Robust operation optimization model for building integrated energy system with demand response mechanism and weighting of consumption responsibility

The integrated energy systems (IESs) in buildings locally supply energy to users and reduce the different types of energy loss during transmission. IESs are inexpensive and highly reliable. However, IESs must simultaneously meet the demands of multiple load types, consider the thermal inertia of energy transmission, meet user comfort requirements, and manage source and load uncertainties. We established a robust operational optimization model for building IESs considering multiple internal and external factors, such as the integrated demand response mechanism, user comfort, and consumption responsibility weighting. We also introduced information gap decision theory. We simulated and analyzed a demonstration project of a building IES, drawing the following conclusions: (1) The operating cost of the system was 32.66% lower in the system with than without the integrated demand response mechanism. (2) For the thermal inertia of buildings, a larger user comfort index or a larger equivalent thermal resistance led to a smaller user-side heating/cooling load demand. (3) The operating cost was 6.27% lower for the system with than without consumption responsibility weighting. (4) The operating cost of the system using the information gap decision theory to solve the operation optimization model was 10.61% higher than that obtained using the traditional fuzzy chance constraints theory, but the information gap decision theory was more flexible and indicative of operator risk appetite. This study provides guidance for promoting low-carbon operations, the green transformation of building integrated energy systems, and guiding operator energy supply strategies.