eco-agentic-micro-grid

⚡ Eco-Agentic Micro-Grid Negotiator 📌 Project Overview The Eco-Agentic Micro-Grid Negotiator is a high-fidelity Reinforcement Learning environment designed to simulate a Decentralized Neighborhood Energy Market. The environment challenges an AI agent to act as a "Smart Home Controller" that must balance energy production, consumption, and economic trade-offs in real-time.

🎯 Problem Statement In a decentralized grid, energy availability is volatile (solar-dependent) and demand is fluctuating. The agent must learn an optimal policy to:

Maximize Financial Gain: Sell excess energy when market prices are high. Ensure Grid Stability: Use battery storage to prevent "blackouts" during low-production periods. Promote Sustainability: Minimize reliance on the primary carbon-heavy grid by optimizing local solar usage. ⚙️ Technical Implementation Framework: Built using the OpenEnv specification and Gymnasium API. Observation Space: A 5-dimensional continuous vector consisting of: Current Hour (Temporal state) Solar Production (Environmental variable) House Demand (Stochastic load) Battery State of Charge (Internal state) Market Price (Economic variable) Action Space: A discrete space of 4 agentic decisions: Charge, Discharge, Sell, and Buy. Reward Shaping: A multi-objective reward function that balances profit, energy security, and green energy utilization. 🚀 Complexity Metrics Temporal Dynamics: The environment uses a sinusoidal function to simulate solar cycles, forcing the AI to learn "diurnal patterns." Economic Volatility: Market prices fluctuate based on a time-of-day curve, requiring the agent to perform "Arbitrage" (buying low, selling high). Constraint Satisfaction: The agent must manage a hard constraint (Battery Capacity) while avoiding a critical failure state (Blackout).