Optimal day ahead power scheduling of microgrids considering demand and generation uncertainties

The inherent intermittency and variability of load demand and renewable energy resources present an ongoing problem for power scheduling of microgrids. To address the operational challenges associated with uncertainty of load and energy resources, this paper proposed a stochastic based optimal day ahead power scheduling of microgrids considering economic and reliability aspects. The proposed problem formulation minimizes the expected operational cost, the pollutants emission cost, cost of power losses as well as energy not supplied (ENS) of the microgrids while accommodating the intermittent nature of load consumption and renewable power generation. The system uncertainties are considered using a set of generated scenarios and also to improve the tradeoff among the computational burden and solution accuracy, the backward technique in order to reduce the generated scenarios has been performed. Grey wolf optimization (GWO) algorithm is implemented to minimize the objective function as an optimization algorithm. Study case has been fulfilled on the modified IEEE 33-bus distribution network. The numerical simulations illustrate the proficiency and performance of the proposed stochastic power scheduling model.