We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user preference. Given the lack of accurate and generalizable battery degradation models, we leverage input convex neural networks (ICNNs) to develop a data-driven degradation model trained on extensive experimental datasets. This approach enables our model to capture nonconvex dependencies on battery temperature and time while maintaining convexity with respect to the charging rate. Such a partial convexity property ensures that the second stage of our methodology remains computationally efficient. In the second stage, we integrate our data-driven degradation model into a multi-objective optimization framework to generate an optimal smart charging profile for each EV. This profile effectively balances the trade-off between financial benefits from V2G participation and battery degradation, controlled by a hyperparameter reflecting the user prioritization of battery health. Numerical simulations show the high accuracy of the ICNN model in predicting battery degradation for unseen data. Finally, we present a trade-off curve illustrating financial benefits from V2G versus losses from battery health degradation based on user preferences and showcase smart charging strategies under realistic scenarios.

In this article, a hybrid model predictive control (MPC) based novel energy management framework for a dc microgrid is proposed to efficiently manage power sharing among photovoltaic (PV) source, battery, fuel cell, and supercapacitor while meeting critical load demand and satisfying operational constraints. In particular, the proposed framework mitigates certain practical operational challenges of the fuel cell and the electrolyzer, as laid down by the manufacturers. Instead of using multiple converters, a multiport converter topology is utilized for integrating the distributed energy resources (DERs) due to fewer conversion stages, compact size, cost-effectiveness, and ease of control. For smooth operation of the multiport converter, a hierarchical control unit is developed to coordinate with the hybrid MPC based supervisory controller and proportional - integral (PI) compensator based local controllers. Finally, a 2 kW laboratory prototype of the five-port converter is integrated with real DERs. The efficacy of the proposed energy management framework is demonstrated through experimental case studies which are designed to create challenging scenarios, such as large power mismatch due to stochastic PV generation and load.

We present a novel user-centric vehicle-to-grid (V2G) framework that enables electric vehicle (EV) users to balance the trade-off between financial benefits from V2G and battery health degradation based on individual preference signals. Specifically, we introduce a game-theoretic model that treats the conflicting objectives of maximizing revenue from V2G participation and minimizing battery health degradation as two self-interested players. Via an enhanced semi-empirical battery health degradation model, we propose a finite-horizon smart charging strategy based on a horizon-splitting approach. Our method determines an appropriate allocation of time slots to each player according to the user's preferences, allowing for a flexible, personalized trade-off between V2G revenue and battery longevity. We conduct a comparative study between our approach and a multi-objective optimization formulation by evaluating the robustness of the charging schedules under parameter uncertainty and providing empirical estimates of regret and sensitivity. We validate our approach using realistic datasets through extensive trade-off studies that explore the impact of factors such as ambient temperature, charger type, and battery capacity, offering key insights to guide EV users in making informed decisions about V2G participation.

Modern active distribution networks possess significant potential to deliver ancillary services to transmission networks owing to the rising integration of renewable energy sources. This study explores the provision of primary frequency support from active distribution network to transmission network via price-based transactive control of microgrid inverters. The proposed method aims to achieve efficient coordination among microgrids, optimizing their utility, while also assisting the active distribution network in optimal primary frequency support provision to transmission network. The feasibility of this approach for real-time application is validated using a real-time simulator.

With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates multiple microgrids in an active distribution network for provisioning passive voltage support-based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed-integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensures passive voltage support. The proposed alternating direction method of multipliers-based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine the scalability and convergence behavior of the distributed algorithm for different choice of hyperparameters and network sizes.