Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles

Governmental incentives to use clean energy, concerns about high and rising prices of fossil fuels, and environmental issues are the most important motivations for adding distributed energy resources to conventional power systems. In these circumstances, new technologies such as combined cooling, heating, and power systems, energy storage systems including battery and thermal storages, and plug-in hybrid electric vehicles not only can improve system efficiency but also can reduce operation and investment cost as well as emissions. In this paper, a residential microgrid consisting of combined cooling, heating and power, plug-in hybrid electric vehicles, photovoltaic unit, and battery energy storage systems is modeled to obtain the optimal scheduling state of these units by taking into account the uncertainty of distributed energy resources. To achieve this goal, a scenario-based method is used to model uncertainties of electrical market price, electrical and thermal demand, and solar irradiance using Normal, Weibull, and Beta probability distribution functions, respectively. The scenario tree is used to generate scenarios and representative scenarios are selected with scenario reduction techniques. The proposed problem is formulated as a mixed-integer nonlinear programming to minimize objectives of operation cost and total emissions. This multi-objective problem is solved by the Augmented epsilon-constraint method and the best solution on the Pareto front set is determined by a fuzzy approach. In order to maintain system reliability during on-peak periods, the time-of-use demand response program is considered to promote a change in energy consumption patterns. Finally, plug-in hybrid electric vehicles are considered as electrical energy storage instead of batteries in optimal energy and emissions management. Proposed energy management is applied on a residential microgrid and the effectiveness of the model is verified. It is found that uncertainties increase the cost by 12.2%; however, energy storage systems reduce cost and emissions by 4.7% and 23%, respectively. (C) 2018 Elsevier Ltd. All rights reserved.