Improved two-stage energy community optimization model considering stochastic behaviour of input data

Integrating renewable energy sources (RES) and the transition of the transport sector to electric vehicles (EV) is a necessary step in reducing global greenhouse gas emissions (GHG). The integration of RES and EV charging stations in the power grid and the optimal scheduling of device operation are problems addressed by the research community. This paper proposes the concept of an energy community (EC) with three business buildings and a local electricity market (LEM) to ensure optimal grid operation and the lowest individual prosumer costs. Furthermore, a charging model for EVs in the building parking lot was introduced to increase overall social welfare. The proposed optimization model is developed as a stochastic two-stage mixed integer nonlinear programming (MINLP) that considers the realization of uncertain input data in the second stage, the virtual cost of battery degradation, and the functional dependencies of variables. Several case studies were conducted to evaluate the proposed model's advantages correctly. The results show a possible reduction in the total daily cost, a reduction of the total daily imported electricity from the grid and a reduction of the total daily exported electricity to the grid up to 100%, depending on the case.