Optimal decarbonization of urban heating systems considering interdependencies between building retrofits and heat supplies

Optimal decarbonization paths of urban heating systems contain both heat demand- and supply-side related measures. Building retrofits, i.e., measures that increase buildings’ energy efficiency, affect the optimal choice of heat generation, which in turn influences optimal building retrofit decisions. Existing energy system models struggle to represent these interdependencies model-endogenously; either a high level of abstraction does not capture key characteristics of the heating system sufficiently accurately or the computational costs are too high if existing, detailed methods are applied at an urban scale. This paper presents mixed-integer linear programming-style optimization conditions that allow to treat these interdependencies model-endogenously at an urban scale with a heterogeneous residential building stock. The urban area is divided into districts, each with multiple archetype buildings representing various building types and energy standards. Retrofit decisions are determined within the model for each district, reducing heat demands and enabling lower heat supply temperatures and thereby more efficient heat generation units. Different existing mixed-integer linear programming-based energy system modeling frameworks can be extended using the proposed new conditions, with only five coupling constraints to the remainder of the system. The technical benefits of the methodology are demonstrated with an experimental case study featuring an urban area with three districts. • The availability of heat sources influences optimal building retrofit decisions. • Mixed integer linear programming conditions to model supply–demand interdependencies. • Optimal energy system design based on the variable number of archetype buildings. • Endogenous modeling of supply–demand interdependencies reveals cost-saving potentials.