---
license: mit
tags:
- reinforcement-learning
- multi-agent
- time-series
- diffusion-model
- energy-management
- smart-grid
---
# ☀️ SolarSys: Scalable Hierarchical Coordination for Distributed Solar Energy

[Source: The SolarSys paper (e.g., your PDF) is the primary source for all claims below.]

[cite_start]SolarSys is a novel **Hierarchical Multi-Agent Reinforcement Learning (HRL)** system designed to manage energy storage and peer-to-peer (P2P) trading across large communities of solar-equipped residences[cite: 10]. This repository contains the full source code for the SolarSys system, including the trained policies, the custom Gym environment, and the hierarchical diffusion model used for data augmentation.

---

## 🚀 Key Features and Performance

[cite_start]SolarSys addresses the scalability limitations of traditional Multi-Agent RL (MARL) methods (like MAPPO and MADDPG) in large Virtual Power Plants (VPPs)[cite: 9, 145].

| Metric | SolarSys Performance (1000 Agents) | Key Mechanism |
| :--- | :--- | :--- |
| **Grid Import Reduction** | [cite_start]$27.48 \pm 0.42\%$ [cite: 18] | [cite_start]Two-tier control scheme [cite: 12, 69] |
| **Daytime Solar Utilization** | [cite_start]$82.76 \pm 5.11\%$ [cite: 18] | [cite_start]Intra-cluster MAPPO optimization [cite: 13] |
| **Fairness (Jain's Index)** | [cite_start]0.773 [cite: 18] | [cite_start]Fairness term in reward function [cite: 391, 511] |
| **Scalability** | [cite_start]Stable convergence at 1000+ agents [cite: 504] | [cite_start]Mean-Field Coordination at the Inter-Cluster layer [cite: 14] |

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## 🧠 System Architecture

The core of SolarSys is a two-level decision hierarchy:

1.  [cite_start]**Low-Level (Intra-Cluster):** Individual households use a **MAPPO** agent to make instantaneous decisions (charge, discharge, local P2P trade, grid trade) based on local meter readings and price signals[cite: 13, 313].
2.  [cite_start]**High-Level (Inter-Cluster):** Cluster Managers use a **Mean-Field** policy to coordinate bulk energy transfers between clusters, ensuring the overall system remains balanced against grid constraints[cite: 14, 314].



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## 📊 Data Generation Framework

[cite_start]To enable large-scale simulation with realistic temporal dynamics, SolarSys includes a **Hierarchical Diffusion Model** for generating synthetic, long-duration energy profiles that maintain both long-term (seasonal/monthly) and short-term (daily/hourly) characteristics[cite: 254, 255].

* [cite_start]**Model:** Hierarchical Diffusion U-Net [cite: 254, 255]
* [cite_start]**Input:** Household ID and Day-of-Year conditioning [cite: 256]
* **Output:** High-resolution time series for Grid Usage and Solar Generation (kWh).



---

## 📁 Repository Structure

The project is organized into core modules and data folders.

```tree
SolarSys/
├── data/
│   ├── per_house/              # Raw CSVs for diffusion model training
│   ├── training/               # Cleaned RL training datasets
│   └── testing/                # Cleaned RL evaluation datasets
├── models/
│   ├── diffusion_models/       # Trained Hierarchical Diffusion Model checkpoints
│   ├── mappo_models/           # Trained MAPPO baselines and low-level agents
│   └── inter_agent_models/     # Trained MeanField high-level coordinator
├── Environment/
│   ├── __init__.py
│   └── solar_sys_environment.py # Custom Gym environment for flat RL
├── cluster/
│   ├── __init__.py
│   └── inter_cluster_coordinator.py # Logic for high-level trade matching
└── trainers/
    ├── __init__.py
    ├── hierarchical_train.py   # Main SolarSys HRL training script
    └── evaluation_scripts/     # Scripts for baselines (PG, MADDPG, MAPPO, MFAC)