Informing heating electrification and prosumer-based solutions toward decarbonisation targets via a multi-objective optimization in grid-connected PV systems in rural buildings

In pursuit of rapid building stock decarbonisation, the electrification of heating through heat pumps, complemented by grid-connected photovoltaic (PV) systems and battery storage, has emerged as a viable solution. This study presents a concise, scalable energy system modelling framework that integrates a regression-based module for dynamic load estimation with an optimisation module to determine optimal system sizing and operation. By incorporating temperature-dependent heating and cooling demand alongside prosumer-based strategies, the framework provides insights into supply-demand balancing, cost considerations, and emissions reduction potential. For rural buildings in representative locations across China's diverse climate zones (Harbin, Beijing, Chengdu and Xiamen), the strategic integration of PV systems, heat pumps, and batteries has been shown to yield substantial carbon emissions reductions. Under scenarios achieving an optimal levelised cost of electricity (LCOE) below 0.04 $/kWh, all 4 regions can realise at least a 50 % reduction in emissions; with a moderate to medium increase in cost (24–60 %), reductions of up to 90 % become feasible. However, if the PV and battery systems' components are oversized, the carbon emissions produced during their manufacturing significantly reduce the benefits of displacing high-carbon grid electricity. Accounting for this embodied carbon burden, the maximum net reductions attainable are 79–85 % for Xiamen, 57–73 % for Chengdu, 75–84 % for Beijing and 72–82 % for Harbin. Overall, this research underscores the transformative potential of coupling electrification with prosumer-based approaches to meet ambitious decarbonisation targets and could be further developed with the assessment of climate-change impacts, dynamic electricity pricing, and community-scale solutions. • Concise, scalable framework for optimising heat pumps, PV, and battery systems. • Regression-based load estimation based on temperature dependence and operational schedules. • Case study covers rural buildings in multiple Chinese climate zones. • Up to 80 %–90 % emissions reduction possible with modest net present cost increase. • Open-source approach enables broad adaptation under evolving technology and climate.