Research on the Probabilistic Incorporation of Renewable Energy into Power Balance in Multi-scenario and Multi-time

Renewable energy resources (RERs) cannot be a reliable source for peak-shaving due to the greatly fluctuation and randomness. RERs participating in day-ahead power balance in inappropriate proportion may increase the risk of power supply in the power system, and even lead to power shortage, especially with the proportion of RERs in the grid gradually increasing. This paper proposes a multi-scenario and multi-time (MSMT) method for calculating the incorporation proportion of RERs into power balance based on the probabilistic. Firstly, anomaly data detection and reconstruction rules are used to improve the quality of historical prediction data of RERs, and composite distance is introduced for scenario division. This reveals more refined characteristics of RERs by taking advantage of the classification ability of dual spectral clustering algorithm. Then, kernel density estimate is utilized to fit the probability distribution of RERs power. Finally, the proportion of RERs participating in power balance in different time periods is calculated. This paper constructs a simulation case based on the power of RERs in a power grid. The simulation results show that the method proposed in this paper can effectively calculate the proportion of RERs integrated into the power balance in different time, which greatly improves power supply reliability and reduces the supply risk of power system.