A three-layer learning architecture coordinates EV-microgrid power sharing, with simulations showing better renewable use, lower peak demand, and higher economic returns

A three-layer learning architecture (prediction, coordination, real-time control) is proposed for optimizing power distribution between electric vehicles and microgrids. Relative to traditional operational strategies, the architecture integrates forecasting, constrained optimization, and real-time adaptive control into a layered structure rather than a single scheduling policy. Described at the abstract level; no algorithmic details or parameter settings for each layer are provided.

The coordination layer solves a constrained optimization problem to allocate energy resources at multiple charging nodes, while the real-time control layer compensates for forecast inaccuracies via adaptive control and reinforcement learning. Handling forecast error is pushed down to the real-time control layer, leaving the coordination layer focused on resource allocation, forming a clear two-level division of labor. Described at the abstract level; no formal statement of the optimization problem or specific reinforcement learning setup is given.

Simulation studies based on representative microgrid use cases show that, relative to traditional operational strategies, the architecture improves renewable energy use, reduces peak demand, and increases economic returns. Improvements over traditional strategies are reported in simulation-comparison form, alongside four key performance indicators: total energy cost, peak shaving, state/charge deviation, and latency. Simulation study; the abstract reports no specific numerical values, number of use cases, or statistical significance.

The architecture is described as scalable, privacy-preserving, and able to accommodate heterogeneous EV fleets while supporting both grid services and users' mobility needs. Beyond power allocation, it considers both privacy and heterogeneous fleet requirements, broadening the architecture's intended applicability. Property statements at the abstract level; no details on privacy mechanisms or heterogeneous-fleet experiments are provided.

The architecture targets power-sharing scenarios between electric vehicles and microgrids, suited to operating environments with multiple charging nodes that must serve both grid services and users' mobility needs; its design goals include scalability, privacy preservation, and heterogeneous fleet support. The abstract mentions sensitivity analyses on forecasting error, fleet size, and communication overhead, indicating the work aims to inform deployments across fleet sizes and under forecast uncertainty. For readers, this layered approach can serve as a starting point for designing charging-scheduling and vehicle-to-grid systems, especially where forecasting, resource allocation, and real-time correction need to be handled separately.

The visible text is only the abstract, without figures, formulas, or experimental details, so it is not possible to judge the representativeness of the simulation use cases, the specific magnitude of improvements, or the direction and extent of each sensitivity factor (forecasting error, fleet size, communication overhead). The trade-offs among the four key performance indicators (total energy cost, peak shaving, state/charge deviation, latency) are not developed in the abstract. The specific privacy-preserving mechanism, how heterogeneous fleet support is implemented, and how the architecture performs under real communication and computation constraints are open questions that require the full text to confirm.