Double-layer planning method for rotary power flow controller and distributed energy storage system

Abstract

The invention provides a double-layer planning method for a rotary power flow controller and a distributed energy storage system, and belongs to the technical field of optimal configuration of a power distribution network. The method comprises the following steps: aiming at the addressing and sizing problems of a rotary power flow controller and a distributed energy storage system, taking the minimum annual comprehensive investment cost of a power distribution area as a first objective function, and constructing an economical-efficiency-based rotary power flow controller and distributed energy storage system upper-layer planning model based on an economical-efficiency objective; based on the upper-layer optimization model, taking the comprehensive bearing capacity of the power distribution area as a second objective function, and based on a lower-layer optimization model of the rotary power flow controller and the distributed energy storage system of the comprehensive bearing capacity of the power distribution area; and an optimization result obtained by solving the lower layer is fed back to the upper layer, the upper layer carries out optimization again, the overall optimization process is completed after multiple iterations, and finally a rotary power flow controller and distributed energy storage system planning scheme based on the economical efficiency and the comprehensive bearing capacity is output.

Description

Technical Field

[0001] This application belongs to the field of distribution network optimization configuration, specifically involving a two-level planning method for a rotating power flow controller and a distributed energy storage system. Background Technology

[0002] In recent years, the emergence of various flexible interconnection devices (FIDs) has provided an effective solution to the problem of loop interconnection in distribution substations. Compared with traditional switches, FIDs not only have switching functions but also add continuous controllable power, flexible switching of operating modes, and diverse control methods. This allows FIDs to avoid power outages and loop-closing shocks caused by conventional switches during switching operations, while effectively solving problems such as voltage exceeding limits and power flow imbalance. By optimizing the voltage and power flow distribution of active distribution substations in a spatial dimension, FIDs have shown broad application prospects in power flow and voltage regulation of the power grid. By interconnecting and supplying power between multiple substations through FIDs, excess renewable energy power in one substation can be transferred to another heavily loaded substation, effectively improving the renewable energy absorption rate while reducing grid losses in the distribution substation.

[0003] Power electronic interconnection controllers (FIDs) have gained significant importance in new distribution substations due to their rapid response and ability to achieve continuous real-time control. The core advantage of these devices lies in their ability to quickly adjust power parameters to meet the dynamic demands of the power grid. However, because the power control circuitry of FIDs is entirely composed of power electronic devices, investment and maintenance costs are relatively high, and fault tolerance is relatively weak. To overcome these limitations, the Rotary Power Flow Controller (RPFC), an electromagnetic flexible interconnection device, has attracted considerable attention. The RPFC achieves effective decoupled control of line power by inputting precise compensation voltages to the interconnected lines. Compared to power electronic devices, the RPFC offers advantages in fault tolerance and operation and maintenance costs, providing a new solution for power flow control and stable operation in new distribution substations.

[0004] On the other hand, distributed energy storage systems (DESS) can effectively absorb excess power from renewable energy sources while shifting peak loads to off-peak areas through flexible charging and discharging, thus achieving the effect of "peak shaving and valley filling." DESS not only optimizes the distribution of electricity at different times, but also significantly enhances the carrying capacity of distribution substations in the time dimension, enabling them to better cope with load changes and dispatch pressures caused by fluctuations in renewable energy sources.

[0005] By optimizing and coordinating the configuration of RPFC and DESS, RPFC can flexibly adjust power flow and dynamically regulate power distribution. Meanwhile, DESS can further balance system energy by storing surplus power and releasing energy during peak demand periods. This combination not only optimizes voltage and power flow distribution spatially, preventing local voltage exceedances and power flow overloads, but also enables dynamic energy scheduling over time, effectively addressing the uncertainties brought about by renewable energy integration and enhancing system flexibility and capacity. Summary of the Invention

[0006] This invention aims to address the planning problem of rotating power flow controllers and distributed energy storage systems, comprehensively considering the economy and carrying capacity of distribution substations, and proposes a two-layer planning method for rotating power flow controllers and distributed energy storage systems. The method constructs a two-layer planning model for rotating power flow controllers and distributed energy storage systems, with the first objective function being the minimum annual comprehensive investment cost of the distribution substation and the second objective function being the comprehensive carrying capacity of the distribution substation. Then, an improved gravitational field algorithm is used to optimize the upper-layer model, obtaining the optimal installation location and capacity of the rotating power flow controller and distributed energy storage system. The lower layer employs a second-order cone programming algorithm to transform the complex nonlinear and non-convex model into a mixed-integer second-order cone model to solve for the optimal operating scheme under various scenarios. The optimization results obtained from the lower layer are fed back to the upper layer. The upper layer further optimizes based on the lower-layer optimization results, completing the overall optimization process after multiple iterations, and finally outputting the optimal planning scheme for the rotating power flow controller and distributed energy storage system.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] A two-level planning method for a rotating power flow controller and a distributed energy storage system includes the following steps:

[0009] Taking the minimum annual comprehensive investment cost of the distribution substation as the first objective function, and using the power constraints, capacity constraints, power flow constraints, and node voltage constraints of the rotating power flow controller and distributed energy storage system connected to the distribution substation as constraints, an economically-based upper-level planning model for the rotating power flow controller and distributed energy storage system is constructed.

[0010] Based on the upper-level planning model, with the comprehensive carrying capacity of the distribution substation as the second objective function and with power flow constraints, node voltage constraints, and line current carrying capacity constraints as constraints, a lower-level optimization model for the rotating power flow controller and distributed energy storage system based on the comprehensive carrying capacity of the distribution substation is constructed.

[0011] An improved gravitational field algorithm combined with a hybrid optimization algorithm of second-order cone programming was used to optimize and solve the two-layer model.

[0012] The optimization results obtained from the lower layer are fed back to the upper layer, which then performs further optimization. After multiple iterations, the overall optimization process is completed, and the final output is a planning scheme for a rotating power flow controller and distributed energy storage system based on economic efficiency and comprehensive carrying capacity.

[0013] Furthermore, the upper-level planning model takes minimizing the annual comprehensive investment cost of the distribution substation area as its first objective function F1, specifically expressed as:

[0014]

[0015] In the formula, Y B Annual network loss cost for distribution radio stations; Y IC.R For the construction cost of RPFC; Y OM.R For the operation and maintenance costs of RPFC; c B Time-of-use pricing; Let p be the injected power of feeder n in the m-th scene during time period t; M is the total number of scenes; N is the total number of feeders in the distribution station area; p m Let y be the probability corresponding to the m-th scenario; μ be the discount rate; y be the probability corresponding to the m-th scenario. RPFC The economic service life of RPFC; Φ R For the set of installation nodes of RPFC; c IN.R The equipment cost per unit capacity RPFC; c CON.R The construction cost per unit capacity RPFC; λ OM.R c is the operating and maintenance cost coefficient per unit capacity RPFC; LC.R The loss cost per unit capacity RPFC; Y represents the power loss of the RPFC in the m-th scenario during time period t; IC.D For the construction cost of DESS; Y OM.D For the operation and maintenance costs of DESS; y DES The economic service life of DESS; Φ D This is the set of installation nodes for DESS; The equipment cost per unit capacity of DESS; c CON.D The construction cost per unit capacity DESS; Equipment cost per unit power DESS; λ OM.D This is the operating and maintenance cost coefficient per unit power DESS; Let be the active power of node i in the m-th scenario during time period t.

[0016] Furthermore, the constraint expressions for the upper-level optimization model are as follows:

[0017] The active power constraint of RPFC is:

[0018]

[0019] The reactive power constraint of RPFC is:

[0020]

[0021] The RPFC capacity constraint is:

[0022]

[0023] The power constraint for DESS is:

[0024]

[0025] The capacity and power constraints for DESS configuration are as follows:

[0026]

[0027] In the formula, These represent the active power flowing into the i and j terminals of the RPFC during time period t, respectively. The active power loss of the RPFC configured for line ij during time period t includes winding coil loss and internal core loss. The RPFC current configured for line ij during time period t; R RPFC X is the equivalent resistance of RPFC; RPFC The equivalent reactance of RPFC; For the internal core loss of RPFC; These represent the reactive power flowing into the i and j terminals of the RPFC during time period t. The RPFC capacity configured for line ij; Let η be the state of charge of the DESS at node i during time period t; cha η dis These are the charging and discharging efficiencies of DESS, respectively. These represent the charging and discharging power of the DESS at node i during time period t; These are the charging and discharging status flags for the DESS at node i, where 0 indicates discharging and 1 indicates charging. Let i be the capacity of the DESS at node i; , respectively, are the maximum charging and discharging power of DESS at node i; T is the total number of time periods in a scheduling cycle, taken as 24h; These are the upper and lower limits of the state of charge, respectively.

[0028] Furthermore, the lower-level optimization model uses the comprehensive carrying capacity of the distribution substation area as the second objective function F2, and its expression is:

[0029] F2 = 0.45 × F′ B.p +0.32×F′ L.p +0.23×F′ C.p

[0030] F i.p =F i.max -F i.c

[0031]

[0032] In the formula, F B For the distribution area interconnection balance index; These represent the apparent power injected into feeder 1 and feeder 2 during time period t in the m-th scene; These are the rated capacities of the main transformers connected to feeders 1 and 2, respectively. It should be noted that when the number of feeders in the distribution substation is 2, the calculation can be done directly. If the number of feeders is greater than 2, then different feeders need to be calculated... Each permutation and combination (where N is the total number of feeders) is substituted into the formula to calculate F. B Then take the average value; F L For distribution area network loss rate indicators; Let F be the active power loss of line ij in the m-th scenario during time period t; L be the total number of branches in the distribution substation area; F C Φ is an indicator of line capacity adequacy. N For the set of feeder nodes of the distribution radio station area; I ij,t,m Let I be the current flowing through line ij in the m-th scenario during time period t; ij.max F represents the maximum current in line ij. i.p F i.c These are the original values ​​of the positive and negative indicators, respectively; F i.max F i.min These represent the maximum and minimum values ​​of the indicator, respectively; F′ i.p This is the normalized value of the positive indicator.

[0033] Furthermore, the constraint expressions for the lower-level optimization model are as follows:

[0034]

[0035] U i.min ≤U i,t ≤U i.max

[0036] 0≤|I ij,t |≤I ij.max

[0037] In the formula, P ij,t Q ij,t Let P be the active and reactive power flowing from node i to node j of line ij during time period t; j,t Q j,t These represent the net active and reactive power flowing into node j during time period t; These represent the active power of the photovoltaic and wind turbines respectively during time period t; These represent the active and reactive power of the load during time period t; U i,t U j,t The voltages at nodes i and j are respectively for time period t; r ij x ij These represent the resistance and reactance of line ij, respectively; U i.min and U i.min These are the lower and upper voltage limits for node i, respectively.

[0038] Furthermore, the improved hybrid optimization algorithm combining the gravitational field algorithm with second-order cone programming includes the following steps:

[0039] (1) Initialize algorithm parameters;

[0040] (2) Introducing the Tent chaotic mapping into the gravitational field algorithm to initialize the population improves the algorithm’s global search capability. Specifically, the Tent mapping generates a more uniformly distributed initial solution by introducing chaotic behavior, thereby avoiding the local clustering problem that may be caused by traditional random initialization. This helps the algorithm escape local optima and improves the global exploration efficiency and convergence speed during the optimization process.

[0041] (3) The improved gravitational field algorithm is used to optimize the solution of the upper-level model, obtain the optimal installation location and capacity of the rotating power flow controller and the distributed energy storage system, and pass the results to the lower level;

[0042] (4) The lower layer adopts the second-order cone programming algorithm to transform the complex nonlinear non-convex model into a mixed integer second-order cone model in order to solve the optimal running scheme under each scenario and feed the optimization results obtained by the lower layer back to the upper layer.

[0043] (5) The upper layer optimizes based on the optimization results of the lower layer, and after multiple iterations, the overall optimization process is completed, and the optimal planning scheme of the rotating power flow controller and the distributed energy storage system is finally output. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram illustrating the solution process of a two-layer planning method for a rotating power flow controller and a distributed energy storage system, provided as an embodiment of the present invention.

[0046] Figure 2This is a schematic diagram of a distribution substation for a two-layer planning method of rotating power flow controller and distributed energy storage system provided in one embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0048] The following detailed description of a two-layer planning method for a rotating power flow controller and a distributed energy storage system according to the present invention, with reference to the accompanying drawings and embodiments, is provided in detail.

[0049] In one embodiment of the present invention, a 10kV triple-feeder distribution substation is selected for description, and its topology is shown below. Figure 2 The RPFC is to be installed at locations S1-S5 in the diagram, and the DESS is to be installed at nodes 4-16. A total of 3 wind turbine units and 3 photovoltaic units will be connected to the distribution area; the connection locations and capacities are shown in Table 1.

[0050] Table 1. Location and Capacity of New Energy Units

[0051]

[0052] To verify the effect of the method of the present invention on improving the economy and carrying capacity of distribution substations, the following four planning schemes were set up:

[0053] Option 1: The distribution radio area is not configured with RPFC and DESS;

[0054] Option 2: Only RPFC planning, configuration, and operational optimization are performed; DESS is not configured.

[0055] Option 3: Only DESS planning, configuration, and operational optimization are performed; RPFC is not configured.

[0056] Option 4: Coordinate and optimize the configuration and operation of RPFC and DESS.

[0057] Table 2 shows the configuration results of RPFC and DESS under the four schemes. Schemes 2 and 4 both configure RPFC at S1 and S4 because the loads at nodes 9 and 11 of feeder 2 are relatively large. Providing power flow transmission channels through S1 and S4 can maximize the transfer of heavy-load power and balance the feeder load rate. Schemes 3 and 4 both configure DESS at nodes 11 and 16 because the timing characteristics of the wind turbine output power and load fluctuations at these two nodes are significantly different. Energy storage can maximize the economy and flexibility of the distribution area through charging and discharging. The configuration capacity of RPFC and DESS in Scheme 4 is lower than that in Schemes 2 and 3, respectively. This indicates that the coordinated planning and configuration scheme of RPFC and DESS can optimize the power flow and power of the distribution area in both time and space dimensions, saving investment in both types of equipment compared to configuration in only one aspect.

[0058] Table 2 RPFC and DESS Configuration Results

[0059]

[0060] Table 3 shows a comparison of the economics of different RPFC and DESS planning schemes. The total cost of a distribution transformer area includes RPFC investment cost, DESS investment cost, and network loss cost. The RPFC and DESS investment costs respectively include the construction cost and operation and maintenance cost of RPFC and DESS. A comparison of the four schemes shows that although the combined configuration of RPFC and DESS in a distribution transformer area will incur certain investment costs, they significantly reduce the network loss cost of the distribution transformer area, thus significantly reducing the total cost of the distribution transformer area and improving overall economic efficiency. Scheme 4 reduces the total cost of the distribution transformer area by 20.99% compared to Scheme 1. This reduction includes both the network loss costs reduced by configuring RPFC and the increased economic benefits after peak shaving and valley filling by configuring DESS, demonstrating the economic efficiency of the combined planning and configuration of RPFC and DESS.

[0061] Table 3. Economic Comparison of RPFC and DESS Planning Schemes

[0062]

[0063] Table 4 compares the comprehensive carrying capacity indicators of the RPFC and DESS planning schemes. In Scheme 1, since RPFC and DESS are not configured, feeder 2 itself is a heavy-load line with insufficient capacity. The new energy power of feeders 1 and 3 is excessive but cannot provide power flow support for feeder 2, resulting in insufficient overall line capacity of the distribution substation and a low interconnection balance index.

[0064] Table 4 Comparison of the comprehensive carrying capacity of RPFC and DESS planning schemes

[0065]

[0066] By comparing the different carrying capacity indicators of schemes 2-4 in the table, it can be seen that configuring RPFC and DESS separately in schemes 2 and 3 can enhance the overall carrying capacity of the distribution substation, with their overall indicators improving by 18.8% and 5.9% respectively compared to scheme 1. Scheme 4, which coordinates and optimizes RPFC and DESS, shows the most significant improvement in carrying capacity, with an improvement of 21%, demonstrating the superiority of coordinated optimization of RPFC and DESS. Analysis of the three specific indicators measuring the carrying capacity level in schemes 2-4 shows that configuring either RPFC or DESS can reduce the network loss rate of the distribution substation, and the standardized indicators of network loss rate have all been improved to a certain extent. Compared with scheme 1, scheme 2 shows the greatest improvement in interconnection balance, because RPFC performs power control and power flow distribution, transferring the power flow of heavily loaded lines spatially and realizing power flow mutual assistance between different feeders in the distribution substation. Compared to Scheme 1, Scheme 3 shows the largest increase in line capacity adequacy among the three indicators. This is because by optimizing the charging and discharging strategy of DESS, the active power of the load is transferred in the time dimension, reducing the peak-to-valley difference of the load and the line load rate, thereby improving the carrying capacity of the distribution substation. Scheme 4 utilizes the coordinated optimization of RPFC and DESS to regulate the balanced distribution of power flow in the spatial dimension and flexibly adjust the load power in the time dimension, thereby improving the comprehensive carrying capacity of the distribution substation in both time and space dimensions.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-level planning method for a rotating power flow controller and a distributed energy storage system, characterized in that, Includes the following steps: (1) Taking the minimum annual comprehensive investment cost of the distribution substation as the first objective function, and using the power constraints, capacity constraints, power flow constraints, and node voltage constraints of the Rotary Power Flow Controller (RPFC) and the distributed energy storage system connected to the distribution substation as constraints, an economically based upper-level planning model of the rotating power flow controller and the distributed energy storage system is constructed. (2) Based on the upper-level planning model, with the comprehensive carrying capacity of the distribution substation as the second objective function and with power flow constraints, node voltage constraints, and line current carrying capacity constraints as constraints, a lower-level optimization model of the rotating power flow controller and distributed energy storage system based on the comprehensive carrying capacity of the distribution substation is constructed. (3) The two-layer model is optimized and solved by using a hybrid optimization algorithm combining the improved gravitational field algorithm and second-order cone programming; (4) Feed the optimization results obtained from the lower layer to the upper layer, and the upper layer will optimize again. After multiple iterations, the overall optimization process is completed, and finally the rotating power flow controller and distributed energy storage system planning scheme based on economy and comprehensive carrying capacity are output.

2. The two-layer planning method for a rotating power flow controller and a distributed energy storage system according to claim 1, characterized in that, The aforementioned upper-level planning model takes minimizing the annual comprehensive investment cost of the distribution substation as its first objective function F1, and its specific expression is as follows: In the formula, Y B Annual network loss cost for distribution radio stations; Y IC.R For the construction cost of RPFC; Y OM.R For the operation and maintenance costs of RPFC; c B Time-of-use pricing; Let p be the injected power of feeder n in the m-th scene during time period t; M is the total number of scenes; N is the total number of feeders in the distribution station area; p m Let y be the probability corresponding to the m-th scenario; μ be the discount rate; y be the probability corresponding to the m-th scenario. RPFC The economic service life of RPFC; Φ R For the set of installation nodes of RPFC; c IN.R The equipment cost per unit capacity RPFC; c CON.R The construction cost per unit capacity RPFC; λ OM.R This is the operating and maintenance cost coefficient per unit capacity RPFC; c LC.R The loss cost per unit capacity RPFC; Y represents the power loss of the RPFC in the m-th scenario during time period t; IC.D For the construction cost of DESS; Y OM.D For the operation and maintenance costs of DESS; y DES The economic service life of DESS; Φ D This is the set of installation nodes for DESS; The equipment cost per unit capacity of DESS; c CON.D The construction cost per unit capacity DESS; Equipment cost per unit power DESS; λ OM.D This is the operating and maintenance cost coefficient per unit power DESS; Let be the active power of node i in the m-th scenario during time period t.

3. The two-layer planning method for a rotating power flow controller and a distributed energy storage system according to claim 1, characterized in that, The constraint expressions for the upper-level optimization model are as follows: The active power constraint of RPFC is: The reactive power constraint of RPFC is: The RPFC capacity constraint is: The power constraint for DESS is: The capacity and power constraints for DESS configuration are as follows: In the formula, These represent the active power flowing into the i and j terminals of the RPFC during time period t, respectively. The active power loss of the RPFC configured for line ij during time period t includes winding coil loss and internal core loss. The RPFC current configured for line ij during time period t; R RPFC X is the equivalent resistance of RPFC; RPFC The equivalent reactance of RPFC; For the internal core loss of RPFC; These represent the reactive power flowing into the i and j terminals of the RPFC during time period t. The RPFC capacity configured for line ij; Let t represent the charge state of DESS at node i during time period t; η cha η dis These are the charging and discharging efficiencies of DESS, respectively. These represent the charging and discharging power of the DESS at node i during time period t; These are the charging and discharging status flags for the DESS at node i, where 0 indicates discharging and 1 indicates charging. Let i be the capacity of the DESS at node i; These are the maximum charging and discharging power of the DESS at node i, respectively; T represents the total number of time periods in a scheduling cycle, which is set to 24 hours. These are the upper and lower limits of the state of charge, respectively.

4. The two-layer planning method for a rotating power flow controller and a distributed energy storage system according to claim 1, characterized in that, The lower-level optimization model uses the comprehensive carrying capacity of the distribution area as the second objective function F2, and its expression is: F2=0.45×F′ B.p +0.32×F′ L.p +0.23×F′ C.p F i.p =F i.max -F i.c In the formula, F B For the distribution area interconnection balance index; These represent the apparent power injected into feeder 1 and feeder 2 during time period t in the m-th scene; These are the rated capacities of the main transformers connected to feeders 1 and 2, respectively. It should be noted that when the number of feeders in the distribution substation is 2, the calculation can be done directly. If the number of feeders is greater than 2, then different feeders need to be calculated... Each permutation and combination (where N is the total number of feeders) is substituted into the formula to calculate F. B Then take the average value; F L For distribution area network loss rate indicators; Let F be the active power loss of line ij in the m-th scenario during time period t; L be the total number of branches in the distribution substation area; F C This is an indicator of line capacity adequacy. Φ N For the set of feeder nodes of the distribution radio area; I ij,t,m Let I be the current flowing through line ij in the m-th scenario during time period t; ij.max F represents the maximum current in line ij. i.p F i.c These are the original values ​​of the positive and negative indicators, respectively; F i.max F i.min These represent the maximum and minimum values ​​of the indicator, respectively; F′ i.p This is the normalized value of the positive indicator.

5. The two-layer planning method for a rotating power flow controller and a distributed energy storage system according to claim 1, characterized in that, The constraint expressions of the lower-level optimization model are as follows: IN i.min ≤U i,t ≤U i.max 0≤|I ij,t |≤I ij.max In the formula, P ij,t Q ij,t Let P be the active and reactive power flowing from node i to node j of line ij during time period t; j,t Q j,t These represent the net active and reactive power flowing into node j during time period t; These represent the active power of the photovoltaic and wind turbines respectively during time period t; These represent the active and reactive power of the load during time period t; U i,t U j,t The voltages at nodes i and j are respectively for time period t; r ij x ij These are the resistance and reactance of line ij, respectively; U i.min and U i.min These are the lower and upper voltage limits for node i, respectively.

6. The two-layer planning method for a rotating power flow controller and a distributed energy storage system according to claim 1, characterized in that, The improved gravitational field algorithm combined with the second-order cone programming hybrid optimization algorithm includes the following steps: (1) Initialize algorithm parameters; (2) Introducing the Tent chaotic mapping into the gravitational field algorithm to initialize the population improves the algorithm’s global search capability. Specifically, the Tent mapping generates a more uniformly distributed initial solution by introducing chaotic behavior, thereby avoiding the local clustering problem that may be caused by traditional random initialization. This helps the algorithm escape local optima and improves the global exploration efficiency and convergence speed during the optimization process. (3) The improved gravitational field algorithm is used to optimize the solution of the upper-level model, obtain the optimal installation location and capacity of the rotating power flow controller and the distributed energy storage system, and pass the results to the lower level; (4) The lower layer adopts the second-order cone programming algorithm to transform the complex nonlinear non-convex model into a mixed integer second-order cone model in order to solve the optimal running scheme under each scenario and feed the optimization results obtained by the lower layer back to the upper layer. (5) The upper layer optimizes based on the optimization results of the lower layer, and after multiple iterations, the overall optimization process is completed, and the optimal planning scheme of the rotating power flow controller and the distributed energy storage system is finally output.

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