Energy Network Scenario Data Generator

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Energy Network Scenario Data Generator

Inspiration

Energy-network planning, testing, and machine-learning workflows require many realistic operating scenarios. In practice, creating those datasets manually is slow, difficult to validate, and often depends on historical network data that cannot be widely distributed.

We wanted to build a reproducible workflow that could transform network topology and historical flow information into synthetic operating scenarios while still giving engineers enough visibility to inspect, validate, and review the results.

The project was also an experiment in AI-assisted software engineering: not only using AI to generate code, but using ChatGPT and Codex across planning, architecture, implementation, testing, documentation, and demo preparation.

What it does

Energy Network Scenario Data Generator provides an end-to-end workflow for producing and reviewing synthetic network operating scenarios.

The project includes:

• A Python processing pipeline for graph transformation, profiling, scenario generation, and reproducibility tracking.
• A FastAPI backend that exposes processed network and scenario data.
• A React dashboard for topology inspection, scenario visualization, subnetwork review, and validation.
• Deterministic generation using fixed seeds and documented configuration.
• Validation covering balance, nodal bounds, structural consistency, and scenario coverage.

Engineers can inspect original and transformed network structures, compare generated distributions, review subnetworks, and verify whether generated datasets satisfy the implemented engineering checks before using them in downstream planning, testing, or machine-learning workflows.

How we built it

We used a structured engineering workflow:

Requirements → Architecture → Implementation → Review → Testing → Refinement → Documentation → Demo

ChatGPT supported planning, architecture discussions, workflow design, documentation strategy, and engineering review.

Codex with GPT-5.6 served as the primary implementation assistant inside Visual Studio Code. It supported development across the Python processing pipeline, FastAPI backend, React dashboard, automated tests, refactoring, repository organization, and documentation.

We also used Codex to build a deterministic demo-production workflow. This included Playwright-based browser automation, reproducible dataset preparation, automated product recording, generated graphics, caption timing, FFmpeg-based video assembly, and technical validation.

Human responsibility remained central throughout the project. Architecture decisions, engineering assumptions, validation scope, data approval, factual claims, and final review were all handled by the project owner.

Challenges we ran into

One major challenge was defining validation carefully. The project verifies balance, bounds, structure, and coverage, but it does not claim to perform full hydraulic or pressure-feasibility analysis. Keeping that distinction clear was important both technically and in the final presentation.

Another challenge was rendering and automating large network graphs. The dashboard uses WebGL-based topology views, and direct canvas node selection was not initially deterministic in headless Chromium. We solved this by creating a maintainable, deterministic selection mechanism that activates the same real application state used by normal user interaction.

We also needed to ensure that the demo remained reproducible. Fixed seeds, stable selectors, health checks, explicit startup procedures, privacy checks, and machine-readable recording manifests were added so the same workflow could be repeated reliably.

Finally, we had to prepare a public demo without distributing the original source dataset. This required careful review of what could be shown in the interface while keeping raw data, private paths, credentials, and internal information out of the repository and final video.

Accomplishments that we're proud of

We are proud that the project became a complete working prototype rather than only a collection of scripts.

The final system includes:

• A reproducible scenario-generation pipeline.
• A documented FastAPI service.
• An interactive React engineering dashboard.
• Automated frontend and backend tests.
• Deterministic scenario generation with verified balance and bound results.
• Topology and subnetwork inspection.
• Scenario distribution and validation visualizations.
• A repeatable Playwright and FFmpeg demo-generation pipeline.
• Clear documentation of AI contributions and human review responsibilities.

We are especially proud that the demo itself was produced through the same engineering principles as the product: deterministic inputs, automated checks, reproducible scripts, evidence-based claims, and explicit review steps.

What we learned

We learned that Codex is most effective when it is given more than a coding task. Clear repository structure, an AGENTS.md file, deterministic runbooks, explicit constraints, tests, and review checkpoints allowed Codex with GPT-5.6 to contribute across the full software lifecycle.

We also learned that AI-assisted development still requires strong human ownership. The model can accelerate implementation, testing, refactoring, and documentation, but engineers must remain responsible for architecture, domain assumptions, privacy, validation boundaries, and final acceptance.

Another important lesson was that reproducibility improves both engineering quality and AI collaboration. Fixed seeds, stable test data, machine-readable manifests, and explicit expected results reduced ambiguity and made it easier to verify every change.

What's next for Energy Network Scenario Data Generator

Next steps include expanding the range of supported network models and scenario-generation strategies, adding larger-scale performance testing, and supporting more configurable engineering constraints.

We also plan to extend the validation layer with additional domain-specific checks and, where suitable models are available, integrate hydraulic or pressure-feasibility analysis as a clearly separate validation stage.

Other future directions include improved comparison across scenario sets, configurable export formats, richer subnetwork analytics, and integration with downstream planning, simulation, and machine-learning workflows.

The broader goal is to turn the prototype into a reusable engineering platform for generating, reviewing, and sharing reproducible scenario datasets while preserving clear boundaries between automated assistance and human engineering responsibility.

Built With

• codex
• fastapi
• gpt-5.6
• playwright
• python
• react
• typescript