Coordinated scheduling optimization of building integrated energy system with flexible load
Qiao Yan, Gongfei Zhang, Yanling Zhang, Haining Yu
Abstract
Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side. • A flexible lighting load adjustment strategy based on office and underground parking scenarios is proposed. • A flexible cooling and heating load adjustment strategy is implemented, considering human comfort and building heat transfer. • An integrated demand response model based on price-based and incentive-based demand response is established. • Considering economics and carbon emissions, A multi-objective source-load optimization model is established. • The model is solved using an improved NSDBO based on logistic chaotic mapping and adaptive t-distribution.