A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control

TL;DR

This work tackles real-time demand response for EV charging by applying Model Predictive Control (MPC) to Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) scenarios. It introduces the open-source EV2Gym simulator and four MPC strategies—eMPC G2V, eMPC V2G, OCMF G2V, and OCMF V2G—that optimize charging/discharging schedules under price signals, grid constraints, and demand response events, while accounting for uncertainty via forecast data. A battery degradation model separating calendar and cyclic losses is incorporated to assess longevity, enabling a trade-off analysis between profitability, flexibility, and battery health. The results show that V2G-enabled MPC can yield substantial profits and grid support, with flexibility-focused variants delivering rapid responses to DR events, albeit with higher cyclic degradation; the tool provides a practical, configurable platform for researchers and practitioners to evaluate MPC-based EV charging strategies.

Abstract

Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules to balance supply and demand while minimizing operational costs and maximizing flexibility. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. Our research focuses on harnessing the potential of MPC in G2V and V2G applications, by providing a simulation tool that allows to maximize EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. In this paper, we propose an open-source MPC controller for G2V and V2G-enabled demand response management. The proposed approach is capable of tackling the uncertainties inherent in demand response operations. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, our controller enables Charge Point Operators (CPOs) to optimize EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences.

Figures (6)

• Figure 1: Overview of EV2Gym's modules power and information flow.
• Figure 2: Flexibility in an EV charger, considering G2V and V2G strategies.
• Figure 3: Comparison of proposed MPC approaches for a case study with $30$ EVSEs connected to a single transformer, and $75$ EVs.
• Figure 4: Average capacity loss per EV because of battery degradation.
• Figure 5: Comparison of EV-user charging costs across different discharge price multipliers ($m$). Note that these figures solely reflect EV-user payments. However, it's important to acknowledge that potential profits from flexibility provision are not included here, which could significantly boost overall profits.