Assessment of Time-Based Demand Response Programs for Electric Vehicle Charging Facilities

In this paper, we apply stochastic optimization techniques to manage the charging and discharging processes of Electric Vehicles (EVs) within parking lots, utilizing various Demand Response Programs (DRPs) like Time-of-Use (TOU), Critical Peak Pricing (CPP), and Real-Time Pricing (RTP). The optimization model aims to balance the interests of both the parking lot owner and EV owners, achieved through a weighted objective function. The primary goal for the parking lot operator is to lower costs related to charge EVs during DRP participation, managed via controlling vehicle charge and discharge cycles. Meanwhile, EV owners seek to mitigate battery degradation and extend battery life by avoiding excessive charging and discharging cycles. To quantify battery degradation, we utilize the Rainflow Counting Algorithm (RCA), assessing the number of charge/discharge cycles and depth of discharge (DoD). The model, based on Mixed-Integer Nonlinear Programming (MINLP), is solved using GAMS software with the BONMIN solver, integrated with MATLAB for executing RCA. Additionally, we employ probability distribution functions (PDFs) that closely match real-world data for modeling the stochastic nature of EV parameters, such as arrival/departure times and initial State of Charge (SOC). Compatibility of these models is validated using statistical tools available in MATLAB's Statistics Toolbox. A simulation of a standard parking lot accommodating 30 vehicles is conducted to test the model, along with a sensitivity analysis of the weighting coefficient (3 in the objective function, which influences the prioritization between the parking lot owner's and EV owners' interests. Results show that at lower (3 values, benefits accrue more to the parking lot owner, favoring RTP programs. Conversely, higher (3 values prioritize EV owners' objectives, resulting in stable energy consumption patterns without grid injections. A comparative analysis of the three DRPs is also provided, offering insights into their effectiveness and implications for both parties involved.