Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price-and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single-and multi objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.