Multi-dimensional and multi-resource comprehensive voltage regulation optimization method for multi-voltage level bipolar direct current distribution network

Abstract

The application discloses a kind of multi-dimension multi-resource comprehensive voltage regulation optimization methods of multi-voltage grade bipolar direct current distribution network, comprising:1 establishes the control model of bipolar direct current transformer DCT, electric vehicle, energy storage equipment and power flow controller;2 establish multi-dimension multi-resource coordinated control model of multi-voltage grade bipolar direct current distribution network;3 establish comprehensive voltage regulation optimization model of multi-voltage grade bipolar direct current distribution network;4 establish the linearization and relaxation model of the constraint of bipolar direct current distribution network comprehensive voltage regulation model;5 by solving multi-voltage grade bipolar direct current distribution network, obtain the comprehensive voltage regulation optimization strategy of bipolar direct current distribution system.The application can realize the quick response load demand and distributed energy fluctuation, ensure the stability of low-voltage side voltage, effectively control the voltage imbalance degree and over-limit problem between positive and negative electrode, improve the stability of system voltage.

Description

Technical Field

[0001] This invention proposes a multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks with multiple voltage levels. Background Technology

[0002] Bipolar DC distribution networks, as a novel power system architecture, exhibit significant advantages in power supply reliability and transmission efficiency due to their bipolar structure and neutral line design, and have gradually become an important direction for addressing future energy system demands in recent years. Compared to traditional AC distribution networks, bipolar DC distribution networks not only reduce losses in energy conversion stages but also better adapt to the integration needs of new energy sources such as distributed photovoltaics, electric vehicles, and energy storage systems. However, with the increasing diversity of renewable energy sources and load types, complex voltage issues are gradually emerging in system operation, requiring in-depth research and solutions.

[0003] In bipolar DC distribution networks, voltage issues mainly manifest as inter-pole voltage imbalance, voltage fluctuations, and node voltage exceeding limits. Inter-pole voltage imbalance is usually caused by asymmetrical power distribution between the positive and negative poles. For example, when the load on one pole is much greater than that on the other, the voltage difference between the positive and negative poles increases significantly. This imbalance not only leads to a decrease in power supply reliability but may also exacerbate power losses in transmission lines, affecting the overall operating efficiency of the system. Furthermore, due to the volatility of distributed photovoltaic (PV) power generation and the randomness of electric vehicle charging and discharging, node voltage may experience drastic fluctuations or even exceed limits. For instance, in high-penetration PV integration scenarios, PV power generation may significantly exceed load demand during low-load periods, leading to excessive voltage; while during peak load periods, node voltage may rapidly drop below the safe operating range, seriously threatening equipment safety and system stability.

[0004] Voltage issues not only affect the power quality and security of bipolar DC distribution networks but also limit the system's ability to integrate high proportions of renewable energy and complex loads. Inter-polar voltage imbalances weaken the system's adaptability to load changes, while node voltage overruns and fluctuations reduce grid operational reliability. As the integration ratio of DC power sources such as distributed photovoltaics, electric vehicles, and energy storage further increases, the scope and complexity of voltage issues will significantly expand. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by proposing a multi-dimensional, multi-resource integrated voltage regulation optimization method for bipolar DC distribution networks with multiple voltage levels. This method aims to achieve rapid response to load demand and distributed energy fluctuations, ensure the stability of the low-voltage side voltage, effectively solve voltage imbalance and limit-crossing problems between the positive and negative poles, thereby improving the voltage regulation capability of bipolar DC distribution networks, ensuring safe system operation, and supporting future energy transition.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] The present invention provides a multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels, characterized by the following steps:

[0008] S1: Establish control models for bipolar DC transformer (DCT), electric vehicle (EV), energy storage device (ESS), and unipolar power flow controller (PFC);

[0009] S2: Construct a power flow model for a multi-voltage-level distribution network including a DC transformer (DCT);

[0010] S3: Establish a comprehensive voltage regulation optimization model for multi-voltage-level bipolar DC distribution networks;

[0011] S4: Transform the comprehensive voltage regulation optimization model of the multi-voltage level bipolar DC distribution network to obtain the transformed comprehensive voltage regulation optimization model;

[0012] S5: Solve the transformed integrated voltage regulation optimization model using the solver to obtain the integrated voltage regulation optimization strategy for the bipolar DC distribution system, including: the control strategy and power transmission strategy of the bipolar DC transformer (DCT), the charging and discharging strategy of electric vehicles, the charging and discharging strategy of energy storage devices, and the control strategy and power transmission strategy of the power flow controller.

[0013] The multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels described in this invention is characterized by the following steps in S1:

[0014] S1-1: Establishing the control model for a bipolar DC-DC transformer (DCT):

[0015] S1-1-1: Using equation (1), construct a model of the relationship between the output power and the voltage on both sides of any single-pole DC transformer (DCT):

[0016] (1)

[0017] In equation (1), To connect different polarities at the ports of a single-pole DC transformer (DCT), and p represents the positive electrode, n represents the negative electrode, and b represents the bipolar electrode. For port polarity The turns ratio of a single-pole DC transformer (DCT); Indicates the port polarity as Compared to the equivalent shift of a single-pole DC transformer DCT, The switching frequency of a single-pole DC transformer (DCT); This is the equivalent inductance value of a single-pole DC transformer (DCT). For port polarity The port voltage on the high-voltage side of a single-pole DC transformer (DCT); Port polarity of a single-pole DC transformer (DCT) Port voltage on the low-voltage side; For port polarity The single-pole DC transformer DCT in Output power at any given moment; The port polarity is The deviation coefficient caused by the inconsistency between the actual voltage turns ratio of the single-pole DC transformer (DCT) and the turns ratio of the high-frequency isolation transformer;

[0018] S1-1-2: Construct the port polarity using equation (2) The power transfer relationship between the input and output sides of a single-pole DC transformer (DCT):

[0019] (2)

[0020] In equation (2), For port polarity The single-pole DC transformer DCT in Input power at time , For port polarity The intermediate circuit of the single-pole DC transformer (DCT) is in Power loss generated at any time;

[0021] S1-1-3: Using equations (3)-(5), construct mathematical models of a single-pole DC transformer DCT in constant ratio control mode, constant power control mode, and constant voltage control mode respectively:

[0022] (3)

[0023] (4)

[0024] (5)

[0025] In equations (3)-(5), For the port polarity in constant ratio control mode, The turns ratio of a single-pole DC transformer (DCT); This indicates that the port polarity is in constant power control mode. The single-pole DC transformer DCT in Output power at any given moment; This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at time, This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at any given moment; , Indicates high pressure. Indicates medium pressure. Indicates low pressure;

[0026] S1-2: Establish the charging and discharging model of electric vehicle (EV) using equations (6)-(8):

[0027] (6)

[0028] (7)

[0029] (8)

[0030] In equations (6)-(8), and They are respectively Time Node The charging and discharging power of the electric vehicle (EV) at the location; for Time Node The equivalent power of charging an electric vehicle (EV) at the location; Representing nodes respectively The connection and disconnection times between the electric vehicle (EV) and the charging equipment at the location; and These are the exchange efficiencies for charging power and discharging power, respectively. for Time Node The location is the state of charge of the electric vehicle (EV). for Time Node The state of charge of the electric vehicle (EV) at the location; For nodes Battery capacity of the electric vehicle (EV) at the location; and They are nodes The maximum charging power and maximum discharging power of the electric vehicle (EV) at the location; and It is a node The lower and upper bounds of the state of charge of the electric vehicle (EV) at the location; and These are nodes The initial state of charge and the expected state of charge of the electric vehicle (EV) at the location; for Time Node The location is the charging and discharging state of the electric vehicle (EV). =0 means Time Node The electric vehicle at the location is in a discharged state. =1 means Time Node The electric vehicle (EV) at the location is charging. Represents the set of all nodes. This indicates the time period during which the electric vehicle (EV) is connected to a charging station. Indicates a time interval;

[0031] S1-3: Establish the mathematical model of the energy storage device ESS using equations (9)-(14):

[0032] (9)

[0033] (10)

[0034] (11)

[0035] (12)

[0036] (13)

[0037] (14)

[0038] In equations (9)-(14), , They are nodes The maximum charging and discharging power of the energy storage device (ESS) at the location; and They are nodes Location-based energy storage device (ESS) at time The charging power and discharging power; For nodes The auxiliary variable set at the location, when the energy storage device ESS is charging, causes... =1, during discharge, let =0; , , They are nodes Minimum, maximum, and initial state of charge (SOC) of the energy storage device (ESS) at the location; , They are nodes The total capacity of the energy storage device ESS at the location and The capacity of a given moment; For nodes Where ESS, an energy storage device with polar connection, is The equivalent load at any given time, if the energy storage device (ESS) is not connected to the node. Where Extreme times, =0; The polarity of the port connected to the energy storage device ESS is The auxiliary variable of the line, if the polarity of the ESS connection port of the energy storage device is When the line is in use, make =1, otherwise, let =0;

[0039] S1-4: Construct the port polarity using equations (15)-(16) Mathematical model of transmission power for a single-pole power flow controller (PFC):

[0040] (15)

[0041] In equation (15), Indicates the port polarity as The turns ratio of a single-pole power flow controller (PFC); Indicates the port polarity as Compared to the equivalent displacement of a single-pole power flow controller (PFC), The switching frequency of a unipolar power flow controller (PFC); This is the equivalent inductance value of a unipolar power flow controller (PFC). , For port polarity The single-pole power flow controller (PFC) connects the nodes on both sides. ,node The inter-electrode voltage value; This is the equivalent inductance value of a unipolar power flow controller (PFC).

[0042] Using equation (16), the maximum transmission power of the unipolar power flow controller PFC under direction shift control, taking the positive pole as an example, is obtained. :

[0043] (16)

[0044] In equation (16), The turns ratio for a unipolar power flow controller (PFC); , The two nodes connected by the unipolar power flow controller (PFC) ,node The positive terminal voltage value.

[0045] Furthermore, in S2, equations (17)-(20) are used to construct a multi-voltage level distribution network power flow model containing a DC transformer (DCT);

[0046] (17)

[0047] (18)

[0048] (19)

[0049] (20)

[0050] In equations (17)-(20), It is a collection of lines in a multi-voltage-level bipolar DC distribution network that do not contain DC transformers; It is a collection of lines containing DC transformers in a multi-voltage-level bipolar DC distribution network; Indicates the port polarity as The DC transformer DCT m The input side is The transmission power of the line connected at any given time; Indicates the port polarity as The DC transformer DCT m On the output side The transmission power of the line connected at any given time; Voltage level Next node Flow to Node Between lines Where Extreme Transmission power at any given moment; Voltage level Down polar lines The resistance; Voltage level Downline Where Extreme The current flowing through at all times; Voltage level Next node Where Extreme Active power injected at all times; Voltage level Next node Flow to Node The lines between Where Extreme Transmission power at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Distributed generation (DG) with polar connections The active power output at all times; Voltage level Next node Where ESS, a polar-connected energy storage system, is Active power for continuous charging; Voltage level Next node Where Electric vehicles (EVs) with high connectivity Active power for continuous charging; Voltage level Next node Where The active power consumed by the load L connected to the pole; ; Indicates the positive electrode. Indicates the negative electrode. Indicates the center line.

[0051] Furthermore, S3 includes the following steps:

[0052] S3-1 uses equations (21)-(22) to establish the objective function of the comprehensive voltage regulation optimization model for a multi-voltage-level bipolar DC distribution network. :

[0053] (twenty one)

[0054] (twenty two)

[0055] In equations (21)-(22), The losses generated during the operation of multi-voltage level bipolar DC distribution networks, This indicates the penalty imposed on multi-voltage-level bipolar DC distribution networks for exceeding voltage limits. and These are the unit operating loss coefficient and the unit load failure coefficient caused by voltage exceeding limits in a multi-voltage-level bipolar DC distribution network, respectively. It optimizes the total number of time periods; Indicates voltage level Next node Where Extreme The penalty caused by exceeding the voltage limit at any given time. This indicates the voltage level of the bipolar DC power distribution system. Next node Where The load L connected to the pole is The active power consumed at all times; Indicates voltage level Next node Where The equilibrium coefficient of the poles; Voltage level Next node Where Extreme Over-limit penalty coefficient at any time, when the voltage level Next node Where The electrode voltage value is within the optimal range. Within the time, order When the voltage level Next node Where The electrode voltage value is within the safe range Outside of this time, , Indicates voltage level The minimum value of the voltage optimization range. Indicates voltage level The maximum value in the lower voltage optimization range. Indicates voltage level The minimum allowable safe voltage for the downstream line voltage. Indicates voltage level The maximum allowable safe voltage for the lower line voltage, and the following:

[0056] (twenty three)

[0057] S3-2: Constructing basic safety constraints using equations (24)-(25):

[0058] (twenty four)

[0059] (25)

[0060] In equations (24)-(25), For multi-voltage level bipolar DC distribution networks The voltage of the reference node ref connected to the pole. and They are respectively voltage levels The multi-voltage level bipolar DC distribution network is located in The upper and lower voltage limits of the nodes connected by polarity; and They are respectively voltage levels The multi-voltage level bipolar DC distribution network is located in polar connection lines The upper and lower limits of the current; For multi-voltage level bipolar DC distribution networks The reference standard voltage of the initial node st of the polar connection. For multi-voltage level bipolar DC distribution networks The voltage at the initial node st of the polar connection;

[0061] S3-3: Constructing inter-electrode unbalanced voltage constraints using equation (26):

[0062] (26)

[0063] In equation (26), and Voltage level Next node The positive and negative voltages at the point. This represents the maximum value of the voltage imbalance. voltage level Next node Inter-electrode voltage imbalance at the point;

[0064] S3-4: Equations (17)-(20), (24)-(26), (1)-(5), (6)-(8), and (9)-(16) are used as comprehensive voltage regulation optimization models for multi-voltage level bipolar DC distribution networks.

[0065] Furthermore, S4 includes the following steps:

[0066] S4-1: Linearization of the squared terms:

[0067] Using current variables respectively and voltage variables After replacing the square terms of current and voltage in equations (17) to (18), we obtain equations (27) to (28):

[0068] (27)

[0069] (28)

[0070] In equations (27)-(28): Voltage level Downline Where Extreme The square of the current flowing through at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment;

[0071] The relationship between node voltage and inter-electrode voltage can be constructed using equation (29):

[0072] (29)

[0073] In equation (29), Represents a node The voltage at the positive terminal, Represents a node The voltage at the negative terminal. Represents a node The voltage at the bipolar port, Represents a node Positive voltage, Represents a node The negative terminal voltage. Represents a node Neutral voltage;

[0074] Transform equations (5) and (7) into equations (30) and (31);

[0075] (30)

[0076] (31)

[0077] In equations (30)-(31), For port polarity The square of the voltage at the high-voltage side port of the DC transformer DCT; For port polarity The square of the voltage at the low-voltage side port of the DC transformer DCT; Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at time , Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at any given moment;

[0078] The unbalanced voltage constraint in equation (26) is transformed into equation (34):

[0079] (32)

[0080] In equation (30), Represents a node The square of the positive voltage; Represents a node The square of the neutral line voltage; Represents a node The square of the voltage at the negative terminal; Represents a node Auxiliary variables at the positive pole, Represents a node Auxiliary variables at the negative pole, Represents a node The auxiliary variable is bipolar, and we have:

[0081] (33)

[0082] S4-2: Linearization of the objective function:

[0083] Assumption Then Linearization is achieved by equations (34)-(36):

[0084] (34)

[0085] (35)

[0086] (36)

[0087] In equations (34)-(36), Voltage level Next node Where Extreme The degree to which the actual voltage deviates from the voltage optimization range at any given time;

[0088] S4-3: Linearization of the flexible equipment is performed using the Big M method, resulting in (37):

[0089] (37)

[0090] In equation (37), M is a positive number; For the Energy Storage System (ESS) Extreme The state variable that is accessed at all times, when When =1, the energy storage system ESS is located Extreme Power at time constrained as ,when When =0, the energy storage system ESS is located Extreme Power at time constrained as ; For nodes without energy storage devices (ESS) connected. Where ESS, an energy storage device with polar connection, is Charging power at any time For nodes with energy storage devices (ESS) connected Where ESS, an energy storage device with polar connection, is The charging power at any given time; and They are nodes Where ESS, an energy storage device with polar connection, is The charging power and discharging power at any given time;

[0091] S4-4: Relaxation of bilinear terms, including:

[0092] In equation (1), the port polarity is introduced as follows: nodes Location connected load Auxiliary variables , and have Thus, we obtain equation (38):

[0093] (38)

[0094] In equation (38), Indicates the port polarity as nodes Location connected load The power; Indicates the port polarity as nodes The coupling coefficient at the point; Indicates the positive line node after decoupling Location connected load The power; Indicates the negative line node after decoupling Location connected load power, Indicates the centerline node after decoupling. Location connected load power, Represents a node Auxiliary variables for positive electrode power decoupling, Represents a node Auxiliary variables for decoupling negative electrode power. Represents a node Auxiliary variables for bipolar power decoupling;

[0095] S4-5: Second-order cone relaxation:

[0096] Equation (19) is relaxed using the second-order cone relaxation method to obtain equation (39):

[0097] (39)

[0098] In equation (39), Represents the L2 norm;

[0099] Converting equation (33) to the standard second-order cone form, we obtain equation (40):

[0100] (40)

[0101] S4-6: Relaxation of hyperbolic terms, including:

[0102] By employing a combination of second-order cone relaxation and approximation, the equation (2) is modified. , , Relaxation is expressed as equations (41)-(42):

[0103] (41)

[0104] (42)

[0105] In equations (41)-(42), and These represent the maximum and minimum values ​​of the positive electrode coefficient at voltage level g, respectively; and These represent the maximum and minimum values ​​of the neutral coefficient at voltage level g, respectively. and These represent the maximum and minimum values ​​of the negative pole coefficient at voltage level g, respectively. Represents a node The auxiliary variable introduced by the relaxation of the positive hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the negative hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the bipolar hyperbolic term.

[0106] 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 multi-dimensional, multi-resource integrated voltage regulation optimization method, and the processor is configured to execute the program stored in the memory.

[0107] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the multi-dimensional, multi-resource integrated voltage regulation and optimization method.

[0108] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0109] 1. This invention achieves comprehensive management of multi-objective voltage problems. Existing technologies typically focus on single voltage problems, such as inter-electrode voltage imbalance or node voltage exceeding limits, lacking the ability to comprehensively manage multiple voltage problems. The strategy of this invention, through global modeling and multi-resource collaborative regulation, simultaneously solves problems such as inter-electrode voltage imbalance, voltage fluctuation, and voltage exceeding limits, significantly improving the voltage quality and stability of the system.

[0110] 2. This invention can adapt to the complexity of multi-voltage level systems. The strategy of this invention is tailored to the characteristics of multi-voltage level bipolar DC distribution networks, constructing an optimal power flow model based on power injection. This model effectively addresses the coupling problems between subsystems of different voltage levels, optimizes voltage and power distribution, and ensures coordinated operation between voltage levels. Compared to traditional technologies, it can better adapt to the complex characteristics of multi-voltage level systems.

[0111] 3. This invention enhances dynamic control capabilities. The strategy of this invention combines real-time optimization technology to flexibly adjust the control parameters of the energy storage system, power flow controller, and DC transformer based on the dynamic changes in distributed photovoltaic power generation and load fluctuations. This significantly improves the system's adaptability and flexibility in high-penetration scenarios, while existing technologies have limited control capabilities in dynamic scenarios. Attached Figure Description

[0112] Figure 1 This is a structural diagram of a three-voltage-level bipolar DC power distribution test system;

[0113] Figure 2 It shows the load consumption and photovoltaic output of the positive and negative electrodes under different voltage levels;

[0114] Figure 3These are diagrams showing the voltage fluctuations of the positive and negative terminals in three different scenarios.

[0115] Figure 4 This is a comparison chart of the positive and negative voltage distribution ranges under three different cases;

[0116] Figure 5 This is a comparison chart of inter-electrode voltage imbalance under three different cases;

[0117] Figure 6 This is a graph analyzing the impact of photovoltaic load capacity on system voltage quality.

[0118] Figure 7 This is a flowchart of a multi-dimensional, multi-resource integrated voltage regulation and optimization method for multi-voltage level bipolar DC distribution networks. Detailed Implementation

[0119] The invention will now be further described with reference to the accompanying drawings.

[0120] Reference Figures 1-6 A multi-dimensional, multi-resource integrated voltage regulation and optimization method for multi-voltage-level bipolar DC distribution networks is proposed. A comprehensive voltage optimization and regulation model for multi-voltage-level bipolar DC distribution networks is established. Specifically, the implementation flowchart is as follows: Figure 7 As shown, the method includes the following steps:

[0121] S1: Establish control models for bipolar DC transformer (DCT), electric vehicle (EV), energy storage device (ESS), and unipolar power flow controller (PFC);

[0122] S1-1: Establishing the control model for a bipolar DC-DC transformer (DCT):

[0123] S1-1-1: Using equation (1), construct a model of the relationship between the output power and the voltage on both sides of any single-pole DC transformer (DCT):

[0124] (1)

[0125] In equation (1), To connect different polarities at the ports of a single-pole DC transformer (DCT), and p represents the positive port, n represents the negative port, and b represents a bipolar port. For port polarity The turns ratio of a single-pole DC transformer (DCT); Indicates the port polarity as Compared to the equivalent shift of a single-pole DC transformer DCT, The switching frequency of a single-pole DC transformer (DCT); This is the equivalent inductance value of a single-pole DC transformer (DCT). For port polarity The port voltage on the high-voltage side of a single-pole DC transformer (DCT); Port polarity of a single-pole DC transformer (DCT) Port voltage on the low-voltage side; For port polarity The single-pole DC transformer DCT in Output power at any given moment; The port polarity is The deviation coefficient caused by the inconsistency between the actual voltage turns ratio of the single-pole DC transformer (DCT) and the turns ratio of the high-frequency isolation transformer.

[0126] S1-1-2: Construct the port polarity using equation (2) The power transfer relationship between the input and output sides of a single-pole DC transformer (DCT):

[0127] (2)

[0128] In equation (2), For port polarity The single-pole DC transformer DCT in Input power at time , For port polarity The intermediate circuit of the single-pole DC transformer (DCT) is in Power loss generated at any time.

[0129] S1-1-3: Using equations (3)-(5), construct mathematical models of a single-pole DC transformer DCT in constant ratio control mode, constant power control mode, and constant voltage control mode respectively:

[0130] (3)

[0131] (4)

[0132] (5)

[0133] In equations (3)-(5), For the port polarity in constant ratio control mode, The turns ratio of a single-pole DC transformer (DCT); This indicates that the port polarity is in constant power control mode. The single-pole DC transformer DCT in Output power at any given moment; This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at time, This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at any given moment; , Indicates high pressure. Indicates medium pressure. This indicates low pressure.

[0134] S1-2: Establish the charging and discharging model of electric vehicle (EV) using equations (6)-(8):

[0135] (6)

[0136] (7)

[0137] (8)

[0138] In equations (6)-(8), and They are respectively Time Node The charging and discharging power of the electric vehicle (EV) at the location; for Time Node The equivalent power of charging an electric vehicle (EV) at the location; Representing nodes respectively The connection and disconnection times between the electric vehicle (EV) and the charging equipment at the location; and These are the exchange efficiencies for charging power and discharging power, respectively. for Time Node The location is the state of charge of the electric vehicle (EV). for Time Node The state of charge of the electric vehicle (EV) at the location; For nodes Battery capacity of the electric vehicle (EV) at the location; and They are nodes The maximum charging power and maximum discharging power of the electric vehicle (EV) at the location; and It is a node The lower and upper bounds of the state of charge of the electric vehicle (EV) at the location; and These are nodes The initial state of charge and the expected state of charge of the electric vehicle (EV) at the location; for Time Node The location is the charging and discharging state of the electric vehicle (EV). =0 means Time Node The electric vehicle at the location is in a discharged state. =1 means Time Node The electric vehicle (EV) at the location is charging. Represents the set of all nodes. This indicates the time period during which the electric vehicle (EV) is connected to a charging station. Indicates a time interval.

[0139] S1-3: Establish the mathematical model of the energy storage device ESS using equations (9)-(14):

[0140] (9)

[0141] (10)

[0142] (11)

[0143] (12)

[0144] (13)

[0145] (14)

[0146] In equations (9)-(14), , They are nodes The maximum charging and discharging power of the energy storage device (ESS) at the location; and They are nodes Location-based energy storage device (ESS) at time The charging power and discharging power; For nodes The auxiliary variable set at the location, when the energy storage device ESS is charging, causes... =1, during discharge, let =0; , , They are nodes Minimum, maximum, and initial state of charge (SOC) of the energy storage device (ESS) at the location; , They are nodes The total capacity of the energy storage device ESS at the location and The capacity of a given moment; For nodes Where ESS, an energy storage device with polar connection, is The equivalent load at any given time, if the energy storage device (ESS) is not connected to the node. Where Extreme times, =0; The polarity of the port connected to the energy storage device ESS is The auxiliary variable of the line, if the polarity of the ESS connection port of the energy storage device is When the line is in use, make =1, otherwise, let =0;

[0147] S1-4: Construct the port polarity using equations (15)-(16) Mathematical model of transmission power for a single-pole power flow controller (PFC):

[0148] (15)

[0149] In equation (15), Indicates the port polarity as The turns ratio of a single-pole power flow controller (PFC); Indicates the port polarity as Compared to the equivalent displacement of a single-pole power flow controller (PFC), The switching frequency of a unipolar power flow controller (PFC); This is the equivalent inductance value of a unipolar power flow controller (PFC). , For port polarity The single-pole power flow controller (PFC) connects the nodes on both sides. , The inter-electrode voltage value; This is the equivalent inductance value of the unipolar power flow controller (PFC).

[0150] Using equation (16), the maximum transmission power of the unipolar power flow controller PFC under direction shift control, taking the positive pole as an example, is obtained. :

[0151] (16)

[0152] In equation (16), The turns ratio for a unipolar power flow controller (PFC); , The two nodes connected by the unipolar power flow controller (PFC) , The positive terminal voltage value.

[0153] S2: To achieve integrated control of multiple voltage levels, DC transformers are used to interconnect subsystems of different voltage levels, enabling power flow between different subsystems. The bipolar DC distribution network consists of three conductors, which, after decoupling, are positive, neutral, and negative, represented by the symbols +, o, and -, respectively. The power, node voltage, and current between different poles are no longer coupled with other poles. Using equations (17)-(20), a power flow model of a multi-voltage level distribution network with a DC transformer (DCT) is constructed:

[0154] (17)

[0155] (18)

[0156] (19)

[0157] (20)

[0158] In equations (17)-(20), It is a collection of lines in a multi-voltage-level bipolar DC distribution network that do not contain DC transformers; It is a collection of lines containing DC transformers in a multi-voltage-level bipolar DC distribution network; Indicates the port polarity as The DC transformer DCT m The input side is The transmission power of the line connected at any given time; Indicates the port polarity as The DC transformer DCT m On the output side The transmission power of the line connected at any given time; Voltage level Next node Flow to Node Between lines Where Extreme Transmission power at any given moment; Voltage level Down polar lines The resistance; Voltage level Downline Where Extreme The current flowing through at all times; Voltage level Next node Where Extreme Active power injected at all times; Voltage level Next node Flow to Node The lines between Where Extreme Transmission power at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Distributed generation (DG) with polar connections The active power output at all times; Voltage level Next node Where ESS, a polar-connected energy storage system, is Active power for continuous charging; Voltage level Next node Where Electric vehicles (EVs) with high connectivity Active power for continuous charging; Voltage level Next node Where The active power consumed by the load L connected to the pole; ; Indicates the positive terminal circuit. Indicates the negative terminal. Indicates the centerline.

[0159] S3: Establish a comprehensive voltage regulation optimization model for multi-voltage-level bipolar DC distribution networks:

[0160] S3-1 uses equations (21)-(22) to establish the objective function of the comprehensive voltage regulation optimization model for a multi-voltage-level bipolar DC distribution network. :

[0161] (twenty one)

[0162] (twenty two)

[0163] In equations (21)-(22), The losses generated during the operation of multi-voltage level bipolar DC distribution networks, This indicates the penalty imposed on multi-voltage-level bipolar DC distribution networks for exceeding voltage limits. and These are the unit operating loss coefficient and the unit load failure coefficient caused by voltage exceeding limits in a multi-voltage-level bipolar DC distribution network, respectively. It optimizes the total number of time periods; Indicates voltage level Next node Where Extreme The penalty caused by exceeding the voltage limit at any given time. This indicates the voltage level of the bipolar DC power distribution system. Next node Where The load L connected to the pole is The active power consumed at all times; Indicates voltage level Next node Where The equilibrium coefficient of the poles; Voltage level Next node Where Extreme Over-limit penalty coefficient at any time, when the voltage level Next node Where The electrode voltage value is within the optimal range. Within the time, order When the voltage level Next node Where The electrode voltage value is within the safe range Outside of this time, , Indicates voltage level The minimum value of the voltage optimization range. Indicates voltage level The maximum value in the lower voltage optimization range. Indicates voltage level The minimum allowable safe voltage for the downstream line voltage. Indicates voltage level The maximum allowable safe voltage for the lower line voltage, and the following:

[0164] (twenty three)

[0165] S3-2: Constructing basic safety constraints using equations (24)-(25):

[0166] (twenty four)

[0167] (25)

[0168] In equations (24)-(25), For multi-voltage level bipolar DC distribution networks The voltage of the reference node ref connected to the pole. and Voltage levels The lower voltage level bipolar DC distribution network is located in The upper and lower voltage limits of the nodes connected by polarity; and Voltage levels The lower voltage level bipolar DC distribution network is located in polar connection lines The upper and lower limits of the current; For multi-voltage level bipolar DC distribution networks The initial node st of the polar connection references the standard voltage. For multi-voltage level bipolar DC distribution networks The voltage of the initial node st of the polar connection.

[0169] S3-3: Constructing inter-electrode unbalanced voltage constraints using equation (26):

[0170] (26)

[0171] In equation (26), and Voltage level Next node The positive and negative voltages at the point. This represents the maximum value of the voltage imbalance. voltage level Next node Inter-electrode voltage imbalance at the point;

[0172] S3-4: Equations (17)-(20), (24)-(26), (1)-(5), (6)-(8), and (9)-(16) are used as comprehensive voltage regulation optimization models for multi-voltage level bipolar DC distribution networks.

[0173] S4: The comprehensive voltage regulation optimization model of the multi-voltage-level bipolar DC distribution network is transformed to obtain the transformed comprehensive voltage regulation optimization model;

[0174] S4-1: Linearization of the squared terms:

[0175] Using current variables respectively and voltage variables After replacing the square terms of current and voltage in equations (17) to (18), we obtain equations (27) to (28):

[0176] (27)

[0177] (28)

[0178] In equations (27)-(28): Voltage level Downline Where Extreme The square of the current flowing through at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment.

[0179] Using equation (29), the relationship between node voltage and inter-electrode voltage can be constructed:

[0180] (29)

[0181] In equation (29), Represents a node The voltage at the positive terminal, Represents a node The voltage at the negative terminal. Represents a node The voltage at the bipolar port, Represents a node Positive voltage, Represents a node The negative terminal voltage. Represents a node Neutral voltage.

[0182] Transform equations (5) and (7) into equations (30) and (31);

[0183] (30)

[0184] (31)

[0185] In equations (30)-(31), For port polarity The square of the voltage at the high-voltage side port of the DC transformer DCT; For port polarity The square of the voltage at the low-voltage side port of the DC transformer DCT; Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at time , Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at any given moment.

[0186] The unbalanced voltage constraint in equation (26) is transformed into equation (34):

[0187] (32)

[0188] In equation (30), Represents a node The square of the positive voltage; Represents a node The square of the neutral line voltage; Represents a node The square of the voltage at the negative terminal; Represents a node Auxiliary variables at the positive pole, Represents a node Auxiliary variables at the negative pole, Represents a node The auxiliary variable is bipolar, and we have:

[0189] (33)

[0190] S4-2: Linearization of the objective function:

[0191] Assumption Then Linearization is achieved by equations (34)-(36):

[0192] (34)

[0193] (35)

[0194] (36)

[0195] In equations (34)-(36), Voltage level Next node Where Extreme The degree to which the actual voltage deviates from the voltage optimization range at any given time.

[0196] S4-3: Linearization of the flexible equipment is performed using the Big M method, resulting in (37):

[0197] (37)

[0198] In equation (37), M is a positive number; For the Energy Storage System (ESS) Extreme The state variable that is accessed at all times, when When =1, the energy storage system ESS is located Extreme Power at time constrained as ,when When =0, the energy storage system ESS is located Extreme Power at time constrained as ; For nodes without energy storage devices (ESS) connected. Where ESS, an energy storage device with polar connection, is Charging power at any time For nodes with energy storage devices (ESS) connected Where ESS, an energy storage device with polar connection, is The charging power at any given time; and They are nodes Where ESS, an energy storage device with polar connection, is The charging power and discharging power at any given time.

[0199] S4-4: Relaxation of bilinear terms, including:

[0200] In equation (1), the port polarity is introduced as follows: nodes Location connected load Auxiliary variables , and have Thus, we obtain equation (38):

[0201] (38)

[0202] In equation (38), Indicates the port polarity as nodes Location connected load The power; Indicates the port polarity as nodes The coupling coefficient at the point; Indicates the positive line node after decoupling Location connected load The power; Indicates the negative line node after decoupling Location connected load power, Indicates the centerline node after decoupling. Location connected load power, Represents a node Auxiliary variables for positive electrode power decoupling, Represents a node Auxiliary variables for decoupling negative electrode power. Represents a node Auxiliary variables for bipolar power decoupling.

[0203] S4-5: Second-order cone relaxation:

[0204] Equation (19) is relaxed using the second-order cone relaxation method to obtain equation (39):

[0205] (39)

[0206] In equation (39), Represents the L2 norm;

[0207] Converting equation (33) to the standard second-order cone form, we obtain equation (40):

[0208] (40)

[0209] S4-6: Relaxation of hyperbolic terms, including:

[0210] By employing a combination of second-order cone relaxation and approximation, the equation (2) is modified. , , Relaxation is expressed as equations (41)-(42):

[0211] (41)

[0212] (42)

[0213] In equations (41)-(42), and These represent the maximum and minimum values ​​of the positive electrode coefficient at voltage level g, respectively; and These represent the maximum and minimum values ​​of the neutral coefficient at voltage level g, respectively. and These represent the maximum and minimum values ​​of the negative pole coefficient at voltage level g, respectively. Represents a node The auxiliary variable introduced by the relaxation of the positive hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the negative hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the bipolar hyperbolic term.

[0214] S5: Solve the transformed integrated voltage regulation optimization model using a solver to obtain the integrated voltage regulation optimization strategy for the bipolar DC distribution system, including: the selection of the control mode of the bipolar DC transformer (DCT) and power transmission. Control strategies, electric vehicle charging and discharging power and Control strategies, charging and discharging power of energy storage devices and Control strategy and control parameters of power flow controller Control strategy and transmission power Control strategy.

[0215] 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.

[0216] 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.

[0217] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components:

[0218] 1) Network Model and Parameter Settings: To verify the effectiveness of the proposed method, this invention sets up the following parameters: Figure 1Simulation tests and numerical analysis were conducted on the three-voltage-level bipolar DC power distribution system shown. In the system, the high-voltage side has a reference voltage of ±10kV, connected to the data center via a dedicated transformer. The data center has a large-capacity DC photovoltaic power supply, and the computing equipment typically uses 240V DC power. This system exhibits characteristics of a single-location, all-time high-power load, and the planning process aims to minimize problems caused by inter-polarity imbalances. The medium-voltage side has a reference voltage of ±375V, supplying power to larger power devices, including electric vehicles, energy storage, distributed photovoltaics, and high-power household loads. The low-voltage side has a reference voltage of ±48V, typically supplying constant-power loads such as 5G base stations. This system has high voltage requirements and is almost unaffected by grid voltage changes, depending only on load usage. The constant-power loads at nodes 15 to 18 are set to 2kW, 1kW, and 2kW, respectively. The time-series photovoltaic output and load consumption at different polarity ports under high-voltage, low-voltage, and data center voltage levels are shown below. Figure 2 As shown in Table 1, four photovoltaic generator sets are connected to the bipolar DC distribution network. Their basic installation parameters are as follows:

[0219] Table 1 Photovoltaic Installation Locations and Parameters

[0220]

[0221] Nodes 8 and 14 are equipped with DC charging stations. Node 8's station has four charging piles located in the work area parking lot. During working hours, multiple vehicles take turns charging, divided into two groups: one group charges upon arrival in the morning and finishes charging during lunch break, while the other group arrives in the afternoon and leaves upon leaving get off work in the evening. Node 14's station has eight charging piles located in a residential parking lot. Its charging pattern is that vehicles begin charging upon arrival home in the evening and finish charging before work the next day. During this time, electric vehicles can freely charge and discharge as energy storage. The expected SOC for all electric vehicles is 1.0. Some parameters of the energy storage devices and electric vehicles are shown in Table 2. Node 32 is equipped with a switchable positive and negative energy storage device.

[0222] Table 2 Parameters for Energy Storage and Electric Vehicles

[0223]

[0224] The power flow controller adopts a DC / DC converter type. The parameter settings of the power flow controller and DC transformer are shown in Table 3, and other parameters in the simulation are shown in Table 4.

[0225] Table 3 Parameters of Power Controller and DC Transformer

[0226]

[0227] Table 4 Other parameters in the simulation

[0228]

[0229] To better verify the advantages of the proposed method in voltage regulation and operation optimization, we further compared three typical cases:

[0230] Case 1: Multi-voltage level bipolar DC distribution network without optimization measures;

[0231] Case 2: Based on Case 1, the objective function of the traditional scheme is adopted, and a DC power flow controller is added to the model. The comprehensive voltage regulation without considering multiple voltage levels and multiple voltage problems is not considered.

[0232] Case 3: The integrated voltage regulation model proposed in this invention.

[0233] 2) Overall voltage situation analysis: Figure 3 From left to right, the figures show the positive and negative node voltages for three typical cases. It is clear from the figures that without optimization measures, due to the large positive load and limited power supply, the positive system experiences a significant power deficit, resulting in generally low voltage levels and many nodes exceeding their downward voltage limits. Conversely, the negative system exhibits the opposite behavior; power redundancy causes some nodes to exceed their upward voltage limits, leading to a severe voltage imbalance between the positive and negative poles. While Scheme 2 optimizes some nodes, the single objective function and limited optimization methods fail to adequately address the inter-pole imbalance and voltage limit exceedance issues. Figure 4 The images, from left to right, show the operating ranges of the positive and negative electrode voltages under different schemes, combined with... Figure 4 As can be seen, the optimization measures of this invention can maintain the voltage within the safe operating range of [0.97, 1.03], effectively improving the voltage operating range. Compared with traditional solutions, it can achieve comprehensive optimization of the entire voltage system with less optimization resources. By interconnecting the low-voltage side and the high-voltage side, the redundant power on the high-voltage side is used to improve the power deficit of the line between node 8 and node 15 on the low-voltage side, significantly reducing the downward voltage limit of this line. Inter-pole switchable energy storage transfers the load of the positive pole to the negative pole, achieving balanced operation of the positive and negative poles. Figure 3-4 It can be clearly seen that in Scheme 3, the original extreme imbalance and partial over-limit situation at nodes 30-33 has been optimized to a situation where the imbalance and voltage are within the safe and allowable range for a period of time.

[0234] 3) System Operation Optimization Comparison: Table 5 shows the system losses and load failures of the three typical schemes. It can be seen that although Scheme 2 improves both system losses and voltage limit exceedance rate, the voltage limit exceedance rate and load failure rate are still relatively high. This is due to the single optimization objective function. Under the constraint of unbalanced voltage, in order to achieve voltage balance between the positive and negative poles, Scheme 2 reduces the voltage limit exceedance rate of the positive pole while not significantly improving the voltage limit exceedance rate of the negative pole. Although it improves voltage balance, it significantly limits the optimization potential of the negative pole voltage quality.

[0235] Under the optimization measures of this invention, compared with the power distribution system without optimization, system losses are reduced by 20.15%, load failures are reduced by 99.54%, and positive and negative voltage over-limit rates are reduced by 99.23% and 67.89%, respectively. Compared with the traditional optimization scheme, the load failure rate and voltage over-limit rate are significantly improved compared with the traditional optimization strategy scheme two, and both are reduced to within the safe operating range.

[0236] Table 5 Comparison of System Operation Optimization Data for the Three Schemes

[0237]

[0238] Since the ±48V low-voltage side serves as a constant power load, its voltage fluctuation is relatively small, and there are fewer unbalanced nodes on the low-voltage side. This invention mainly studies the voltage imbalance on the high-voltage side. Figure 5 The paper presents the voltage imbalance of two typical nodes, 12 and 32, under three case studies. Scheme 2 can reduce voltage imbalance to some extent, but it cannot achieve overall system optimization. Taking the 32-node system as an example, Scheme 2 does not effectively improve the voltage imbalance compared to the unoptimized case. Considering the average voltage imbalance of the three schemes in the table, the optimization strategy of this invention can achieve comprehensive overall optimization, showing a significant improvement in voltage imbalance compared to Scheme 2. It achieves an optimization of 56.49% compared to Scheme 1 and 43.00% compared to Scheme 2 using the traditional optimization strategy.

[0239] 4) Comparative Analysis of System Photovoltaic Carrying Capacity: This invention tests the impact of photovoltaic (PV) access at different penetration rates on the voltage regulation performance of the proposed method by changing the access capacity of distributed PV. To this end, the following two typical scenarios are selected for comparative analysis. Scenario 1: Power flow calculation without integrated voltage regulation in a multi-voltage level bipolar DC distribution network; Scenario 2: The PV access capacity of the integrated voltage regulation model proposed in this invention increases from 1.0 times the standard capacity to 1.8 times in increments of 0.05 times. Figure 6The results demonstrate the inter-electrode voltage imbalance and voltage limit exceedance rate under different photovoltaic (PV) grid connection capacities. The results show that the proposed scheme can significantly improve system voltage quality and enhance the load-bearing capacity for distributed PV when voltage problems arise due to large-scale distributed PV grid connection. Compared to traditional models, the proposed model improves PV grid connection capacity by 15.79% while maintaining stable system voltage. Furthermore, the model effectively controls voltage imbalance in high-proportion PV grid connection scenarios, maintaining it at around 0.5%, a reduction of over 30.23% compared to no regulation, significantly enhancing the system's adaptability to high PV penetration scenarios.

[0240] In summary, the multi-resource collaborative control method proposed in the multi-dimensional, multi-resource integrated voltage regulation optimization method for multi-voltage-level bipolar DC distribution networks enhances the flexible allocation capability of heterogeneous resources and demonstrates significant advantages in improving the voltage performance and power flow distribution capability of bipolar DC distribution systems. Compared with traditional optimization methods, this strategy significantly reduces overall system losses in multi-voltage-level scenarios and effectively controls voltage imbalance and limit-crossing issues between positive and negative poles, thereby improving system voltage stability. Simultaneously, the proposed model can significantly improve the integration capability of distributed photovoltaic (PV) systems, enhancing the system's adaptability to high-penetration PV integration.

[0241] In the description of this specification, the illustrative representations of the invention do not necessarily refer to the same embodiments or examples. Those skilled in the art can combine and integrate the different implementations or examples described in this specification. Furthermore, the additional content described in this specification is merely an enumeration of implementation forms of the inventive concept, and the scope of protection of this invention should not be regarded as limited to the specific forms stated in the embodiments. The scope of protection of this invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

[0242] In the description of this specification, the illustrative representations of the invention do not necessarily refer to the same embodiments or examples. Those skilled in the art can combine and integrate the different implementations or examples described in this specification. Furthermore, the additional content described in this specification is merely an enumeration of implementation forms of the inventive concept, and the scope of protection of this invention should not be regarded as limited to the specific forms stated in the embodiments. The scope of protection of this invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels, characterized in that, Includes the following steps: S1: Establish control models for bipolar DC transformer (DCT), electric vehicle (EV), energy storage device (ESS), and unipolar power flow controller (PFC); S1-1: Establishing the control model for a bipolar DC-DC transformer (DCT): S1-1-1: Constructing a model showing the relationship between the output power and the voltages on both sides of any single-pole DC transformer (DCT): S1-1-2: Construct the port polarity using equation (2) The power transfer relationship between the input and output sides of a single-pole DC transformer (DCT): (2) In equation (2), For port polarity The single-pole DC transformer DCT in Input power at time , For port polarity The intermediate circuit of the single-pole DC transformer (DCT) is in Power loss generated at any time; For port polarity The single-pole DC transformer DCT in Output power at any given moment; S1-1-3: Using equations (3)-(5), construct mathematical models of a single-pole DC transformer DCT under constant ratio control mode, constant power control mode, and constant voltage control mode respectively: (3) (4) (5) In equations (3)-(5), For the port polarity in constant ratio control mode, The turns ratio of a single-pole DC transformer (DCT); This indicates that the port polarity is in constant power control mode. The single-pole DC transformer (DCT) in Output power at any given moment; This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at time, This indicates the voltage level of the port of the single-pole DC transformer (DCT) under constant voltage control mode. Down Voltage at any given moment; , Indicates high pressure. Indicates medium pressure. Indicates low pressure; S2: Construct a power flow model for a multi-voltage-level distribution network including a DC transformer (DCT); S3: Establish a comprehensive voltage regulation optimization model for a multi-voltage-level bipolar DC distribution network, including: the model constructed in steps S1 and S2, as well as basic safety constraints and inter-pole unbalanced voltage constraints; S4: Transform the comprehensive voltage regulation optimization model of the multi-voltage level bipolar DC distribution network to obtain the transformed comprehensive voltage regulation optimization model; S5: Solve the transformed integrated voltage regulation optimization model using the solver to obtain the integrated voltage regulation optimization strategy for the bipolar DC distribution system, including: the control strategy and power transmission strategy of the bipolar DC transformer (DCT), the charging and discharging strategy of electric vehicles, the charging and discharging strategy of energy storage devices, and the control strategy and power transmission strategy of the power flow controller.

2. The multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels according to claim 1, characterized in that, S1 includes the following steps: S1-1-1: Using equation (1), construct a model of the relationship between the output power and the voltage on both sides of any single-pole DC transformer (DCT): (1) In equation (1), To connect different polarities at the ports of a single-pole DC transformer (DCT), and p represents the positive electrode, n represents the negative electrode, and b represents the bipolar electrode. For port polarity The turns ratio of a single-pole DC transformer (DCT); Indicates the port polarity as Compared to the equivalent shift of a single-pole DC transformer DCT, The switching frequency of a single-pole DC transformer (DCT); This is the equivalent inductance value of a single-pole DC transformer (DCT). For port polarity The port voltage on the high-voltage side of a single-pole DC transformer (DCT); Port polarity of a single-pole DC transformer (DCT) Port voltage on the low-voltage side; For port polarity The single-pole DC transformer (DCT) in Output power at any given moment; The port polarity is The deviation coefficient caused by the inconsistency between the actual voltage turns ratio of the single-pole DC transformer (DCT) and the turns ratio of the high-frequency isolation transformer; S1-2: Establish the charging and discharging model of electric vehicle (EV) using equations (6)-(8): (6) (7) (8) In equations (6)-(8), and They are respectively Time Node The charging and discharging power of the electric vehicle (EV) at the location; for Time Node The equivalent power of charging an electric vehicle (EV) at the location; Representing nodes respectively The connection and disconnection times between the electric vehicle (EV) and the charging equipment at the location; and These are the exchange efficiencies for charging power and discharging power, respectively. for Time Node The location is the state of charge of the electric vehicle (EV). for Time Node The state of charge of the electric vehicle (EV) at the location; For nodes Battery capacity of the electric vehicle (EV) at the location; and They are nodes The maximum charging power and maximum discharging power of the electric vehicle (EV) at the location; and It is a node The lower and upper bounds of the state of charge of the electric vehicle (EV) at the location; and These are nodes The initial state of charge and the expected state of charge of the electric vehicle (EV) at the location; for Time Node The location is the charging and discharging state of the electric vehicle (EV). =0 means Time Node The electric vehicle at the location is in a discharged state. =1 means Time Node The electric vehicle (EV) at the location is charging. Represents the set of all nodes. This indicates the time period during which the electric vehicle (EV) is connected to a charging station. Indicates a time interval; S1-3: Establish the mathematical model of the energy storage device ESS using equations (9)-(14): (9) (10) (11) (12) (13) (14) In equations (9)-(14), , They are nodes The maximum charging and discharging power of the energy storage device (ESS) at the location; and They are nodes Location-based energy storage device (ESS) at time The charging power and discharging power; For nodes The auxiliary variable set at the location, when the energy storage device ESS is charging, causes... =1, during discharge, let =0; , , They are nodes Minimum, maximum, and initial state of charge (SOC) of the energy storage device (ESS) at the location; , They are nodes The total capacity of the energy storage device ESS at the location and The capacity of a given moment; For nodes Where ESS, an energy storage device with polar connection, is The equivalent load at any given time, if the energy storage device (ESS) is not connected to the node. Where Extreme times, =0; The polarity of the port connected to the energy storage device ESS is The auxiliary variable of the line, if the polarity of the ESS connection port of the energy storage device is When the line is in use, make =1, otherwise, let =0; S1-4: Construct the port polarity using equations (15)-(16) Mathematical model of transmission power for a single-pole power flow controller (PFC): (15) In equation (15), Indicates the port polarity as The turns ratio of a single-pole power flow controller (PFC); Indicates the port polarity as Compared to the equivalent displacement of a single-pole power flow controller (PFC), The switching frequency of a unipolar power flow controller (PFC); This is the equivalent inductance value of a unipolar power flow controller (PFC). , For port polarity The single-pole power flow controller (PFC) connects the nodes on both sides. ,node The inter-electrode voltage value; This is the equivalent inductance value of a unipolar power flow controller (PFC). Using equation (16), the maximum transmission power of the unipolar power flow controller PFC under direction shift control, taking the positive pole as an example, is obtained. : (16) In equation (16), The turns ratio for a unipolar power flow controller (PFC); , The two nodes connected by the unipolar power flow controller (PFC) ,node The positive terminal voltage value.

3. The multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels according to claim 2, characterized in that, In S2, equations (17)-(20) are used to construct a power flow model of a multi-voltage level distribution network containing a DC transformer (DCT); (17) (18) (19) (20) In equations (17)-(20), It is a collection of lines in a multi-voltage-level bipolar DC distribution network that do not contain DC transformers; It is a collection of lines containing DC transformers in a multi-voltage-level bipolar DC distribution network; Indicates the port polarity as The DC transformer DCT m The input side is The transmission power of the line connected at any given time; Indicates the port polarity as The DC transformer DCT m On the output side The transmission power of the line connected at any given time; Voltage level Next node Flow to Node Between lines Where Extreme Transmission power at any given moment; Voltage level Down polar lines The resistance; Voltage level Downline Where Extreme The current flowing through at all times; Voltage level Next node Where Extreme Active power injected at all times; Voltage level Next node Flow to Node The lines between Where Extreme Transmission power at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Extreme Voltage at any given moment; Voltage level Next node Where Distributed generation (DG) with polar connections The active power output at all times; Voltage level Next node Where ESS, a polar-connected energy storage system, is Active power for continuous charging; Voltage level Next node Where Electric vehicles (EVs) with high connectivity Active power for continuous charging; Voltage level Next node Where The active power consumed by the load L connected to the pole; ; Indicates the positive electrode. Indicates the negative electrode. Indicates the center line.

4. The multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels according to claim 3, characterized in that, S3 includes the following steps: S3-1 uses equations (21)-(22) to establish the objective function of the comprehensive voltage regulation optimization model for a multi-voltage-level bipolar DC distribution network. : (21) (22) In equations (21)-(22), The losses generated during the operation of multi-voltage level bipolar DC distribution networks, This indicates the penalty imposed on multi-voltage-level bipolar DC distribution networks for exceeding voltage limits. and These are the unit operating loss coefficient and the unit load failure coefficient caused by voltage over-limit in a multi-voltage-level bipolar DC distribution network, respectively. It optimizes the total number of time periods; Indicates voltage level Next node Where Extreme The penalty caused by exceeding the voltage limit at any given time. This indicates the voltage level of the bipolar DC power distribution system. Next node Where The load L of the pole connection is The active power consumed at all times; Indicates voltage level Next node Where The equilibrium coefficient of the poles; Voltage level Next node Where Extreme The penalty factor for exceeding the limit at any given time, when the voltage level Next node Where The electrode voltage value is within the optimal range. Within the time, order When the voltage level Next node Where The electrode voltage value is within the safe range Outside of this time, , Indicates voltage level The minimum value of the voltage optimization range. Indicates voltage level The maximum value in the lower voltage optimization range. Indicates voltage level The minimum allowable safe voltage for the downstream line voltage. Indicates voltage level The maximum allowable safe voltage for the lower line voltage, and the following: (23) S3-2: Constructing basic safety constraints using equations (24)-(25): (24) (25) In equations (24)-(25), For multi-voltage level bipolar DC distribution networks The voltage of the reference node ref connected to the pole. and They are respectively voltage levels The multi-voltage level bipolar DC distribution network is located in The upper and lower voltage limits of the nodes connected by polarity; and They are respectively voltage levels The multi-voltage level bipolar DC distribution network is located in polar connection lines The upper and lower limits of the current; For multi-voltage level bipolar DC distribution networks The reference standard voltage of the initial node st of the polar connection. For multi-voltage level bipolar DC distribution networks The voltage at the initial node st of the polar connection; S3-3: Constructing inter-electrode unbalanced voltage constraints using equation (26): (26) In equation (26), and Voltage level Next node The positive and negative voltages at the point. This represents the maximum value of the voltage imbalance. voltage level Next node Inter-electrode voltage imbalance at the point; S3-4: Equations (17)-(20), (24)-(26), (1)-(5), (6)-(8), and (9)-(16) are used as comprehensive voltage regulation optimization models for multi-voltage level bipolar DC distribution networks.

5. The multi-dimensional, multi-resource integrated voltage regulation and optimization method for bipolar DC distribution networks under multiple voltage levels according to claim 4, characterized in that, S4 includes the following steps: S4-1: Linearization of the squared terms: Using current variables respectively and voltage variables After replacing the square terms of current and voltage in equations (17) to (18), we obtain equations (27) to (28): (27) (28) In equations (27)-(28): Voltage level Downline Where Extreme The square of the current flowing through at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment; Voltage level Next node Where Extreme The square of the voltage at any given moment; The relationship between node voltage and inter-electrode voltage can be constructed using equation (29): (29) In equation (29), Represents a node The voltage at the positive terminal, Represents a node The voltage at the negative terminal. Represents a node The voltage at the bipolar port, Represents a node Positive voltage, Represents a node The negative terminal voltage. Represents a node Neutral voltage; Transform equations (5) and (7) into equations (30) and (31); (30) (31) In equations (30)-(31), For port polarity The square of the voltage at the high-voltage side port of the DC transformer DCT; For port polarity The square of the voltage at the low-voltage side port of the DC transformer DCT; Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at time , Indicates voltage level Multi-voltage level bipolar DC distribution network in The square of the voltage at any given moment; The unbalanced voltage constraint in equation (26) is transformed into equation (34): (32) In equation (30), Represents a node The square of the positive voltage; Represents a node The square of the neutral line voltage; Represents a node The square of the voltage at the negative terminal; Represents a node Auxiliary variables at the positive pole, Represents a node Auxiliary variables at the negative pole, Represents a node The auxiliary variable is bipolar, and we have: (33) S4-2: Linearization of the objective function: Assumption Then Linearization is achieved by equations (34)-(36): (34) (35) (36) In equations (34)-(36), Voltage level Next node Where Extreme The degree to which the actual voltage deviates from the voltage optimization range at any given time; S4-3: Linearization of the flexible equipment is performed using the Big M method, resulting in (37): (37) In equation (37), M is a positive number; For the Energy Storage System (ESS) Extreme The state variable that is accessed at all times, when When =1, the energy storage system ESS is located Extreme Power at time constrained as ,when When =0, the energy storage system ESS is located Extreme Power at time constrained as ; For nodes without energy storage devices (ESS) connected. Where ESS, an energy storage device with polar connection, is Charging power at any time For nodes with energy storage devices (ESS) connected Where ESS, an energy storage device with polar connection, is The charging power at any given time; and They are nodes Where ESS, an energy storage device with polar connection, is The charging power and discharging power at any given time; S4-4: Relaxation of bilinear terms, including: In equation (1), the port polarity is introduced as follows: nodes Location connected load Auxiliary variables , and have Thus, we obtain equation (38): (38) In equation (38), Indicates the port polarity as nodes Location connection load The power; Indicates the port polarity as nodes The coupling coefficient at the point; Indicates the positive line node after decoupling Location connection load The power; Indicates the negative line node after decoupling Location connected load power, Indicates the centerline node after decoupling. Location connected load power, Represents a node Auxiliary variables for positive electrode power decoupling, Represents a node Auxiliary variables for decoupling negative electrode power. Represents a node Auxiliary variables for bipolar power decoupling; S4-5: Second-order cone relaxation: Equation (19) is relaxed using the second-order cone relaxation method to obtain equation (39): (39) In equation (39), Represents the L2 norm; Converting equation (33) to the standard second-order cone form, we obtain equation (40): (40) S4-6: Relaxation of hyperbolic terms, including: By employing a combination of second-order cone relaxation and approximation, the equation (2) is modified. , , Relaxation is expressed as equations (41)-(42): (41) (42) In equations (41)-(42), and These represent the maximum and minimum values ​​of the positive electrode coefficient at voltage level g, respectively. and These represent the maximum and minimum values ​​of the neutral line coefficient at voltage level g, respectively. and These represent the maximum and minimum values ​​of the negative pole coefficient at voltage level g, respectively. Represents a node The auxiliary variable introduced by the relaxation of the positive hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the negative hyperbolic term. Represents a node The auxiliary variable introduced by the relaxation of the bipolar hyperbolic term.

6. 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 multi-dimensional multi-resource integrated voltage regulation optimization method according to any one of claims 1-5, and the processor is configured to execute the program 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 multi-dimensional, multi-resource integrated voltage regulation and optimization method according to any one of claims 1-5.

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