Power distribution network optimal operation method based on modular mobile energy storage and intelligent soft switch cooperation

Abstract

The application discloses a power distribution network optimal operation method based on modular mobile energy storage and intelligent soft switch cooperation, comprising the following steps: 1, modeling the energy storage type intelligent soft switch SOP; 2, establishing a modular mobile energy storage road network model; 3, modeling the power distribution system based on modular mobile energy storage and intelligent soft switch cooperation, and obtaining the optimal operation strategy of the power distribution system by solving the optimal operation model. The application can effectively integrate the space-time scheduling capability of the modular mobile energy storage system and the tide flow flexible adjustment capability of the intelligent soft switch, realize the double optimal distribution of electric energy in the time and space dimensions, has obvious advantages in dealing with the voltage out-of-limit, light abandonment and tide flow congestion problems caused by high proportion distributed photovoltaic access, and enhances the reliability and safety of power grid operation.

Description

Technical Field

[0001] This invention belongs to the field of power distribution system operation optimization, specifically a power distribution network optimization operation method based on the synergy of modular mobile energy storage and intelligent soft switching. Background Technology

[0002] The large-scale integration of distributed photovoltaic (PV) power brings new challenges to the safe operation of active distribution networks. Due to the mismatch between peak and off-peak load characteristics and insufficient line capacity, problems such as power backflow overload, increased system operational safety risks, and low PV absorption rates arise. Mobile Energy Storage Systems (MESS), as flexible energy balancing devices, can be connected to different nodes in the distribution network to provide power support according to grid operation needs, thereby playing a capacity-sharing role and promoting power balance. Soft open points (SOPs), as new flexible distribution devices, operate in a closed-loop manner when connected to the distribution network and can control active and reactive power between feeders in real time, enabling flexible adjustment of the power flow in the active distribution network and energy transfer between lines, thus effectively improving the absorption of new energy in the distribution network. The coordinated operation of MESS and SOP can effectively cope with high-proportion distributed PV power output fluctuations and promote the safe and economical operation of the distribution network.

[0003] As a crucial component of flexible distribution networks (FDNs), Standard Operating Procedures (SOPs) enable joint adjustment of active and reactive power, enhancing the flexibility and response speed of the distribution network. However, SOPs can only transfer electrical energy on a spatial scale, not on a temporal scale. Measurable Energy Storage Systems (MESSs), with their compact size and rapid response, have broad application prospects in ensuring the safe, stable, and economical operation of distribution networks. However, considering factors such as traffic congestion, their energy transfer efficiency on a spatial scale is relatively low. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by proposing a distribution network optimization operation method based on the synergy of modular mobile energy storage and intelligent soft switching. The aim is to integrate the Mobile Energy Storage System (MESS) into the Standard Operating Procedure (SOP) via a DC link, enabling collaborative optimization of the SOP and MESS within the distribution network. This helps resolve issues such as voltage overruns, line overloads, and curtailment caused by high photovoltaic penetration, thereby achieving reasonable power allocation in the grid and ensuring its safe and reliable operation.

[0005] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0006] The present invention provides a method for optimizing the operation of a distribution network based on the synergy of modular mobile energy storage and intelligent soft switching, characterized by the following steps:

[0007] Step 1: Construct the operational constraints of the smart soft switch SOP;

[0008] Step 2: Construct operational constraints for the road-network model of modular mobile energy storage;

[0009] Step 3: Construct a controllable equipment operation model for the power distribution system, including: the operation model of the on-load tap-changing transformer (OLTC) and the operation model of the switchable capacitor banks (CBs);

[0010] Step 4: Construct a distributed power supply operation model;

[0011] Step 5: Construct an operation model for a power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft switching system (SOP);

[0012] Step 6: Construct the objective function for the operation of the power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft switching system (SOP). ;

[0013] Step 7: After transforming the operating constraints of the power distribution system into mixed-integer second-order cone programming constraints, apply the objective function... The solution is obtained by solving the problem and obtaining the operation scheme of the power distribution system, which includes the operation of the intelligent soft switch SOP, the operation of the on-load tap-changing transformer, the operation of the switchable capacitor, and the operation of the modular mobile energy storage EM.

[0014] The characteristic of the distribution network optimization operation method based on modular mobile energy storage and intelligent soft switching described in this invention is that step 1 includes:

[0015] Step 1.1: Obtain the power balance constraint of SOP from equations (1) to (3):

[0016] (1)

[0017] (2)

[0018] (3)

[0019] In equations (1)-(3), for Time of the first A voltage source converter DC-side active power; for time The actual active power transmitted; for time Active power loss; This represents the total number of voltage source converters. for The loss coefficient; express The capacity; For the simulation time set;

[0020] Step 1.2: Obtain the capacity constraint of SOP from equations (4)-(5):

[0021] (4)

[0022] (5)

[0023] In equations (4)-(5), express The capacity; for time The actual reactive power transmitted; for Maximum output reactive power.

[0024] Furthermore, in step 2, the road-network model operation constraints for modular mobile energy storage are constructed using equations (6) to (21):

[0025] (6)

[0026] (7)

[0027] (8)

[0028] (9)

[0029] (10)

[0030] (11)

[0031] (12)

[0032] (13)

[0033] (14)

[0034] (15)

[0035] (16)

[0036] (17)

[0037] (18)

[0038] (19)

[0039] (20)

[0040] In equations (6)-(20), Nodes under zero traffic flow conditions With nodes The distance between the roads; The ideal vehicle speed under zero traffic flow conditions; Indicates the degree of congestion in the transportation network; Configuration time for modular mobile energy storage (ME) under zero traffic flow conditions; A collection of battery blocks in modular mobile energy storage (ME); for In the modular mobile energy storage ME system, the first Block battery With nodes The connection status, if the two are connected, then =1, if the two are disconnected, then =0; and They are respectively The first modular mobile energy storage ME Block battery block The charging and discharging indicators, if =1 indicates Charging, if =1 indicates Discharge; and They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; and They are respectively time The active power of charging and discharging; and They are respectively time The reactive power of charging and discharging; and These are the upper limits of the active power for charging and discharging the battery pack, respectively. and These are the upper limits of the reactive power for charging and discharging the battery pack, respectively. and These are the charging and discharging efficiencies of the battery pack, respectively. and They are respectively The upper and lower limits of energy storage capacity; This represents the total number of mobile energy storage battery modules. It is a set of nodes.

[0041] Furthermore, step 3 includes:

[0042] Step 3.1: Construct the operating model of the on-load tap-changing transformer (OLTC) using equations (21) and (22):

[0043] (twenty one)

[0044] (twenty two)

[0045] In equations (22)-(23), for Nodes that are always connected to the voltage source converter The voltage is equal to the voltage on the secondary side of the OLTC. for Nodes that are always connected to the voltage source converter With nodes Between lines The tap position of the OLTC; For nodes exist Voltage at any given moment; for Timetable Voltage regulation rate of the OLTC; For the line The initial voltage regulation rate of the OLTC; For the line The change in voltage regulation at adjacent tap positions;

[0046] Step 3.2: Construct the operating model of CBs using equations (23)-(24):

[0047] (twenty three)

[0048] (twenty four)

[0049] In equations (23)-(24), For nodes exist The capacity of the capacitors constantly connected to the power distribution system. yes capacitor capacitance, yes Time Node The number of capacitor banks installed at the location This is the maximum number of capacitor banks that can be connected.

[0050] Furthermore, in step 4, the distributed power supply operation model is constructed using equations (25) to (27):

[0051] (25)

[0052] (26)

[0053] (27)

[0054] In equations (25)-(27), M and P are the sets of nodes connected to modular mobile energy storage and photovoltaics, respectively; Represents various distributed power sources and nodes The connection status; and They are nodes The upper limits of active and reactive power output of distributed power sources; and They are nodes The upper and lower limits of the power factor of the distributed power source; and They are nodes The active and reactive power outputs of the distributed power source.

[0055] Furthermore, in step 5, an operational model of the power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft-switching system (SOP) is constructed using equations (28) to (33):

[0056] (28)

[0057] (29)

[0058] (30)

[0059] (31)

[0060] (32)

[0061] (33)

[0062] In equations (28)-(33), and They are nodes The set of child and parent nodes; , for Time Node With nodes Branch road ,node With nodes Branch roads between Active power transmitted upstream; , for Time Branch Branch roads Reactive power transmitted upstream; , For nodes The maximum active and reactive power of the load; for DG injection node at any time The active power; for Time Node The square of the voltage applied; and Branch roads Resistance and reactance; and They are nodes Upper and lower limits of the voltage square term; branch road The upper limit of the square term of the current.

[0063] Furthermore, step 6 includes:

[0064] Step 6.1: Construct the line losses of the power distribution system using equation (34). :

[0065] (34)

[0066] In equation (34), This is the line loss coefficient. A collection of power grid branches;

[0067] Step 6.2: Construct the voltage over-limit loss of the power distribution system using equation (35). :

[0068] (35)

[0069] In equation (35), This is the energy loss coefficient caused by voltage exceeding the limit; A set of power grid nodes; for Time Node Due to the energy loss caused by voltage exceeding the limit, and the following:

[0070] (36)

[0071] In equation (36), for Time Node The load power; for Time Node The injection power; for Time Node Voltage; For voltage regulation dead zone, , These are the upper and lower limits of the voltage regulation dead zone; when No voltage loss occurs when the voltage regulation dead zone is in effect; This is the minimum allowable system voltage. This represents the maximum allowable voltage for the system.

[0072] Step 6.3: Construct the objective function of the power distribution system using equation (37). :

[0073] (37)

[0074] Furthermore, step 7 includes the following steps:

[0075] Step 7.1: Transform equation (11) into the linear constraint shown in equation (38) using a linearization method:

[0076] (38)

[0077] In equation (38), For mobile energy storage With nodes exist The connection status at any given moment, For mobile energy storage With nodes exist Connection status within a given time period;

[0078] Step 7.2: Transform equation (33) into the second-order cone constraint shown in equation (39) using the second-order cone relaxation method:

[0079] (39)

[0080] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the power distribution network optimization operation method, and the processor is configured to execute the program stored in the memory.

[0081] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when run by a processor, executes the steps of the power distribution network optimization operation method.

[0082] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0083] 1. This invention achieves efficient and coordinated regulation of electrical energy in both time and space by constructing a flexible interconnected system based on modular mobile energy storage. The mobile energy storage system has the ability to be flexibly deployed and dynamically moved, enabling it to migrate between different areas to adapt to dynamic changes in load and distributed photovoltaic output, significantly improving the flexibility and operational reliability of the power distribution system.

[0084] 2. This invention connects a modular mobile energy storage system to the DC side of an intelligent soft switch, achieving cross-regional coordination of power between feeders through the flexible power flow regulation capability of the intelligent soft switch. This design effectively alleviates local overvoltage problems, significantly improves the absorption capacity of distributed photovoltaic power, reduces curtailment, and improves energy utilization efficiency.

[0085] 3. This invention, through a modular mobile energy storage system, can release stored electrical energy during peak grid load periods, effectively supporting voltage, preventing voltage collapse, and significantly improving the voltage quality and stability of the power distribution system. Simultaneously, through coordinated operation with intelligent soft switches, it achieves optimal power flow distribution in the power distribution system, reducing system operating losses and improving system operating efficiency.

[0086] 4. The multi-period optimization scheduling model constructed in this invention comprehensively considers factors such as the spatiotemporal coupling of modular mobile energy storage, traffic constraints, distributed photovoltaic output, and load uncertainty. It aims to maximize photovoltaic absorption, ensure voltage stability, and simultaneously reduce system operating losses. Simulation results show that the model performs excellently in terms of photovoltaic absorption capacity, network loss reduction, and voltage quality improvement, achieving coordinated optimization of multiple objectives. Attached Figure Description

[0087] Figure 1 This is a topology diagram of a smart soft switch;

[0088] Figure 2 It is a topology and road network diagram of an IEEE 33-bus system containing modular mobile energy storage and intelligent soft switching;

[0089] Figure 3 It is a dynamic scheduling diagram of modular mobile energy storage;

[0090] Figure 4 This is a diagram showing the active power transmitted through intelligent soft switches in feeders 12, 18, 22, and 33 of the modular mobile energy storage (Case 3) added to the DC side of the SOP.

[0091] Figure 5 This is a comparison of the active power diagrams of the original IEEE 33-node system with the addition of a four-port SOP, connecting the four ports to nodes 12, 18, 22, and 33, and adding fixed energy storage on the DC side of the SOP (Case 2) versus replacing the fixed energy storage with modular mobile energy storage in Case 2 (Case 3) and transmitting it through feeders 12, 18, 22, and 33.

[0092] Figure 6 It is a dynamic graph showing the number of battery blocks connected to a node that can access modular mobile energy storage at various times.

[0093] Figure 7 This is a diagram showing the operating strategies of controllable equipment installed in the power distribution system (on-load tap-changing transformer, switchable capacitor bank);

[0094] Figure 8 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0095] In this embodiment, a distribution network optimization operation method based on the synergy of modular mobile energy storage and intelligent soft switching effectively integrates the time-space scheduling capability of modular mobile energy storage systems with the power flow flexibility adjustment capability of intelligent soft switching. This achieves dual optimization of power allocation in both time and space dimensions and has significant advantages in addressing voltage overruns, curtailment, and power flow congestion issues caused by high-proportion distributed photovoltaic (PV) grid integration. Therefore, it enhances the reliability and security of grid operation. Specifically, for example... Figure 8 As shown, the specific steps of this method are as follows:

[0096] Step 1 Figure 1 The topology of the intelligent soft switch is shown, which contains four VSCs with equal capacity. The operating model of the intelligent soft switch SOP is obtained from equations (1) to (5):

[0097] 1) The power balance constraints are obtained from equations (1) to (3):

[0098] (1)

[0099] (2)

[0100] (3)

[0101] Equation (1) represents the active power balance of the VSC; Equation (2) indicates that each DC feeder in the VSC needs to satisfy active power balance; Equation (3) is the formula for calculating the active power loss of the VSC. In equations (1)-(3), for Time of the first A voltage source converter DC-side active power; for time The actual active power transmitted; for time Active power loss; Total number of VSCs; for The loss coefficient; express The capacity; This is the system simulation time set.

[0102] 2) The capacity constraint is obtained from equations (4) and (5):

[0103] (4)

[0104] (5)

[0105] Equation (4) is Capacity constraints. In equations (4)-(5), express The capacity; for time The actual reactive power transmitted; for Maximum output reactive power.

[0106] Step 2: Construct a modular mobile energy storage road-network model using equations (6)-(20):

[0107] MESS (Mechanical Energy Storage System) mainly consists of energy storage devices and mobile equipment. The energy storage devices are typically composed of lithium battery packs and connected to the DC link of the system operating point (SOP) via an AC / DC converter. The AC / DC converter's charging and discharging power can be adjusted by system settings. Compared to stationary energy storage, MESS can be flexibly connected to the grid at various nodes, improving operational flexibility.

[0108] Mobile energy storage At the node With nodes Travel time between Equivalent travel distance With actual vehicle speed It can be represented as:

[0109] (6)

[0110] (7)

[0111] (8)

[0112] In equations (6)-(8), For nodes With nodes The distance between the roads; The ideal vehicle speed under zero traffic flow conditions; It indicates the degree of congestion in the transportation network and can be estimated based on the condition of the transportation network. For mobile energy storage collection, .

[0113] After receiving a dispatch instruction, if mobile energy storage needs to be supplied by a node... Transfer to node To perform charging and discharging, the optimal route is first selected based on the congestion level of the traffic network to achieve the shortest travel time. Next destination node ; Configure time for energy storage. Before mobile energy storage reaches the node. Previously, i.e., time interval At that time, it is related to the node The connection status is always 0. In addition, the following conditions must be met:

[0114] At any given time, a mobile energy storage device can be connected to at most one node or be in an idle state.

[0115] Mobile energy storage can only be charged and discharged when it is connected throughout a certain scheduling period;

[0116] The charging and discharging power and state of charge constraints of mobile energy storage;

[0117] The above-mentioned spatiotemporal dynamic scheduling constraints can be expressed as follows:

[0118] (9)

[0119] (10)

[0120] (11)

[0121] (12)

[0122] (13)

[0123] (14)

[0124] (15)

[0125] (16)

[0126] (17)

[0127] (18)

[0128] In equations (9)-(18), for The first modular mobile energy storage ME Block battery With nodes The connection status, if the two are connected, then =1, if the two are disconnected, then =0; and They are respectively The first modular mobile energy storage ME Block battery block The charging and discharging indicators, if =1 indicates Charging, if =1 indicates Discharge; and They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; and They are respectively time The active power of charging and discharging; and They are respectively time The reactive power of charging and discharging; and These are the upper limits of the active power for charging and discharging the battery pack, respectively. and These are the upper limits of the reactive power for charging and discharging the battery pack, respectively. and These are the charging and discharging efficiencies of the battery pack, respectively. and They are respectively The upper and lower limits of energy storage capacity; This represents the total number of mobile energy storage battery modules. Let be the set of nodes. Equation (9) indicates that when the time interval is less than the sum of the passage and configuration time, the connection state of mobile energy storage at node k is 0; Equation (10) indicates that mobile energy storage can connect to at most one node at the same time (i.e., it can be in a connected state or an idle state); Equation (11) indicates the coupling relationship between the charging and discharging state of mobile energy storage and the spatial state, ensuring that it charges and discharges when it is in a connected state; Equation (12) ensures consistent charging and discharging states: the charging and discharging state of mobile energy storage must be consistent with the charging and discharging state of the nodes it is connected to; Equations (13) and (14) are the upper and lower limits of the active power of charging and discharging of mobile energy storage, respectively; Equations (15) and (16) are the upper and lower limits of the reactive power of charging and discharging of mobile energy storage; Equations (17) and (18) are the state of charge constraints of mobile energy storage.

[0129] Since the energy storage is connected to the DC side of the SOP, the energy storage block can be charged and discharged on the DC side of the SOP. Therefore, equation (2) of the SOP constraint is modified as follows:

[0130] (19)

[0131] In equation (19), This represents the total number of modular mobile energy storage battery modules. Also, because:

[0132] Each node can connect to multiple energy storage blocks;

[0133] The charging and discharging states of energy storage devices connected to the same node must remain consistent.

[0134] The constraints are expressed as follows:

[0135] (20)

[0136] State consistency within the same node: if energy storage and All connected to the node Then, through constraint (20), and This ensures that both are in the same charging state. The same applies to the discharging state.

[0137] Idle state handling: When the energy storage is not connected to any node, constraint (11) forces This means that charging and discharging are prohibited.

[0138] Step 3: Construct the controllable equipment operation model of the power distribution system using equations (21)-(24), including: the operation model of the on-load tap-changing transformer (OLTC) and the operation model of the switchable capacitor bank (CBs):

[0139] Step 3.1: Construct the operating model of the on-load tap-changing transformer (OLTC) using equations (21) and (22):

[0140] (twenty one)

[0141] (twenty two)

[0142] In equations (21)-(22), for Nodes that are always connected to the voltage source converter The voltage is equal to the voltage on the secondary side of the OLTC. for Nodes that are always connected to the voltage source converter With nodes Between lines The tap position of the OLTC; For nodes exist Voltage at any given moment; for Timetable Voltage regulation rate of the OLTC; For the line The initial voltage regulation rate of the OLTC; For the line The change in voltage regulation at adjacent tap positions.

[0143] Step 3.2: Construct the operating model of CBs using equations (23)-(24):

[0144] (twenty three)

[0145] (twenty four)

[0146] In equations (23)-(24), For nodes exist The capacity of the capacitors constantly connected to the power distribution system. yes capacitor capacitance, yes Time Node The number of capacitor banks installed at the location This is the maximum number of capacitor banks that can be connected.

[0147] Step 4: Construct the distributed power source operation model using equations (25)-(27):

[0148] (25)

[0149] (26)

[0150] (27)

[0151] In equations (25)-(27), M and P are the sets of nodes connected to modular mobile energy storage and photovoltaics, respectively; Represents various distributed power sources and nodes The connection status; and These are the upper limits of active and reactive power output of distributed power sources, respectively. and These are the upper and lower limits of the power factor for distributed generation, respectively; the upper and lower limits of the power factor for photovoltaics are the same.

[0152] Step 5: Construct an operation model for a power distribution system based on the coordination of modular mobile energy storage and intelligent soft switching using equations (28) to (33):

[0153] (28)

[0154] (29)

[0155] (30)

[0156] (31)

[0157] (32)

[0158] (33)

[0159] In equations (28)-(33), For the system simulation time set; and They are nodes The set of child and parent nodes; , for Time Node With nodes Branch road ,node With nodes Branch roads between Active power transmitted upstream; , for Time Branch and branch roads Reactive power transmitted upstream; , For nodes The maximum active and reactive power of the load; for DG injection node at any time The active power; for Time Node The square of the voltage; and These are the branch resistance and reactance, respectively. and They are nodes Upper and lower limits of the voltage square term; This is the upper limit of the square term of the branch current.

[0160] Step 6: Construct the objective function for the operation of the power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft switching system (SOP). :

[0161] Step 6.1: Construct the line losses of the power distribution system using equation (34). :

[0162] (34)

[0163] In equation (34), This is the line loss coefficient. It is a collection of power grid branches.

[0164] Step 6.2: Construct the voltage over-limit loss of the power distribution system using equation (35). :

[0165] (35)

[0166] In equation (35), This is the energy loss coefficient caused by voltage exceeding the limit; A set of power grid nodes; for Time Node Energy loss due to voltage exceeding limits; and:

[0167] (36)

[0168] In equation (36), for Time Node The load power; for Time Node The injection power; for Time Node Voltage; For voltage regulation dead zone, , For the upper and lower limits of the voltage regulation dead zone; when No voltage loss occurs when the voltage regulation dead zone is in effect; This is the minimum allowable system voltage. This represents the maximum allowable voltage for the system.

[0169] Step 6.3: Construct the objective function of the power distribution system using equation (37). :

[0170] (37)

[0171] Step 7: After transforming the operating constraints of the power distribution system into mixed-integer second-order cone programming constraints, apply the objective function... The solution is obtained by solving the problem and obtaining the operation scheme of the power distribution system, which includes the operation of the intelligent soft switch SOP, the operation of the on-load tap-changing transformer, the operation of the switchable capacitor, and the operation of the modular mobile energy storage EM.

[0172] Step 7.1: Transform equation (11) into the linear constraint shown in equation (38) using a linearization method:

[0173] (38)

[0174] In equation (38), For mobile energy storage With nodes exist The connection status at any given moment, For mobile energy storage With nodes During the period The connection status within.

[0175] Step 7.2: Transform equation (33) into the second-order cone constraint shown in equation (39) using the second-order cone relaxation method:

[0176] (39)

[0177] Using the above method, the operation constraints of the power distribution system based on the coordination of modular mobile energy storage and intelligent soft switching are transformed into a mixed integer second-order cone programming problem. By solving the problem with the help of a solver, the operation strategy of the power distribution system, including the operation of intelligent soft switching, on-load tap-changing transformer, switchable capacitor, and modular mobile energy storage system, is obtained.

[0178] 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.

[0179] 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.

[0180] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components:

[0181] I. Case Description and Simulation Result Analysis

[0182] To verify its effectiveness, the present invention employs, as follows: Figure 2 The IEEE 33-node system shown, featuring a 4-port SOP and modular mobile energy storage, serves as a test system, and a computational example analysis is conducted. Figure 3 In the test system shown, the active and reactive power requirements of each node load are set according to the standard IEEE 33-node test system. The system reference voltage is set to 12.66kV, and the voltage safety range is set to 0.95pu-1.05pu. The total capacity of the equipped 4-port SOP is 1MVA, and the nodes selectable by the feeder selector switch are set at nodes 12, 18, 22, and 33. The system additionally deploys photovoltaic systems at nodes 7, 9, 13, 15, and 27. CBs are set at node 29, with each CB having a capacity of 0.15MVar, and a maximum of 4 capacitor banks can be connected. The simulation timescale is set to 1 hour, and the simulation time is from 1:00 to 24:00.

[0183] To fully demonstrate the effectiveness of the proposed distribution network optimization operation method based on modular mobile energy storage and intelligent soft switching, three schemes, Case 1, Case 2, and Case 3, are set up for comparison in the case study section. Case 1 is based on the original distribution network of IEEE 33 nodes. Case 2 adds a four-port SOP to Case 1 and connects the four ports to nodes 12, 18, 22, and 33, and adds fixed energy storage on the DC side of the SOP. Case 3 replaces the fixed energy storage in Case 2 with modular mobile energy storage.

[0184] Case 1: Based on the original distribution network of the IEEE 33-node network;

[0185] Case 2: Add a four-port SOP to Case 1, connect the four ports to nodes 12, 18, 22 and 33, and add fixed energy storage on the DC side of the SOP;

[0186] Case 3: In Case 2, the stationary energy storage was changed to modular mobile energy storage;

[0187] All numerical simulations in the examples section were performed in MATLAB 2020a and solved using the YALMIP toolbox and Gurobi solver in a 64-bit Windows environment.

[0188] exist Figure 3 In the IEEE 33-node test system shown, both schemes were executed simultaneously. The light loss, network loss, and voltage limit exceedance rates of Case 1, Case 2, and Case 3 are shown in Table 1.

[0189] Table 1

[0190]

[0191] Table 1 shows the simulation results of the three cases. It can be seen that the optimization model proposed in this paper (Case 3) performs best in terms of photovoltaic absorption capacity, network loss reduction and voltage quality improvement. Its overall operation effect is significantly better than the other two schemes, which further verifies the effectiveness and advancement of the proposed model in multi-objective coordinated optimization.

[0192] Mobile energy storage dispatch results are as follows Figure 3 As shown, where Figure 3 Part (a) shows the modular mobile energy storage scheduling and the active power of each battery during charging; Figure 3 Section (b) represents the active power of each battery during discharge; Figure 3 Section (c) represents the remaining electrical energy of each battery. Modular mobile energy storage is concentrated during peak photovoltaic output periods (12:00-15:00) for centralized charging to store energy and prevent curtailment. During peak morning and evening electricity consumption periods (8:00-10:00 and 19:00-21:00), it is concentrated for centralized discharge to prevent grid voltage from falling below the lower limit due to excessive load. Simultaneously, mobile energy storage transfers electrical energy between nodes 9, 13, 15, 25, 32, and 34 24 hours a day, and interacts with the DC side of the SOP (System-on-Plug-in). The SOP then transmits the energy to nodes 12, 18, 22, and 33 via feeders, greatly expanding the positive benefits of the SOP in the grid.

[0193] like Figure 4 As shown, Figure 4 Parts (a) to (d) of the diagram show the power transmission of each voltage source converter (VSC) in the SOP, respectively. Figure 4 Part (a) in the diagram refers to feeder 1-12, which connects VSC1 to node 12. Figure 4 Part (b) in the diagram refers to feeder 2-18, which connects VSC2 to node 18. Figure 4 Part (c) in the diagram refers to feeder 3-22, which connects VSC3 to node 22. Figure 4 Part (d) in the diagram represents feeder 4-33, which connects VSC4 to node 33. As shown in the diagram, during the peak photovoltaic output period from 8:00 to 10:00, since nodes 12 and 18 are close to the photovoltaic access point, SOP transmits excess photovoltaic power to feeders 3-22 and 4-33 via feeders 1-12 and 2-18. This achieves power transfer from nodes 12 and 18 to nodes 22 and 33, effectively alleviating local overvoltage problems and improving photovoltaic absorption capacity.

[0194] Depend on Figure 5 It can be seen that, Figure 5 Parts (a) to (d) in the table show the power transmission of each voltage source converter (VSC) on the feeder in the SOP of Case 2 and Case 3, respectively: Figure 5 Part (a) in the diagram refers to feeder 1-12, which connects VSC1 to node 12. Figure 5 Part (b) in the diagram refers to feeder 2-18, which connects VSC2 to node 18. Figure 5 Part (c) in the diagram refers to feeder 3-22, which connects VSC3 to node 22. Figure 5 Part (d) in the diagram represents feeder 4-33, where VSC4 connects to node 33. As shown in the figure, compared to Case 2, the power transmission of the SOP feeder is significantly enhanced in Case 3 after the introduction of the mobile energy storage system. Simultaneously, the operating period of the SOP has expanded from primarily concentrated during peak photovoltaic output (12:00–15:00) to the entire day, achieving more continuous and balanced operation. This indicates that the mobile energy storage system plays a positive role in optimizing spatiotemporal energy allocation and improving the system's flexible adjustment capabilities, effectively enhancing the operating efficiency and resource coordination capabilities of the SOP.

[0195] Figure 6 It is the number of battery blocks configured on a node that can access modular mobile energy storage at any given time. Figure 7 It refers to the changes in OLTC gear position and the number of capacitor banks connected to the CBs during the entire optimization process.

[0196] In this specification, the illustrative descriptions of the invention are not necessarily directed at the same embodiments or examples. Those skilled in the art can combine and integrate the different embodiments or examples described in this specification. Furthermore, the embodiments in this specification are merely enumerations of implementation forms of the inventive concept, and the scope of protection of the invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of the invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A power distribution network optimal operation method based on modular mobile energy storage and intelligent soft switch coordination, characterized in that, Includes the following steps: Step 1: Construct the operational constraints of the smart soft switch SOP; Step 1.1: Obtain the power balance constraint of SOP from equations (1) to (3): (1) (2) (3) In equations (1)-(3), for Time of the first A voltage source converter DC-side active power; for time The actual active power transmitted; for time Active power loss; This represents the total number of voltage source converters. for The loss coefficient; express The capacity; For the simulation time set; Step 1.2: Obtain the capacity constraint of SOP from equations (4)-(5): (4) (5) In equations (4)-(5), express The capacity; for time The actual reactive power transmitted; for Maximum output reactive power; Step 2: Construct the road-network model operation constraints for modular mobile energy storage using equations (6)-(21); (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) In equations (6)-(19), Nodes under zero traffic flow conditions With nodes The distance between the roads; The ideal vehicle speed under zero traffic flow conditions; Indicates the degree of congestion in the transportation network; Configuration time for modular mobile energy storage (ME) under zero traffic flow conditions; A collection of battery blocks in modular mobile energy storage (ME); for In the modular mobile energy storage ME system, the first Block battery With nodes The connection state, if the two are connected, then =1, if the two are disconnected, then =0; and They are respectively The first modular mobile energy storage ME Block battery block The charging and discharging indicators, if =1 indicates Charging, if =1 indicates Discharge; and They are respectively Time Node The charging and discharging indicators, if =1 indicates Time Node Charging, if =1 indicates Time Node Discharge; and They are respectively time The active power of charging and discharging; and They are respectively time The reactive power of charging and discharging; and These are the upper limits of the active power for charging and discharging the battery pack, respectively. and These are the upper limits of the reactive power for charging and discharging the battery pack, respectively. and These are the charging and discharging efficiencies of the battery pack, respectively. and They are respectively The upper and lower limits of energy storage capacity; This represents the total number of mobile energy storage battery modules. A set of nodes; Step 3: Construct a controllable equipment operation model for the power distribution system, including: the operation model of the on-load tap-changing transformer (OLTC) and the operation model of the switchable capacitor banks (CBs); Step 4: Construct a distributed power supply operation model; Step 5: Construct an operation model for the power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft-switching system (SOP) using equations (27)-(32): (27) (28) (29) (30) (31) (32) In equations (27)-(32), and They are nodes The set of child and parent nodes; , for Time Node With nodes Branch road ,node With nodes Branch roads between Active power transmitted upstream; , for Time Branch Branch roads The reactive power transmitted upstream; , For nodes The maximum active and reactive power of the load; for DG injection node at any time The active power; for Time Node The square of the voltage applied; and Branch roads Resistance and reactance; and 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 6: Construct the objective function for the operation of the power distribution system based on the collaboration of modular mobile energy storage (ME) and intelligent soft switching system (SOP). ; Step 7: After transforming the operating constraints of the power distribution system into mixed-integer second-order cone programming constraints, apply the objective function... The solution is obtained to obtain the operation scheme of the power distribution system, which includes the operation of the intelligent soft switch SOP, the operation of the on-load tap-changing transformer, the operation of the switchable capacitor, and the operation of the modular mobile energy storage EM. Among them, the battery of the modular energy storage system can be disassembled and spliced, and the mobile energy storage vehicle transfers the battery to the demand side.

2. The method for optimizing the operation of a distribution network based on the synergy of modular mobile energy storage and intelligent soft switching as described in claim 1, characterized in that, Step 3 includes: Step 3.1: Construct the operating model of the on-load tap-changing transformer (OLTC) using equations (20) and (21): (20) (21) In equations (20)-(21), for Nodes that are always connected to the voltage source converter The voltage is equal to the voltage on the secondary side of the OLTC. for Nodes that are always connected to the voltage source converter With nodes Between lines The tap position of the OLTC; For nodes exist Voltage at any given moment; for Timetable Voltage regulation rate of the OLTC; For the line The initial voltage regulation rate of the OLTC; For the line The change in voltage regulation at adjacent tap positions; Step 3.2: Construct the operating model of CBs using equations (22)-(23): (22) (23) In equations (22)-(23), For nodes exist The capacity of the capacitors constantly connected to the power distribution system. yes capacitor capacitance, yes Time Node The number of capacitor banks installed at the location This is the maximum number of capacitor banks that can be connected.

3. The method for optimizing the operation of a distribution network based on the synergy of modular mobile energy storage and intelligent soft switching as described in claim 2, characterized in that, The distributed power source operation model is constructed from equations (24) to (26): (24) (25) (26) In equations (24)-(26), M and P are the sets of nodes connected to modular mobile energy storage and photovoltaics, respectively; Represents various types of distributed power sources and nodes The connection status; and They are nodes The upper limits of active and reactive power output of distributed power sources; and They are nodes The upper and lower limits of the power factor of the distributed power source; and They are nodes The active and reactive power outputs of the distributed power source.

4. The method for optimizing the operation of a distribution network based on the synergy of modular mobile energy storage and intelligent soft switching as described in claim 3, characterized in that, Step 6 includes: Step 6.1: Construct the line loss of the power distribution system using equation (33). : (33) In equation (33), This is the line loss coefficient. A collection of power grid branches; Step 6.2: Construct the voltage over-limit loss of the power distribution system using equation (34). : (34) In equation (34), This is the energy loss coefficient caused by voltage exceeding the limit; A set of power grid nodes; for Time Node Energy loss due to voltage exceeding limits; and: (35) In equation (35), for Time Node The load power; for Time Node The injection power; for Time Node Voltage; For voltage regulation dead zone, , The upper and lower limits of the voltage regulation dead zone; when No voltage loss occurs when the voltage regulation dead zone is in effect; This is the minimum allowable system voltage. This represents the maximum allowable voltage for the system. Step 6.3: Construct the objective function of the power distribution system using equation (36). : (36)。 5. The method for optimizing the operation of a distribution network based on the synergy of modular mobile energy storage and intelligent soft switching as described in claim 4, characterized in that, Step 7 includes the following steps: Step 7.1: Transform equation (11) into the linear constraint shown in equation (37) using a linearization method: (37) In equation (37), For mobile energy storage With nodes exist The connection status at any given moment, For mobile energy storage With nodes exist Connection status within a given time period; Step 7.2: Transform equation (32) into the second-order cone constraint shown in equation (38) using the second-order cone relaxation method: (38)。 6. 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 power distribution network optimization operation method according to any one of claims 1-5, and the processor is configured to execute the programs stored in the memory.

7. 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 power distribution network optimization operation method according to any one of claims 1-5.

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