PV/PV-Battery hosting capacity estimation method based on hidden Markov model for effective stochastic computation

Monte Carlo is commonly applied to model uncertainties in the penetration of photovoltaic (PV) systems as random processes. However, Monte Carlo simulations require a large number of stochastic calculations to obtain the desired accuracy. This paper develops a hosting capacity estimation model using hidden Markov to provide an effective stochastic calculation of PV/PV-battery penetration. To improve the representative in modeling actual penetration scenarios, the proposed model considers the probabilities among candidates on the basis of the customer types, the customer with PV-only or the customer with PV-battery, and the size of the PV/PV-battery. To be used in the simulations, a technique is proposed to calculate the min–max load demand and PV generation curves. To assess the computational load of the proposed model, this work provides an accuracy evaluation with respect to the number of stochastic simulations. The findings indicate that the proposed solution can achieve a cost-effective calculation of the hosting capacity. In practice, this work can provide the distribution planner with useful direction to help make informed decisions about the distribution network reinforcement strategy to deal with high PV/PV-battery penetration.