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.
System Architecture
The core of SolarSys is a two-level decision hierarchy:
- [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].
- [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].
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.
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)