A power distribution network flexibility expansion method based on multiple recombination driving

Abstract

The application discloses a kind of based on multiple recombination drive distribution network flexibility expansion method, comprising:1, the model of multiple recombination space-time adjustable equipment is constructed;2, the flexible interaction model of direct current side of reconfigurable intelligent soft switch and modular mobile energy storage is constructed;3, the flexibility expansion model of distribution network based on multiple recombination space-time adjustable equipment drive is constructed;4, by linearization and cone relaxation, the constraint condition of the flexibility expansion model of distribution network is converted into the constraint condition under second-order cone programming, then the objective function is solved, and the optimal operation scheme of distribution network is obtained.The application can fully improve the utilization efficiency of distributed controllable resources by multiple equipment recombination, and realize the flexibility improvement of distribution network in space and time dimension.

Description

Technical Field

[0001] This invention belongs to the field of distribution network operation optimization, specifically a method for expanding the flexibility of distribution networks based on multiple reorganization drives. Background Technology

[0002] The large-scale integration of renewable energy and the gradual empowerment of various intelligent services have placed new demands on the flexible and controllable resources and flexible control paradigms of modern distribution networks. Traditional distribution networks, limited by short-circuit capacity and electromagnetic ring networks, generally adopt a "closed-loop design, open-loop operation" architecture. This involves configuring tie switches to construct ring or mesh topologies to achieve segmented management and load transfer after faults. However, this operating mode often suffers from inherent defects such as insufficient control flexibility and limited dynamic response speed, making it difficult to adapt to the new distribution network operation requirements of high-proportion distributed renewable energy and diversified load integration, as well as to meet the dynamic control needs of multiple network types, multiple nodes, and multiple time periods. Against this backdrop, how to expand the system's resource and control flexibility across space and time to support the safe and economical operation of the distribution network has become an urgent issue that needs to be addressed. Summary of the Invention

[0003] This invention aims to address the shortcomings of the prior art by proposing a method for expanding the flexibility of distribution networks based on multiple reorganization drives. This method effectively solves the problems of limited control capabilities and low capacity utilization in traditional distribution networks. While ensuring cost control, it can greatly improve the flexibility of distribution networks, thereby reducing resource redundancy and the possibility of failures, and improving the reliability and economy of distribution network operation.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0005] This invention discloses a distribution network flexibility enhancement method based on multiple reconfiguration drive, applicable to a distribution network driven by a multiple reconfiguration spatiotemporally adjustable device (M-SCD). The M-SCD comprises a reconfigurable intelligent soft switch (R-SOP) and a modular mobile energy storage system (M-TESS). The R-SOP consists of a voltage source converter (VSC) and a feeder selection switch, while the M-TESS consists of battery modules and a mobile energy storage vehicle. The mobile energy storage vehicle docks on the DC side of the R-SOP and performs loading and unloading operations on the battery modules. The battery modules are charged and discharged through the VSC on the DC side of the R-SOP. The method for enhancing distribution network flexibility includes the following steps:

[0006] Step 1: Construct a model of the multiple reconfigurable spatiotemporally adjustable device M-SCD, including: the battery module reconfiguration model, the battery module charging and discharging model, and the reconfigurable intelligent soft switch R-SOP operation model.

[0007] Step 2: Based on the model of the multi-recombinable spatiotemporally adjustable device M-SCD, construct a flexible interaction model between the DC side of the recombinable intelligent soft switch R-SOP and the modular mobile energy storage M-TESS.

[0008] Step 3: Based on the flexible interaction model, construct a flexible extension model for the distribution network driven by the M-SCD multi-recombination spatiotemporally adjustable device, including: objective function and its constraints;

[0009] Step 4: After transforming the constraints of the distribution network flexibility extension model into constraints under second-order cone programming through linearization and cone relaxation, solve the objective function to obtain the optimal operation scheme of the distribution network, including: the operation of reconfigurable smart soft switch R-SOP and the operation of modular mobile energy storage M-TESS.

[0010] The characteristic of the distribution network flexibility expansion method based on multiple reorganization drives described in this invention is that step 1 includes:

[0011] Step 1.1: Construct the battery module reconfiguration model using equations (1) and (2):

[0012] (1)

[0013] (2)

[0014] In equations (1)-(2), A time set for one running cycle; , They are respectively Modular 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 Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; for Modular mobile energy storage battery modules With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0; for Modular mobile energy storage battery modules With nodes The connection status; A collection of battery modules in modular mobile energy storage; A set of nodes;

[0015] Step 1.2: Construct the charging and discharging model of the battery module using equations (3)-(8):

[0016] (3)

[0017] (4)

[0018] (5)

[0019] (6)

[0020] (7)

[0021] (8)

[0022] In equations (3)-(8), , They are respectively t Time Battery Module The charging and discharging active power; , They are respectively t Time Battery Module The reactive power of charging and discharging; for t Time Battery Module The capacity; for Time Battery Module The capacity; , These are the upper limits of the charging and discharging active power of the battery module, respectively. , Battery modules The upper limit of the charging and discharging reactive power; , Battery modules The charging and discharging efficiency; , Battery modules The upper and lower limits of capacity; For time intervals;

[0023] Step 1.3: Construct the operational model of the reconfigurable intelligent soft switch R-SOP using equations (9)-(15):

[0024] (9)

[0025] (10)

[0026] (11)

[0027] (12)

[0028] (13)

[0029] (14)

[0030] (15)

[0031] In equations (9)-(15), for t Real-time reconfigurable intelligent soft-switching R-SOP voltage source converter h DC-side active power; for t Time-of-flight voltage source converter h The actual active power transmitted; for t Time-of-use voltage source converter h Active power loss during transmission; Voltage source converter h Capacity utilization rate; A collection of voltage source converters; Indicates voltage source converter h The capacity; Indicates the first connected to R-SOP n Power transmission capacity on each branch; for Time-of-use voltage source converter h The actual reactive power transmitted; Indicates voltage source converter h With the n The switch status on the branch line; Indicates voltage source converter h Maximum output reactive power; N This represents the total number of branches connected to R-SOP.

[0032] Furthermore, in step 2, a flexible interaction model between the reconfigurable smart soft-switching R-SOP DC side and modular mobile energy storage is constructed using equation (16):

[0033] (16).

[0034] Furthermore, step 3 includes:

[0035] Step 3.1: Using equations (17)-(25), construct the constraints for the flexibility extension model of the distribution network driven by the M-SCD multi-recombination spatiotemporally adjustable device:

[0036] (17)

[0037] (18)

[0038] (19)

[0039] (20)

[0040] (twenty one)

[0041] (twenty two)

[0042] (twenty three)

[0043] (twenty four)

[0044] (25)

[0045] In equations (17)-(25), M and P are the sets of energy storage nodes and photovoltaic nodes, respectively; Represents a node The connection status of the connected distributed power sources; , Representing nodes respectively The active and reactive power outputs of the connected distributed power sources; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the connected distributed power sources; , They are nodes The upper and lower limits of the power factor of the connected distributed power sources; , They are nodes The set of child and parent nodes; , for t Time Node and nodes Branch roads between Uptransmitted active power, nodes and nodes Branch roads between Active power transmitted upstream; , for t Time Branch Reactive power transmitted from the branch The reactive power transmitted upstream; , For nodes superior t At any given time, the load's active power and reactive power are measured. for Distributed power injection node The active power; 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; 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;

[0046] Step 3.2: Construct the objective function of the distribution network flexibility extension model based on the M-SCD driven by multiple reconfiguration spatiotemporally adjustable devices using equations (26) and (27). :

[0047] (26)

[0048] (27)

[0049] In equations (26)-(27), The unit cost of line loss. For distribution network branch collection; , The unit cost of load reduction and curtailment; , They are respectively Time-based load reduction and light curtailment; for Time Node Energy loss due to voltage exceeding limits; for Time Node The load power; for Time Node The voltage; This is the lower limit of the voltage regulation dead zone. This is the upper limit of the voltage regulation dead zone. This is the minimum allowable voltage for the distribution network. This represents the maximum allowable voltage for the distribution network.

[0050] Furthermore, step 4 includes:

[0051] Step 4.1: Use equation (28) to transform the bilinear terms in equation (1) into linear constraints:

[0052] (28)

[0053] In equation (28), for Modular mobile energy storage battery modules With nodes The connection status; Indicates battery module u With nodes j During the period Internal connection state;

[0054] Step 4.2: Using equation (29), the capacity constraint of the reconfigurable intelligent soft switch R-SOP in equation (12) is converted into a rotating second-order cone constraint:

[0055] (29)

[0056] Step 4.3: Use equations (30)-(35) to transform the quadratic terms in equations (20)-(25). and Transform into a linear representation:

[0057] (30)

[0058] (31)

[0059] (32)

[0060] (33)

[0061] (34)

[0062] (35)

[0063] In equations (30)-(35), 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;

[0064] Step 4.4: Multiply the quadratic terms in equation (25) using equation (36). After performing second-order cone relaxation, equation (36) is transformed into the standard second-order cone form using equation (37):

[0065] (36)

[0066] (37)

[0067] 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 distribution network flexibility extension method based on multiple reorganization drive, and the processor is configured to execute the program stored in the memory.

[0068] The present invention provides 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 distribution network flexibility expansion method based on multiple reorganization drive.

[0069] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0070] 1. This invention combines and recombines various controllable time and space devices such as reconfigurable intelligent soft switch R-SOP, voltage source converter VSC, and modular mobile energy storage M-TESS. On the basis of giving full play to their own functions, the mutual cooperation between the devices forms a more complete, robust and reliable power distribution network operation system, which improves the utilization rate of flexible resources and maximizes the optimization effect of the power distribution system.

[0071] 2. This invention extends the system's scheduling capabilities from local to network-wide, enabling cross-regional energy transfer. Battery modules, vehicle queues, and energy injection locations can be flexibly distributed across multiple nodes, effectively improving the spatial operational capabilities of the distribution network. Simultaneously, electricity is fully utilized in the multi-layered transmission channels of "node-branch-region," achieving network-wide coverage and multi-regional energy coordination.

[0072] 3. This invention enables the power distribution network to have dynamic energy distribution capabilities in the time dimension through the reorganization and timing scheduling of battery modules. It also adjusts the distribution of battery modules, vehicle driving paths, and energy injection locations in real time according to the fluctuations in new energy output and dynamic changes in load, thereby achieving refined management of the system's operating status. Attached Figure Description

[0073] Figure 1 This is a schematic diagram illustrating the increased flexibility of the power distribution network according to the present invention;

[0074] Figure 2 This is a topology diagram of the M-SCD, a multiple recombination spatiotemporally adjustable device of the present invention;

[0075] Figure 3 This 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.

[0076] Figure 4 This is a diagram showing the reorganization and distribution of the battery module of the present invention;

[0077] Figure 5 This is a diagram of energy interaction and transfer between distribution network nodes according to the present invention;

[0078] Figure 6 This is a feeder power transmission diagram of the reconfigurable intelligent soft switch R-SOP of the present invention;

[0079] Figure 7 This is a diagram showing the capacity change of the voltage source converter VSC after reconfiguration according to the present invention;

[0080] Figure 8 This is a power distribution network voltage performance diagram of the present invention;

[0081] Figure 9 This is a flowchart illustrating the implementation of the method described in this invention. Detailed Implementation

[0082] In this embodiment, a distribution network flexibility extension method based on multiple reconfiguration drives creates a new distributed adjustable resource utilization model driven by multiple reconfiguration. Through multi-level reconfiguration of feeders, voltage source converters, and battery modules, a novel response mode incorporating distributed adjustable resources such as energy storage, voltage source converters, remote switches, and mobile vehicles is proposed. Figure 1As shown, if the four-port reconfigurable smart soft switch R-SOP is individually connected to nodes A, B, C, and D of the system, its effective range is mainly limited to Region I, where voltage regulation and power flow control can be achieved. However, after large-scale mobile energy storage is integrated into the system, the vehicle's movement path between nodes is similar to a "virtual feeder," undertaking the function of cross-node power transmission. The battery module acts as the energy carrier, and vehicle dispatching acts as the transmission path, realizing the equivalent transfer of power across nodes. Combining feeder switching and voltage source converter (VSC) reconfiguration not only expands the multi-level transmission channels of electricity in the "node-branch-region" hierarchy but also further extends the coverage of energy dispatching to the entire Region II. Specifically, as... Figure 9 As shown, the specific steps of this method are as follows:

[0083] Step 1: Construct a model of a multi-recombination spatiotemporally adjustable device:

[0084] Topology of multiple reconfigurable spatiotemporally adjustable devices, such as Figure 2 As shown, the multiple recombination of this device is specifically reflected in the following three levels:

[0085] 1) Feeder Reconfiguration: The reconfigurable intelligent soft switch R-SOP enables flexible connection between external nodes and internal asymmetrical voltage source converters (VSCs) via feeder selection switches, thereby achieving dynamic power transfer between feeders. This improves the spatial flexibility of power dispatch and enhances the utilization efficiency of VSC capacity.

[0086] 2) Voltage source converter VSC reconfiguration: Relying on the asymmetrical capacity configuration and flexible topology of the reconfigurable intelligent soft switch R-SOP, multiple voltage source converter VSC modules can be reconfigured as needed to meet system power regulation requirements.

[0087] 3) Battery module reconfiguration: The modular mobile energy storage T-MESS consists of multiple battery modules and multiple vehicles, and has the ability to quickly assemble, dispatch across regions and reuse.

[0088] Step 1.1: Construct the battery module reconfiguration model using equations (1) and (2):

[0089] (1)

[0090] (2)

[0091] In equations (1)-(2), A time set for one running cycle; , They are respectively Modular 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 Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; for Modular mobile energy storage battery modules With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0; for Modular mobile energy storage battery modules With nodes The connection status; A collection of battery modules for modular mobile energy storage; It is a set of nodes.

[0092] Step 1.2: Construct the charge and discharge model of the battery module using equations (3)-(8):

[0093] (3)

[0094] (4)

[0095] (5)

[0096] (6)

[0097] (7)

[0098] (8)

[0099] In equations (3)-(8), , They are respectively t Time Battery Module The charging and discharging active power; , They are respectively t Time Battery Module The reactive power of charging and discharging; for tTime Battery Module The capacity; for Time Battery Module The capacity; , These are the upper limits of the charging and discharging active power of the battery module, respectively. , Battery modules The upper limit of the charging and discharging reactive power; , Battery modules The charging and discharging efficiency; , Battery modules The upper and lower limits of capacity; For time intervals.

[0100] Step 1.3: Construct the operational model of the reconfigurable intelligent soft switch R-SOP using equations (9)-(15):

[0101] (9)

[0102] (10)

[0103] (11)

[0104] (12)

[0105] (13)

[0106] (14)

[0107] (15)

[0108] In equations (9)-(15), for t Real-time reconfigurable intelligent soft-switching R-SOP voltage source converter h DC-side active power; for t Time-of-use voltage source converter h The actual active power transmitted; for t Time-of-use voltage source converter h Active power loss during transmission; The loss factor of the voltage source converter; A collection of voltage source converters; Indicates voltage source converter h The capacity; This indicates the first one that can be connected to R-SOP. n Power transmission capacity on each branch; for Time-of-use voltage source converter h The actual reactive power transmitted; Indicates voltage source converter h With the n The switch status on the branch line; Indicates voltage source converter h Maximum output reactive power; N This represents the total number of branches that can be connected to R-SOP.

[0109] Step 2: Construct a flexible interaction model between the DC side of the reconfigurable intelligent soft-switching R-SOP and the modular mobile energy storage M-TESS:

[0110] Step 2.1: Introduce a "traffic node" on the DC side of the reconfigurable smart soft switch R-SOP as a docking and loading / unloading hub for the modular mobile energy storage M-TESS. The modular mobile energy storage M-TESS docks at the "traffic node" and performs loading and unloading operations on the battery modules. The battery modules are charged and discharged through the DC side voltage source converter VSC.

[0111] Step 2.2: Construct a flexible interaction model between the DC side of the reconfigurable intelligent soft switch R-SOP and the modular mobile energy storage M-TESS using equation (16):

[0112] (16)

[0113] Step 3: Construct a flexible extension model of the distribution network driven by the multi-reorganization spatiotemporally adjustable device M-SCD. Under the multi-reorganization mechanism, the operation of the distribution network involves the dynamic reorganization of feeders, voltage source converters (VSCs), and battery modules. The power flow distribution and resource scheduling of the distribution network exhibit strong coupling and multi-dimensional characteristics.

[0114] Step 3.1: Using equations (17)-(25), construct the constraints for the flexibility extension model of the distribution network driven by the M-SCD multi-recombination spatiotemporally adjustable device:

[0115] (17)

[0116] (18)

[0117] (19)

[0118] (20)

[0119] (twenty one)

[0120] (twenty two)

[0121] (twenty three)

[0122] (twenty four)

[0123] (25)

[0124] In equations (17)-(25), M and P are the sets of energy storage nodes and photovoltaic nodes, respectively; Represents a node The connection status of the connected distributed power sources; , Representing nodes respectively The active and reactive power outputs of the connected distributed power sources; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the connected distributed power sources; , They are nodes The upper and lower limits of the power factor of the connected distributed power sources; , They are nodes The set of child and parent nodes; , for t Time Node and nodes Branch roads between Uptransmitted active power, nodes and nodes Branch roads between Active power transmitted upstream; , for t Time Branch Reactive power transmitted from the branch The reactive power transmitted upstream; , For nodes superior t At any given time, the load's active power and reactive power are measured. for Distributed power injection node The active power; 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; 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.

[0125] Step 3.2: Construct the objective function of the distribution network flexibility extension model based on the M-SCD driven by multiple reconfiguration spatiotemporally adjustable devices using equations (26) and (27). :

[0126] (26)

[0127] (27)

[0128] In equations (26)-(27), The unit cost of line loss. For distribution network branch collection; , The unit cost of load reduction and curtailment; , They are respectively Time-based load reduction and light curtailment; for Time Node Energy loss due to voltage exceeding limits; for Time Node The load power; for Time Node The voltage; This is the lower limit of the voltage regulation dead zone. This is the upper limit of the voltage regulation dead zone. This is the minimum allowable voltage for the distribution network. This represents the maximum allowable voltage for the distribution network.

[0129] Step 4: After transforming the constraints of the distribution network flexibility extension model into constraints under second-order cone programming through linearization and cone relaxation, the objective function is solved to obtain the optimal operation scheme of the distribution network.

[0130] Step 4.1: Since equation (1) contains bilinear terms, equation (28) is transformed into a linear constraint using a linearization method:

[0131] (28)

[0132] In equation (28), for Modular mobile energy storage battery modules With nodes The connection status; The intermediate variable introduced represents the battery module. u With nodes j During the period The connection status within.

[0133] Step 4.2: Use equation (29) to convert the capacity constraint of the reconfigurable intelligent soft switch R-SOP in equation (12) into a rotating second-order cone constraint:

[0134] (29)

[0135] Step 4.3: Since there are quadratic terms in equations (20)-(25) and ,use and Linearization is achieved through variable substitution. Equations (20)-(25) are transformed into linearization constraints using equations (30)-(35):

[0136] (30)

[0137] (31)

[0138] (32)

[0139] (33)

[0140] (34)

[0141] (35)

[0142] In equations (30)-(35), 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.

[0143] Step 4.4: After using variable substitution in equation (25), the product of quadratic terms... It is still nonlinear, using equation (36) to multiply the quadratic terms in equation (25). After performing second-order cone relaxation, equation (36) is transformed into the standard second-order cone form using equation (37):

[0144] (36)

[0145] (37)

[0146] By transforming the nonlinear model into a linear model in step 4, and then solving it with the Gurobi solver, the optimal solution strategy for the distribution network flexibility extension method based on multiple reorganization drives of this invention can be obtained.

[0147] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0148] 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.

[0149] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components:

[0150] Numerical simulation tests were conducted on a coupled system of an improved IEEE 33-node system with a rated voltage of 12.66 kV and a transportation network containing 14 nodes. The system's safe voltage range was set to [0.95, 1.05] pu. The reconfigurable intelligent soft switch R-SOP DC-side "transportation node" was designated as node number 34, and nodes 9, 13, 15, 25, and 32 were designated as modular mobile energy storage access nodes.

[0151] The four-port reconfigurable intelligent soft switch R-SOP has a total capacity of 1MVA. The capacity of the four voltage source converters is allocated according to the golden ratio method, and the nodes for feeder selection switch selection are set at nodes 12, 18, 22, and 33. Photovoltaic generator sets with capacities of 900kVA, 800kVA, 700kVA, 600kVA, and 500kVA are installed at nodes 7, 9, 13, 15, and 27, respectively.

[0152] Active power related costs Set at 0.5 yuan / kW.h; Take 6 yuan / kW.h Value and equal; Take 0.98. A total of 15 batteries and 4 cars are set up. Each car can carry a maximum of 4 batteries. Each battery has a capacity of 20 kWh and a maximum charging and discharging power of 10 kW. The ideal speed of the car is 60 km / h.

[0153] Depend on Figure 3 As shown in (a), the battery modules are concentrated in the peak photovoltaic output period from 10:00 to 14:00 for charging to store electrical energy and avoid wasting solar power; Figure 3 As shown in (b), the battery module discharges intensively during the peak electricity consumption periods of 8:00-10:00 and 19:00-21:00 in the morning and evening;

[0154] Depend on Figure 4 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.

[0155] Depend on Figure 5 It can be seen that modular mobile energy storage transfers 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 periods of high photovoltaic output, and move to the load concentration area to discharge during the evening peak period. Through the movement path of modular mobile energy storage M-TESS, 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.

[0156] Depend on Figure 6 It can be seen that the power transmitted by each feeder in the reconfigurable smart soft switch R-SOP is as follows: feeders connected to nodes 12, 18, 22, and 33. During the peak photovoltaic output period of 10:00–14:00, since nodes 12 and 18 are close to the photovoltaic access point, the reconfigurable smart soft switch R-SOP transmits the excess photovoltaic power to feeders 22 and 33 through feeders 12 and 18, realizing power transfer, effectively alleviating the local overvoltage problem and improving photovoltaic absorption.

[0157] Depend on Figure 7 It can be seen that the total capacity of the feeders connected to each voltage source converter (VSC) will dynamically change throughout the day according to the control requirements. During the midday period when photovoltaic output is high, the reconfigurable intelligent soft switch will reconfigure the voltage source converter (VSC) to concentrate the capacity to feeders 12 and 18, thereby supporting the transmission of more power.

[0158] Depend on Figure 8It can be seen that the system voltage is always strictly maintained within the range of [0.95, 1.05] pu; at the same time, the multi-recombination spatiotemporally adjustable device proposed in this invention achieves 100% photovoltaic absorption in the distribution network and reduces network losses by 60.57% under the premise of no load cut-off;

[0159] The results of the numerical examples show that the multi-recombination spatiotemporally adjustable device proposed in this invention not only realizes flexible multi-level energy scheduling across nodes and feeders in the power distribution network, but also achieves dynamic identification and fine management of battery modules, avoiding problems such as overcharging, over-discharging and state mismatch.

Claims

1. A method for extending the flexibility of a distribution network based on multiple reconfiguration drive, applied to a distribution network driven by a multiple reconfiguration spatiotemporally adjustable device (M-SCD), wherein the multiple reconfiguration spatiotemporally adjustable device (M-SCD) includes: The reconfigurable smart soft switch R-SOP and modular mobile energy storage M-TESS are described. The reconfigurable smart soft switch R-SOP consists of a voltage source converter (VSC) and a feeder selection switch. The modular mobile energy storage M-TESS consists of battery modules and a mobile energy storage vehicle. The mobile energy storage vehicle docks on the DC side of the reconfigurable smart soft switch R-SOP and performs loading and unloading operations on the battery modules. The battery modules are charged and discharged through the voltage source converter (VSC) on the DC side of the reconfigurable smart soft switch R-SOP. The method for expanding the flexibility of the distribution network includes the following steps: Step 1: Construct a model of the multiple reconfigurable spatiotemporally adjustable device M-SCD, including: the battery module reconfiguration model, the battery module charging and discharging model, and the reconfigurable intelligent soft switch R-SOP operation model. Step 1.1: Construct the battery module reconfiguration model using equations (1) and (2): (1) (2) In equations (1)-(2), A time set for one running cycle; , They are respectively Modular 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 Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; for Modular mobile energy storage battery modules With nodes The connection state, if the two are connected, then =1; if the two are disconnected, then =0; for Modular mobile energy storage battery modules With nodes The connection status; A collection of battery modules in modular mobile energy storage; A set of nodes; Step 1.2: Construct the charging and discharging model of the battery module using equations (3)-(8): (3) (4) (5) (6) (7) (8) In equations (3)-(8), , The battery modules at time t are respectively The charging and discharging active power; , The battery modules at time t are respectively The charging and discharging reactive power; Battery module at time t The capacity; for Time Battery Module The capacity; , These are the upper limits of the charging and discharging active power of the battery module, respectively. , Battery modules The upper limit of the charging and discharging reactive power; , Battery modules The charging and discharging efficiency; , Battery modules The upper and lower limits of capacity; For time intervals; Step 1.3: Construct the operational model of the reconfigurable intelligent soft switch R-SOP using equations (9)-(15): (9) (10) (11) (12) (13) (14) (15) In equations (9)-(15), Let h be the DC-side active power of the voltage source converter h in the reconfigurable intelligent soft-switching R-SOP at time t. Let h be the actual active power transmitted by the voltage source converter at time t. Let h be the active power loss transmitted by the voltage source converter at time t. The capacity utilization rate of voltage source converter h; A collection of voltage source converters; This indicates the capacity of the voltage source converter h; This represents the power transmission capacity of the nth branch connected to the R-SOP; for The actual reactive power transmitted by the voltage source converter h at any given time; This indicates the switching state of the voltage source converter h and the nth branch; This represents the maximum output reactive power of the voltage source converter h; N is the total number of branches connected to R-SOP. Step 2: Based on the model of the multi-recombinable spatiotemporally adjustable device M-SCD, construct a flexible interaction model between the DC side of the recombinable intelligent soft switch R-SOP and the modular mobile energy storage M-TESS. Step 3: Based on the flexible interaction model, construct a flexible extension model for the distribution network driven by the M-SCD multi-recombination spatiotemporally adjustable device, including: objective function and its constraints; Step 4: After transforming the constraints of the distribution network flexibility extension model into constraints under second-order cone programming through linearization and cone relaxation, solve the objective function to obtain the optimal operation scheme of the distribution network, including: the operation of reconfigurable smart soft switch R-SOP and the operation of modular mobile energy storage M-TESS.

2. The method for expanding distribution network flexibility based on multiple reorganization drives according to claim 1, characterized in that, Step 2 involves using equation (16) to construct a flexible interaction model between the reconfigurable smart soft-switching R-SOP DC side and modular mobile energy storage: (16)。 3. The method for expanding the flexibility of distribution networks based on multiple reorganization drives according to claim 2, characterized in that, Step 3 includes: Step 3.1: Using equations (17)-(25), construct the constraints for the flexibility extension model of the distribution network driven by the M-SCD multi-recombination spatiotemporally adjustable device: (17) (18) (19) (20) (21) (22) (23) (24) (25) In equations (17)-(25), M and P are the sets of energy storage nodes and photovoltaic nodes, respectively; Represents a node The connection status of the connected distributed power sources; , Representing nodes respectively The active and reactive power outputs of the connected distributed power sources; , They are nodes The upper limit of active power output and the upper limit of reactive power output of the connected distributed power sources; , They are nodes The upper and lower limits of the power factor of the connected distributed power sources; , They are nodes The set of child and parent nodes; , The node at time t and nodes Branch roads between Uptransmitted active power, nodes and nodes Branch roads between Active power transmitted upstream; , branch at time t Reactive power transmitted from the branch Reactive power transmitted upstream; , For nodes The active power and reactive power of the load at time t; for Distributed power injection node The active power; The node at time t The square of the voltage applied; The node at time t The square of the voltage applied; branch at time t 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; Step 3.2: Construct the objective function of the distribution network flexibility extension model based on the M-SCD driven by multiple reconfiguration spatiotemporally adjustable devices using equations (26) and (27). : (26) (27) In equations (26)-(27), The unit cost of line loss. For distribution network branch collection; , The unit cost of load reduction and curtailment; , They are respectively Time-based load reduction and light curtailment; for Time Node Energy loss due to voltage exceeding limits; for Time Node The load power; for Time Node The voltage; This is the lower limit of the voltage regulation dead zone. This is the upper limit of the voltage regulation dead zone. This is the minimum allowable voltage for the distribution network. This represents the maximum allowable voltage for the distribution network.

4. The method for expanding the flexibility of distribution networks based on multiple reorganization drives according to claim 3, characterized in that, Step 4 includes: Step 4.1: Use equation (28) to transform the bilinear terms in equation (1) into linear constraints: (28) In equation (28), for Modular mobile energy storage battery modules With nodes The connection status; This indicates that battery module u and node j are in the same time period. Internal connection state; Step 4.2: Using equation (29), the capacity constraint of the reconfigurable intelligent soft switch R-SOP in equation (12) is converted into a rotating second-order cone constraint: (29) Step 4.3: Use equations (30)-(35) to transform the quadratic terms in equations (20)-(25). and Transform into a linear representation: (30) (31) (32) (33) (34) (35) In equations (30)-(35), Represents the node at time t The square of the voltage applied; Represents the node at time t The square of the voltage applied; Represents the branch at time t The square of the current; Step 4.4: Multiply the quadratic terms in equation (25) using equation (36). After performing second-order cone relaxation, equation (36) is transformed into the standard second-order cone form using equation (37): (36) (37)。 5. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing the distribution network flexibility extension method based on multiple reorganization drive as described in any one of claims 1-4, wherein the processor is configured to execute the programs 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 performs the steps of the distribution network flexibility extension method based on multiple reorganization drive as described in any one of claims 1-4.

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