Configure, simulate &
assess energy communities — no code.
This web platform extends the open-source Python Energy Communities (PyECOM) toolkit so practitioners, policymakers, and community stakeholders can upload datasets, configure scenarios, run optimisation, and explore results without programming. It adds performance and sustainability metrics for operational efficiency, economic outcomes, and grid interaction — supporting transparent decision-making for energy community planning.
Madeira benchmark or your Excel template · Deterministic, decentralised & stochastic algorithms · Interactive charts & KPIs
From datasets to dispatch — in the browser
The frontend connects to a Pyomo-based optimisation backend via FastAPI: you define the EC, choose the algorithm and time resolution, then compare outputs and indicators tailored to each method — closing the gap between research-grade optimisation and usable, no-code workflows.
Benchmark & Excel-based inputs
Use the built-in Madeira Island benchmark (five residential households, second half of 2019), with backend scaling to synthesise larger or smaller communities, or upload the EC configuration spreadsheet template defining members and assets (loads, PV, EVs, BESSs).
Measured, forecast, or your own series
Choose measured time series from the benchmark, forecasts derived from those measurements for day-ahead or rolling studies, or user-provided series in Excel. Set the optimisation resolution to 15, 30, or 60 minutes and tune method-specific parameters.
PV, EVs, BESS & flexible loads
The optimisation layer schedules typical EC assets and flexibility: photovoltaic generation, electric vehicles, battery storage, and controllable loads that can be reduced or curtailed — so operational decisions stay aligned with technical limits.
Three scheduling algorithms
Deterministic optimisation with seven alternative objective functions; a decentralised bi-level method (peer layer plus EC layer, iterative until convergence); and stochastic optimisation with scenario-based load and generation uncertainty and simple scale-factor controls for robustness.
Seven objectives (deterministic)
Select among baseline operation, reducing the energy invoice, reducing grid import, improving battery longevity, reducing environmental impact, increasing comfort, and reducing operational costs — as a single shared objective, multiple shared objectives, or different objectives per member.
Interactive charts & KPI tables
Explore dispatch and outcomes with Highcharts (line, stacked area, bar, panning, tooltips). Export chart images and data from the interface. KPI sets adapt automatically to the chosen optimisation method and scenario so you see the most relevant performance and sustainability indicators.
Up and running in three steps
Sign in, import or select data, configure the optimisation method and objectives, then review interactive visualisations and KPI tables — with export options for reporting and further analysis.
Configure the EC & data source
Sign in, then select the benchmark dataset or upload the Excel template. Pick measured, forecast, or uploaded time series, set resolution (15–60 minutes), and choose the optimisation algorithm and its parameters.
Run optimisation
The backend preprocesses inputs into Pyomo-ready structures, executes deterministic, decentralised, or stochastic scheduling, and returns results via the API — no scripting required.
Interpret results
View algorithm-specific charts and KPI tables, switch chart types, and export figures or data for reporting — supporting transparent comparison of operational, economic, and grid-interaction outcomes.
Open tools, clearer EC decisions
PyECOM parses EC datasets, models distributed assets, and applies optimisation to scheduling and operation problems (see the original SoftwareX publication). The usability of such tools, however, remains a barrier for users without advanced programming skills — which this web interface is designed to address, within the European U2DEMO project on open peer-to-peer energy sharing.
The stack follows a client–server architecture: the GUI is built in Nuxt 3; the backend standardises inputs for Pyomo models and serves results through FastAPI; authentication uses MariaDB. Together, these pieces let you assess how deterministic, decentralised, and stochastic formulations behave — and interpret algorithm-specific results through charts and KPIs aligned with each run.
No-code workflows
Designed so users without advanced programming skills can configure scenarios, run routines, and compare metrics interactively — addressing a key adoption barrier for EC optimisation tools.
Nuxt · FastAPI · Pyomo
The frontend collects inputs and displays results; the backend standardises data for Pyomo models and exposes outputs through a REST API, with secure sign-in backed by MariaDB.
Transparent assessment
Method-specific visualisations and KPIs help you see how deterministic, decentralised, and stochastic formulations differ — supporting reporting and stakeholder engagement.