Net zero carbon rural integrated energy system design optimization based on the energy demand in temporal and spatial dimensions

The energy demand of rural residential buildings has not received sufficient attention in previous research on rural integrated energy system (IES) design. The predominant "full-time and full-space" design principle results in the IES capacity not corresponding to the actual energy demand of rural buildings, and the impact of energy demand on IES design capacity has not been quantified. Therefore, energy demands for four scenarios ("full-time and full-space", "full-time and partial-space", "partial-time and full-space", and "partial-time and partial-space") were obtained through field investigations and simulation. Meanwhile, a rural IES capacity configuration optimization model was constructed with the economic and net zero carbon emissions constraints. The two-stage fast and elitist multi-objective genetic algorithm (Top-NSGAII) was employed to determine the optimal design for four scenarios. Results showed that the design based on "partial-time and partial-space" performs best in terms of economics, carbon emissions, and autonomy, reducing annual total costs by 6.96 %, net carbon emissions by 3492.30kgCO2, and external electricity ratio by 8.32percentage pionts, compared with the "full-time and full-space". The study quantifies the impact of energy demand on capacity design and provides a new path to realizing net zero carbon rural residential buildings.