A hybrid planning framework for virtual capacity-enhanced distribution transformer considering time-division multiplexing of energy storage unit
Year: 2025
Type: article
Abstract: • A concept about virtual capacity-enhanced distribution transformer is proposed. • A hybrid planning framework is developed to determine the most economical and technically effective capacity-enhanced scheme. • A time-division multiplexing strategy is introduced to improve utilization of energy storage unit. • The peak-valley disparities and the “duck curve” phenomenon are addressed using the proposed method. Conventional (distribution transformers) DT capacity enhancement through replacement with larger units often incurs high investment costs and suboptimal asset utilization. To address these issues, this paper proposes a virtual capacity-enhanced scheme for DTs based on battery (energy storage unit) ESUs. A TDM strategy is introduced to improve ESU operation efficiency, and an optimal configuration model for ESU capacity is proposed to balance economic benefits and operational performance. A hybrid capacity-enhanced decision-making framework is further developed to guide DT capacity enhancement decisions by systematically evaluating virtual, conventional, and combined strategies. Case studies show that the virtual capacity-enhanced DT performs better under low overload conditions. Compared with traditional and purely virtual schemes, the hybrid scheme reduces five-year cumulative costs by 14.18 % and 11.61 %, respectively. In addition, the proposed TDM strategy increases the annual cumulative discharge duration of the ESU by 1233 h, and reduces the average daily peak-to-valley load difference by 6.08 %. In a real-world 10 kV feeder, the hybrid scheme reduces cumulative costs by 21.13 % compared with traditional scheme.
Authors: Qingqi Sun, Luona Xu, Yuan Chi, Yongjie Luo, Yiyao Zhou, Yao Zou, Qianggang Wang, Niancheng Zhou
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
Awards: G2218230852, G6725686808
OpenAlex ID: W4414525448
