Preventive Energy Management Strategy Before Extreme Weather Events by Modeling EVs' Opt-In Preferences

Abstract

In recent literature, the value of electric vehicles (EVs) for the resilience enhancement of urban microgrids has been shown. Furthermore, on a larger scale, there has been a growing recognition of the potential of EV cooperation in enhancing the overall resilience of smart cities. To this end, the city can be partitioned into a set of blocks, each encompassing buildings. Within each block, EV traveling time can be ignored. As a step forward, this study presents a Preventive Energy Management (PEM) strategy along with a rescheduling procedure by cooperation of EVs, local distributed energy resources (DERs), and buildings in different city blocks. Based on the available information related to the amount of curtailed loads, two cases are modeled and studied. In the proposed PEM strategy, EV owners' opt-in preferences such as arrival and departure times, and the city block in which they are willing to give energy services are modeled. As a more realistic consideration, the proposed model does not consider the buildings' load as a lumped load, instead the PEM strategy is designed to consider each of the buildings separately. The resulting optimization model is flexible enough to enable EVs to switch from one building to another to provide energy in different time slots. By applying disjunctive-constraint-based transformation, the model is recast as a Mixed Integer Linear Programming (MILP) that could be efficiently solved by commercial optimization solvers. The proposed approach is applied to a benchmark and the results are analyzed. According to the results, using EVs in the PEM strategy has been proven to be effective and the importance of the length of the period of service and opt-in preferences for optimal scheduling are highlighted.