Coordinated Optimization of Robustness and Flexibility of Building Air-Conditioning Systems for Demand Response Control Considering Prediction Uncertainty

Abstract

Robust strategies are essentially needed in building air-conditioning (AC) systems for meeting heating/cooling demands stably under uncertainty, whereas demand response can serve the grid through flexible operation and achieve remarkable economic benefits. However, formulating optimal control strategies that enable weighing robustness and flexibility for AC systems is quite challenging and rarely addressed owing to their conflicting control objectives. In this paper, a dual-objective coordinated optimization approach is proposed to balance the robustness and flexibility of demand response under building load uncertainty. The load distribution interval is predicted by the quantile regression neural network model. And optimal demand response strategies are performed via modulating temperature set-points. Using the genetic algorithm and multi-objective decision-making optimization framework, the optimal strategy can be obtained by weighing the confidence level of load prediction interval and the operating cost of flexible operation. According to the case study of an office building in Tianjin, compared with the robust optimal strategy, the coordinated optimal strategy reduces the operating cost by up to 35.8% with an 80% load guarantee rate. In contrast to the flexible optimal strategy, it sacrifices only 0.05% cost but strengthens the load guarantee rate by 33.3%.

Keywords: Load prediction uncertainty, Air-conditioning system, Demand response control, Coordinated optimization method, Flexibility evaluation