Data-Clustering Based Method for Developing Battery Behaviour CDFs for BESS Grid Integration

The planning and operation of power systems necessitates accurate modeling of generation, demand and energy storage. This includes taking into account the stochastic nature of renewable resources, electricity prices and load profiles. This study presents an innovative approach to develop cumulative distribution functions (CDFs) for Li-ion battery dispatch during its charging and discharging states. The CDFs are obtained by executing an hourly dispatch model over a one-year time-frame utilizing actual zonal electricity price data of New York City (NYC) to determine the optimal hour-by-hour dispatches of the battery. This dispatch data are clustered by four distinct seasons and two different day-types (weekdays, weekends) to replicate the energy price profile. The battery charging, discharging and inactive states are derived from the clustered profiles, along with their respective time-frames. Thus, the CDFs are formulated for each season, day type and battery condition (charging or discharging) according to a probabilistic analysis conducted on an hourly basis.