Method for optimizing reserve in congestion management using weather forecast information with uncertainty

In recent years, photovoltaic systems and other renewable energy sources have been significantly introduced into the power grid. However, when renewable energy is unevenly distributed across the grid, grid congestion occurs. This is defined as a situation where the utilization rate of transmission lines and transformers increases locally and exceeds their capacity. One method to mitigate grid congestion is re-dispatch. This is a method of reducing the output of reserve power on the grid where congestion occurs and increasing the output on the grid where congestion does not occur. However, reserve power is not always available to increase production. Therefore, it is necessary to forecast the output of renewable energy sources to ensure the availability of reserve power in advance to relieve congestion. Additionally, since the predicted renewable energy output contains errors, these errors must be considered. This paper utilizes the Meso-scale Ensemble Prediction System (MEPS), a weather forecasting system with publicly distributed uncertainty information provided by the Japan Meteorological Agency (JMA). We propose a novel congestion management planning method that incorporates the MEPS data and accounts for the uncertainty. Our results demonstrate that the proposed method outperforms conventional methods that always ensure a fixed amount of reserve power in terms of congestion avoidance and cost.