Integrated scheduling of renewable generation and demand response programs in a microgrid

Wind and solar energy introduced significant operational challenges in a Microgrid (MG), especially when renewable generations vary from forecasts. In this paper, forecast errors of wind speed and solar irradiance are modeled by related probability distribution functions and then, by using the Latin hypercube sampling (LHS), the plausible scenarios of renewable generation for day-head energy and reserve scheduling are generated. A two-stage stochastic objective function aiming at minimizing the expected operational cost is implemented. In the proposed method, the reserve requirement for compensating renewable forecast errors is provided by both responsive loads and distributed generation units. All types of customers such as residential, commercial and industrial ones can participate in demand response programs which are considered in either energy or reserve scheduling. In order to validate the proposed methodology, the proposed approach is finally applied to a typical MG and simulation results are carried out.