DAO DRES

DAO DRES is a distributed resilience and energy-operations suite for coordinating hybrid power assets under disruption scenarios. It combines environmental data ingestion, predictive analytics, optimization planning, microgrid device control, geothermal baseload stabilization, orchestration loops, and audit capture into a single technical system.

This repository is intentionally technical. Its main value is to demonstrate system design depth: service decomposition, control-loop behavior, optimization under uncertainty, operational telemetry, and cross-service execution flow.

See PUBLIC_EDITION_SCOPE.md for public-boundary notes.

System Role

DAO DRES represents the resilient energy operations layer within the broader Sampo AI OS ecosystem. It is not a consumer-facing application. It is an infrastructure-grade backend system designed for resilient control, contingency planning, and operational decision support.

Core Services

1. Data Integration Service Loads and normalizes environmental event data into weather, flood, and seismic risk signals.

2. Predictive Analytics Engine Generates short-horizon production forecasts and infrastructure risk assessments.

3. Dynamic Optimization Engine Produces action plans for storage and baseload behavior under risk constraints.

4. Microgrid Device Controller Simulates controllable energy assets and records state transitions.

5. Geothermal Baseload Stability Service Maintains simulated geothermal output using PID control.

6. Orchestration Service Executes the control loop by fetching plans and dispatching commands.

7. Audit Log Service Collects operational state-transition events for traceability.

Technical Highlights

• decomposed multi-service runtime instead of a single monolith
• deterministic seeded event dataset for reproducible scenarios
• real optimization logic using binary decision variables and constrained planning
• closed-loop orchestration across multiple service boundaries
• simulated plant-control logic with PID-based geothermal regulation
• operational event history for auditability
• Prometheus-compatible metrics on critical services

Example Use Cases

• resilient energy control in remote or disrupted environments
• microgrid orchestration demonstrations
• technical portfolio evidence for distributed systems and control engineering
• research and prototyping for energy resilience workflows
• architecture reviews for operational AI systems

Architecture

• data_integration_service.py normalizes seeded event data into structured system signals
• predictive_analytics_engine.py converts operating context into production and risk predictions
• dynamic_optimization_engine.py creates actionable plans using constrained optimization
• microgrid_device_controller.py provides controllable asset endpoints and event recording
• geothermal_stability_service.py runs a PID control loop over a simulated plant
• orchestration_service.py drives the end-to-end control loop
• audit_log_service.py gathers device-event traces into an audit surface

Quick Start

Docker Compose

docker compose up --build

Main service endpoints:

• orchestration status: http://localhost:18118/status
• optimization plan: http://localhost:18114/optimize
• device list: http://localhost:18112/devices
• geothermal status: http://localhost:18115/status
• audit logs: http://localhost:18117/logs

Local Validation

pip install -r requirements.txt
pytest -q

Operational Notes

The public edition is designed to be technically representative and locally runnable. It keeps the distributed control pattern visible while using seeded data and lighter message-flow assumptions suitable for public release.

Ecosystem Context

DAO DRES is one of the highest-complexity infrastructure services within Sampo AI OS. In ecosystem terms, DAO Hub remains the orchestration center, while DAO DRES demonstrates the energy-resilience and operational-control layer that can sit beneath larger governance and analytics surfaces.