Stochastic modelling of community energy storage system based on diffusion approximation

With the high demand for renewable energy sources such as wind turbines, the future distribution systems and/or microgrids will face more challenges in energy management, due to the intermittency of renewable power generation. By buffering such uncertain power supplies, community energy storage (CES) systems can provide dispatchable capacities and are effective tools to harness renewable power in a community. However, the dispatch of a CES system is complicated due to the randomness in its state-of-charge (SoC) and thus, the randomness in dispatchable capacities. In order to address this problem, a stochastic model of a CES system with wind power generation is reported in this paper. The power generation of each wind turbine is modelled using a Markov modulated rate process (MMRP), while the CES system is modelled as a queueing system with heterogeneous sources and constant output. Based on a diffusion approximation of the queue length, a closed-form representation of the cumulative distribution function (CDF) of the SoC of CES system can be derived. The analytical model is validated by a case study based on the wind power generation data obtained from the Changling Wind Farm in Jilin Province of Northeast China.