Improving the sustainability of existing buildings in Nordic countries through energy system optimization

Authors
Eloranta, Vilppu; Woszczek, Aleksandra; Grönman, Aki

Abstract
Distributed energy generation is a major trend in the ongoing global energy transition process that requires flexibility in energy systems, also in buildings. Lately, old industrial buildings have been renovated and repurposed as offices or campuses to extend their service lives, often retrofitted with photovoltaic (PV) power plants. In the Nordic countries, PV electricity production can exceed the building consumption in the summer, while very low production is occurring in the winter. Possible solutions for this discrepancy include local energy storages or exports to energy networks. However, the optimization of these energy systems from sustainability point of view requires further investigation. From this premise, this study provides new insights on life cycle sustainability potential in large buildings, provided through energy system optimization. An old factory building in Lahti, Finland, lately renovated to become a campus, is serving as a case building. It is equipped with a ground source heat pump (GSHP), a PV power plant, and is connected to the regional district heat network. A 3D building energy consumption model is built, and its performance is validated with measured data. Next, an energy system optimization model that dimensions various production and storage components is developed. The model enables flexible energy flows: the excess PV electricity can be used to produce heat with GSHP and stored, or alternatively directly stored as electricity or exported to the electric grid. The used optimization objectives are life cycle emissions and monetary costs. The results indicate that an optimized building-level energy system with local generation and storage components reduces imports from energy networks compared to the reference scenario. This directly reduces the life cycle emissions caused by energy production. Further, it is shown that the optimization objectives can be adjusted to obtain a both economically and environmentally balanced solution.