Climate-driven load shifts and the optimal design of district heating and cooling systems: Planning energy supply for a warming century

This paper introduces a multi-stage stochastic optimization framework based on Stochastic Dual Dynamic Programming (SDDP) to plan long-term District Heating and Cooling (DHC) systems under deep climate uncertainty. Integrating Shared Socioeconomic Pathways (SSPs) with uncertain CO2 prices and thermal demands, the framework provides adaptive investment strategies until 2100. Applied to a Belgian case, results project a fundamental shift from heating to cooling, with heat losses declining by up to −57%±34% and heat gains increasing by up to +291%±25% by 2100 across scenarios. The model consistently converges to robust, electrified configurations dominated by Air-Source Heat Pump (ASHP) and Ground-Source Heat Pump (GSHP) supported by seasonal Borehole Thermal Energy Storage (BTES), with Natural Gas Boiler (NG) relegated to marginal backup roles. While transition mechanisms differ, driven by warming in high-emission pathways and by carbon pricing in mitigation pathways, system costs and emissions converge across scenarios. This work demonstrates that electrified DHC systems with seasonal storage offer a cost-effective, resilient strategy for temperate climates under deep uncertainty, though outcomes are sensitive to regional climate and demand profiles.