A single-file browser-only learning model for linear capacity expansion and hourly dispatch. It is intended for education, screening-level analysis and strategy framing. It does not require a local Python install.
- Open the HTML file in a modern browser.
- Use Start here for the first-run workflow.
- Use Dashboard to run the embedded Mini-grid case.
- Use Grid Load Builder, Resource profiles, Network, Technology Candidates and Settings to review inputs.
- Read Results in this order: Outcome, Build & energy, Cost, Network & storage, Node operations, Scenarios, Flat-world, Stress, Dispatch, Export checks, Detailed tables, Saved and Finance.
- Use Fixed system comparator, Scenarios, Flat-world and Stress for comparison workflows.
Settings includes reliability constraints, storage settings, runtime settings and the Generated linear program preview.
The Generated linear program preview is collapsed by default. It can be generated from the current inputs, copied, downloaded as a plain-text .lp file, or downloaded as an inspection bundle JSON containing the .lp text, a hash and basic model-size metadata. This is intended for learning, external solver inspection and review of the linear formulation approach. It does not change the optimisation result.
The model is a linear transport abstraction. It includes demand, candidate supply, storage, external grid interface, transport links, losses, build limits, reliability penalties and selected constraints.
It excludes unit commitment, alternating-current power flow, security-constrained dispatch, binary build decisions, market price formation, nodal pricing, reserves, frequency and voltage stability. Outputs are screening-grade evidence, not a bankable system design, grid connection study or investment approval.
The embedded demand-profile reconstruction is derived from PLEXOS-World 2015, Brinkerink and Deane (2020), Harvard Dataverse, DOI 10.7910/DVN/CBYXBY. The app is open-source under GPL-3.0-or-later.
Publish the generic HTML app only. Do not publish saved setup JSON files containing confidential locations, costs, demand traces, capacities or commercial assumptions.