A Novel Shared Energy Storage Planning Method Considering the Correlation of Renewable Uncertainties on the Supply Side

The shared energy storage service provided by independent energy storage operators (IESO) has a wide range of application prospects, but when faced with the interrelated and uncertain output of renewable energy on the supply side, how to size for energy storage capacity is a highly challenging problem. To this end, this paper firstly proposes a hybrid shared energy storage framework, in which the private energy storage of power suppliers and IESO jointly provide shared energy storage services for users. To generate low-dimensional scenarios that consider the correlation between the multiple uncertainties of wind and solar generation, combining the Kernel function estimation and the Gaussian mixture model forms the Bayesian estimation to fit the historical generation curve precisely. The uncertain output of same generation resources is coupled based on Kullback-Leibler divergence. The Frank-Copula function is used to couple the uncertain output between diverse generation resources. The energy storage planning problem is formulated as a Bayesian distributionally robust optimization model. The related algorithms are designed to solve the model. The effectiveness of the proposed uncertainties coupling and capacity sizing method is validated through a case study in the IEEE 118-bus system.