WIND AND SOLAR RENEWABLE ENERGY SYSTEM ESTIMATION WITH BATTERIES USING THE MONTE CARLO SAMPLING APPROACH

The factors involved in the declining costs of electricity produced from renewable energy are government regulations, policy obligations, and incentives and the goal to reduce the carbon footprint in the energy sector. Therefore, installing renewables can be considered an advantage in the energy industry, but determining the proper sizes for large-scale renewable energy can be challenging due to their inherent intermittency and generation uncertainty in the face of climate change. These concerns can be mitigated by installing energy storage systems (ESSs) of a suitable size. Therefore, to avoid oversizing of renewable plus ESSs an optimization program is needed. To make the optimization program estimations reliable the impact of the uncertainties associated with wind speed and solar irradiance must assessed, studied, and integrated into the algorithm. These uncertainties influence energy generation and the system size estimation. Here we present the Monte Carlo method to quantify the effect of uncertainty on system sizing. The proposed algorithm is shown through a case study of a utility near Oklahoma City, which has significant renewable energy potential. Using the probability distributions from the 15-year data from atmospheric radiometric measurements (ARM) of solar and wind in 15-minute time intervals 1000 possible scenarios of year to year are generated by the Monte Carlo sampling method. These scenarios are used in the optimization program for least-cost system sizing for 10 years. This proposed optimization framework is applicable to estimate the system sizes for residential and utilities that are interested in minimizing the electricity cost with renewable energy and BESS.