Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation

•Energy Hub optimization of asset design and operation to supply data-driven demand.•Novel comparison of distinct asset sets regarding total cost and LCA emissions.•Purely stationary asset upgrades are preferential to pure mobility upgrades.•Joint optimization of MES and EVs enables large CO2eq mitigation at negative costs.•Uncertainty analysis highlights robust benefits of cross-sector optimization. Residential energy and mobility demand are responsible for a substantial share of global greenhouse gas emissions due to high dependency on fossil fuels in heating and motorized individual transport. Technology upgrades might enable cost-effective climate change mitigation in decentralized mobility-including Multi-energy Systems (MIMES). Their holistic and cross-sectoral optimization with respect to design and operation can innovatively identify tradeoffs between ecological and economical solutions, quantify potential benefits and show drawbacks over current solutions. To this end, this work provides a novel, hourly-resolved, consumer-centric, multi-objective optimization framework based on the Energy Hub concept that includes private mobility investments. It compares four distinct technology portfolios for minimal lifecycle emissions and total annualized cost when supplying residential demands to promote beneficial solutions. In a case study, consumers may modify their combustion-based heating and mobility systems (1) by switching to e-mobility (2), by switching to stationary Multi-energy Systems (3), or by switching to e-mobility and Multi-energy Systems simultaneously (4). Optimizations on 83 stochastically selected single- and multi-family buildings in St. Gallen, Switzerland, demonstrate that all three technological upgrades offer improved performance over (1): While costs decrease moderately in cost-driven optimizations, emission reductions range from 16% up to 68% when emission-driven optimizations are performed. The joint electrification of stationary and mobile assets (4) is particularly attractive and outperforms all other technology cases. Scenario (3) offers the second-best performance. While the optimal design and operation of assets depend on the technology availability, emission reduction targets, building size, and demand properties, an uncertainty analysis underpins the overall benefits of upgrades and accredits robustness to abovementioned ranking for wide techno-economic parameter variations and objectives. Pathways for further emission redu