A simplified model for estimating the monthly performance of autonomous wind energy systems with battery storage

This paper presents a simplified algorithm to estimate the monthly performance of autonomous small-scale wind energy systems with battery storage. The novel model is drawn based on the simulation results, using eight-year long hour-by-hour measured wind speed data from five different locations throughout the world. An hourly constant load profile is used. The renewable energy simulation program (ARES) of the Cardiff School of Engineering is used. The ARES simulates the battery state of voltage (SoV) and is able to predict the system performance.The monthly performance values obtained from the simulations are plotted against increasing energy to load ratios for varying battery storage capacities to obtain performance curves. The novel method correlates the monthly system performance with the parameters of the Weibull distribution function, thus offering a universal use. The monthly performance curves are mathematically represented using a 2-parameter function. The novel method is validated by comparing the simulated performance values with those estimated from the simplified algorithm. The standard errors calculated in estimation of the system performance using the simplified algorithm are further presented for each battery capacity.