Intelligent parking lot power management: Augmented epsilon-constraint concept with correlation analysis

This paper addresses essential aspects of decision-making and management in energy resources. To achieve this, a tri-objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi-objective optimization problem as a scenario-based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load-shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug-in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83-bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi-objective problem defined for each scenario is solved by the augmented epsilon-constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision-making method.