Decentralized, Efficient, and Fair: Mean-Field Predictive Control for Bidirectional EV Coordination Under Uncertainty

A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation

This study introduces a unified control mechanism for integrated charging and service allocation of Mobility-on-Demand Electric Vehicles (MoD-EVs) operating under a flexible A-to-B rental model. The charging operation of MoD-EVs is influenced by stochastic customer rental requests and time-varying electricity prices. The framework captures the nonlinear battery dynamics and optimizes the charging operations against dynamic electricity prices and to meet stochastic customer demand while prioritizing battery health and cost efficiency. To address the computational challenges posed by the nonlinear battery dynamics, the framework employs a piece-wise linear model integrated into a multi-objective, chance-constrained mixed-integer linear programming (MILP) Model Predictive Control (MPC) formulation. A mixed logical switching mechanism is utilized to determine optimal charging sequences. Furthermore, a distributed approach is implemented to ensure computational scalability compared to centralized alternatives. Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation compared to the prevailing business-as-usual (BAU) approach and state of the art Laxity-based charging (LC) approach.

A Bilevel Reinforcement Learning Framework for Coordinated EV Charging and Dynamic Pricing

This paper presents a bilevel Reinforcement Learning (RL) framework for optimizing Electric Vehicle (EV) charging through price-mediated coordination between grid operators and charging stations. Unlike prior work relying on direct control or manual subgoal engineering, the proposed approach uses dynamic pricing as an implicit coordination signal to address a complex multi-objective optimization problem involving grid stability, user satisfaction, and economic efficiency. To manage this complexity, the problem is decomposed into two levels comprised of an upper-level Distribution System Operator (DSO) that determines dynamic pricing strategies, and multiple lower-level Load Aggregators (LAs) responsible for EV charging decisions at individual stations in response to these prices. This bilevel structure captures the leader–follower interaction between DSOs and LAs, with each level operating at different temporal scales. Deep Deterministic Policy Gradient (DDPG) agents are deployed at both levels, enabling adaptive decision-making under operational constraints. Extensive simulations compare the framework against multiple Rule-Based Control (RBC) baselines. Results demonstrate that the DDPG-based DSO achieves a 42.4% higher mean reward and 19.1% higher profit compared to the best-performing RBC baseline, while preserving grid stability and user satisfaction. These results validate the effectiveness of bilevel RL for complex energy optimization problems, highlighting its potential as a scalable control paradigm for smart management systems.

Multi-Agent Reinforcement Learning Based Latency-Aware Hierarchical Control Enabling Electric-Vehicle Based Virtual Power Plants to Participate in Balancing Markets

Balancing markets require flexible resources that can promptly follow dispatch signals. Aggregated fleets of electric vehicles (EVs) operated as electric-vehicle virtual power plants (EV VPPs) are promising candidates. Aggregators must control the total power of EV chargers to track dispatch signals while satisfying individual EV users' charging demands. Conventional centralized optimization methods can achieve high tracking performance. However, they rely on global information and require solving large-scale optimization problems, which impose high computational and communication burdens and limit scalability. To address this issue, this paper proposes a two-level hierarchical control scheme based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. At the upper level, each charging station is modeled as an agent, and at the lower level its policy allocates charging and discharging power to the individual EV chargers. At runtime, each station-level agent uses only local observations and the broadcast dispatch signal. We present a case study on participation in Japan's balancing market Secondary 2 (S2) product. The study evaluates the controller on an EV VPP consisting of five stations with a total of 50 Level 2 chargers. The proposed method achieves a dispatch tracking rate (fraction of dispatch intervals with aggregate power inside the market-defined tracking error band) of 97 percent within the allowable tracking error band around the dispatch signal. It also achieves an 80 percent Target SoC satisfaction rate, where the Target SoC is the user-specified departure-time state of charge (SoC). Overall, this method reduces online computation time and communication latency while maintaining high tracking performance and userdemand satisfaction. These results suggest that MADDPG-based hierarchical control provides a practical control scheme for large EV fleets when latency constraints hinder centralized control.

Coordinated Optimization of Orderly Charging and Grid Interaction at Electric Vehicle Charging Stations Based on Multi-Agent Reinforcement Learning

A collaborative optimization framework based on multi-agent reinforcement learning is proposed for orderly charging at electric vehicle charging stations and coordinated interaction with the power grid, providing a technical reference for intelligent charging coordination under grid interaction and electromagnetic compatibility constraints.

Optimal Scheduling of Virtual Power Plants with Electric Vehicles: A Dual-Incentive Framework

Integrated Planning and Scheduling of Charging Infrastructure for Battery Electric Buses Under Effective Capacity Uncertainty

The electrification of urban transport has made battery electric buses (BEBs) an important option for reducing carbon emissions and improving urban air quality. However, the high investment cost of charging infrastructure and the uncertainty in effective usable battery capacity at the day-ahead scheduling stage—caused by accumulated degradation, heterogeneous operating conditions, and imperfect state estimation—create major challenges for charging infrastructure siting and daily bus operations. This study proposes a joint optimization model for infrastructure siting and BEB charging scheduling, in which effective capacity uncertainty is handled using a distributionally robust optimization (DRO) framework. To solve the resulting mixed-integer nonlinear program efficiently, we develop a matheuristic decomposition method that integrates Adaptive Large Neighborhood Search (ALNS) with small gaps relative to a relaxation-based lower bound. Computational experiments based on real-world bus route data indicate that the proposed framework obtains high-quality solutions with small gaps relative to a relaxation-based lower bound, performs better than representative benchmark heuristics, and scales well to large instances.