SCARE – Community-based Adaptive Resilience for Energy Systems

A distributed multi-agent system for resilient multi-energy restoration.

Built on top of:

Dependency	Role
mango-agents	Agent framework, roles, simulation world
mango-energy-environments	Multi-energy network physics & failure injection
distributed-resource-optimization	ADMM cross-sector optimisation
monee	Multi-energy network model
plotly	Visualisation
networkx	Graph algorithms

Architecture

Network component          Agent roles
─────────────────          ─────────────────────────────────────────────
Child (load / gen)    →    EnergyBalanceNegotiator + GenerationController
Node (bus)            →    ProblemDetector + GridReconfigurator
  ↳ CHP / P2G / G2P   →    + EnergyConverterRole (ADMM)
Branch (switchable)   →    GridTieSwitchOperator
Branch (heat exch.)   →    EnergyBalanceNegotiator + GenerationController
Branch (P2G / G2P)    →    EnergyConverterRole (ADMM)

Three named topologies are maintained per agent via NamedTopologies:

• groups – fully-connected clusters per connected component × sector (used by energy-balance negotiation)
• grid – physical network graph (used by grid reconfiguration path-finding)
• cps – cross-sector coupling points (used by CP optimisation)

Quick start

import asyncio
from mango_energy_environments import Failure, fetch_example_net
from scare.scenario.restoration import (
    create_restoration_scenario_world,
    start_restoration_simulation,
)

async def main():
    net = fetch_example_net()
    world = create_restoration_scenario_world(net)
    failures = [Failure(delay_s=2.0, branch_ids=[(3, 4)])]
    await start_restoration_simulation(world, failures, simulation_duration_s=30.0)

asyncio.run(main())

Or run the ready-made experiment:

python -m experiment.restoration

Installation

pip install -e .

All dependencies are resolved from local clones under ~/git/.

Package structure

src/scare/
├── base/
│   ├── model.py        # enums, dataclasses, message types
│   ├── util.py         # unit conversions, observation helpers, ADMM factories
│   └── visu.py         # plotly visualisation
├── community/role.py   # Community leader + CommunityParticipant
├── detection/role.py   # ProblemDetector
├── service/
│   ├── balance.py      # EnergyBalanceNegotiator (consensus chain algorithm)
│   ├── stability.py    # GenerationController
│   ├── reconfiguration.py  # GridReconfigurator + GridTieSwitchOperator
│   ├── cp.py           # EnergyConverterRole (ADMM)
│   └── chs.py          # CommunityHostingService
└── scenario/
    └── restoration.py  # create_restoration_scenario_world()

experiment/
├── restoration.py      # 30 s demo run
├── evaluation.py       # batch eval across scenarios
└── plotting.py         # comparison plots from saved results

License

MIT – see LICENSE.