Case
District energy
District heating
Energy efficiency in buildings
+5
Our expertise is analysis of energy systems, energy markets and potential impact of new technologies and policies. We make recommendations of the most feasible and low-cost solutions to optimize energy systems on all levels, from building and district to country and regional level. Using the TIMES energy systems modelling framework is our preferred method, ensuring a scientific approach. Our services include capacity building.
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The Swedish company Trollhättan Energi is developing its district heating systems in the Swedish municipality of Trollhättan. They need a flexible tool to analyze existing district heating operations, explore options for new technologies, and identify feasible and cost-optimal solutions.
Trollhättan Energi provides heating to 18,000 homes and 300 companies. The Company has set a strategic aim that its energy production should be fossil-free by 2030.
EML built a tailored energy systems model, the TIMES-TE model. The model represents the existing district heating systems.
The TIMES-TE model is an optimization model using advanced mathematical modelling. It’s based on the TIMES energy systems modelling framework that is internationally recognized and developed by a working group under the IEA (ETSAP).
The TIMES-TE model data spans the period 2027- 2040. The municipality is divided into four zones. The model uses a detailed hourly time slice structure to capture the hourly district heating demand and supply for representative weeks throughout the four seasons.
It has a representation of available energy resources, including not yet exploited sources such as wastewater. Waste heat potential in Trollhättan was identified using registers of all municipal activities and mapping through GIS modelling.
Furthermore, input data include the current infrastructure and the technology catalog used by the Danish Energy Agency, allowing the model to choose the optimal technology solution.
Assumptions of heating and cooling demand projections and energy price projections are key features.
EML generated about 30 scenarios, allowing for an in-depth analysis and testing of multiple options. The running of scenarios was carried out as an iterative process. We presented scenarios to Trollhättan Energi management and based on their feedback, we updated assumptions and targets and designed and generated more scenarios.
Among the results of the scenario analysis were recommendations to invest in heat pump technology and not reinvest in certain plants.
Furthermore, we estimated the payback time for recommended investments in heat pump technology.
The TIMES-TE model can be regularly updated, and Trollhättan Energi can use it as a flexible tool for continuous strategic energy planning, dovetailing system developments, and financial planning.
The project was carried out in cooperation with The Swedish Environmental Institute (IVL).
