Application of the Multi-Parent Biased Random-Key Genetic Algorithm (MP-BRKGA) with Geo-Information Data for Automated Heating Network Planning

Germany's Energy Transition aims to significantly reduce CO2 emissions by 2045. This requires a substantial increase in the integration of renewable energies across the electricity, mobility and heating sectors. The decarbonization of the latter is primarily driven by municipal heat planning: Cities must outline strategies for incorporating more renewable energy sources. The city state of Hamburg (Germany's second largest city with approx. 2 million inhabitants) plans to expand its heating network, connecting new urban areas and decarbonizing supply through waste heat and large heat pumps. The Hamburg heat plan identifies areas that are well and not so well suited for district heating. For areas without access to district heating, alternative heating options must be explored. Our research analyzes conditions under which local heating networks are (more) viable vis-à-vis building-based solutions. We employ an adapted biased random-key genetic algorithm (BRKGA) utilizing geo-information from Hamburg and cost data for heating supply and networks. This paper shows the application of BRKGA to a new domain and contributes to the ongoing discourse on urban sustainable heating solutions.