Distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation

Abstract

This invention proposes a distribution network zonal coordination optimization method that considers both local renewable energy consumption and flexible voltage regulation. The method first introduces a comprehensive index of source-load mismatch considering 5G base station access, namely the maximum net load factor and the minimum net load of distribution areas, to screen for transformer overload and DPV overcapacity distribution areas. Then, based on power flow calculation results, the distribution network voltage-power sensitivity is calculated, and the selection criteria for dominant nodes are defined to screen the voltage-dominant nodes. Next, a genetic algorithm is used to optimize the zonal scheme, dividing the distribution network into multiple interconnected physical sub-regions. Finally, a distribution network zonal coordination optimization model incorporating 5G base stations and DPVs is established, and the SADMM algorithm is used for distributed solution to achieve zonal coordination optimization operation of the distribution network. This invention can effectively solve the problem of limited voltage management capacity within a region, improve the local renewable energy consumption capacity of the distribution network, and enhance the flexibility of distribution network voltage regulation.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a method for coordinated optimization of distribution network zones that considers local consumption of new energy sources and flexible voltage regulation. Background Technology

[0002] The power distribution network will integrate a large number of distributed photovoltaic (DPV) and 5G base stations, among other sources and loads. High DPV penetration and high-energy-consuming devices will be a major characteristic of the new power system. However, the high-dimensional integration of DPV and 5G base stations into the distribution network will significantly increase the computational burden on the distribution network's central processing unit, posing new challenges to economic dispatch and power quality. Therefore, research on regional coordinated optimization methods for the distribution network is of great significance for local consumption of renewable energy and voltage quality management.

[0003] The sheer number of DPV and 5G base stations makes full utilization difficult. The challenge lies in the increased decision-making workload, which can lead to heavy computational burdens and poor reliability in centralized systems. Therefore, suitable distributed coordination optimization methods are needed to flexibly and effectively control controllable resources in the distribution network. However, existing distributed active and reactive power coordination optimization models for distribution networks do not yet consider distribution network indices related to active and reactive power, making it difficult to fully regulate resources, ensure sufficient reserves in each zone to respond to reactive power compensation, and limit voltage management capabilities in each area, resulting in large voltage deviations. Furthermore, current research does not consider the source-load mismatch problem caused by high-energy-consuming base station equipment in controllable resources, which can exacerbate transformer power losses in each distribution substation. Therefore, distributed coordination optimization methods for distribution networks need to introduce source-load mismatch as a zoning indicator to ensure the safe and stable operation of the distribution network. Summary of the Invention

[0004] To address the technical problems of traditional distributed network coordination optimization methods being unable to fully regulate resources and having poor voltage deviation management capabilities, this invention provides a distribution network zonal coordination optimization method that considers both local consumption of new energy sources and flexible voltage regulation.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical method: a distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation, comprising:

[0006] Step S1: The comprehensive index of source-load mismatch degree and the comprehensive index of voltage-power sensitivity are used as the comprehensive evaluation index for regional division. An optimal zoning model of the distribution network that comprehensively considers the local consumption of new energy and flexible voltage regulation is constructed. The optimal zoning model of the distribution network is optimized and solved by the genetic algorithm to obtain the regional division result of the distribution network and divide the distribution network into several sub-regions.

[0007] Step S2: Based on the distribution network area division results obtained in Step S1, and with the constraint that the state variables of the coupled branches in adjacent distribution network areas are equal, a distributed power optimization control coupling model is constructed.

[0008] Step S3: Using the optimization of distribution network losses and average voltage deviation as the objective function, a distribution network zonal coordinated optimization model incorporating 5G base stations and distributed photovoltaics is constructed. The constraints of this distribution network zonal coordinated optimization model include: power system flow constraints, power security constraints, group switching of capacitor banks constraints, static var compensator constraints, 5G base station constraints, distributed photovoltaic constraints, regional power balance constraints, and zonal regulation constraints; wherein, the zonal regulation constraints are determined based on the distributed power optimization control coupling model.

[0009] Step S4: The SADMM algorithm is used to solve the coordination optimization model corresponding to each sub-region of the distribution network in a distributed manner to obtain the real-time operating power and voltage distribution of the distribution network, thereby realizing the zoned coordinated optimization operation of the distribution network.

[0010] Furthermore, step S1, the process of constructing the optimal zoning model of the distribution network, includes:

[0011] S101, by introducing the maximum load rate and minimum net load of the transformer in the distribution area, a comprehensive index of source-load mismatch is determined. As shown in the following formula:

[0012] (1)

[0013] (2)

[0014] In the formula: , They are respectively Time zone Active and reactive loads; Taiwan District The assembled photovoltaic power station Actual output at any given moment; Taiwan District exist Total load of 5G base stations at any given time; The transformer capacity for each distribution area; , Representing the respective districts The transformer's maximum load rate and minimum net load, when At that time, it indicates the station area. The transformer is under heavy load, so it is categorized into a set. Conversely, they are classified into sets. ,when At that time, it indicates the station area. There is an overcapacity in distributed photovoltaic power generation, which is categorized into a collective. Conversely, they are classified into sets. ; , These are the weighting coefficients for the maximum load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively.

[0015] S102, Determine the basis for selecting the dominant nodes of the distribution network;

[0016] First, determine the voltage-active power sensitivity and voltage-reactive power sensitivity of each branch of the distribution network;

[0017] (3)

[0018] (4)

[0019] In the formula: branch road Voltage-active power sensitivity; Indicates a branch ; Represented as node 0 to node The set of line nodes; Resistance of the distribution network lines; For nodes Voltage; For nodes Voltage; For nodes The outflow of active power; The active power flowing through branch k; This refers to the active power loss of the transformer. branch road Voltage-reactive power sensitivity; For the reactance of the distribution network lines; , They are nodes and nodes reactive power; This refers to the reactive power loss of the transformer.

[0020] Then define the observability and controllability metrics for the dominant node;

[0021] (5)

[0022] (6)

[0023] In the formula: Number the dominant node; It is the set of all nodes in the distribution network; Number the other nodes in the area; as the dominant node observability indicators; This indicates the degree of influence of the dominant node voltage on the active voltage of other nodes within the region; This indicates the degree of influence of the dominant node voltage on the reactive voltage of other nodes within the region; as the dominant node Controllability indicators;

[0024] Finally, the selection criteria for the dominant nodes of the distribution network were determined.

[0025] (7)

[0026] In the formula: It is a comprehensive indicator of voltage-power sensitivity; , These are the weighting coefficients for observability and controllability;

[0027] S103 uses the comprehensive index of source-load mismatch and the comprehensive index of voltage-power sensitivity as comprehensive evaluation indicators for regional division, and constructs an optimal zoning model for the distribution network that comprehensively considers local consumption of new energy and flexible voltage regulation, as follows:

[0028] (8)

[0029] In the formula: This represents the objective function of the distribution network optimization zoning model; , These are the weighting coefficients for each indicator. .

[0030] Furthermore, the distributed power optimization control coupling model is as follows:

[0031] (9)

[0032] (10)

[0033] (11)

[0034] In the formula: Represents the set of coupled branches in a distribution network; Indicates the coupled branches in adjacent areas of the distribution network; , Representing regions , The collection of branch roads, the area and region They are adjacent; This represents the set of related physical sub-regions into which a power distribution network is divided; , Representing regions , State variables of a medium-coupled branch; Indicates the region Active power transmitted in the coupled branch; Indicates the region Reactive power transmitted in coupled branches; Representing regions Nodes at both ends of the coupled branch , The node voltage.

[0035] Furthermore, the objective function of the distribution network zoning coordination optimization model is as follows:

[0036] (12)

[0037] (13)

[0038] (14)

[0039] (15)

[0040] (16)

[0041] In the formula: This represents the objective function of the distribution network zone coordination optimization model; , These are distribution network losses and average voltage deviation, respectively. , These are the weighting factors for distribution network losses and average voltage deviation, respectively. , These are the weighting coefficients for distribution network losses and average voltage deviation, respectively. , These are the initial values ​​of distribution network loss and average voltage deviation before optimization; for Price per unit of network loss per moment; For nodes and nodes Branch current between; The duration of a single time period; This refers to the number of nodes in the distribution network. The reference voltage; for Time Node Voltage; One optimization cycle;

[0042] Preferably, the power flow constraints of the power system are as follows:

[0043] (17)

[0044] In the formula, , They are nodes The active and reactive power injected; , They are nodes Next node The outflow of active and reactive power; , Each is the previous node Inflow node Active and reactive power; For nodes and nodes Branch currents between; , For nodes The conductivity and susceptance to ground are constant; , They are nodes , The voltage;

[0045] The power safety constraints are as follows:

[0046] (18)

[0047] In the formula, , They are nodes and nodes The upper and lower limits of the current in the branch circuits; , These are the active and reactive power exchanged with the upper-level power grid, respectively. , These are the maximum and minimum interactive active power allowed to pass through the connection branch between the distribution network and the upper-level power grid, respectively. , These are the maximum and minimum reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively. , They are nodes The upper and lower limits of the voltage; For nodes and nodes The square of the branch current between; For nodes The square of the voltage;

[0048] The constraints for the grouped switching capacitor banks are as follows:

[0049] (19)

[0050] In the formula: The number of capacitor banks in operation is represented by the value of a discrete variable. For nodes Maximum number of capacitor banks connected; The compensation power for each group of capacitors is constant; This represents the maximum number of operations. For the reactive power of switching capacitor banks in groups; , These represent an optimization time period and an optimization cycle, respectively.

[0051] The static var compensator is subject to the following constraints:

[0052] (20)

[0053] In the formula: The reactive power of the static var compensator; , These represent the lower and upper limits of the reactive power of the static var compensator, respectively.

[0054] The constraints of the 5G base station include:

[0055] 1) Constraints of the 5G base station load model:

[0056] (twenty one)

[0057] (twenty two)

[0058] In the formula, for Total load of 5G base stations at any given time; This indicates the working status of the 5G base station. When the value is 1, the 5G base station is in an active state; when the value is 0, the 5G base station is in a sleep state. , They are respectively Time of the first The active and sleep loads of a 5G base station; for Time of the first The static load of a 5G base station; express Time of the first The communication load of each 5G base station is a dynamic load. For the first The load scaling factor of a 5G base station; A coefficient that reflects the communication data of mobile users; This represents the predicted maximum value of the dynamic load of a 5G base station, which is the radio frequency output power of the active antenna element in the 5G base station communication device when the mobile user communication load is predicted.

[0059] 2) Constraints on the backup power capacity model for 5G base stations:

[0060] (twenty three)

[0061] In the formula: For 5G base stations The required backup power capacity for a given period of time; The shortest backup power time for 5G base stations;

[0062] 3) Constraints on the backup energy storage power control model for 5G base stations:

[0063] (twenty four)

[0064] (25)

[0065] (26)

[0066] (27)

[0067] In the formula, express or ; The active charging identifier for backup energy storage of 5G base stations is a variable of 0 or 1. When the identifier is 0, no charging is performed, and when the identifier is 1, charging is performed. This is a flag for the active power discharge of backup energy storage for 5G base stations. It is a variable that can be 0 or 1. When the flag is 0, no discharge occurs, and when the flag is 1, discharge occurs. For energy storage batteries Active power absorbed or released at any time; for The minimum value; for The maximum value; Energy storage for 5G base stations Actual operating capacity during the time period; The self-discharge rate of backup energy storage for 5G base stations; , These are the charging and discharging efficiencies of backup energy storage for 5G base stations; , These are the maximum and minimum actual operating capacities of backup energy storage for 5G base stations, respectively. , These represent the actual operating capacity of 5G base station backup energy storage during the initial and final periods of the scheduling cycle, respectively. for Actual charging and discharging power of 5G base station energy storage during the time period;

[0068] 4) Constraints of the 5G base station backup energy storage SOC model:

[0069] (28)

[0070] In the formula: , , They are respectively The backup energy storage SOC of 5G base stations during the time period and its maximum and minimum values;

[0071] The constraints of the distributed photovoltaic system are as follows:

[0072] (29)

[0073] In the formula, , They are nodes The assembled photovoltaic power station in the first Actual and projected output over a given period;

[0074] The power balance constraints within the region are as follows:

[0075] (30)

[0076] In the formula, , These are the sum of active power and reactive power injected into each node of the distribution network, respectively. , These are the total active load and the total reactive load, respectively. This indicates the total load of 5G base stations; This represents the total active power purchased by the distribution network, calculated from the data for each time period. Add them together to get; The total reactive power purchased by the distribution network is determined by the amount of reactive power purchased in each time period. Add them together to get; The total charge and discharge capacity of the backup energy storage battery for 5G base stations is determined by the active power absorbed in each time period. and released active power Add them together to get; , , These are the total output of the DPV, the total reactive power of the grouped switching capacitor banks, and the total reactive power of the static var compensator;

[0077] The zoning control constraints are as shown in equations (9) to (11).

[0078] This invention proposes a distribution network zoning coordination optimization method that considers both local renewable energy consumption and flexible voltage regulation. First, it introduces a comprehensive index of source-load mismatch considering 5G base station access, namely the maximum net load factor and the minimum net load of distribution areas, to screen for transformer overload and DPV overcapacity distribution areas. Then, it calculates the distribution network voltage-power sensitivity based on power flow calculations, defines the criteria for selecting dominant nodes, and screens the dominant voltage nodes. Next, it uses a genetic algorithm to optimize the zoning scheme, dividing the distribution network into multiple interconnected physical sub-regions. Finally, it establishes a distribution network zoning coordination optimization model including 5G base stations and DPVs, and uses the SADMM algorithm for distributed solution to achieve zoning coordination optimization operation of the distribution network. This invention introduces a comprehensive source-load mismatch index on top of the voltage-power sensitivity index, avoiding the phenomenon of no controllable resources within a region caused by a single index zoning, solving the problem of limited voltage management capabilities within a region, and improving the distribution network's local renewable energy consumption capacity and the flexibility of voltage regulation. Attached Figure Description

[0079] Figure 1 This is a flowchart of the distribution network zone coordination optimization method that considers local consumption of new energy and flexible voltage regulation proposed in this invention;

[0080] Figure 2 This is a flowchart of the solution process for the distribution network zone coordination optimization model proposed in this invention;

[0081] Figure 3 This is an IEEE 33-node distribution network topology diagram in an embodiment of the present invention;

[0082] Figure 4 This is a schematic diagram of load and DPV output prediction data in an embodiment of the present invention;

[0083] Figure 5 This is a diagram showing the minimum net load distribution of the transformer area in an embodiment of the present invention;

[0084] Figure 6 This is a distribution diagram of the maximum net load rate of transformers in the distribution area according to an embodiment of the present invention;

[0085] Figure 7 This is a voltage-power sensitivity matrix distribution diagram in an embodiment of the present invention;

[0086] Figure 8 This is a schematic diagram of the power distribution network partition obtained by using the method of the present invention in an embodiment of the present invention;

[0087] Figure 9 This is a schematic diagram of the power distribution network partition obtained using a conventional method in an embodiment of the present invention;

[0088] Figure 10This is a schematic diagram of the action scheme of the reactive power compensation device obtained by using the method of the present invention in an embodiment of the present invention;

[0089] Figure 11 This is a schematic diagram of the voltage distribution at node 1 in scenario 1 of this invention.

[0090] Figure 12 This is a schematic diagram of the voltage distribution at node 2 in scenario 2 of this invention. Detailed Implementation

[0091] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0092] like Figure 1 As shown, a method for coordinated optimization of distribution network zones that considers local consumption of new energy sources and flexible voltage regulation mainly includes the following steps.

[0093] Step S1: Construct and solve an optimal zoning model for the distribution network that comprehensively considers local consumption of new energy sources and flexible voltage regulation.

[0094] S101, introduce the source-load mismatch index considering 5G base station access, namely the maximum load rate of the transformer in the distribution area and the minimum net load of the distribution area, to screen distribution areas with heavy transformer load and excess DPV output, as shown in Equation (1):

[0095] (1)

[0096] In the formula: , They are respectively Time zone Active and reactive loads; Taiwan District The assembled photovoltaic power station Actual output at any given moment; Taiwan District exist Total load of 5G base stations at any given time; The transformer capacity for each distribution area; , Representing the respective districts The transformer's maximum load rate and minimum net load, when At that time, it indicates the station area. The transformer is under heavy load, so it is categorized into a set. Conversely, they are classified into sets. ,when At that time, it indicates the station area. There is an overcapacity in distributed photovoltaic power generation, which is categorized into a collective. Conversely, they are classified into sets. The above four sets can be used to filter transformer areas with significant differences in source load.

[0097] Define a comprehensive index for source-load mismatch. for:

[0098] (2)

[0099] In the formula: , These are the weighting coefficients for the maximum load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively. In this embodiment, we take... .

[0100] S102, Determine the basis for selecting the dominant nodes of the distribution network.

[0101] First, determine the voltage-active power sensitivity and voltage-reactive power sensitivity of each branch of the distribution network;

[0102] (3)

[0103] (4)

[0104] In the formula: branch road Voltage-active power sensitivity; Indicates a branch ; Represented as node 0 to node The set of line nodes; Resistance of the distribution network lines; For nodes Voltage; For nodes Voltage; For nodes The outflow of active power; The active power flowing through branch k; This refers to the active power loss of the transformer. branch road Voltage-reactive power sensitivity; For the reactance of the distribution network lines; , They are nodes and nodes reactive power; This refers to the reactive power loss of the transformer.

[0105] Then, the observability index of the dominant node is defined by the influence of the dominant node voltage on the voltages of other nodes within the partition.

[0106] (5)

[0107] In the formula: Number the dominant node; It is the set of all nodes in the distribution network; Number the other nodes in the area; as the dominant node observability indicators; This indicates the degree of influence of the dominant node voltage on the active voltage of other nodes within the region; This indicates the degree of influence of the dominant node voltage on the reactive voltage of other nodes within the region.

[0108] Next, the controllability represents the impact of changes in the power of the dominant node on the voltage of other nodes within the partition. By controlling the power of the dominant node, the voltage level of the entire partition is controlled, and a controllability index is defined.

[0109] (6)

[0110] In the formula: as the dominant node Controllability indicators.

[0111] Finally, the selection criteria for the dominant nodes of the distribution network were determined.

[0112] (7)

[0113] In the formula: It is a comprehensive indicator of voltage-power sensitivity; , As the weighting coefficient for observability and controllability, this embodiment takes... .

[0114] S103, Construct the optimal zoning model for the distribution network.

[0115] Taking into account both local renewable energy consumption and flexible voltage regulation, the comprehensive index of source-load mismatch and the comprehensive index of voltage-power sensitivity are used as comprehensive evaluation indicators for regional division. An optimal zoning model for the distribution network that comprehensively considers local renewable energy consumption and flexible voltage regulation is constructed. The objective function of this model is as follows:

[0116] (8)

[0117] In the formula: This represents the objective function of the distribution network optimization zoning model; , These are the weighting coefficients for each indicator. By adjusting the weighting coefficients, partitioning results for different objectives can be obtained. Since this invention addresses distribution networks with high proportions of 5G base stations and DPV access, source-load mismatch is prominent; therefore, it can... The setting is relatively large; in this implementation, we take... It is 0.65.

[0118] S104 uses a genetic algorithm to solve the optimal partitioning model of the distribution network, dividing the distribution network into several interconnected physical sub-regions.

[0119] Using the comprehensive evaluation index for distribution network area division (i.e., formula (8)) as the fitness function, the upper limit of the number of distribution network partitions is limited. Considering the electrical distance characteristics between nodes, the similarity of the operating characteristics between nodes is quantified through the node similarity calculation formula, and the partition optimization is performed by a genetic algorithm. In the partitioning process, the concept of complex network modularity function is introduced, and the number of partitions and the specific nodes contained in each partition are obtained by optimizing the modularity function. Since the use of genetic algorithm for distribution network partition optimization is a conventional technique in this field, it will not be discussed in detail here.

[0120] Step S2: Based on the distribution network area division results obtained in Step S1, construct a distributed power optimization control coupling model with the constraint that the state variables of the coupled branches in adjacent distribution network areas are equal.

[0121] Each region is equipped with one terminal controller. Each terminal controller only performs data measurements within its controlled region and only collects boundary coordination information from adjacent terminal controllers. The coupling between adjacent regions is the branch on the region boundary, referred to as the coupling branch. The set of coupled branches is represented by equation (9), as follows:

[0122] (9)

[0123] In the formula: Represents the set of coupled branches in a distribution network; Indicates the coupled branches in adjacent areas of the distribution network; , Representing regions , The collection of branch roads, the area and region They are adjacent; This represents the set of related physical subregions into which a power distribution network is divided.

[0124] Coupled branch State variables include the power transmitted through the branches and the square of the node voltage. Region The state variables of the intermediate coupled branch are represented by equation (10), as follows:

[0125] (10)

[0126] In the formula: Indicates the area The state variables of the intermediate coupled branch, Indicates the area Active power transmitted in the coupled branch; Indicates the area Reactive power transmitted in coupled branches; Representing regions Nodes at both ends of the coupled branch , The node voltage.

[0127] To make the problem solved by partitioning equivalent to the original problem, the region... The coupled branch state X obtained from the subproblem a,ij With adjacent areas Coupled branch states obtained from subproblems They must be equal. The coupling constraints of distributed power optimization control based on partition coordination are shown in Equation (11), as follows:

[0128] (11)

[0129] In the formula: Indicates the area State variables of the coupled branch.

[0130] Step S3: Construct a distribution network zone coordination optimization model that includes 5G base stations and distributed photovoltaics.

[0131] The purpose of regional coordinated optimization is to optimize the overall voltage level of the distribution network. The overall voltage level of the distribution network is evaluated by the voltage reference deviation. Based on this, a regional coordinated optimization model of the distribution network including 5G base stations and distributed photovoltaics is constructed with the goal of optimizing the network loss and average voltage deviation.

[0132] The objective function of the regional coordinated optimization model for the power distribution network is as follows:

[0133] (12)

[0134] (13)

[0135] (14)

[0136] (15)

[0137] In the formula: This represents the objective function of the distribution network zone coordination optimization model; , These are distribution network losses and average voltage deviation, respectively. , These are the weighting factors for distribution network losses and average voltage deviation, respectively. , These are the weighting coefficients for distribution network losses and average voltage deviation, respectively. , The initial values ​​for distribution network loss and average voltage deviation before optimization are as follows:

[0138] (16)

[0139] In the formula: for Price per unit of network loss per moment; For nodes and nodes Branch current between; The duration of a single time period; This refers to the number of nodes in the distribution network. The reference voltage; for Time Node Voltage; This is one optimization cycle.

[0140] The constraints of the distribution network zone coordination optimization model include: power system flow constraints, power security constraints, group switching of capacitor banks constraints, static var compensator constraints, 5G base station constraints, distributed photovoltaic constraints, regional power balance constraints, and zone regulation constraints.

[0141] ① The power flow constraints of the power system are as follows:

[0142] (17)

[0143] In the formula, , They are nodes The active and reactive power injected; , They are nodes Next node The outflow of active and reactive power; , Each is the previous node Inflow node Active and reactive power; For nodes and nodes Branch current between; , For nodes The conductivity and susceptance to ground are constant; , They are nodes , The voltage.

[0144] ② The power safety constraints are as follows:

[0145] (18)

[0146] In the formula, , They are nodes and nodes The upper and lower limits of the current in the branch circuits; , These are the active and reactive power exchanged with the upper-level power grid, respectively. , These are the maximum and minimum interactive active power allowed to pass through the connection branch between the distribution network and the upper-level power grid, respectively. , These are the maximum and minimum reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively. , They are nodes The upper and lower limits of the voltage; For nodes and nodes The square of the branch current between; For nodes The square of the voltage.

[0147] ③ The constraints for grouped switching of capacitor banks (CB) are as follows:

[0148] (19)

[0149] In the formula: The number of capacitor banks in operation is represented by the value of a discrete variable. For nodes Maximum number of capacitor banks connected; The compensation power for each group of capacitors is constant; This represents the maximum number of operations. For the reactive power of switching capacitor banks in groups; , These represent an optimization time period and an optimization cycle, respectively.

[0150] ④ The constraints of the static var generator (SVG) are as follows:

[0151] (20)

[0152] In the formula: The reactive power of the static var compensator; , These represent the lower and upper limits of the reactive power of the static var compensator, respectively.

[0153] ⑤ 5G base station constraints

[0154] 1) The 5G base station load model constraint references the linear equation of the 5G base station load, expressed as:

[0155] (twenty one)

[0156] (twenty two)

[0157] In the formula, for Total load of 5G base stations at any given time; This indicates the working status of the 5G base station. When the value is 1, the 5G base station is in an active state; when the value is 0, the 5G base station is in a sleep state. , They are respectively Time of the first The active and sleep loads of a 5G base station; for Time of the first The static load of a 5G base station; express Time of the first The communication load of each 5G base station is a dynamic load. For the first The load scaling factor of a 5G base station; A coefficient that reflects the communication data of mobile users; This represents the predicted maximum value of the dynamic load of a 5G base station, which is the radio frequency output power of the active antenna unit in the 5G base station communication device when the mobile user communication load is predicted.

[0158] 2) The safe backup power capacity of 5G base station backup energy storage in each time period can be calculated from the total base station load mentioned above. Therefore, the model constraint for the safe backup power capacity of 5G base station backup energy storage is:

[0159] (twenty three)

[0160] In the formula: For 5G base stations The required backup power capacity for a given period of time; The shortest backup power time for 5G base stations.

[0161] 3) To fully reflect the flexibility of the backup energy storage device, a power factor control method is adopted, using the maximum charging and discharging capacity of the converter to constrain active / reactive power. Therefore, the constraints of the 5G base station backup energy storage power control model include:

[0162] (twenty four)

[0163] (25)

[0164] In the formula: express or ; The active charging identifier for backup energy storage of 5G base stations is a variable of 0 or 1. When the identifier is 0, no charging is performed, and when the identifier is 1, charging is performed. This is a flag for the active power discharge of backup energy storage for 5G base stations. It is a variable that can be 0 or 1. When the flag is 0, no discharge occurs, and when the flag is 1, discharge occurs. For energy storage batteries Active power absorbed or released at any time; for The minimum value; for The maximum value.

[0165] The backup energy storage capacity of 5G base stations is directly related to their charging and discharging power. It is also necessary to ensure that the backup energy storage capacity does not exceed its limit throughout the entire cycle, and that the backup energy storage capacity is equal at the beginning and end of the cycle. Therefore, the constraints of the 5G base station backup energy storage power control model also include:

[0166] (26)

[0167] In the formula: Energy storage for 5G base stations Actual operating capacity during the time period; The self-discharge rate of backup energy storage for 5G base stations; , These are the charging and discharging efficiencies of backup energy storage for 5G base stations; , These are the maximum and minimum actual operating capacities of backup energy storage for 5G base stations, respectively. , These represent the actual operating capacity of 5G base station backup energy storage during the initial and final periods of the scheduling cycle.

[0168] Energy storage batteries experience energy loss during actual charging and discharging, leading to a slight difference between the actual charging and discharging power of the base station's backup energy storage and its internal power throughout the entire cycle. Therefore, the constraints of the 5G base station backup energy storage power control model also include:

[0169] (27)

[0170] In the formula: for Actual charging and discharging power of 5G base station energy storage during the specified time period.

[0171] 4) Constraints of the 5G base station backup energy storage SOC model:

[0172] (28)

[0173] In the formula: , , They are respectively The backup energy storage SOC of 5G base stations during the time period and its maximum and minimum values.

[0174] ⑥ Constraints of Distributed Photovoltaics

[0175] In actual power grids, most DPV (Dynamic Power Generation) resources are of the type where active power is uncontrollable but reactive power is controllable. Active power output is the predicted DPV output, as follows:

[0176] (29)

[0177] In the formula, , They are nodes The assembled photovoltaic power station in the first Actual and predicted output over a given period.

[0178] ⑦ The power balance constraints within the region are as follows:

[0179] (30)

[0180] In the formula, , These are the sum of active power and reactive power injected into each node of the distribution network, respectively. , These are the total active load and the total reactive load, respectively. This indicates the total load of 5G base stations; This represents the total active power purchased by the distribution network, calculated from the data for each time period. Add them together to get; The total reactive power purchased by the distribution network is determined by the amount of reactive power purchased in each time period. Add them together to get; The total charge and discharge capacity of the backup energy storage battery for 5G base stations is determined by the active power absorbed in each time period. and released active power Add them together to get; , , These are the total output of the DPV, the total reactive power of the grouped switching capacitor banks, and the total reactive power of the static var compensator.

[0181] ⑧ Zoning control constraints

[0182] The distribution network zone coordination optimization model needs to meet the zone control constraints with state variables of coupled branches, as shown in equations (9) to (11).

[0183] Step S4: The synchronous alternating direction method of multipliers (SADMM algorithm) is used to solve the coordination optimization model corresponding to each sub-region of the distribution network in a distributed manner, so as to obtain the real-time operating power and voltage distribution of the distribution network and realize the zoned coordinated optimization operation of the distribution network.

[0184] The distribution network zoning coordination optimization model rationally divides the distribution network into zones based on voltage-power sensitivity and source-load mismatch index, thereby mitigating voltage deviations in each zone. The following section uses the coordination subproblem of zones a and b as an example to briefly describe the distributed solution process of the SADMM algorithm (since using the SADMM algorithm for distribution network zoning coordination optimization is a standard technique in this field, it will not be elaborated upon here). The solution process can be found by referring to [reference needed]. Figure 2 .

[0185] 1) Establish a sub-optimization problem for each region. Construct the region... , The augmented Lagrangian function corresponding to the objective function of the subproblem and And through appropriate transformations, the distribution is transformed into equations (31) and (32):

[0186] (31)

[0187] (32)

[0188] In the formula: , They are respectively regions , Decision variables for subproblems; , They are respectively regions , The objective function of the subproblem; This represents the number of iterations. For penalty parameters; , They are respectively regions , The vector consisting of dual variables, for example Corresponding coupled branch state . , Other regions , No. The fixed reference value for the next iteration is used to select the region. , No. The average state of the coupled branch in the next iteration is given by equation (33):

[0189] (33)

[0190] After the objective function is determined, the constraints for each region are determined as Equations (17) to (18) and Equations (21) to (30).

[0191] 2) No. In the next iteration, the augmented Lagrangian function is calculated. and The minimum decision variable values ​​are given in equations (34) to (25), and the states of the coupled branches in each region are obtained simultaneously:

[0192] (34)

[0193] (35)

[0194] In the formula: , The first The area updated in the next iteration , Sub-problem decision variables.

[0195] 3) The average value of the coupled branch state calculated based on the coupled branch state of each region is used as a fixed reference value for the next iteration, as shown in the following formula:

[0196] (36)

[0197] 4) Each region updates its dual variable as follows:

[0198] (37)

[0199] (38)

[0200] 5) Algorithm Iteration Convergence Criterion. The algorithm iteration convergence criterion is reflected in whether the square of the L2 norm of the difference between the states of the coupled branches obtained from adjacent regions satisfies the condition. To ensure convergence accuracy, the iteration ends when condition (39) is satisfied:

[0201] (39)

[0202] This invention calculates the voltage-power sensitivity of the distribution network and combines it with the source-load mismatch index to rationally partition the distribution network, constructing a distribution network partition coordination optimization model. The distribution network partition coordination optimization model is a convex programming model with a separable objective function and linear boundary coupling constraints. It can be solved in a distributed manner using the aforementioned SADMM algorithm to address voltage deviations in each partition.

[0203] To verify the effectiveness and superiority of the method involved in this invention, this embodiment uses... Figure 3 Taking the distribution network system shown as an example, the method of this invention is used to formulate a backup energy storage power control strategy for 5G base stations and to regulate the power flow of the distribution network. To meet the research needs of this invention, nodes 4, 11, 16, 22, and 32 are connected to the DPV system; nodes 9 and 13 are connected to the CB system; nodes 21 and 30 are connected to the SVG system; and nodes 6, 15, 19, and 28 are connected to the 5G base station system. Each device is numbered with Arabic numerals according to the above connection order. The 24-hour load and DPV predicted total output curves are shown below. Figure 4 As shown. The relevant parameters for the example are shown in Tables 1 and 2:

[0204]

[0205]

[0206] To verify the effectiveness and accuracy of the method proposed in this invention, two planning methods and scenarios were set up.

[0207] Scenario 1: The traditional distribution network zoning coordination optimization method is adopted, which only considers voltage-power sensitivity for distribution network zoning.

[0208] Scenario 2: The distribution network zoning coordination optimization method involved in this invention is adopted.

[0209] Table 3 compares the costs of various items and the overall cost for two scenarios under the IEEE 33-node example. Regarding network loss optimization, the network loss value under the method of this invention is lower, reduced by 22.41% compared to scenario 1; regarding overall operating cost, the overall cost of the method of this invention is reduced by 7.27% compared to scenario 1; regarding voltage deviation, the average voltage deviation of the method of this invention is 0.786, the lowest among the two scenarios. Therefore, the method proposed in this invention has a more significant voltage regulation effect, ensuring the safe and economical operation of the distribution network.

[0210]

[0211] Based on the optimization results of the method of this invention, the power supply mismatch index of the distribution network is obtained as follows: Figures 5-6 As shown in the figure, the set is obtained from this diagram. , , and This allows for the selection of transformer substations that are overloaded and DPV output that are excessive. Substations with overloaded transformers are then matched with substations with excessive DPV output to form a set of substations that are mutually supportive due to proximity, Ω={(3,4),(6,7),(8,9),(13,14),(16,17),(20,21),(23,24),(24,25),(24,26),(28,29),(31,32)}.

[0212] The voltage-power sensitivity matrix distribution of the IEEE 33-node distribution system is as follows: Figure 7 As shown, nodes 17, 21, and 32 are located at the end of the line, where the voltage drop caused by power changes is greater; therefore, they are placed in different zones. Considering both the system's source-load mismatch and the distribution network's voltage management capabilities, the distribution network is zoned using the minimum net load of the distribution area, the maximum net load factor of the transformer in the distribution area, and voltage-power sensitivity, as shown below. Figure 8 As shown, these are regions 1 {33, 1-7, 19-24}, 2 {5, 25-32}, and 3 {7-17}, with nodes 5 and 7 as the coupling regions. (The rest of the text appears to be a continuation of the previous sentence and can be left as is.) Figure 9 It is evident that traditional zoning methods only consider voltage-power sensitivity but neglect regional source-load distribution, resulting in a lack of matching controllable resources within region 4 and weak regional autonomy. Furthermore, traditional zoning methods prioritize the availability of controllable resources within their own region, dividing the distribution network into numerous regions, leading to excessive coupling and increasing the computational burden on the algorithm.

[0213] Figure 10 The operating scheme of the reactive power compensation device obtained by the method of this invention is calculated to have an operating cost of 1131.4 yuan for CB and SVG, accounting for 31.72% of the total operating cost. Table 4 shows the calculation results of voltage distribution under various scenarios. Figures 11-12 The diagram shows the voltage distribution for each scenario. In the traditional method, the maximum voltage of the distribution network is 1.036, the minimum is 0.992, and the overall voltage deviation is 10.472. Regions 3 and 5 lack sufficient controllable resources for voltage regulation, resulting in limited voltage control capabilities within these regions. Consequently, the node voltages in regions 3 and 5 are significantly higher than in other regions. In this invention, the backup energy storage of 5G base stations absorbs some of the DPV output and buffers the rise in distribution network voltage during periods of high DPV activity. Simultaneously, the remaining capacity of both DPV and 5G base station backup energy storage is fully utilized. The overall voltage deviation of the distribution network using this method is 6.227, a 68.84% reduction compared to scenario 1, meeting the national standard requirement of ±7%.

[0214]

[0215] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0216] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this invention.

Claims

1. A distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation, characterized in that, include: Step S1: The comprehensive index of source-load mismatch degree and the comprehensive index of voltage-power sensitivity are used as the comprehensive evaluation index for regional division. An optimal zoning model of the distribution network that comprehensively considers the local consumption of new energy and flexible voltage regulation is constructed. The optimal zoning model of the distribution network is optimized and solved by the genetic algorithm to obtain the regional division result of the distribution network and divide the distribution network into several sub-regions. The comprehensive index of source-load mismatch As shown in the following formula: （1） （2） In the formula: , They are respectively Time zone Active and reactive loads; Taiwan District The assembled photovoltaic power station Actual output at any given moment; Taiwan District exist Total load of 5G base stations at any given time; The transformer capacity for each distribution area; , Representing the respective districts The transformer's maximum load rate and minimum net load, when At that time, it indicates the station area. The transformer is under heavy load, so it is categorized into a set. Conversely, they are classified into sets. ,when At that time, it indicates the station area. There is an overcapacity in distributed photovoltaic power generation, which is categorized into a collective. Conversely, they are classified into sets. ; , These are the weighting coefficients for the maximum load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively. Step S2: Based on the distribution network area division results obtained in Step S1, and with the constraint that the state variables of the coupled branches in adjacent distribution network areas are equal, a distributed power optimization control coupling model is constructed. Step S3: Using the optimization of distribution network losses and average voltage deviation as the objective function, a distribution network zonal coordinated optimization model incorporating 5G base stations and distributed photovoltaics is constructed. The constraints of this distribution network zonal coordinated optimization model include: power system flow constraints, power security constraints, group switching of capacitor banks constraints, static var compensator constraints, 5G base station constraints, distributed photovoltaic constraints, regional power balance constraints, and zonal regulation constraints; wherein, the zonal regulation constraints are determined based on the distributed power optimization control coupling model. Step S4: The synchronous alternating direction multiplier method is used to solve the coordination optimization model corresponding to each sub-region of the distribution network in a distributed manner, so as to obtain the real-time operating power and voltage distribution of the distribution network and realize the zoned coordinated optimization operation of the distribution network.

2. The distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation according to claim 1, characterized in that: Step S1, the process of constructing the optimal zoning model of the distribution network, includes: S101, Determine the basis for selecting the dominant nodes of the distribution network; First, determine the voltage-active power sensitivity and voltage-reactive power sensitivity of each branch of the distribution network; （3） （4） In the formula: branch road Voltage-active power sensitivity; Indicates a branch ; Represented as node 0 to node The set of line nodes; Resistance of the distribution network lines; For nodes Voltage; For nodes Voltage; For nodes The outflow of active power; The active power flowing through branch k; This refers to the active power loss of the transformer. branch road Voltage-reactive power sensitivity; For the reactance of the distribution network lines; , They are nodes and nodes reactive power; This refers to the reactive power loss of the transformer. Then define the observability and controllability metrics for the dominant node; （5） （6） In the formula: Number the dominant node; It is the set of all nodes in the distribution network; Number the other nodes in the area; as the dominant node observability indicators; This indicates the degree of influence of the dominant node voltage on the active voltage of other nodes within the region; This indicates the degree of influence of the dominant node voltage on the reactive voltage of other nodes within the region; as the dominant node Controllability indicators; Finally, the selection criteria for the dominant nodes of the distribution network were determined. （7） In the formula: It is a comprehensive indicator of voltage-power sensitivity; , These are the weighting coefficients for observability and controllability; S102 uses the comprehensive index of source-load mismatch and the comprehensive index of voltage-power sensitivity as comprehensive evaluation indicators for regional division, and constructs an optimal zoning model for the distribution network that comprehensively considers local consumption of new energy and flexible voltage regulation, as follows: （8） In the formula: This represents the objective function of the distribution network optimization zoning model; , These are the weighting coefficients for each indicator. .

3. The distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation according to claim 2, characterized in that: The distributed power optimization control coupling model is as follows: （9） （10） （11） In the formula: Represents the set of coupled branches in a distribution network; Indicates the coupled branches in adjacent areas of the distribution network; , Representing regions , The collection of branch roads, the area and region They are adjacent; This represents the set of related physical sub-regions into which a power distribution network is divided; , Representing regions , State variables of a centrally coupled branch; Indicates the area Active power transmitted in the coupled branch; Indicates the area Reactive power transmitted in coupled branches; Representing regions Nodes at both ends of the coupled branch , The node voltage.

4. The distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation according to claim 3, characterized in that: The objective function of the distribution network zone coordination optimization model is as follows: （12） （13） （14） （15） （16） In the formula: This represents the objective function of the distribution network zone coordination optimization model; , These are distribution network losses and average voltage deviation, respectively. , These are the weighting factors for distribution network losses and average voltage deviation, respectively. , These are the weighting coefficients for distribution network losses and average voltage deviation, respectively. , These are the initial values ​​of distribution network loss and average voltage deviation before optimization; for Price per unit of network loss per moment; For nodes and nodes Branch current between; The duration of a single time period; This refers to the number of nodes in the distribution network. The reference voltage; for Time Node Voltage; This is one optimization cycle.

5. The distribution network zone coordination optimization method considering local consumption of new energy and flexible voltage regulation according to claim 4, characterized in that: The power flow constraints of the power system are as follows: （17） In the formula, , They are nodes The active and reactive power injected; , They are nodes Next node The outflow of active and reactive power; , Each is the previous node Inflow node Active and reactive power; For nodes and nodes Branch current between; , For nodes The conductivity and susceptance to ground are constant; , They are nodes , The voltage; The power safety constraints are as follows: （18） In the formula, , They are nodes and nodes The upper and lower limits of the current in the branch circuits; , These are the active and reactive power exchanged with the upper-level power grid, respectively. , These are the maximum and minimum interactive active power allowed to pass through the connection branch between the distribution network and the upper-level power grid, respectively. , These are the maximum and minimum reactive power exchange allowed to pass through the interconnection branch between the distribution network and the upper-level power grid, respectively. , They are nodes The upper and lower limits of the voltage; For nodes and nodes The square of the branch current; For nodes The square of the voltage; The constraints for the grouped switching capacitor banks are as follows: （19） In the formula: The number of capacitor banks in operation is represented by the value of a discrete variable. For nodes Maximum number of capacitor banks connected; The compensation power for each group of capacitors is constant; This is the maximum number of operations. For the reactive power of switching capacitor banks in groups; , These represent an optimization time period and an optimization cycle, respectively. The static var compensator is subject to the following constraints: （20） In the formula: The reactive power of the static var compensator; , These represent the lower and upper limits of the reactive power of the static var compensator, respectively. The constraints of the 5G base station include: 1) Constraints of the 5G base station load model: （21） （22） In the formula, for Total load of 5G base stations at any given time; This indicates the working status of a 5G base station. When the value is 1, the 5G base station is in an active state; when the value is 0, the 5G base station is in a sleep state. , They are respectively Time of the first The active and sleep loads of a 5G base station; for Time of the first The static load of a 5G base station; express Time of the first The communication load of each 5G base station is a dynamic load. For the first The load scaling factor of a 5G base station; A coefficient that reflects the communication data of mobile users; This represents the predicted maximum value of the dynamic load of a 5G base station, which is the radio frequency output power of the active antenna element in the 5G base station communication device when the mobile user communication load is predicted. 2) Constraints on the backup power capacity model for 5G base stations: （23） In the formula: For 5G base stations The required backup power capacity for a given period of time; For the shortest backup power time of 5G base stations; 3) Constraints on the backup energy storage power control model for 5G base stations: （24） （25） （26） （27） In the formula, express or ; The active charging identifier for backup energy storage of 5G base stations is a variable of 0 or 1. When the identifier is 0, no charging is performed, and when the identifier is 1, charging is performed. This is a flag for the active power discharge of backup energy storage for 5G base stations. It is a variable that can be 0 or 1. When the flag is 0, no discharge occurs, and when the flag is 1, discharge occurs. For energy storage batteries Active power absorbed or released at any time; for The minimum value; for The maximum value; Energy storage for 5G base stations Actual operating capacity during the time period; The self-discharge rate of backup energy storage for 5G base stations; , These are the charging and discharging efficiencies of backup energy storage for 5G base stations; , These are the maximum and minimum actual operating capacities of backup energy storage for 5G base stations, respectively. , These represent the actual operating capacity of 5G base station backup energy storage during the initial and final periods of the scheduling cycle, respectively. for Actual charging and discharging power of 5G base station energy storage during the time period; 4) Constraints of the 5G base station backup energy storage SOC model: （28） In the formula: , , They are respectively The backup energy storage SOC of 5G base stations during the time period and its maximum and minimum values; The constraints of the distributed photovoltaic system are as follows: （29） In the formula, , They are nodes The assembled photovoltaic power station in the first Actual and projected output over a given period; The power balance constraints within the region are as follows: （30） In the formula, , These are the sum of active power and reactive power injected into each node of the distribution network, respectively. , These are the total active load and the total reactive load, respectively. This represents the total load of 5G base stations; This represents the total active power purchased by the distribution network, calculated from the data for each time period. Add them together to get; The total reactive power purchased by the distribution network is determined by the amount of reactive power purchased in each time period. Add them together to get; The total charge and discharge capacity of the backup energy storage battery for 5G base stations is determined by the active power absorbed in each time period. and released active power Add them together to get; This refers to the total output of the DPV. The total reactive power of the capacitor banks switched on and off in groups; This represents the total reactive power of the static var compensator. The zoning control constraints are as shown in equations (9) to (11).

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