Method for improving source and load carrying capacity of transformer area based on virtual feeder-recombinant mobile energy storage

Abstract

The application provides a method for improving source and load carrying capacity of a transformer area based on virtual feeder-reconfigurable mobile energy storage, comprising: 1. constructing a mobile energy storage space-time transfer model based on reconfigurable decoupling and virtual feeder; 2. establishing a battery module on-demand reconfiguration model; 3. constructing a battery block charging and discharging consistency sorting and safety control model based on state of charge difference; 4. under the conditions of meeting the operation of the distribution network, virtual feeder transfer and battery module physical constraints, establishing a mixed integer second-order cone programming model, optimizing the vehicle scheduling path and battery module reconfiguration scheme, and realizing flexible energy allocation and space-time collaborative control across transformer area nodes. The method breaks through the traditional fixed capacity limit of mobile energy storage, improves the configuration accuracy and utilization efficiency of energy storage resources among multiple nodes; at the same time, the virtual feeder technology breaks through the physical topology constraint of the distribution network, and can construct a cross-time and space energy interconnection channel without building new lines, thereby enhancing the flexibility of the distribution network structure and the scheduling elasticity.

Description

Technical Field

[0001] This invention belongs to the field of distribution network operation optimization, specifically a method for improving the load-carrying capacity of distribution areas based on virtual feeders and reconfigurable mobile energy storage. Background Technology

[0002] As the scale of distributed renewable energy (such as photovoltaic and wind power) integration into distribution networks continues to expand, these networks are increasingly exhibiting characteristics such as uneven load distribution and increased power volatility. Some distribution areas are prone to voltage exceeding limits, line overload, or limited renewable energy absorption at different times. Traditional distribution network structures and equipment capacities are typically planned based on static loads, making it difficult to adapt to dynamic changes in load over time and space. Therefore, there is an urgent need to improve the load carrying capacity of distribution networks through flexible resource adjustment. Energy storage technology enables the transfer of electrical energy over time and is an important means of improving the flexibility of distribution networks. However, traditional fixed energy storage systems have fixed installation locations, making spatial allocation between different nodes difficult, resulting in limited energy storage resource utilization efficiency. Mobile energy storage, which transports energy storage devices by vehicle, allows energy to be transferred between different distribution area nodes, improving resource scheduling flexibility to some extent. However, existing mobile energy storage systems mostly adopt an integrated structure, making it difficult to dynamically adjust capacity according to the needs of different nodes. On the other hand, reconfigurable battery technology enables energy storage systems to consist of multiple standardized battery modules. However, in actual operation, there are often differences in the state of charge (SOC) between the modules. Without effective coordination and control, this can easily limit the overall charging and discharging capacity of the system and bring safety risks. Meanwhile, energy exchange between nodes in traditional distribution networks mainly relies on existing physical lines, with a fixed topology and limited cross-regional energy distribution capabilities. Summary of the Invention

[0003] To address the shortcomings of the prior art, this invention proposes a method for enhancing the load-carrying capacity of distribution networks based on virtual feeders and reconfigurable mobile energy storage. This method aims to combine reconfigurable mobile energy storage with virtual feeder technology to achieve flexible reconfiguration of battery modules, efficient scheduling of mobile energy storage, and spatiotemporal coordinated allocation of energy across distribution networks. While ensuring the safety and consistency of battery operation, this method also enhances the load-carrying capacity of the distribution network.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: This invention discloses a method for enhancing the load-carrying capacity of distribution substations based on virtual feeders and reconfigurable mobile energy storage. This method is applied to a coupled system of distribution networks and transportation networks. The reconfigurable mobile energy storage consists of transport vehicles and multiple standardized battery modules. The transport vehicles enable the spatial transfer of battery modules between different distribution substation nodes, and the unloading and reassembly of battery modules enable on-demand reconfiguration and access. The method is characterized by the following steps: Step 1: Construct a virtual feeder spatiotemporal transfer model for reconfigurable mobile energy storage to describe the dynamic relationship between the spatial transfer path, travel delay, and parking status of transport vehicles and their battery modules at different nodes of the distribution network. Step 2: Construct a battery module reconfiguration model to address the differences in source-load consumption across different distribution areas; Step 3: Dynamically adjust the charging and discharging power of each battery module connected to the same node through a power allocation mechanism, thereby constructing a battery equalization model based on the difference in state of charge. Step 4: Under the conditions of satisfying the power flow security constraints of the distribution network, the virtual feeder transfer constraints, and the physical constraints of the battery modules, based on the models in Steps 1, 2, and 3, construct a power flow model for improving the load-carrying capacity of the transformer substation with virtual feeder-reconfigurable mobile energy storage, and then solve it after converting it into a model under linear constraints to obtain the optimal vehicle dispatching route and the dynamic reconfiguration scheme of the battery modules, including: battery module charging and discharging power, battery module reconfiguration status, battery module availability status, and vehicle dispatching route.

[0005] The method for improving the load-carrying capacity of transformer substations based on virtual feeders and reconfigurable mobile energy storage, as described in this invention, is characterized in that, in step 1, a spatiotemporal transfer model of virtual feeders for reconfigurable mobile energy storage is constructed using equations (1) to (11): (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In equations (1)-(16), Indicates vehicle Is it in Time is determined by nodes Transfer to node ; Indicates vehicle exist Is the time starting from the node? Depart; if so, then order. Otherwise, let , for Time vehicle With nodes The connection status; if connected, then let If disconnected, then let ; Indicates vehicle From node To the node The distance; Indicates battery module From node To the node The distance; Indicates battery module exist From the node Scheduled to node The time required; Indicates vehicle exist From the node Scheduled to node The time required; This indicates the battery module in reconfigurable mobile energy storage. exist Is the delivery direction at any given time from the node? Scheduled to node If so, then let Otherwise, let , Indicates battery module exist Is the time starting from the node? Delivery to other nodes; if so, then... Otherwise, let , for Time Battery Module With nodes The connection status; if connected, then let If disconnected, then let ; For the installation and configuration time of the battery module; A collection of battery modules in reconfigurable mobile energy storage; This indicates the maximum number of battery modules that can be installed in each vehicle; A collection of vehicles; For a coupled system, it is the set of nodes. A set of moments for one running cycle; for Time Battery Module With vehicles The connection status; for Time vehicle The speed of travel; This represents the ideal vehicle speed under zero-flow traffic conditions. for Traffic congestion coefficient of a time-coupled system; , They are respectively t Time Battery Module The charging and discharging active power; , for Virtual feeder at node Equivalent absorbed and equivalent released active power; , These are the upper limits of the charging and discharging active power of the battery module, respectively. For energy storage module u in The state of charge at any given moment; For battery module At any moment The state of charge; It is a positive number.

[0006] Furthermore, in step 2, the battery module reconfiguration model is constructed using equations (17) to (22): (17) (18) (19) (20) (twenty one) (twenty two) In equations (17)-(22), for Time Battery Module At the node The continuous docking and stable connection state is only achieved when the battery module... exist Time and Always connected to the node When it was time, he ordered =1; , They are respectively Battery modules in mobile energy storage that can be reconfigured at any time The charging and discharging indicators, if =1 indicates the battery module Charging, if =1 indicates the battery module Discharge; , They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; for Time Battery Module With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0.

[0007] Furthermore, in step 3, an energy storage equilibrium control model based on the difference in state of charge is constructed using equations (23) to (33): (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) (31) (32) (33) In equations (23)-(33), For battery module exist The state of charge at any given moment; , These represent the maximum and minimum nuclear power states of the battery module. For battery module With battery module exist The variable representing the difference in state of charge (SOC) at any given time is used to indicate the relationship between the battery module and the SOC. exist Is the state of charge at any given time higher than that of the battery module? exist The state of charge at any given moment; Indicates battery module exist Intermediate variables related to the charging state at any given time; Indicates battery module exist Intermediate variables of the discharge state at any given time; It is a positive number. Represent another positive number, and Less than , Indicates battery module exist Battery level at any moment Indicates battery module exist The maximum battery level at any given time.

[0008] Furthermore, step 4 includes: Step 4.1, Constraints for constructing the power flow model of virtual feeder-reconfigurable mobile energy storage to enhance the source-load carrying capacity of the transformer area using equations (34)-(42): (34) (35) (36) (37) (38) (39) (40) (41) (42) In equations (34)-(42), express t Distributed power supply and nodes The connection status; , They represent t Time Node The active and reactive power outputs of the distributed power sources connected above; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the distributed power sources connected to it; , They are nodes The upper and lower limits of the power factor of the distributed power source connected to the above; , They are nodes The set of child and parent nodes; for t Time Node and its child nodes Branch roads between Active power transmitted upstream, for t Time Node parent node and nodes Branch roads between Active power transmitted upstream; , for t Time Branch reactive power transmitted from the branch Reactive power transmitted upstream; , For nodes superior t At any given time, the load's active power and reactive power are measured. for t Time Node The square of the voltage applied; for t Time Node The square of the voltage applied; for t Time Branch The square of the current, for t Time Branch The square of the current; and Branch roads Resistance and reactance; and Branch roads Resistance and reactance; , They are nodes Upper and lower limits of the voltage square term; branch road The upper limit of the square term of the current; , for t Time Node Waste light and waste load; Step 4.2: Construct the objective function of the power flow model for improving the load-carrying capacity of the transformer area using the virtual feeder-reconfigurable mobile energy storage system based on equation (43). : (43) In equation (43), This is the unit loss coefficient of the line in the coupled system; The photovoltaic reduction factor of the line in the coupled system; This is the load reduction factor for the lines in the coupled system; For a coupled system, it is the set of branches; The cost of vehicle movement and battery reconfiguration in the coupled system; Indicates vehicle The unit operating cost This represents the reconfiguration cost of each battery module used for each vehicle bonding operation; , Indicates battery module The charging and discharging efficiency; Indicates a time interval; Step 4.3: Use equations (44)-(49) to transform the quadratic terms in equations (37)-(42). and Transform into a linear representation: (44) (45) (46) (47) (48) (49) In equations (44)-(49), express t Time Node The square of the voltage applied; express t Time Node The square of the voltage applied; express t Time Branch The square of the current; Step 4.4: Multiply the quadratic terms in equation (49) using equation (50). After performing second-order cone relaxation, equation (50) is transformed into the standard second-order cone form using equation (51): (50) (51) Step 4.5, the objective function After forming a linearly constrained model with equations (1)-(36), (44)-(48) and (51), the model is solved using the Gurobi solver to obtain the proposed scheme for improving the load-carrying capacity of the transformer area based on virtual feeder-reconfigurable mobile energy storage.

[0009] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program that supports the processor in executing the method for improving the source-load carrying capacity of a transformer area based on virtual feeder-reconfigurable mobile energy storage, and the processor is configured to execute the program stored in the memory.

[0010] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, performs the steps of the method for improving the load-carrying capacity of a transformer substation based on virtual feeder-reconfigurable mobile energy storage.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention enables flexible transfer and capacity reconfiguration of battery modules between different power distribution nodes by constructing a spatiotemporal transfer model for mobile energy storage and an on-demand reconfiguration model for battery modules. This breaks through the limitation of fixed capacity in traditional mobile energy storage and improves the configuration accuracy and utilization efficiency of energy storage resources among multiple nodes.

[0012] 2. This invention combines a battery module equalization control model based on the difference in state of charge, which can dynamically coordinate the charging and discharging power of each battery module, reduce the deviation in state of charge between modules, effectively avoid the "barrel effect" in traditional reconfigurable mobile energy storage systems, and improve the overall available capacity and operational safety of the energy storage system.

[0013] 3. This invention utilizes virtual feeder technology to construct energy transmission channels across distribution area nodes at the scheduling level, achieving energy mutual assistance and coordinated allocation between different distribution areas without the need for additional physical lines, thereby enhancing the flexibility of the distribution network structure and the elasticity of scheduling.

[0014] 4. Under the conditions of satisfying the power flow constraints of the distribution network, the operation constraints of the battery module, and the vehicle scheduling constraints, the present invention obtains the optimal scheduling scheme by establishing a mixed integer second-order cone programming optimization model. This not only reduces line losses and the curtailment rate of new energy sources, but also reduces local overload and voltage fluctuations, thereby improving the overall reliability and economy of the distribution network operation. Attached Figure Description

[0015] Figure 1This is a charging power diagram of the battery modules of the present invention, wherein (a) is the charging power of each battery module; and (b) is the discharging power of each battery module. Figure 2 This is a diagram showing the reorganization and distribution of the battery module of the present invention; Figure 3 This is a diagram of energy interaction and transfer between distribution network nodes according to the present invention; Figure 4 This is a power distribution network voltage performance diagram of the present invention; Figure 5 This is a flowchart illustrating the implementation of the method described in this invention. Detailed Implementation

[0016] In this embodiment, a method for enhancing the load-carrying capacity of distribution substations based on virtual feeders and reconfigurable mobile energy storage utilizes a reconfigurable mobile energy storage system and its multi-level collaborative control scheme. This involves using transport vehicles to spatially transfer battery modules between different distribution substation nodes, and employing a reconfigurable structure for flexible assembly and dynamic connection of the battery modules. Simultaneously, a balance control scheme based on state of charge (SOC) differences is combined to coordinate charging and discharging power and schedule the state of battery modules connected to the same node, thereby constructing a multi-level control mechanism across nodes and time periods: "bottom-level module reconfiguration—middle-level state balancing—top-level energy coordination." This method not only effectively eliminates operational inconsistencies caused by SOC differences within the reconfigurable mobile energy storage system and ensures the safe and stable operation of battery modules, but also significantly improves the energy transfer capacity of the energy storage system in both spatial and temporal dimensions. Furthermore, by constructing energy exchange channels across distribution substation nodes at the scheduling level using virtual feeder technology, energy coordination and allocation between multiple nodes can be achieved without adding new physical lines, thereby improving the distribution network's capacity to absorb distributed energy and the overall flexibility and reliability of the system. Specifically, as... Figure 5 As shown, the specific steps of this method are as follows: Step 1: Construct a virtual feeder spatiotemporal transfer model for reconfigurable mobile energy storage using equations (1)-(11): (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In equations (1)-(16), Indicates vehicle Is it in Time is determined by nodes Transfer to node ; Indicates vehicle exist Is the time starting from the node? Depart; if so, then order. Otherwise, let , for Time vehicle With nodes The connection status; if connected, then let If disconnected, then let ; Indicates vehicle From node To the node The distance; Indicates battery module From node To the node The distance; Indicates battery module exist From the node Scheduled to node The time required; Indicates vehicle exist From the node Scheduled to node The time required; This indicates the battery module in reconfigurable mobile energy storage. exist Is the delivery direction at any given time from the node? Scheduled to node If so, then let Otherwise, let , Indicates battery module exist Is the time starting from the node? Delivery to other nodes; if so, then... Otherwise, let , for Time Battery Module With nodes The connection status; if connected, then let If disconnected, then let ; For the installation and configuration time of the battery module; A collection of battery modules in reconfigurable mobile energy storage; This indicates the maximum number of battery modules that can be installed in each vehicle; A collection of vehicles; For a coupled system, it is the set of nodes. A set of moments for one running cycle; for Time Battery Module With vehicles The connection status; for Time vehicle The speed of travel; This represents the ideal vehicle speed under zero-flow traffic conditions. for Traffic congestion coefficient of a time-coupled system; , They are respectively t Time Battery Module The charging and discharging active power; , for Virtual feeder at node Equivalent absorbed and equivalent released active power; , These are the upper limits of the charging and discharging active power of the battery module, respectively. For energy storage module u in The state of charge at any given moment; For battery module At any moment The state of charge; It is a positive number.

[0017] Step 2: Construct the battery module reconfiguration model based on equations (17)-(22): (17) (18) (19) (20) (twenty one) (twenty two) In equations (17)-(22), for Time Battery Module At the node The continuous docking and stable connection state is only achieved when the battery module... exist Time and Always connected to the node When it was time, he ordered =1; , They are respectively Battery modules in mobile energy storage that can be reconfigured at any time The charging and discharging indicators, if =1 indicates the battery module Charging, if =1 indicates the battery module Discharge; , They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; for Time Battery Module With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0.

[0018] Step 3: Construct an energy storage balance control model based on the difference in state of charge using equations (23)-(33): (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) (31) (32) (33) In equations (23)-(33), For battery module exist The state of charge at any given moment; , These represent the maximum and minimum nuclear power states of the battery module. For battery module With battery module exist The variable representing the difference in state of charge (SOC) at any given time is used to indicate the relationship between the battery module and the SOC. exist Is the state of charge at any given time higher than that of the battery module? exist The state of charge at any given moment; Indicates battery module exist Intermediate variables related to the charging state at any given time; Indicates battery module exist Intermediate variables of the discharge state at any given time; Indicates battery module exist Battery level at any moment Indicates battery module exist The maximum battery level at any given time; It is a large positive number. This represents another, smaller, positive number. Less than .

[0019] Step 4.1: Construct a power flow model for improving the source-load carrying capacity of the virtual feeder-reconfigurable mobile energy storage area using equations (34)-(42): (34) (35) (36) (37) (38) (39) (40) (41) (42) In equations (34)-(42), express t Distributed power supply and nodes The connection status; , They represent t Time Node The active and reactive power outputs of the distributed power sources connected above; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the distributed power sources connected to it; , They are nodes The upper and lower limits of the power factor of the distributed power source connected to the above; , They are nodes The set of child and parent nodes; for t Time Node and its child nodes Branch roads between Active power transmitted upstream, for t Time Node parent node and nodes Branch roads between Active power transmitted upstream; , for t Time Branch reactive power transmitted from the branch Reactive power transmitted upstream; , For nodes superior t At any given time, the load's active power and reactive power are measured. for t Time Node The square of the voltage applied; for t Time Node The square of the voltage applied; for t Time Branch The square of the current, for t Time Branch The square of the current; and Branch roads Resistance and reactance; and Branch roads Resistance and reactance; , They are nodes Upper and lower limits of the voltage square term; branch road The upper limit of the square term of the current; , for t Time Node The discarded light and load.

[0020] Step 4.2: Construct the objective function of the power flow model for improving the load-carrying capacity of the transformer area using the virtual feeder-reconfigurable mobile energy storage system based on equation (43). : (43) In equation (43), This is the unit loss coefficient of the line in the coupled system; The photovoltaic reduction factor of the line in the coupled system; This is the load reduction factor for the lines in the coupled system; For a coupled system, it is the set of branches; The cost of vehicle movement and battery reconfiguration in the coupled system; Indicates vehicle The unit operating cost This represents the reconfiguration cost of each battery module used for each vehicle bonding operation; , Indicates battery module The charging and discharging efficiency; Indicates a time interval.

[0021] Step 4.3: Use equations (44)-(49) to transform the quadratic terms in equations (37)-(42). and Transform into a linear representation: (44) (45) (46) (47) (48) (49) In equations (44)-(49), express t Time Node The square of the voltage applied; express t Time Node The square of the voltage applied; express t Time Branch The square of the current.

[0022] Step 4.4: Multiply the quadratic terms in equation (49) using equation (50). After performing second-order cone relaxation, equation (50) is transformed into the standard second-order cone form using equation (51): (50) (51) Step 4.5, the objective function After forming a linearly constrained model with equations (1)-(36), (44)-(48) and (51), the model is solved using the Gurobi solver to obtain the proposed scheme for improving the load-carrying capacity of the transformer area based on virtual feeder-reconfigurable mobile energy storage.

[0023] In summary, the method of this invention effectively solves the problems of inflexible cross-regional allocation of fixed energy storage, inflexible capacity configuration of mobile energy storage, and inconsistent state of charge of reconfigurable battery modules in traditional distribution networks. While ensuring controllable system operating costs, it achieves dynamic allocation and coordinated scheduling of energy storage resources among different transformer substation nodes by constructing a spatiotemporal transfer model for mobile energy storage, an on-demand reconfiguration mechanism for battery modules, and a balance control scheme based on state of charge differences. This improves the utilization efficiency of energy storage resources and the control flexibility of the distribution network. Simultaneously, this invention utilizes virtual feeder technology to overcome the limitations of the traditional physical topology of distribution networks, achieving energy exchange and spatiotemporal coordinated allocation across transformer substation nodes without the need for new lines. This reduces problems such as curtailment of solar and wind power or local overload caused by source-load mismatch, and improves the overall reliability and economy of the distribution network while ensuring the operational safety and state consistency of battery modules.

[0024] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the methods described above, and the processor is configured to execute the program stored in the memory.

[0025] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0026] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components: Numerical simulation tests were conducted on an improved IEEE 33-bus system with a rated voltage of 12.66kV. The system's safe voltage range was set to [0.95, 1.05] pu, and nodes 9, 13, 15, 25, 32, and one traffic node (node ​​34) were designated as reconfigurable mobile energy storage nodes. The active power-related cost was set at 0.5 yuan / kW·h; a total of 15 batteries were installed, each with a capacity of 20 kWh and a maximum charge / discharge power of 10 kW. Photovoltaic generator sets with capacities of 900 kVA, 800 kVA, 700 kVA, 600 kVA, and 500 kVA were installed at nodes 7, 9, 13, 15, and 27, respectively.

[0027] Depend on Figure 1 As shown in (a), the battery modules are concentrated in charging during the peak photovoltaic output period from 10:00 to 14:00 to store electrical energy and avoid wasting solar power; Figure 1 As shown in (b), the battery modules discharge intensively during the peak electricity consumption periods of 8:00-10:00 and 19:00-21:00 in the morning and evening; at the same time, the charging and discharging power of each battery module is within the safe range.

[0028] Depend on Figure 2 It can be seen that the dynamic combination and movement of the battery modules are constantly changing. During the peak period of photovoltaic output, the battery modules are mainly concentrated at nodes 9, 13 and 15 connected to the photovoltaic system to achieve centralized charging to absorb excess power, effectively alleviate the problem of curtailment and suppress local voltage over-limit. Depend on Figure 3 It can be seen that the reconfigurable mobile energy storage can transfer electrical energy between nodes 9, 13, 15, 25, 32 and 34 throughout the day. Through combination and allocation, the battery modules can move to the photovoltaic installation site to absorb and store electrical energy during the peak photovoltaic output period, and move to the load concentration area to discharge during the evening peak period. Through the movement path of the reconfigurable mobile energy storage, a dynamic "virtual feeder" is constructed, realizing the transfer of electrical energy in time and space, thereby effectively improving the flexible control capability of the power distribution system. Depend on Figure 4 It can be seen that the system voltage is always strictly maintained within the range of [0.95, 1.05] pu; The results of the numerical examples show that the proposed method for enhancing the load-carrying capacity of distribution substations based on virtual feeders and reconfigurable mobile energy storage not only enables flexible cross-node and cross-time-period scheduling of mobile energy storage among different distribution substation nodes during distribution network operation, effectively expanding the system's energy allocation capabilities in both spatial and temporal dimensions and improving the flexibility of distribution network regulation; but also, by constructing a battery module on-demand reconfiguration model and a balance control scheme based on state-of-charge differences, it achieves dynamic identification and refined management of the battery module's operating status. Through flexible reconfiguration of battery modules and optimized allocation of charging and discharging power, the state-of-charge deviation between modules can be gradually reduced, avoiding problems such as overcharging, over-discharging, and operational mismatch caused by inconsistent SOC. Furthermore, under the coordinated scheduling of the virtual feeder mechanism and mobile energy storage, this embodiment can achieve energy mutual assistance between different distribution substations without changing the original physical topology of the distribution network. While ensuring voltage stability at distribution network nodes and efficient absorption of distributed power sources, it effectively reduces line congestion and power loss, and slows down the cyclic aging of the energy storage system. This achieves a comprehensive optimization of energy storage resource utilization efficiency and distribution network operation reliability and economy.

Claims

1. A method for enhancing the load-carrying capacity of distribution substations based on virtual feeders and reconfigurable mobile energy storage, applied to a coupled system of distribution network and transportation network, wherein the reconfigurable mobile energy storage consists of transport vehicles and multiple standardized battery modules. The transport vehicles enable the spatial transfer of battery modules between different distribution substation nodes, and the unloading and reassembly of battery modules enable on-demand reconfiguration and access. The method is characterized by... The method for improving the load-bearing capacity of the transformer substation includes the following steps: Step 1: Construct a virtual feeder spatiotemporal transfer model for reconfigurable mobile energy storage to describe the dynamic relationship between the spatial transfer path, travel delay, and parking status of transport vehicles and their battery modules at different nodes of the distribution network. Step 2: Construct a battery module reconfiguration model to address the differences in source-load consumption across different distribution areas; Step 3: Dynamically adjust the charging and discharging power of each battery module connected to the same node through the power allocation mechanism, thereby constructing a battery balancing model based on the difference in state of charge using equations (23)-(33). (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) In equations (23)-(33), For energy storage module u in The state of charge at any given moment; For battery module exist The state of charge at any given moment; , These represent the maximum and minimum states of charge of the battery module. For battery module With battery module exist The variable representing the difference in state of charge (SOC) at any given time is used to indicate the relationship between the battery module and the SOC. exist Is the state of charge at any given time higher than that of the battery module? exist The state of charge at any given moment; Indicates battery module exist Intermediate variables related to the charging state at any given time; Indicates battery module exist Intermediate variables of the discharge state at any given time; It is a positive number. Represent another positive number, and Less than , Indicates battery module exist Battery level at any moment Indicates battery module exist The maximum battery level at any given time; , They are respectively Reconfigurable mobile energy storage battery modules The charging and discharging indicators, if =1 indicates the battery module Charging; if =1 indicates the battery module Discharge; , They are respectively t Time Battery Module The charging and discharging active power; , They are respectively t Time Battery Module The charging and discharging active power; , They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; Step 4: Under the conditions of satisfying the power flow security constraints of the distribution network, the virtual feeder transfer constraints, and the physical constraints of the battery modules, based on the models in Steps 1, 2, and 3, construct a power flow model for improving the load-carrying capacity of the transformer substation with virtual feeder-reconfigurable mobile energy storage, and then solve it after converting it into a model under linear constraints to obtain the optimal vehicle dispatching route and the dynamic reconfiguration scheme of the battery modules, including: battery module charging and discharging power, battery module reconfiguration status, battery module availability status, and vehicle dispatching route.

2. The method for improving the source-load carrying capacity of a transformer substation based on virtual feeder-reconfigurable mobile energy storage according to claim 1, characterized in that, In step 1, a virtual feeder spatiotemporal transfer model for reconfigurable mobile energy storage is constructed using equations (1) to (11): (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In equations (1)-(16), Indicates vehicle Is it in Time is determined by nodes Transfer to node ; Indicates vehicle exist Is the time starting from the node? Depart; if so, then order. Otherwise, let , for Time vehicle With nodes The connection status; if connected, then let ; If disconnected, then let ; Indicates vehicle From node To the node The distance; Indicates battery module From node To the node The distance; Indicates battery module exist From the node Scheduled to node The time required; Indicates vehicle exist From the node Scheduled to node The time required; This indicates the battery module in reconfigurable mobile energy storage. exist Is the delivery direction at any given time from the node? Scheduled to node If so, then let Otherwise, let , Indicates battery module exist Is the time starting from the node? Delivery to other nodes; if so, then... Otherwise, let , for Time Battery Module With nodes The connection status; if connected, then let ; If disconnected, then let ; For the installation and configuration time of the battery module; A collection of battery modules in reconfigurable mobile energy storage; This indicates the maximum number of battery modules that can be installed in each vehicle; A collection of vehicles; For a coupled system, it is the set of nodes. A set of moments for one running cycle; for Time Battery Module With vehicles The connection status; for Time vehicle The speed of travel; This represents the ideal vehicle speed under zero-flow traffic conditions. for Traffic congestion coefficient of a time-coupled system; , for Virtual feeder at node Equivalent absorbed and equivalent released active power; , These are the upper limits of the charging and discharging active power of the battery module, respectively. For battery module At any moment The state of charge; It is a positive number.

3. The method for improving the source-load carrying capacity of a transformer substation based on virtual feeder-reconfigurable mobile energy storage according to claim 2, characterized in that, Step 2 involves constructing the battery module reconfiguration model based on equations (17) to (22): (17) (18) (19) (20) (21) (22) In equations (17)-(22), for Time Battery Module At the node The continuous docking and stable connection state is only achieved when the battery module... exist Time and Always connected to the node When it was time, he ordered =1, for Time Battery Module With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0.

4. The method for improving the source-load carrying capacity of a transformer substation based on virtual feeder-reconfigurable mobile energy storage according to claim 3, characterized in that, Step 4 includes: Step 4.1, Constraints for constructing the power flow model of virtual feeder-reconfigurable mobile energy storage to enhance the source-load carrying capacity of the transformer area using equations (34)-(42): (34) (35) (36) (37) (38) (39) (40) (41) (42) In equations (34)-(42), express t Distributed power supply and nodes The connection status; , They represent t Time Node The active and reactive power outputs of the distributed power sources connected above; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the distributed power sources connected to it; , They are nodes The upper and lower limits of the power factor of the distributed power source connected to the above; , They are nodes The set of child and parent nodes; for t Time Node and its child nodes Branch roads between Active power transmitted upstream, for t Time Node parent node and nodes Branch roads between Active power transmitted upstream; , for t Time Branch reactive power transmitted from the branch Reactive power transmitted upstream; , For nodes superior t At any given time, the load's active power and reactive power are measured. for t Time Node The square of the voltage applied; for t Time Node The square of the voltage applied; for t Time Branch The square of the current, for t Time Branch The square of the current; and Branch roads Resistance and reactance; and Branch roads Resistance and reactance; , They are nodes Upper and lower limits of the voltage square term; branch road The upper limit of the square term of the current; , for t Time Node Waste light and waste load; Step 4.2: Construct the objective function of the power flow model for improving the load-carrying capacity of the transformer area using the virtual feeder-reconfigurable mobile energy storage system based on equation (43). : (43) In equation (43), This is the unit loss coefficient of the line in the coupled system; The photovoltaic reduction factor of the line in the coupled system; This is the load reduction factor for the lines in the coupled system; For a coupled system, it is the set of branches; The cost of vehicle movement and battery reconfiguration in the coupled system; Indicates vehicle The unit operating cost This represents the reconfiguration cost of each battery module used for each vehicle bonding operation; , Indicates battery module The charging and discharging efficiency; Indicates a time interval; Step 4.3: Use equations (44)-(49) to transform the quadratic terms in equations (37)-(42). and Transform into a linear representation: (44) (45) (46) (47) (48) (49) In equations (44)-(49), express t Time Node The square of the voltage applied; express t Time Node The square of the voltage applied; express t Time Branch The square of the current; Step 4.4: Multiply the quadratic terms in equation (49) using equation (50). After performing second-order cone relaxation, equation (50) is transformed into the standard second-order cone form using equation (51): (50) (51) Step 4.5, the objective function After forming a linearly constrained model with equations (1)-(36), (44)-(48) and (51), the model is solved using the Gurobi solver to obtain the proposed scheme for improving the load-carrying capacity of the transformer area based on virtual feeder-reconfigurable mobile energy storage.

5. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the method for improving the source load carrying capacity of a transformer area based on virtual feeder-reconfigurable mobile energy storage as described in any one of claims 1-4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the method for improving the load-carrying capacity of a transformer substation based on virtual feeder-reconfigurable mobile energy storage as described in any one of claims 1-4.

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