Comprehensive stochastic optimal scheduling in residential micro energy grid considering pumped-storage unit and demand response

The improvement of energy efficiency, generation cost, emissions, and reliability are some of the most significant problems in the energy industry. Energy storage systems and demand response programs (DRPs) are regarded as essential tools to optimally solve these problems. In this paper, an improved residential micro energy grid (MEG) is proposed to decrease the operation cost and emissions by integrating combined cooling, heating and power (CCHP), photovoltaic (PV) units, wind turbine (WT) units, a pumped-storage unit, heating storage, and cooling storage. Due to the disadvantages of batteries, such as short life, high investment cost, detriment to the environment, and difficult maintenance, investors are sometimes unwilling to choose batteries for electricity storage, in which case pumped-storage units are often selected as an alternative. For better performance on the demand side, priceand incentive-based DRPs are employed to reduce the operation cost and maintain reliability during the high load period. Also, the uncertainties of wind speed, solar radiation, electricity price, and electrical load are taken into account to gain more accurate results by using a scenario-based methodology. Latin hypercube sampling (LHS) is used to generate scenarios, and a k-means algorithm is adopted to reduce the number of scenarios. Multi-objective and single-objective problems are formulated and solved by the multi-objective artificial sheep algorithm (MOASA) and artificial sheep algorithm (ASA), respectively. The max-min fuzzy method is used to select the trade-off solution among the Pareto-front. In addition, both grid-connected and off-grid modes are considered. Three cases are studied to evaluate the proposed model. The results of Case 1 and Case 2 show that the operation cost and emissions are reduced by 23.39% and 1.35%, respectively, with the implementation of the price-based DRP.