Optimizing Building Energy Systems Using Generalized Disjunctive Programming: Improving Model Precision and Decision‐Making Efficiency

This paper examines the optimization of building energy systems (BESs) through the lens of generalized disjunctive programming (GDP), a methodology that streamlines the modeling of intricate logical structures and discrete decisions. The study concentrates on three pivotal aspects of BES optimization: the imposition of minimum part‐load constraints, the selection of discrete equipment sizes and pricing, and the integration of subsidy policies. These elements are of critical importance due to the operational limitations of real‐world technologies, the necessity for precise equipment investment strategies, and the significant influence of policy incentives on system design. The results of comprehensive case studies demonstrate the significant impact of these constraints on decision‐making and system performance. Specifically, part‐load constraints result in a shift in the operational priorities of equipment, with an increased reliance on energy storage systems, particularly during periods of low demand. Additionally, pricing models have a significant impact on equipment selection, while subsidy integration has the effect of reducing overall costs and encouraging the adoption of energy‐efficient technologies, such as heat pumps. However, the adoption of GDP introduces computational challenges, particularly due to the complex disjunctive constraints applied across multiple time steps, which require careful consideration in BES optimization.