Data aggregation impacts on sizing of battery used for peak shaving

Inhalt:
There has been an increasing interest in peak-shaving (PS) for industrial customers seeking to achieve large reductions in network charges. Data aggregated in quarterly hour measurements are used for electricity billing calculation and are typically used in modern algorithms to optimally size Battery Storage Energy System (BESS) for industrial peak shaving. BESSs’ power and capacity sizing miscalculations might occur if load profiles exhibit large power variations over the averaging interval considered. This work investigates the impact that data aggregation has on the sizing of BESSs used for peak-shaving. For this, a one year high resolution commercial power demand profile (HR profile) sampled every 2 seconds is analyzed. By means of a linear model, BESSs’ sizing at different aggregation periods is performed and the errors that could arise from real operation are simulated. The results show that battery sizing miscalculation can lead to an error up to 13 % of a targeted PS power.