Comprehensive, fast, user-friendly and thoroughly validated open-source energy system planning framework

Projektinformationen

Mopo

ID Finanzhilfevereinbarung: 101095998

DOI

10.3030/101095998

EK-Unterschriftsdatum 9 Dezember 2022

Startdatum 1 Januar 2023

Enddatum 31 Dezember 2026

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 6 580 837,50

EU-Beitrag

€ 6 580 837,50

Koordiniert durch

TEKNOLOGIAN TUTKIMUSKESKUS VTT OY
Finland

Periodic Reporting for period 2 - Mopo (Comprehensive, fast, user-friendly and thoroughly validated open-source energy system planning framework)

Berichtszeitraum: 2024-07-01 bis 2025-12-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The energy landscape is in rapid transition and it is crucial to keep up with emerging technologies and changing systems. Society needs tools that can capture the changes while enabling cost-effective, sustainable and reliable energy systems to be planned.

Building on the legacy of the European project Spine, Mopo will further develop and refine a set of workflow management, energy system model and data processing tools. The goal is to provide decision-makers with effective, high-performance and modular tools to facilitate energy modelling within the energy, the transport and other sectors. Energy modelling is used at national and EU levels to support policy and regulatory decisions that aim to steer European energy systems to carbon neutrality. Energy modelling is also used in private and public organisations engaged in investing and operating energy related assets, ranging across energy generation, transfer, storage and consumption.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

Mopo iproduces high resolution datasets for energy system modelling and a system to build model instances fit for purpose (e.g. high resolution for the country under study while keeping other regions in lower resolution for computational efficiency). The datasets will cover all important energy domains: demand in industries, buildings, transport, availability and time series for renewable energy, costs and characteristics of energy conversion, transmission and storage technologies as well as existing infrastructures. The first versions of the datasets have been made available.

Mopo enhances the open-source workflow, data and scenario management tool Spine Toolbox. Handling large amounts of complex data for scenario-based modelling is time consuming and error-prone endeavor. Spine Toolbox helps to avoid mistakes, automate processes, connect data to models, link different models and visualize results. During Mopo, several improvements have already been made, for example, to the core data structures and user interfaces.

Mopo enhances the state-of-the-art energy system model generator SpineOpt.jl. As the existing SpineOpt is already a very extensive model with lot of capabilities, the focus will be on improving the performance and usability of SpineOpt. During Mopo, the execution speed of SpineOpt has already been improved, its user constraints have been made more flexible, and the input data structure has been simplified without taking away any of its flexibility.

Mopo will perform three case studies: European-wide full sector energy system model, high-detail industrial region and analysis for the decarbonisation of Baltic countries in high resolution.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The project will produce exclusively open source tools and open access data. The further exploitation will therefore take place through the adoption of these tools and data across stakeholders: policy support, industries, and academia. The open tools and data can also support consultancy work and there will be need for support functions when the tools start to have wider adoption.

Expected results include data pipelines that provide energy system relevant datasets, a workflow to create model instances from those datasets, a tool for workflow, data and scenario management, an energy system model, tools to visualize input data and results, a process to certify correct functioning of energy system models, course material for energy systems modelling.