Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids

Retrofitting the existing building stock is among the most important objectives and imperative to meet societal goals to reduce primary energy demand and anthropogenic greenhouse gas emissions. District heating systems have proven to supply heat for buildings both energy and cost efficiently. Thermal microgrids (TMGs) can be understood as a subcategory of district heating systems: small scale, bidirectional, and potentially fed by different thermal sources. Given a suitable combination of loads, the number of and distance between buildings, they can offer economic and environmental advantages compared to the supply by individual heating systems per building. We present a novel method using data analysis techniques on georeferenced building stock data to identify suitable configurations of buildings that yield a cost-efficient TMG. For the identification, both semantic and spatial data from a database are combined using fuzzy logics and cost-benefit analysis. We apply the method using a case study featuring a database of 306 buildings potentially to be retrofitted. As a result, we can identify nine groups of 25 buildings that would form a microgrid featuring up to 17.4% cost benefits compared to an individual heat supply. This would save approximately 30% of the building-induced CO
₂
emission of the community.