Recommended Air Conditioner Temperature Based on Probabilistic Power Flow Considering High-Dimensional Stochastic Variables

Current power management systems face multiple reliability challenges associated with integrating a population of air conditioner (AiCo) loads into future grids. These challenges, such as the AiCo overload issue, result in a substantial discrepancy between supply and demand during the summer peak, and a few power consumers have to bear the pain of switch power brownouts. The main theme of this paper is the engineering application of AiCo control laws considering the flexible tradeoff between power economics and thermal comfort. The recommended AiCo operating temperature should be reproduced according to different micrometeorological environments. The novelty of the study is that the air conditioner temperature is recommended based on probabilistic power flow (PPF) considering high-dimensional stochastic variables. A fast calculation method is presented to solve PPF calculation problems based on singular value decomposition, principal component analysis and a polling method. This experiment aims to obtain different PPF results corresponding to different AiCo operating temperatures. The effectiveness and efficiency of the proposed method are verified by comparing the method with the Latin hypercube sampling algorithm in a 118-bus power system. Numerical tests verify the benefits of nonnegative matrix factorization and the stochastic response surface method (SRSM) for PPF calculations considering high-dimensional stochastic variables.