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.

Electricity procurement constitutes a significant share of operational costs for large electricity consumers, and thus exposure to extreme prices poses a substantial financial risk. This paper proposes a method to generate EUPHEMIAcompatible bids for flexible demand to enable their participation in the European day-ahead electricity market while minimizing risks. Two strategies are considered, resulting in two bid formats: hourly bids (HBs), representing flexibility via marginal price responsiveness through price-quantity pairs, and exclusivegroup bids (EBs), representing flexibility via mutually exclusive operational schedules submitted at opportunity cost. Our method is evaluated on a hypothetical electrolyzer system and a realworld steel plant under different market conditions. Results show that the economic performance of each strategy depends on the operational characteristics of the load and market conditions. Under volatile market conditions, highly flexible systems achieve better economic outcomes with EBs, while less flexible systems with stronger intertemporal constraints perform better with HBs.

Coordinating the interactions among flexibility assets in multi-carrier integrated energy systems (MIES) can lead to a cost-efficient energy transition. However, the proliferation of flexibility assets and their growing participation in active demand response increases the complexity of coordinating these interactions. This paper investigates several approaches to model the coordination of flexibility scheduling in MIES with many autonomous flexibility providers. We propose runtime model coupling as an alternative modeling paradigm to overcome the limitations of monolithic centralized co-optimization. Specifically, we introduced two model coupling approaches—a distributed price-response and a decentralized market auction approach—to address practical challenges such as preserving the autonomy and privacy of flexibility providers while ensuring scalability. We conduct a quantitative benchmark of these approaches against co-optimization across varying problem sizes, complexities, and computing infrastructures. This benchmark provides new empirical insights into the trade-offs between optimality, autonomy, and scalability that have so far remained unquantified in the energy system literature. We show that model coupling offers a method to balance optimality and realism (autonomy and privacy) while delivering substantial scalability gains. The proposed model coupling approaches are formalized as open-source software with several practical applications: modelers can experiment with different flexibility modeling approaches and choose the one that best matches their modeling objectives and constraints; flexibility providers can couple their models to simulate interactions between their systems to make informed operational decisions without disclosing any confidential information.

Carbon capture and storage (CCS) has substantial potential for deep decarbonization of the steel sector. However, long-term transformations within this sector lead to significant changes in steel units, posing challenges for CCS deployment. Here, we integrate sector-level transformation pathways by 2060 to simulate the distribution of China’s steel units and generate optimal CCS deployment schemes using a source-sink matching model. Results indicate that CCS accounts for 31.4-40.7% of carbon mitigation effects in China’s steel sector by 2060. Following the sector-level pathways, over 650 steel units will either be eliminated or retrofitted. The optimal CCS deployment schemes can achieve carbon mitigation effects of 472.4-609.6 Mt at levelized costs of 187.4-193.5 Chinese Yuan t⁻¹ CO₂, demonstrating cost-effectiveness under future carbon price levels. Nevertheless, the proposed schemes will lead to energy and water consumption of 951.0-1427.3 PJ and 1.60-1.69 million m³, respectively, posing a risk of resource scarcity. These insights inform the development of CCS implementation strategies in China’s steel sector and beyond, promoting deep decarbonization throughout society.

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.