Energy-Aware Aggregation of Input Data for the Optimisation of Heat Supply of Municipal Districts

TL;DR

This paper presents a methodology for aggregating buildings in municipal heat planning by incorporating energy performance indicators, balancing geographical and energy factors to optimize heat supply models.

Contribution

It introduces a novel approach that integrates energy performance indicators into building grouping for energy system optimization, considering temporal energy profiles.

Findings

Incorporating energy indicators improves aggregation accuracy.

Balancing geographical and energy factors enhances model efficiency.

The workflow supports Pareto-optimal heat supply solutions.

Abstract

In the context of municipal heat planning, it is imperative to consider the numerous buildings, numbering in the hundreds or thousands, that are involved. This poses particular challenges for model-based energy system optimization, as the number of variables increases with the number of buildings under consideration. In the worst case, the computational complexity of the models experiences an exponential increase with the number of variables. Furthermore, within the context of heat transition, it is often necessary to map extended periods of time (i.e., the service life of systems) with high resolution (particularly in the case of load peaks that occur at the onset of the day). In response to these challenges, the aggregation of input data is a common practice. In general, building blocks or other geographical and urban formations, such as neighbourhoods, are combined. This article explores the potential of incorporating energy performance indicators into the grouping of buildings. The case study utilizes authentic data from the Neu-Schwachhausen district, grouped based on geographical location, building geometry, and energy performance indicators. The selection of energy indicators includes the annual heat consumption as well as the potential for solar energy generation. To this end, a methodology is hereby presented that considers not only the anticipated annual energy quantity, but also its progression over time. We present a full workflow from geodata to a set of techno-socio-economically Pareto-optimal heat supply options. Our findings suggest that it is beneficial to find a balance between geographical position and energy properties when grouping buildings for the use in energy system models.