A hybrid transformer-PPO framework for multi-objective energy management in renewable-based microgrids.

This project gives a step‑by‑step framework for managing energy in renewable microgrids using a mix of Transformer models and Proximal Policy Optimization (PPO). Microgrids that rely on variable sources like solar and wind need strong control methods to keep generation, storage, and demand in balance while keeping costs and emissions low. The framework uses the Transformer’s ability to model sequences to predict future load and renewable output. Those predictions are then used by a deep reinforcement‑learning agent, trained with PPO, to decide how to dispatch power in real time. The optimization looks at three goals: preserving the battery’s state‑of‑charge, reducing costs of grid interaction, and minimizing load shedding. The guide

shows how to build a Python‑based microgrid simulator with tools such as PyTorch and OpenAI Gym. It also helps you model system dynamics, create reward functions, and compare the hybrid controller with traditional rule‑based methods and standard reinforcement‑learning approaches. This work helps undergraduate engineers see how modern machine‑learning techniques can be applied to power distribution systems.

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