Case
Energy efficiency in buildings
Geothermal energy
Heat pumps
+3
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 American company Brightcore Energy wishes to identify the optimal energy mix for a customer embarking on renovating and optimizing a facility’s heating and cooling systems. The facility is a school connected to an adjacent residential neighborhood in Arlington, Massachusetts. At the outset, the systems are mainly supplied by natural gas and a bit of electricity. The buildings have individual AC units.
EML built a tailored energy systems model, the TIMES-Arlington model. The model represents the heating and cooling systems and includes estimates on local energy resources such as river and sewage water.
The TIMES-Arlington model is an optimization model based on the TIMES energy systems modelling framework that is internationally recognized and developed by a working group under the IEA (ETSAP).
We set the TIMES-Arlington model to optimize the systems for the milestones 2024, 2030, 2040, and 2050.
To ensure accurate system sizing according to the load demands and to reflect peaks, we selected eight representative weeks based on load profiles. Each selected week captures the load behavior of a specific season. The resulting time series distinguishes between weekdays and non-weekdays and represents 24 hours per day. This method also minimizes computational time.
In numerous scenarios, we tested the impact of different combinations of technologies on primary energy production and the need for storage and grid capacities. On more detailed levels, we tested the impact on the production of space heat, space cooling, and hot water production.
The running of scenarios was carried out as an iterative process. We presented scenarios to Brightcore and based on the feedback, we updated assumptions and targets and designed and generated more scenarios.
Our analysis showed that a combination of different kinds of pumps is the most feasible and lowest-cost solution for a future system.
The analysis included estimated costs of grid expansion and building new pipes. Furthermore, we tested the optimal percentage of residential buildings to be connected to the different kinds of pumps. We could demonstrate significant differences in the total discounted costs of systems between tested mixes.
In addition to the total system costs of different technology mixes, our analysis included CapEx breakdowns and estimates of primary energy supply and final energy consumption.
