Optimal coordinated design of building mix ratios and energy systems under uncertainty

As renewable energy production surges, traditional load-leveling strategies for stabilizing terminal loads during planning have shown significant limitations, particularly in the face of uncertainties within integrated energy systems (IES) that can diminish energy efficiency. This study introduces a pioneering two-stage robustness model for IES configuration, offering a comprehensive analysis of how terminal load affects system performance by varying the mix of building types in a region. It uniquely assesses the simultaneous impact of thermal, cooling, and electricity storage under various uncertainties, shedding light on how regional building composition influences the economic viability of energy systems. Findings indicate that uncertainties in renewable energy and demand drive up investment costs, with each 6-unit increment in the robustness parameter increasing total costs by 3.7–4.9 %. The study identifies office-centric areas as optimal for IES implementation and residential areas as less favorable. It also suggests that with an increasing gas-to-electricity price ratio, there should be a strategic increase in energy storage installations, prioritizing thermal/cold energy storage (T/CES) given current pricing. The study concludes with regression models that facilitate expedited system design and feasibility assessments, providing valuable insights for optimizing IES under diverse uncertainties and identifying cost-effective scenarios.