Joint optimization of day-ahead of a microgrid including demand response and electric vehicles

In this work, we discuss how to schedule responsive loads and electric vehicles at the same time in a microgrid that utilizes wind and PV electricity to save running costs and pollutants. The proposed methodology utilizes EVs for reducing high levels of electricity consumption during peak periods and modifying demand curves, and responsive loads for supplying reserves required to offset the inherent uncertainties of PV and wind generation. In addition, a two-step approach is provided for estimating the anticipated operating cost of the microgrid, which includes both energy and reserve. Minimizing generating and reserve power costs is the first step. The second step is to minimize costs connected with unit scheduling modifications caused by variations in wind and PV production. As a powerful and efficient optimization strategy, improved sunflower optimization (ISFO) algorithm is subsequently employed to solve the corresponding objective optimization problem. Results obtained for an MG on an hourly basis throughout the day show that the ISFO algorithm outperforms other conventional methods. It should be noted that three scenarios have been established to examine how the MG's day-ahead performance is affected by the combined scheduling of EVs and controlled loads. Scenarios 3 remarkably lower than the values presented in Scenarios 1 and 2, the three cost terms the generating cost, the reserve cost, and the starting cost of units are determined as $742.87, $10.16, and $6.01, correspondingly, in this scenario.