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license: mit |
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tags: |
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- reinforcement-learning |
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- multi-agent |
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- time-series |
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- diffusion-model |
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- energy-management |
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- smart-grid |
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--- |
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# ☀️ SolarSys: Scalable Hierarchical Coordination for Distributed Solar Energy |
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[Source: The SolarSys paper (e.g., your PDF) is the primary source for all claims below.] |
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[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. |
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--- |
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## 🚀 Key Features and Performance |
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[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]. |
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| Metric | SolarSys Performance (1000 Agents) | Key Mechanism | |
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| :--- | :--- | :--- | |
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| **Grid Import Reduction** | [cite_start]$27.48 \pm 0.42\%$ [cite: 18] | [cite_start]Two-tier control scheme [cite: 12, 69] | |
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| **Daytime Solar Utilization** | [cite_start]$82.76 \pm 5.11\%$ [cite: 18] | [cite_start]Intra-cluster MAPPO optimization [cite: 13] | |
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| **Fairness (Jain's Index)** | [cite_start]0.773 [cite: 18] | [cite_start]Fairness term in reward function [cite: 391, 511] | |
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| **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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--- |
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## 🧠 System Architecture |
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The core of SolarSys is a two-level decision hierarchy: |
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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]. |
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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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--- |
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## 📊 Data Generation Framework |
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[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]. |
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* [cite_start]**Model:** Hierarchical Diffusion U-Net [cite: 254, 255] |
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* [cite_start]**Input:** Household ID and Day-of-Year conditioning [cite: 256] |
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* **Output:** High-resolution time series for Grid Usage and Solar Generation (kWh). |
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--- |
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## 📁 Repository Structure |
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The project is organized into core modules and data folders. |
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```tree |
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SolarSys/ |
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├── data/ |
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│ ├── per_house/ # Raw CSVs for diffusion model training |
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│ ├── training/ # Cleaned RL training datasets |
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│ └── testing/ # Cleaned RL evaluation datasets |
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├── models/ |
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│ ├── diffusion_models/ # Trained Hierarchical Diffusion Model checkpoints |
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│ ├── mappo_models/ # Trained MAPPO baselines and low-level agents |
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│ └── inter_agent_models/ # Trained MeanField high-level coordinator |
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├── Environment/ |
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│ ├── __init__.py |
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│ └── solar_sys_environment.py # Custom Gym environment for flat RL |
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├── cluster/ |
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│ ├── __init__.py |
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│ └── inter_cluster_coordinator.py # Logic for high-level trade matching |
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└── trainers/ |
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├── __init__.py |
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├── hierarchical_train.py # Main SolarSys HRL training script |
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└── evaluation_scripts/ # Scripts for baselines (PG, MADDPG, MAPPO, MFAC) |
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