---
title: Smart Grid Environment Server
emoji: 🎯
colorFrom: yellow
colorTo: blue
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
---

# ⚡ Smart Grid OpenEnv Environment

A realistic reinforcement learning environment that simulates **energy
distribution in a smart electrical grid** powered by renewable sources.

Built for the Meta x PyTorch OpenEnv Hackathon, this environment enables
AI agents to learn how to balance demand, renewable generation, and
battery storage.

------------------------------------------------------------------------

## 🌍 Problem Overview

Modern power grids must handle:

-   Fluctuating energy demand
-   Intermittent solar and wind generation
-   Limited battery storage

The goal is to optimally distribute energy across regions while
minimizing:
- Unmet demand
- Energy waste
- Battery misuse

------------------------------------------------------------------------

## 🧠 Environment Design

### ⏱ Time-based Simulation

-   Each episode = 24 timesteps (hours)
-   Demand and generation vary dynamically over time

------------------------------------------------------------------------

## 📦 Observation Space

```
  Feature            Description
  ------------------ --------------------------
  hour               Current timestep (0--23)
  demand_r1          Demand in Region 1
  demand_r2          Demand in Region 2
  demand_r3          Demand in Region 3
  solar_generation   Solar power available
  wind_generation    Wind power available
  battery_level      Current battery storage
  battery_capacity   Maximum battery capacity
```

------------------------------------------------------------------------

## 🎯 Action Space

The agent must output:

supply_r1, supply_r2, supply_r3, charge_battery

------------------------------------------------------------------------

## 🧩 Tasks

-   Easy: balanced_grid_easy
-   Medium: solar_management, wind_uncertainty
-   Hard: peak_demand, full_grid_challenge

------------------------------------------------------------------------

## 🚀 Running Locally

pip install -r requirements.txt
uvicorn server.app:app --reload

------------------------------------------------------------------------

## 🐳 Docker

docker build -t smart_grid_env .\
docker run -p 8000:8000 smart_grid_env

------------------------------------------------------------------------

## 🤖 Inference

python inference.py

------------------------------------------------------------------------

## 📁 Project Structure

```
smart_grid/
├── client.py
├── models.py
├── openenv.yaml
├── inference.py
├── server/
├── tasks/
```

------------------------------------------------------------------------

## 🏁 Goal

Train an AI agent that balances supply, demand, and storage efficiently.