Chance-constrained optimal schedule of battery energy storage considering the uncertainties of renewable generation

Since renewable energy generation has strong uncertainties and pure conventional unit dispatch schemes are limited by the unit-operating capacities, such scheduling is inapplicable for power systems with high proportions of renewable energy sources (RESs). We propose an optimal scheduling model for battery energy storage systems (BESSs) by considering the uncertainties of RESs. The probability distribution of renewable energy generation is characterized using a Gaussian mixture model that effectively captures its stochastic nature. Chance constraints are incorporated into the dispatch model to enhance system security while ensuring sufficient reserve capacity to mitigate fluctuations in the RES outputs. Furthermore, these constraints are transformed into their deterministic equivalents using quantile-based methods. Case studies were then conducted on two systems to demonstrate the ability of the proposed model to improve system security and economic efficiency. The results indicate that incorporating BESSs can significantly reduce the system risk probability and operational costs, particularly under scenarios with high RES penetration. The model also highlights the tradeoffs between BESS capacity and system risk levels as well as constraint settings and economic benefits to provide valuable insights for practical applications. It is expected that future efforts in this field will be focused on extending the model to include the impacts of BESSs on branch power transmission risks.