Development of Probability Estimation Method for Transmission Line Overload due to Forecast Errors of Renewable Energy Sources

To achieve decarbonization, the market share of renewable energy sources (RESs), such as wind and photovoltaic generators, is gradually increasing. However, the outputs of RESs are hard to accurately predict, which introduces uncertainty to a stable electric power system. This would pose a risk of violations of power system constraints, such as thermal overloads of transmission lines. To maintain a stable power supply, it is necessary to recognize the overload risk so that the system operators can take countermeasures in advance to prevent them. This paper presents a probability estimation method for transmission line overloads caused by uncertainties in the real-time outputs of RESs. The problem is modeled as a multi-dimensional integration problem based on the probability density function of the RES forecast output errors and be solved with a Monte-Carlo integration-based method. In numerical analysis, the modified IEEE-RTS 24-bus system was used to obtain a ranking of the overload probabilities for the transmission lines. It is also demonstrated that the overload probability can be significantly reduced by modifying the unit commitment schedule.