with extremely large photovoltaic generation to reduce supply-demand imbalances.
研究成果
The proposed method effectively reduces supply-demand imbalances in power systems with large PV generation by utilizing BESS and PV forecasting with prediction intervals. The wider the prediction interval used for scheduling, the smaller the energy shortfall, and the larger the BESS capacity, the smaller the PV curtailment. Future work will focus on developing methods for BESS in current day operation to reduce both PV curtailment and energy shortfall.
研究不足
The study does not consider the losses and constraints of the network. The BESS charges or discharges only following the day-ahead schedule, not adjusting for actual PV power output deviations in the current day operation.
1：Experimental Design and Method Selection:
The study uses a mixed integer linear programming (MILP) approach for the day-ahead unit commitment (UC) of thermal generators and optimal BESS charging/discharging scheduling, considering PV forecasting errors through prediction intervals.
2：Sample Selection and Data Sources:
The simulation uses actual 30-minute solar irradiation and load demand data from the Kanto area in Japan for April to June
3：List of Experimental Equipment and Materials:
20 The study involves thermal generators, hydro and nuclear power plants, and BESS with varying inverter and energy capacities.
4：Experimental Procedures and Operational Workflow:
The methodology includes forecasting PV power with prediction intervals, scheduling thermal generators and BESS operations day-ahead, and evaluating supply-demand imbalances in the current day operation.
5：Data Analysis Methods:
The effectiveness of the proposed method is evaluated by analyzing total energy shortfall and PV curtailment over the simulation period.
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