城市轨道交通柔直变流器参与电网无功补偿的多时间尺度协同优化技术

doi:10.19595/j.cnki.1000-6753.tces.250676

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Abstract A flexible DC traction power supply system for urban rail transit can meet traction demands and provide surplus reactive power regulation capacity to support urban power grids. In existing power structures, the traction power system and the distribution network are supplied by the grid independently, lacking coordinated dispatch. However, the four-quadrant capability of flexible converters enables full exploitation of reactive power support from the traction system. Thus, this paper proposes a multi-time-scale coordinated optimization method for flexible converters in urban rail systems to participate in reactive power compensation.
Firstly, at the day-ahead time scale, an improved particle swarm optimization (PSO) algorithm is employed to minimize the sum of network losses, voltage deviation variance, and the operation cost of discrete voltage regulation devices. Secondly, at the intra-day timescale, a flexible DC converter model and a method for evaluating reactive power compensation capability are established. Leveraging the four-quadrant adjustable reactive power compensation characteristics, a second-order cone programming (SOCP) approach is adopted to minimize grid losses and voltage deviation variance, achieving finer-grained reactive power optimization. Finally, a collaborative optimization strategy for multiple converters is proposed to enable mutual reactive power support among converters, addressing long-term equipment overloading issues.
In the Changping regional grid of Beijing integrated with Metro Line 13 A, simulation results show that the proposed method restricts the maximum daily operations of on-load tap changer (OLTC) and capacitor bank (CB) to 5, reducing the total daily operational cost of these devices to ¥520, which is a 70.62% decrease compared to the static optimization method. By employing multi-time-scale reactive power optimization, the approach effectively suppresses voltage fluctuations and mitigates additional network losses arising from prediction errors in photovoltaic and load data. Compared with the non-optimized scenario and the day-ahead strategy-fixed scenario, network losses across the entire grid decrease by 7.30% and 2.09%, respectively, while voltage deviation variances drop by 30.09% and 13.02%, respectively. In the Huilongguan area, network losses are reduced by 12.79% and 6.50%, and voltage deviation variances by 17.60% and 1.78%, respectively. Moreover, the multi-converter collaborative optimization strategy maintains network losses and voltage deviation variance at similar levels. Meanwhile, the average high-load operation time ratio decreases from 92.6% to 74.7%. Thus, converter operational stress and losses are reduced, and the risk of equipment overloading is alleviated.
The following conclusions can be drawn from simulation analyses. (1) The multi-timescale reactive power optimization strategy addresses temporal mismatch issues among devices and mitigates voltage violations and additional network losses caused by prediction errors. Fisher’s optimal partitioning method is applied to impose temporal constraints on day-ahead device operations, ensuring power system security. (2) A reactive power optimization framework incorporating urban rail flexible DC converters is developed. By constructing minute-level traction substation power models for traction load impact characteristics and metro operational peak-valley patterns, intra-day optimization further suppresses voltage fluctuations and reduces network losses. (3) A collaborative optimization technology for multiple rail transit flexible DC converters enables dynamic reactive power allocation among converters, addressing short-term reactive power inadequacy and long-term overloading issues, thereby extending equipment service life.

Key words： Traction power supply system multi-time scale optimization flexible DC converter second order cone programming multi-converter coordination

Received: 24 April 2025

PACS:	TM922.3
	TM73

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	Yu Wenjun
	Lin Junjie
	Cai Shilong
	Lu Chao
	Li Xiaoqian

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Yu Wenjun,Lin Junjie,Cai Shilong等. Multi-Time Scale Collaborative Optimization Technology for Urban Rail Transit Flexible DC Converters Participating in Grid Reactive Power Compensation[J]. Transactions of China Electrotechnical Society, 2026, 41(8): 2836-2852.

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