Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach

Abstract

Electric vehicle (EV) drivers aim to charge their vehicles cost-effectively and with minimal charging time. Meanwhile, the ever-increasing number of EVs without charging control strategies could result in a massive surge in peak demand, potentially overloading distribution equipment and violating voltage constraints. To tackle this challenge, this paper introduces a transactive energy market (TEM) framework for an EV parking lot (EVPL) equipped with photovoltaic (PV) panels and battery systems (BSs), considering the preferences of both EV drivers and the EVPL operator. In this framework, EVs parked in the EVPL participate in the TEM by submitting their charging flexibility through response curves, which indicate the compensation required for different values of flexibility. Furthermore, the proposed model allows the EVPL operator to utilize the flexibility of EVs in the vehicle-to-grid (V2G) program by incentivizing EV drivers, considering their preferences and the degradation cost of EV batteries. The study employs the stochastic programming method to model uncertainties in PV output, electricity prices, and EV availability. It also incorporates BS degradation costs and carbon emissions constraints into the EVPL scheduling problem. Linearization techniques are then applied to transform the non-linear optimization problem into a mixed-integer linear programming (MILP) model. Finally, applying the model to a case study validates its superiority in satisfying the preferences of both EV drivers and EVPL.

Keywords

• Transactive energy market
• electric vehicle parking lot
• vehicle-to-grid (V2G) operation
• uncertainty modeling
• mixed-integer linear programming
• carbon emissions constraint