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| Coordinated Planning of Rotary Power Flow Controller and Distributed Energy Storage System Considering the Economic and Carrying Capacity of Distribution Station Area |
| Yan Xiangwu1, Lu Junda1, Wu Ming2, Shao Chen1, Jia Jiaoxin1 |
1. Hebei Provincial Key Laboratory of Distributed Energy Storage and Microgrid North China Electric Power University Baoding 071003 China;
2. State Grid Shanghai Energy Internet Research Institute Co. Shanghai 201210 China |
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Abstract As a critical component of the power system, the power supply capacity of distribution station areas directly influences urban economic development and the quality of life for residents. With the growing integration of renewable energy sources, electric vehicles, and diverse load types, distribution station areas face significant challenges, including weak grid structures, uneven load distribution, and insufficient carrying capacity. The rotary power flow controller (RPFC), as an electromagnetic flexible interconnection device, offers advantages such as low unit investment cost and ease of operation and maintenance, enabling flexible control of line power flow in the spatial dimension. Meanwhile, the distributed energy storage system (DESS) enables power transfer between sources and loads in the temporal dimension. Their coordinated operation can significantly improve the carrying capacity of distribution station areas.
Firstly, the basic structures of RPFC and DESS are introduced, followed by the establishment of their mathematical models. The RPFC is a flexible interconnection device that regulates power flow based on the principle of the rotary phase shifting transformer (RPST). By adjusting both the amplitude and phase of the series voltage, it allows for precise control of power flow along the line. The DESS, utilizing battery energy storage, exchanges power with the grid via a DC-AC inverter.
Secondly, a three-layer planning framework for RPFC and DESS is constructed. In the spatial dimension, the framework first identifies the optimal power flow transfer channel, positioning the siting and sizing of the RPFC at the upper layer. This layer generates the optimal location and capacity for the RPFC with the objective of minimizing its total investment cost. The middle layer addresses the siting and sizing of the DESS, determining its installation location and capacity based on the RPFC configuration from the upper layer, aiming to minimize the DESS investment cost. The results from the middle layer are then passed to the bottom layer, which focuses on coordinated operation optimization. The goal of this bottom layer is to maximize the overall load-bearing capacity of the distribution area. Based on the siting and sizing plans from the upper and middle layers, the bottom layer determines the optimal operational power of both the RPFC and DESS for various scenarios. It further communicates the real-time charging and discharging operations of the DESS to the middle layer, while providing feedback on RPFC power transmission and branch flows to the upper layer. Through iterative optimization across the three layers, the model achieves an optimal configuration and operational strategy.
Then, based on the three-layer planning framework, a three-layer coordinated planning model of RPFC and DESS is established. To solve the proposed model, a hybrid optimization algorithm is introduced, which combines an improved gravitational field algorithm (IGFA) with second-order cone programming (SOCP). The IGFA improves the population initialization process by leveraging the randomness and ergodicity characteristics of the Tent chaotic map, which enhances the algorithm's global search capability and convergence speed.
Finally, the proposed model and algorithm are validated through simulation case studies. The results demonstrate that the coordinated planning of RPFC and DESS significantly improves power flow distribution and allocation in both temporal and spatial dimensions within the distribution area. This leads to a reduction in the annual network loss cost and enhances both the economic efficiency and load-bearing capacity of the distribution network. Moreover, the proposed IGFA-SOCP hybrid optimization algorithm improves global search efficiency while simplifying the solution process for multi-layer models, optimizing the overall calculation and reducing solution time.
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Received: 06 February 2024
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2025, Vol. 40 
