District heating networks (DHNs) have significant potential to decarbonize residential heating and accelerate the energy transition. However, designing carbon-neutral DHNs requires balancing several objectives, including economic costs, social acceptance, long-term uncertainties, and grid-integration challenges arising from electrification. By combining modeling-to-generate-alternatives with power flow simulation techniques, we develop a decision-support method for designing carbon-neutral DHNs that are cost-effective, socially acceptable, and impose minimal impacts on the electricity grid. Applying our method to a Dutch case, we find substantial diversity in how carbon-neutral DHNs can be designed. The flexibility in technology choice, sizing, and location enables accommodating different real-world needs and achieving high electrification levels without increasing grid loading. For instance, intelligently located heat pumps and thermal storage can limit grid stress even when renewable baseload heat sources and green-fuel boilers are scarce. Using our method, planners can explore diverse carbon-neutral DHN designs and identify the design that best balances stakeholders’ preferences.

Coordinating the interactions between increasingly interconnected energy sectors and carriers can lead to an efficient integration of variable renewable energy (VRE) resources, and a more cost-efficient energy transition. This paper proposes a model coupling approach that uses a market-based mechanism to efficiently coordinate the interactions among electricity, heat, and (hydrogen) gas systems, and (near) optimally schedule flexibility to maximize social welfare. The proposed approach is benchmarked against traditional co-optimization, and is shown to achieve comparable results with a moderate "optimality gap"in terms of reduction in system costs, peak load, and VRE curtailment. Its added value is the ability to enable each system to interact in an integrated energy system and locally optimize their decisions without sharing confidential information. The practical implication of this new approach is to provide a modeling environment where system operators and flexibility aggregators can obtain insights into the impacts of decarbonization of other parties on their systems - thereby avoiding myopic operational or investment decisions.