Load pattern recognition based optimization method for energy flexibility in office buildings. (1st September 2022)

• Record Type: Journal Article
• Authors: Wang, Qiaochu; Ding, Yan; Kong, Xiangfei; Tian, Zhe; Xu, Linrui; He, Qing
• Is Part Of: Energy. Volume 254:Part C(2022)
• Publication Date: 2022-09-01
• Subjects: Two-step clustering -- Load pattern recognition -- Flexibility factor -- Operation strategy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124475
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Air conditioning systems are generally considered to have the greatest flexibility potential in buildings that can be flexibly regulated with thermal storage to reduce the interaction with the power grid and increase demand response benefits. In previous studies, the flexibility of air-conditioning systems was reflected through time-of-use tariffs. However, a strategy that only factors the tariffs incurs a greater operational energy consumption. In this study, a flexibility factor was established and incorporated into the multi-objective optimization process, together with the operational energy consumption, as two optimization objectives. After obtaining typical load patterns using a two-step clustering method, for multi-objective decision-making in the day-ahead operation, the entropy-grey technique for order preference by similarity to an ideal solution method is used. Considering an office building as a case study, we found that the optimized flexibility factor can reach 0.31 and 0.99 during a week of operation in winter and summer, on average, respectively, and achieved a cumulative energy-saving effect of 17.98% and 35.49%. In addition, the two-step clustering method can better demonstrate the flexibility factor than the single-step clustering. Highlights: Typical air conditioning load patterns are selected by a two-step clustering method. Detailed load boundary division leads to better energy-saving operation strategies. Flexibility factor is incorporated into the multi-objective optimization process. Demand response benefits are fully realized by balancing cost and efficiency.