PowerGrid Case Study#
Multi-agent microgrid control using HERON with PandaPower integration.
Overview#
The PowerGrid case study demonstrates HERON applied to power systems with:
IEEE 13, 34, 123-bus test networks
Device models (Generator, ESS, Transformer)
Multi-agent environments with pre-configured setups
MAPPO training examples with RLlib
Component |
Description |
|---|---|
Networks |
IEEE 13, 34, 123-bus test systems via PandaPower |
Devices |
Generator, ESS (Energy Storage), Transformer |
Agents |
|
Features |
Electrical (P, Q, V), Storage (SOC), Network metrics |
Setups |
Pre-configured environments with config and time series data |
Installation#
# Install with power grid support
pip install -e ".[powergrid]"
# Or full installation with RL support
pip install -e ".[all]"
Quick Start#
from powergrid.envs.multi_agent_microgrids import MultiAgentMicrogrids
from powergrid.setups.loader import load_setup
# Load environment configuration from a setup
env_config = load_setup("ieee34_ieee13")
env_config.update({
"centralized": True,
"max_episode_steps": 24,
"train": True,
})
env = MultiAgentMicrogrids(env_config)
obs_dict, info = env.reset()
# Each agent acts independently
for _ in range(24):
actions = {agent_id: env.action_spaces[agent_id].sample()
for agent_id in env.agents}
obs_dict, rewards, terminateds, truncateds, infos = env.step(actions)
Running Examples#
cd case_studies/power
# Example 1: Single microgrid
python examples/01_single_microgrid_basic.py
# Example 5: MAPPO training
python examples/05_mappo_training.py --test
See Examples for detailed example documentation.