Title:	Optimization of positive energy districts
DNr:	NAISS 2025/22-334
Project Type:	NAISS Small Compute
Principal Investigator:	Jenny Enerbäck <jennyene@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2025-03-04 – 2025-07-01
Classification:	10105
Keywords:

Abstract

In an effort to reduce the climate impact of the built environment stakeholders such as politicians, building owners, and municipalities, are looking at cost-efficient strategies to improve the energy footprint of buildings. These strategies can include envelope renovations (insulating walls, roofs, windows) and investments in energy conversion and storage technologies. The effort is motivated by the significant contribution the built environment has to total energy consumption and carbon emissions: within the EU this amounts to 40% of the total energy demand and 35% of green house gas emissions. In this project, we have developed an optimization model for investments in energy efficiency measures for buildings within a district, with the objective to minimize carbon emissions as well as investment and operational costs. By optimizing investments for all buildings simultaneously, we can account for each buildings electricity imports and exports while also modeling electricity trade within the district. This approach aligns with the concept of a "Positive Energy District": an area that through energy efficiency measures, local energy production, and internal trade reduces its reliance on energy imports and ideally becomes a net supplier of energy. The model is formulated as a mixed integer linear program, with binary variables for investment decisions and continuous variables for dimensioning of technical systems, and for energy flows. Since the two objective functions are conflicting, the aim is to explore the Pareto front of non-dominated solutions. These solutions can be used to make techno-economic analyses of the problem. Two aspects of the mathematical formulation can make it difficult to solve: the number of binary variables, as well as the long time horizon, up to 30 years, which is necessary because of the long pay-back period of many of the investments. To handle these challenges, we will explore different decomposition methods, such as Benders decomposition, and/or coupling of operation years.