Intelligent adaptive energy management framework for hybrid solar-grid EV fast-charging stations with energy storage and time-based demand response optimization

This research looks at the grid‑integration problems caused by electric‑vehicle (EV) fast‑charging stations. When they draw all their power from the conventional grid, they create peak‑demand spikes that stress the system and can make the grid unstable. To reduce these issues, the paper proposes an **Intelligent Adaptive Energy Management Framework (IAEMF)** for a **1 MW hybrid solar‑grid EV DC fast‑charging station** that includes a **600 kWh energy storage system (ESS)**. The core of the framework is a **time‑adaptive multi‑mode control algorithm** that continuously decides how to split power among photovoltaic (PV) generation, the ESS, and the utility grid. The decisions use: * real‑time solar irradiance, * time‑of‑use (ToU) electricity pricing,

and * dynamic demand‑response (DR) requirements. Power can flow both ways on a shared DC bus, operating in five distinct modes: – PV‑to‑ESS – PV‑to‑EV – Grid‑to‑EV – ESS‑to‑EV – PV‑to‑Grid A **variable‑step Perturb‑and‑Observe (P&O) MPPT algorithm** maximizes solar energy capture under changing weather conditions. For B.Tech students, the paper offers a clear foundation for: * simulating multi‑source DC microgrids, * modeling bidirectional power converters, and * implementing state‑machine‑based energy‑management strategies. Because a full‑scale 1 MW hardware test isn’t feasible in the lab, the work relies mainly on MATLAB/Simulink simulations for student projects.

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