A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems

Abstract

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak electricity cost times and outage protection in BIPV (building integrated photovoltaic) systems. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, the weather conditions, outage events and demand peaks are not deterministic in nature. Therefore, the sizing of battery storage capacity should also be based on a probabilistic approach. The Monte Carlo simulation is a rigorous method to sizing BIPV system as it takes into account a real building load profiles, the weather information and the local historical outage distribution. The simulation is split into seasonal basis for the analysis of demand shifting and outage events in order to match the seasonal weather conditions and load profiles. Five configurations of PV (photovoltaic) are assessed that cover different areas and orientations. The simulation output includes the predicted PV energy yield, the amount of energy required for demand management and outage event. Therefore, consumers can base sizing decisions on the historical data and local risk of outage statistics and the success rate of meeting the demand shift required. Finally, the economic evaluations together with the sensitivity analysis and the assessment of customers’ outage cost are discussed.