Affine optimization operation method of power distribution network considering adjustable potential of base station backup energy storage

Abstract

The application relates to a power distribution network affine optimization operation method and device considering adjustable potential of base station backup energy storage. The method comprises the following steps: for each power distribution network node in a power distribution network, determining the schedulable capacity of the base station backup energy storage of the power distribution network node based on the base station power consumption at the power distribution network node and the node load level of the power distribution network node; constructing an affine objective function for representing the operation cost of the power distribution network according to the cost information of the power distribution network; constructing a base station backup energy storage operation constraint according to the schedulable capacity, the charging power and the discharging power of the base station backup energy storage at the power distribution network node; constructing a power distribution network affine optimization operation model according to the affine objective function and the base station backup energy storage operation constraint; and solving the power distribution network affine optimization operation model to obtain a power distribution network affine optimization operation result. The method can improve the evaluation accuracy of the schedulable capacity of the base station backup energy storage and further improve the accuracy of affine optimization of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a method and apparatus for affine optimization operation of distribution networks that takes into account the adjustable potential of backup energy storage at base stations. Background Technology

[0002] Solar and wind energy are highly favored due to their sustainability and environmental friendliness, with the installed capacity of photovoltaic (PVG) and wind turbine (WTG) power generation continuing to grow. However, the output of PVG and WTG is random and fluctuating, posing a serious challenge to the safe and economical operation of power distribution networks. At the same time, the rapid development of communication technology has led to a surge in the number of base stations and the widespread deployment of backup energy storage facilities.

[0003] Currently, the methods for assessing the schedulable capacity of backup energy storage for base stations suffer from low accuracy. Summary of the Invention

[0004] Therefore, it is necessary to provide a distribution network affine optimization operation method and apparatus that considers the adjustable potential of base station backup energy storage, which can improve the accuracy of assessing the schedulable capacity of base station backup energy storage and address the above-mentioned technical problems.

[0005] Firstly, this application provides an affine optimization operation method for distribution networks that considers the adjustable potential of base station backup energy storage, comprising: for each distribution network node in the distribution network, determining the dispatchable capacity of base station backup energy storage at the distribution network node based on the power consumption of the base station at the distribution network node and the node load level of the distribution network node; constructing an affine objective function to characterize the operating cost of the distribution network based on the cost information of the distribution network; constructing operating constraints for base station backup energy storage based on the dispatchable capacity, the charging power and discharging power of the base station backup energy storage at the distribution network node; constructing an affine optimization operation model for the distribution network based on the affine objective function and the operating constraints for base station backup energy storage; and solving the affine optimization operation model for the distribution network to obtain the affine optimization operation results of the distribution network.

[0006] Secondly, this application provides an affine optimization operation device for distribution networks that considers the adjustable potential of base station backup energy storage. The device includes: a determination module, used to determine the dispatchable capacity of base station backup energy storage at each distribution network node based on the power consumption of the base station at the distribution network node and the node load level of the distribution network node; a first construction module, used to construct an affine objective function characterizing the operating cost of the distribution network based on the cost information of the distribution network; a processing module, used to construct operating constraints for base station backup energy storage based on the dispatchable capacity, the charging power and discharging power of the base station backup energy storage at the distribution network node; a second construction module, used to construct an affine optimization operation model for the distribution network based on the affine objective function and the operating constraints for base station backup energy storage; and an analysis module, used to solve the affine optimization operation model for the distribution network and obtain the affine optimization operation results of the distribution network.

[0007] The aforementioned affine optimization operation method and apparatus for distribution networks, considering the adjustable potential of base station backup energy storage, determines the dispatchable capacity of base station backup energy storage at each distribution network node based on the base station power consumption and node load level of the distribution network node. This improves the accuracy of assessing the dispatchable capacity of base station backup energy storage by considering the node load level. Furthermore, an affine objective function characterizing the operating cost of the distribution network is constructed based on the cost information of the distribution network. Operating constraints for base station backup energy storage are established based on the backup power capacity, the charging power, and the discharging power of the base station backup energy storage at the distribution network node. An affine optimization operation model for the distribution network is then constructed based on the affine objective function and the operating constraints. Solving the affine optimization operation model yields the affine optimization operation results for the distribution network. Therefore, by accurately assessing the dispatchable capacity of base station backup energy storage, the accuracy of affine optimization operation of the distribution network can be further improved. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating an affine optimization operation method for a distribution network that considers the adjustable potential of backup energy storage at base stations in one embodiment.

[0010] Figure 2 This is a flowchart illustrating a distribution network affine optimization operation method that considers the adjustable potential of base station backup energy storage in another embodiment.

[0011] Figure 3 This is a schematic diagram of the distribution network topology in one embodiment;

[0012] Figure 4 This is a schematic diagram showing the results of 5G base station load characteristics for four functional regions in one embodiment.

[0013] Figure 5 This is a schematic diagram illustrating the daily variation trends of WTG and PVG output and load power coefficient in one embodiment;

[0014] Figure 6 This is a schematic diagram of the load level weights and reliability indicators of a distribution network node in one embodiment;

[0015] Figure 7 This is a schematic diagram showing the minimum backup power time for different models in one embodiment;

[0016] Figure 8 This is a schematic diagram showing the schedulable capacity of backup energy storage for 5G base stations under different models in one embodiment.

[0017] Figure 9 This is a schematic diagram showing the real-time output of backup energy storage for 5G base stations at various distribution network nodes in one embodiment.

[0018] Figure 10 This is a structural block diagram of a distribution network affine optimization operation device that considers the adjustable potential of base station backup energy storage in one embodiment.

[0019] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] Currently, existing methods for assessing the adjustable potential of backup energy storage for 5G base stations do not consider the impact of node load levels. Power restoration times vary significantly across different node load levels, resulting in different backup power times for 5G base station backup energy storage. Therefore, by considering the impact of node loads in the distribution network, a more accurate assessment model for the dispatchable capacity of 5G base station backup energy storage can be constructed. Furthermore, based on an accurate assessment of the dispatchable capacity of 5G base station backup energy storage, optimized operation of the distribution network can be achieved to address uncertainties.

[0022] For distribution network uncertainty optimization operation methods considering 5G base station backup energy storage regulation, probabilistic methods are included. Existing probabilistic methods take into account the probabilistic characteristics of 5G base station loads and employ multi-scenario methods to handle the uncertainty of power from new energy sources and 5G base station loads in the distribution network, constructing a typical "source-load" scenario set. However, this requires massive historical data to support it in obtaining the distribution characteristics of uncertainty variables, thus limiting its application scenarios. Non-probabilistic methods, such as robust optimization methods and interval optimization methods, are also available for distribution network uncertainty optimization operation methods considering 5G base station backup energy storage regulation. These methods can be modeled based on the boundary information of uncertainty variables and have higher engineering application value when historical data is limited. However, robust optimization methods tend to be overly conservative in their optimization results to ensure the feasibility of the solution under the worst-case scenario; while interval optimization methods, due to the interval expansion phenomenon, also face the problem of conservative optimization results or even no feasible solutions.

[0023] In view of this, such as Figure 1 As shown, an affine optimization operation method for distribution networks considering the adjustable potential of base station backup energy storage is provided. The method is illustrated using a server in a distribution network affine optimization operation system as an example. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Specifically, the method includes the following steps:

[0024] S102, for each distribution network node in the distribution network, based on the base station power consumption at the distribution network node and the node load level of the distribution network node, determine the dispatchable capacity of the base station backup energy storage of the distribution network node.

[0025] Base stations can be connected at distribution network nodes, and the power consumption of the base station can include the static power consumption and maximum dynamic power of the base station at the distribution network node during the target time period. For example, the base station can be a 5G base station.

[0026] In one embodiment, the power consumption of base stations at distribution network nodes and the node load level of distribution network nodes can be analyzed based on a capacity assessment model to obtain the dispatchable capacity of base station backup energy storage at distribution network nodes.

[0027] S104. Based on the cost information of the distribution network, construct an affine objective function to characterize the operating cost of the distribution network.

[0028] As an improvement on the interval algorithm, the affine algorithm locates the propagation trajectory of uncertain factors through noise elements and realizes the correlation between different variables by sharing noise elements. It can solve the problem of interval expansion caused by the inability of the interval algorithm to consider the correlation between variables.

[0029] Specifically, affine variables satisfy:

[0030]

[0031] in, Let x0 be the affine variable, x0 be the affine center value, and ε be the affine variable. k Let x be the noise element k, whose value range is [-1, 1]; k is the affine wave value; n is the total number of noise elements.

[0032] Specifically, the upper and lower bounds of the affine variable satisfy the following:

[0033]

[0034] in, Let x be the upper bound of the affine variable, and let x be the lower bound of the affine variable.

[0035] Based on the range of load variation or WTG / PVG output at each node in the distribution network, the source load output uncertainty affine model can be derived by inversely using the formulas corresponding to the upper and lower limits of the affine variables. The source load output uncertainty affine model satisfies:

[0036]

[0037] in, These are the affine forms of the active load, WTG active output, and PVG active output of distribution network node i in the target time period t, respectively. These are the affine center values ​​of the active load, WTG active output, and PVG active output of distribution network node i in the target time period t, respectively. ε represents the fluctuation values ​​of the active load, WTG active power output, and PVG active power output of distribution network node i during the target time period t, respectively, caused by the influence of noise element k; k Let k be the noise element and Ω be the Ω. e Ω T Ω ε These are the distribution network node set, time period set, and noise element set, respectively.

[0038] In light of the above, and considering the impact of the uncertainty of the "source-load" relationship, this application can express the affine objective function as an affine form combining the central value and the fluctuation value, based on the basic structure of affine numbers.

[0039] The operating costs of the distribution network may include the cost of electricity purchased by the distribution network operator from the upstream power grid, and the operating subsidy for 5G base station backup energy storage paid by the distribution network operator to the communication network operator. Based on this, the cost information of the distribution network in this application may include: the electricity purchase cost of the distribution network operator, the operating subsidy for base station backup energy storage paid by the distribution network operator to the communication network operator, the electricity purchase cost of the distribution network operator, and the operating subsidy cost for base station backup energy storage paid by the distribution network operator to the communication network operator.

[0040] In one embodiment, an affine objective function can be constructed to characterize the operating cost of the distribution network based on the affine central value of the electricity purchase cost, the affine central value of the base station backup energy storage operation subsidy cost, the affine fluctuation value of the electricity purchase cost, and the affine fluctuation value of the base station backup energy storage operation subsidy cost.

[0041] S106. Based on the dispatchable capacity, the charging power and discharging power of the base station backup energy storage at the distribution network node, construct the base station backup energy storage operation constraints.

[0042] Among them, the charging power of the base station backup energy storage at the distribution network node can refer to the power of the base station backup energy storage system (such as a battery) charging from the grid at the distribution network node. It reflects the ability of the energy storage system to obtain electrical energy from the grid per unit time.

[0043] In one embodiment, the operating constraints of the base station backup energy storage can be constructed based on the affine center value of the charging power of the base station backup energy storage at the distribution network node, the affine fluctuation value of the charging power affected by the noise element, the affine center value of the discharging power of the base station backup energy storage at the distribution network node, the affine fluctuation value of the discharging power affected by the noise element, and the dispatchable capacity.

[0044] S108. Based on the affine objective function and the operational constraints of the base station backup energy storage, an affine optimization operation model for the distribution network is constructed.

[0045] The affine objective function is used to characterize the operating cost of the distribution network. In one embodiment, an affine optimization operation model for the distribution network can be constructed based on the operational constraints of base station backup energy storage while minimizing the affine objective function.

[0046] S110, solve the affine optimization operation model of the distribution network to obtain the affine optimization operation results of the distribution network.

[0047] The constructed affine optimization operation model for the distribution network can be used in... Core TMThe simulation was performed on a computer with an i5-10500 1.8GHz CPU, using the Matlab R2022a platform for programming and calling the CPLEX toolbox for solving.

[0048] The affine optimization operation results of the distribution network can include: the cost range, cost center value, cost fluctuation value corresponding to the operating cost of the distribution network, and the actual output of the backup energy storage of the base station at each distribution network node.

[0049] based on Figure 1 The method described above determines the dispatchable capacity of base station backup energy storage for each distribution network node based on the base station power consumption and node load level at that node. This improves the accuracy of assessing the dispatchable capacity of base station backup energy storage by considering the node load level. Furthermore, an affine objective function characterizing the operating cost of the distribution network is constructed based on its cost information. Operating constraints for base station backup energy storage are established based on the backup capacity, charging power, and discharging power of the base station backup energy storage at the distribution network node. An affine optimization operation model for the distribution network is then constructed based on the affine objective function and these constraints. Solving this model yields the affine optimization operation results. Therefore, by accurately assessing the dispatchable capacity of base station backup energy storage, the accuracy of affine optimization operation of the distribution network can be further improved.

[0050] In one embodiment, the base station power consumption at the distribution network node includes: the static power consumption and the maximum dynamic power consumption of the base station at the distribution network node during the target time period. Specifically, based on the base station power consumption at the distribution network node and the node load level of the distribution network node, the dispatchable capacity of the base station backup energy storage at the distribution network node is determined (i.e., S102), including the following steps:

[0051] Step 1: Determine the minimum backup time for backup energy storage of distribution network nodes based on the total number of distribution network nodes, the power supply reliability of distribution network nodes, the weight corresponding to the node load level of distribution network nodes, and the average backup power duration.

[0052] Among them, the minimum backup power time is directly proportional to the failure rate of the distribution network node and inversely proportional to the weight corresponding to the node load level. The failure rate of the distribution network node is the difference between 1 and the power supply reliability of the distribution network node.

[0053] Specifically, the minimum backup power time for backup energy storage at distribution network nodes must meet the following requirements:

[0054]

[0055] Among them, T i BSλ represents the minimum backup power time for the backup energy storage of distribution network node i, n represents the total number of distribution network nodes, and λ represents the minimum backup power time for the backup energy storage of distribution network node i. i Indicates the power supply reliability of node i in the distribution network; w i This represents the weight corresponding to the node load level of node i in the distribution network. In some cases, the weight values ​​for level I, II, and III loads can be 1, 0.5, and 0.3, respectively. This indicates the average backup power duration.

[0056] Taking 5G base stations as an example, 5G base stations can include two types: macro base stations and small base stations (micro, pico, and femto). Considering that macro base stations have higher power consumption and wider coverage, and that only macro base stations currently have backup energy storage, this application can use macro base stations as an example for illustration.

[0057] Specifically, base station power consumption includes static power consumption and maximum dynamic power consumption, and the power consumption model satisfies:

[0058]

[0059] in, This represents the total power consumption of the base station at node i in the distribution network during the target time period t. P represents the static power consumption of the base station at node i in the distribution network during the target time period t. i dyn,max ρ represents the maximum dynamic power consumption of the base station at node i in the distribution network during the target time period t. i,t This represents the load rate of the base station at node i in the distribution network during the target time period t, where the base station can be a 5G base station.

[0060] There are no restrictions on how the reliability of distribution network nodes can be determined. Examples of possible methods are given below.

[0061] In one embodiment, the power supply reliability of the distribution network nodes satisfies:

[0062]

[0063] Where m is the minimum path count, referring to the total number of paths with the minimum path length from the balance node to node i in the distribution network; p is the number of network components, referring to the total number of components in the distribution network, including transformers, switches, and distribution lines; λ k λ is the failure rate of the minimum path k. s P represents the failure rate of component s. ks ∈{0,1}, P ks Indicates whether the minimum path k contains element s, P ks =1 indicates that the minimum path k contains element s, P ks =0 indicates that the minimum path k does not contain element s.

[0064] Step 2: Based on the minimum backup power time, perform integral calculations on the static power consumption and the maximum dynamic power consumption to determine the backup power capacity of the base station backup energy storage of the distribution network node during the target time period.

[0065] Specifically, the backup energy storage capacity of base stations at distribution network nodes should meet the following requirements during the target time period:

[0066]

[0067] in, This represents the backup energy storage capacity of distribution network node i during the target time period t.

[0068] Step 3: Determine the dispatchable capacity of the base station backup energy storage of the distribution network node based on the maximum backup energy storage capacity and backup power capacity of the distribution network node.

[0069] Specifically, the schedulable capacity satisfies:

[0070]

[0071] in, This represents the dispatchable capacity of the base station backup energy storage at distribution network node i during the target time period t. This represents the maximum standby energy storage capacity of node i in the distribution network.

[0072] Based on the above, by considering the power supply reliability and node load level of distribution network nodes, the accuracy of the assessment of the dispatchable capacity of base station backup energy storage can be improved.

[0073] Taking 5G base stations as an example, backup energy storage for 5G base stations can provide power when the power grid fails or power supply is interrupted, ensuring the continuous operation of the base station and avoiding communication interruptions. As the reliability of urban power grids gradually improves, backup energy storage becomes idle for a long time. Therefore, the idle portion can be dispatched to improve the operational economy of the distribution network.

[0074] In one embodiment, the base station backup energy storage operation constraints include at least one of the following: a first operation constraint, a second operation constraint, a third operation constraint, a fourth operation constraint, and a fifth operation constraint.

[0075] Specifically, the first operational constraints satisfy the following: the difference between the affine center value of the discharge power and the first operational statistics value is greater than or equal to the first preset value and less than or equal to the first target value; the sum of the affine center value of the discharge power and the first operational statistics value is greater than or equal to the first preset value and less than or equal to the first target value; the first target value is the product of the state value corresponding to the charging and discharging state of the backup energy storage of the distribution network node and the rated power of the backup energy storage of the distribution network node; and the first operational statistics value is the statistical value after performing absolute value calculation on the affine fluctuation value of the discharge power corresponding to each distribution network node.

[0076] For example, taking the first preset value as 0, the first running constraint satisfies:

[0077]

[0078] in, Ω e For the set of noise elements, Ω T For time periods; The affine center value represents the discharge power of the base station backup energy storage at node i in the distribution network during the target time period t. γ represents the affine fluctuation value of the base station backup energy storage at distribution network node i during the target time period t caused by the influence of noise element k, i.e., the affine fluctuation value of the discharge power corresponding to distribution network node i; i,t γ characterizes the charging and discharging state of the backup energy storage at node i in the distribution network during the target time period t. i,t 1 represents discharge, γ i,t 0 represents charging; P i BS This represents the rated power of the standby energy storage at node i in the distribution network.

[0079] Specifically, the second operational constraints satisfy the following: the difference between the affine center value of the charging power and the second operational statistics value is greater than or equal to the first preset value and less than or equal to the second target value; the sum of the affine center value of the charging power and the second operational statistics value is greater than or equal to the first preset value and less than or equal to the second target value; the second target value is the product of the third target value, which is inversely correlated with the state value corresponding to the charging and discharging state, and the rated power; the second operational statistics value is the statistical value after performing absolute value calculation on the affine fluctuation value of the charging power corresponding to each distribution network node.

[0080] For example, taking the first preset value as 0, the second running constraint satisfies:

[0081]

[0082] in, Let represent the affine center value of the charging power of the base station's backup energy storage at node i in the distribution network during the target time period t. The value represents the affine fluctuation of the base station backup energy storage at node i in the distribution network during the target time period t, caused by the influence of noise element k. That is, the affine fluctuation of the charging power corresponding to node i in the distribution network.

[0083] Specifically, the third operational constraint satisfies the following: the difference between the affine center value of the capacity of the backup energy storage of the distribution network node in the target time period and the third operational statistical value is greater than or equal to the backup power capacity of the base station backup energy storage of the distribution network node in the target time period, and less than or equal to the maximum backup energy storage capacity of the distribution network node; the sum of the affine center value of the capacity and the third operational statistical value is greater than or equal to the backup power capacity and less than or equal to the maximum backup energy storage capacity; the third operational statistical value is the statistical value after performing absolute value calculation on the capacity affine fluctuation value corresponding to each distribution network node; the difference between the maximum backup energy storage capacity and the backup capacity is equal to the dispatchable capacity.

[0084] Here, the affine capacity fluctuation value corresponding to the distribution network node refers to the fluctuation value of the backup energy storage capacity of the distribution network node during the target period caused by the influence of noise elements. For example, the third operating constraint satisfies:

[0085]

[0086] in, This represents the backup energy storage capacity of distribution network node i during the target time period t. The affine center value represents the capacity of the backup energy storage of node i in the distribution network during the target time period t; This represents the corresponding fluctuation value of the backup energy storage capacity of distribution network node i during the target time period t caused by the influence of noise element k, i.e., the affine fluctuation value of the capacity corresponding to the distribution network node. This represents the maximum standby energy storage capacity of node i in the distribution network. This indicates the schedulable capacity.

[0087] Specifically, the fourth operational constraint satisfies the following: the sum of the affine center value of the capacity and the fourth operational statistic is equal to the affine center value of the capacity of the backup energy storage of the distribution network node in the next time period of the target time period; the fourth operational statistic is the difference between the first multiplier and the first ratio, the first multiplier is the product of the charging efficiency of the backup energy storage of the distribution network node and the affine center value of the charging power, and the first ratio is the ratio of the affine center value of the discharge power to the discharge efficiency.

[0088] For example, the fourth operational constraint condition is satisfied:

[0089]

[0090] in, The affine center value represents the capacity of the backup energy storage of node i in the distribution network in the next time period (t+1) after the target time period t. The affine center value represents the capacity of the backup energy storage of node i in the distribution network during the target time period t; This represents the charging efficiency of the backup energy storage at node i in the distribution network.

[0091] Specifically, the fifth operating constraint satisfies the following: the sum of the capacity affine fluctuation value corresponding to the distribution network node and the fifth operating statistical value is equal to the capacity affine fluctuation value corresponding to the next time period; the fifth operating statistical value is the difference between the second multiplier and the second ratio, the second multiplier is the product of the charging efficiency and the affine fluctuation value corresponding to the charging power, and the second ratio is the ratio of the affine fluctuation value corresponding to the discharging power to the discharging efficiency.

[0092] For example, the fifth operational constraint satisfies:

[0093]

[0094] in, This represents the affine fluctuation value of the backup energy storage capacity of distribution network node i in the next time period after the target time period t, caused by the influence of noise element k.

[0095] In one embodiment, cost information includes: the electricity purchase cost of the distribution network operator, the base station backup energy storage operation subsidy paid by the distribution network operator to the communication network operator, the electricity purchase cost of the distribution network operator, and the base station backup energy storage operation subsidy cost paid by the distribution network operator to the communication network operator.

[0096] Among them, the electricity purchase cost of the distribution network operator can refer to the fee paid by the distribution network operator to purchase electricity from power generation enterprises or other electricity market entities; the electricity purchase cost of the distribution network operator can refer to the total fee paid by the distribution network operator to purchase electricity from power generation enterprises or other electricity markets to meet the electricity demand of users, and the electricity purchase cost includes the electricity purchase fee; the base station backup energy storage operation subsidy fee can refer to the subsidy provided by the subsidy agency to support the equipment and operation of energy storage systems (such as battery energy storage) for communication base stations; the base station backup energy storage operation subsidy cost can refer to the total cost involved in the subsidy provided by the subsidy agency to support the equipment and operation of backup energy storage systems for communication base stations, and the subsidy cost includes the subsidy fee.

[0097] Specifically, based on the cost information of the distribution network, the implementation of an affine objective function (i.e., S104) to characterize the operating cost of the distribution network can include the following steps:

[0098] Step 1: Determine the first affine statistical value between the affine center value of electricity purchase cost and the affine center value of base station backup energy storage operation subsidy cost, and the second affine statistical value between the affine fluctuation value of electricity purchase cost and the affine fluctuation value of base station backup energy storage operation subsidy cost.

[0099] Step 2: Based on the weighting coefficients, the first affine statistical value, and the second affine statistical value, construct an affine objective function to characterize the operating cost of the distribution network.

[0100] In one embodiment, the affine objective function satisfies:

[0101]

[0102] Where F represents the affine objective function; w represents the weighting coefficient, which is used to balance the affine center value and the interval value in the affine objective function; The affine center value representing the cost of electricity purchase. The affine center value representing the operating subsidy cost of base station backup energy storage. The affine fluctuation value representing the cost of electricity purchase. This represents the affine fluctuation value of the operating subsidy cost of base station backup energy storage. and It characterizes the range of cost fluctuations caused by the uncertainty of the "source-load" relationship.

[0103] In one embodiment, the affine center value of the electricity purchase cost for the distribution network operator can be determined based on the unit electricity purchase cost of the distribution network during the target time period and the affine center value of the active power of the substation during the target time period.

[0104] Specifically, the affine center value of the electricity purchase cost satisfies:

[0105]

[0106] in, U represents the affine center value of electricity purchase costs. Sub,t This represents the unit electricity purchase cost of the distribution network during the target time period. The affine center value of the active power of the substation during the target time period is represented by Δt, which represents the unit time period, Ω. T Represents a set of time periods.

[0107] In one embodiment, the affine center value of the base station backup energy storage operation subsidy can be determined based on the unit subsidy cost of the base station backup energy storage, the affine center value of the charging power of the base station backup energy storage at the distribution network node during the target period, and the affine center value of the discharging power.

[0108] Specifically, the affine center value of the base station backup energy storage operation subsidy fee satisfies:

[0109]

[0110] in, The affine center value of the base station backup energy storage operation subsidy cost, u BS This represents the unit subsidy cost of backup energy storage for base stations. Let represent the affine center value of the charging power of the base station's backup energy storage at node i in the distribution network during the target time period. Ω represents the affine center value of the discharge power of the base station backup energy storage at node i of the distribution network during the target time period. e This represents the set of nodes in the distribution network.

[0111] In one embodiment, the affine fluctuation value of the power purchase cost of the distribution network can be determined based on the unit power purchase cost of the distribution network during the target time period, the affine fluctuation value of the active power corresponding to the substation, and the unit time period. Here, the affine fluctuation value of the active power corresponding to the substation refers to the fluctuation value of the active power of the substation during the target time period caused by the influence of noise elements.

[0112] Specifically, the affine fluctuation value of electricity purchase cost satisfies:

[0113]

[0114] in, The affine fluctuation value representing the cost of electricity purchase. This represents the affine fluctuation value of the active power corresponding to the substation.

[0115] In one embodiment, the affine fluctuation value of the base station backup energy storage operation subsidy cost can be determined based on the unit subsidy cost of base station backup energy storage, the affine fluctuation value of the charging power of base station backup energy storage at the distribution network node during the target period affected by noise elements, and the affine fluctuation value of the discharging power of base station backup energy storage at the distribution network node during the target period affected by noise elements.

[0116] Specifically, the affine fluctuation value of the operating subsidy cost of base station backup energy storage satisfies:

[0117]

[0118] in, Ω represents the affine fluctuation value of the operating subsidy cost of base station backup energy storage. ε This represents the set of noise elements.

[0119] In one embodiment, based on the affine objective function and the operational constraints of the base station backup energy storage, an affine optimization operation model for the distribution network (i.e., S108) is constructed, including the following steps:

[0120] Step 1: Based on the affine center value of the active power injected into the distribution network node during the target time period, the affine fluctuation value of the active power affected by the noise element, the affine fluctuation value of the reactive power injected into the distribution network node during the target time period, and the corresponding affine fluctuation value of the reactive power, construct the power balance constraint of the distribution network node.

[0121] The affine center value of active power refers to the center value of active power expressed in affine form. Therefore, based on the basic structure of affine numbers, the power balance constraint of distribution network nodes can be expressed as an affine form combining the affine center value and the affine fluctuation value. The affine fluctuation value corresponding to reactive power refers to the fluctuation value of reactive power injected into the distribution network node during the target time period caused by the influence of noise elements.

[0122] In one embodiment, the power balance constraint of the distribution network node includes at least one of the following constraints: a first balance constraint, a second balance constraint, a third balance constraint, and a fourth balance constraint.

[0123] Specifically, the first balance constraint satisfies the following: the difference between the sum of the affine center values ​​of the active power injected into the substation by the distribution network node in the target time period, the affine center values ​​of the active power output of wind power generation and photovoltaic power generation injected into the distribution network node in the target time period, and the sum of the affine center values ​​of the discharge power, and the first balance statistical value, is equal to the affine center value of the active power injected into the distribution network node in the target time period; the first balance statistical value is the sum of the affine center values ​​of the total power consumption of the base station at the distribution network node in the target time period, the affine center value of the charging power, and the affine center value of the active power demand of the load injected into the distribution network node in the target time period.

[0124] For example, the first equilibrium constraint satisfies:

[0125]

[0126] in, This represents the affine center value of the active power injected by distribution network node i during the target time period t. This represents the affine center value of the active power injected into the substation by distribution network node i during the target time period t. This represents the affine center value of the active power output of wind power generation injected by distribution network node i in the target time period t. This represents the affine center value of the active power output of photovoltaic power generation injected by distribution network node i in the target time period t. The affine center value represents the total power consumption of the base station at node i in the distribution network during the target time period t. This represents the affine center value of the active power demand of the load injected into node i of the distribution network during the target time period t.

[0127] Specifically, the second balance constraint satisfies the following: the difference between the sum of the affine fluctuation values ​​corresponding to the active power of the substation, the active power output of wind power generation, the active power output of photovoltaic power generation, the affine fluctuation values ​​corresponding to the discharge power, and the affine fluctuation values ​​corresponding to the total power consumption, and the second balance statistical value, is equal to the affine fluctuation value corresponding to the injected active power; the second balance statistical value is the sum of the affine fluctuation values ​​corresponding to the charging power and the affine fluctuation values ​​corresponding to the active power demand of the load.

[0128] Among them, the affine fluctuation value corresponding to the active power of the substation refers to the corresponding fluctuation value of the active power of the substation injected by the distribution network node in the target period affected by the noise element, and the affine fluctuation value corresponding to the active power output of wind power generation refers to the corresponding fluctuation value of the active power output of wind power generation injected by the distribution network node in the target period affected by the noise element.

[0129] For example, the second equilibrium constraint satisfies:

[0130]

[0131] in, This represents the fluctuation value of the active power injected by distribution network node i during the target time period t, which is affected by the noise element k, i.e., the affine fluctuation value corresponding to the injected active power. This represents the affine fluctuation value corresponding to the active power of the substation. This represents the affine wave value corresponding to the active power output of wind power generation. This represents the affine wave value corresponding to the active power output of photovoltaic power generation. This represents the affine fluctuation value corresponding to the total power consumption. This represents the affine fluctuation value corresponding to the active power demand of the load.

[0132] Specifically, the third balance constraint satisfies the following: the sum of the affine center value of the reactive power injected by the distribution network node into the substation during the target time period and the affine center value of the reactive power demand of the load is equal to the affine center value of the injected reactive power.

[0133] For example, the third equilibrium constraint satisfies:

[0134]

[0135] in, These represent the affine center values ​​of reactive power injected by distribution network node i during the target time period t, the affine center values ​​of reactive power in the substation, and the affine center values ​​of reactive power demand from the load, respectively.

[0136] Specifically, the fourth balance constraint satisfies the following: the difference between the affine fluctuation value corresponding to the reactive power of the substation and the affine fluctuation value corresponding to the reactive power demand of the load is equal to the affine fluctuation value corresponding to the injected reactive power.

[0137] For example, the fourth equilibrium constraint satisfies:

[0138]

[0139] in, This represents the affine fluctuation value of the reactive power injected by distribution network node i during the target time period t, which is affected by the noise element k. That is, the affine fluctuation value corresponding to the injected reactive power. These represent the affine fluctuation values ​​corresponding to the reactive power of the substation and the affine fluctuation values ​​corresponding to the reactive power demand of the load, respectively.

[0140] Step 2: For the target distribution network node connected to each distribution network node in the distribution network, construct the linearized power flow equation constraint of the distribution network based on the electrical parameters of the line between the two distribution network nodes, the affine center value of the voltage amplitude of the target distribution network node in the target time period, and the affine fluctuation value of the voltage amplitude.

[0141] Here, the target distribution network node refers to other distribution network nodes connected to the distribution network node. For example, ρ(i) can be represented as the set of all nodes connected to distribution network node i.

[0142] Among them, the affine center value of voltage amplitude refers to the center value of voltage amplitude expressed in affine form, and the affine fluctuation value of voltage amplitude refers to the corresponding fluctuation value of voltage amplitude caused by the influence of noise elements.

[0143] In one embodiment, the electrical parameters include conductance and susceptance, and the linearized power flow equation constraints of the distribution network include at least one of the following: a first linearized power flow constraint, a second linearized power flow constraint, a third linearized power flow constraint, and a fourth linearized power flow constraint.

[0144] Specifically, the first linearized power flow constraint satisfies the following: the difference between the first power flow statistics and the second power flow statistics is equal to the affine center value of the injected active power; the first power flow statistics are the statistical values ​​of the first correlation values ​​of each target distribution network node corresponding to the distribution network node, and the first correlation value is the product of the conductance and voltage amplitude affine center values ​​of the target distribution network node; the second power flow statistics are the statistical values ​​of the second correlation values ​​of each target distribution network node corresponding to the distribution network node, and the second correlation value is the product of the susceptance and phase angle affine center values ​​of the target distribution network node.

[0145] Wherein, the conductance corresponding to the target distribution network node refers to the conductance of the line between the target distribution network node and the corresponding distribution network node, and the susceptance corresponding to the target distribution network node refers to the susceptance of the line between the target distribution network node and the corresponding distribution network node.

[0146] For example, the first linearized power flow constraint satisfies:

[0147]

[0148] in, Ω L Represents a set of distribution network lines; ρ(i) represents the affine center value of the active power injected by distribution network node i in the target time period t; ρ(i) can be represented as the set of all nodes connected to distribution network node i, including i = j; G ij V represents the conductance of the line ij between distribution network node i and the corresponding target distribution network node j. j,t,0 B represents the affine center value of the voltage amplitude at the target distribution network node j during the target time period t. ij θ represents the susceptance of the line ij between distribution network node i and the corresponding target distribution network node j. j,t,0 This represents the affine center value of the phase angle of the target distribution network node j during the target time period t.

[0149] Specifically, the second linearized power flow constraint satisfies the following: the difference between the third and fourth power flow statistics is equal to the affine fluctuation value corresponding to the injected active power; the third power flow statistics are the statistical values ​​of the third correlation values ​​of each target distribution network node corresponding to the distribution network node, and the third correlation value is the product of the conductance and voltage amplitude affine fluctuation value corresponding to the target distribution network node; the fourth power flow statistics are the statistical values ​​of the fourth correlation values ​​of each target distribution network node corresponding to the distribution network node, and the fourth correlation value is the product of the conductance and phase angle affine fluctuation value corresponding to the target distribution network node.

[0150] Among them, the affine fluctuation value of the voltage amplitude corresponding to the target distribution network node refers to the fluctuation value of the voltage amplitude of the target distribution network node caused by the influence of noise elements. The affine fluctuation value of the phase angle corresponding to the target distribution network node refers to the fluctuation value of the phase angle of the target distribution network node caused by the influence of noise elements.

[0151] For example, the second linearized power flow constraint satisfies:

[0152]

[0153] in, V represents the fluctuation value of the active power injected by distribution network node i during the target time period t, caused by the influence of noise element k, i.e., the affine fluctuation value corresponding to the injected active power; j,t,k θ represents the affine voltage amplitude fluctuation value of the target distribution network node j during the target time period t, i.e., the affine voltage amplitude fluctuation value corresponding to the target distribution network node; j,t,k This represents the affine phase angle fluctuation value of the target distribution network node j during the target time period t, i.e., the affine phase angle fluctuation value corresponding to the target distribution network node.

[0154] Specifically, the third linearized power flow constraint satisfies the following: the difference between the negative value of the fifth power flow statistics and the sixth power flow statistics is equal to the affine center value of the injected reactive power; the fifth power flow statistics are the statistical values ​​of the fifth correlation values ​​of each target distribution network node corresponding to the distribution network node, and the fifth correlation value is the product of the susceptance and voltage amplitude affine center value of the target distribution network node; the sixth power flow statistics are the statistical values ​​of the sixth correlation values ​​of each target distribution network node corresponding to the distribution network node, and the sixth correlation value is the product of the conductance and phase angle affine center value of the target distribution network node.

[0155] For example, the third linearization power flow constraint satisfies:

[0156]

[0157] in, This represents the affine center value of the reactive power injected by distribution network node i during the target time period t.

[0158] Specifically, the fourth linearized power flow constraint satisfies the following: the difference between the negative value of the seventh power flow statistics and the eighth power flow statistics is equal to the affine fluctuation value corresponding to the injected reactive power; the seventh power flow statistics are the statistical values ​​of the seventh correlation values ​​of each target distribution network node corresponding to the distribution network node, and the seventh correlation value is the product of the susceptance and voltage amplitude affine fluctuation value corresponding to the target distribution network node; the eighth power flow statistics are the statistical values ​​of the eighth correlation values ​​of each target distribution network node corresponding to the distribution network node, and the eighth correlation value is the product of the conductance and phase angle affine fluctuation value corresponding to the target distribution network node.

[0159] For example, the fourth linearization power flow constraint satisfies:

[0160]

[0161] in, This represents the corresponding fluctuation value of the reactive power injected by distribution network node i during the target time period t, caused by the influence of noise element k, i.e., the affine fluctuation value corresponding to the injected reactive power.

[0162] Step 3: Based on the power balance constraints of the distribution network nodes, the linearized power flow equation constraints of the distribution network, and the operation constraints of the backup energy storage, construct the affine constraint conditions corresponding to the affine objective function.

[0163] Step 4: Construct an affine optimization operation model for the distribution network based on the affine objective function and affine constraints.

[0164] In one embodiment, an affine optimization operation model for the distribution network can be constructed based on affine constraints while minimizing the affine objective function.

[0165] Based on the above, the accuracy of model construction can be improved by introducing power balance constraints at distribution network nodes and constraints on linearized power flow equations of the distribution network.

[0166] In one embodiment, based on the power balance constraints of the distribution network nodes, the linearized power flow equation constraints of the distribution network, and the operational constraints of the backup energy storage, the affine constraint conditions corresponding to the affine objective function are constructed, including the following steps:

[0167] Step 1: Based on the current affine center value and current affine fluctuation value of the line during the target time period, as well as the current transmission range of the line, construct the line current constraint.

[0168] Among them, the current affine center value refers to the center value of the current of the line in the target time period expressed in affine form, and the current affine fluctuation value refers to the corresponding fluctuation value of the current of the line in the target time period caused by the influence of noise elements.

[0169] In one embodiment, the line current constraint satisfies the following: the difference between the current affine center value and the current constraint statistical value is greater than or equal to the lower limit of the current transmission range and less than or equal to the upper limit of the current transmission range; the sum of the current affine center value and the current constraint statistical value is greater than or equal to the lower limit of the current transmission range and less than or equal to the upper limit of the current transmission range; wherein, the current constraint statistical value is the statistical value obtained by performing an absolute value operation on the current affine fluctuation value corresponding to each line.

[0170] For example, the line current constraint satisfies:

[0171]

[0172] Among them, I ij Indicates the lower limit of the current transmission range. Indicates the upper limit of the current transmission range; I ij,t,0 This represents the affine center value of the current in the line ij between distribution network node i and the corresponding target distribution network node j during the target time period t; I ij,t,k This represents the corresponding fluctuation value of the current of line ij during the target time period t caused by the influence of noise element k, that is, the affine fluctuation value of the current of the line.

[0173] Step 2: Construct voltage deviation constraints based on the affine center value and affine fluctuation value of the voltage amplitude corresponding to the distribution network node, as well as the voltage deviation range of the node.

[0174] Among them, the affine center value of voltage amplitude corresponding to the distribution network node refers to the affine center value of voltage amplitude of the distribution network node in the target time period, and the affine fluctuation value of voltage amplitude corresponding to the distribution network node refers to the corresponding fluctuation value of voltage amplitude of the distribution network node in the target time period caused by the influence of noise element.

[0175] In one embodiment, the difference between the affine center value of the voltage amplitude corresponding to a distribution network node and the voltage deviation statistical value is greater than or equal to the lower limit of the node voltage deviation range and less than or equal to the upper limit of the node voltage deviation range; the sum of the affine center value of the voltage amplitude corresponding to a distribution network node and the voltage deviation statistical value is greater than or equal to the lower limit of the node voltage deviation range and less than or equal to the upper limit of the node voltage deviation range; wherein, the voltage deviation statistical value is the statistical value after performing an absolute value operation on the voltage affine fluctuation value corresponding to each distribution network node.

[0176] For example, the voltage deviation constraint satisfies:

[0177]

[0178] Among them, V i This indicates the lower limit of the node voltage deviation range. Indicates the upper limit of the node voltage deviation range; V i,t,0 V represents the affine center value of the voltage amplitude of distribution network node i during the target time period t, i.e., the affine center value corresponding to the distribution network node; i,t,k This represents the fluctuation value of the voltage amplitude of distribution network node i during the target time period t caused by the influence of noise elements, i.e., the affine fluctuation value corresponding to the distribution network node.

[0179] Step 3: Based on the power balance constraints of the distribution network nodes, the linearized power flow equation constraints of the distribution network, the line current constraints, the voltage deviation constraints, and the standby energy storage operation constraints of the base station, construct the affine constraint conditions corresponding to the affine objective function.

[0180] Among them, the affine constraints include power balance constraints at distribution network nodes, linearized power flow equation constraints for the distribution network, line current constraints, voltage deviation constraints, and standby energy storage operation constraints for base stations.

[0181] Based on the above, the accuracy of model construction can be improved by introducing line current constraints and voltage deviation constraints.

[0182] Furthermore, an affine optimization operation model for the distribution network can be constructed based on the power balance constraints of distribution network nodes, the linearized power flow equation constraints of the distribution network, the line current constraints, the voltage deviation constraints, the standby energy storage operation constraints of base stations, and the affine objective function.

[0183] In one embodiment, the affine optimization operation model of the distribution network satisfies:

[0184]

[0185] Among them, F0 and F εThese represent the affine central value and the affine fluctuation value of cost, respectively. Specifically, the affine central value of cost is the sum of the affine central value of the distribution network operator's electricity purchase cost and the affine central value of the subsidy paid by the distribution network operator to the communication network operator for the operation of base station backup energy storage; the affine fluctuation value of cost is the sum of the affine fluctuation value of the distribution network operator's electricity purchase cost and the affine fluctuation value of the subsidy paid by the distribution network operator to the communication network operator for the operation of base station backup energy storage. Specifically, F0 and F... ε They respectively satisfy:

[0186]

[0187] Where h(u0,u k ,v0,v k ) represents inequality constraints, namely, inequality constraints in distribution network node power balance constraints, distribution network linearized power flow equation constraints, line current constraints, voltage deviation constraints, and base station backup energy storage operation constraints; g(u0,v0)=0 and g(u k ,v k ) = 0 indicates equality constraints, namely equality constraints in distribution network node power balance constraints, distribution network linearized power flow equation constraints, line current constraints, voltage deviation constraints, and base station backup energy storage operation constraints; u0 and u k Let v0 and v represent the central value and fluctuation value of the decision variable, respectively; k These represent the central value and fluctuation value of the state variable, respectively.

[0188] As shown in the above equation, all variables (including decision variables and state variables) in the affine optimization operation model of the distribution network consist of a central value and N fluctuation values. This is achieved by constraining h(u0, u...) using inequalities. k ,v0,v k This ensures that the affine value formed by the central and fluctuating values ​​of the variables satisfies the upper and lower limits of the system's operation; the equality constraint g(u0,v0)=0 ensures that the central value of the variables satisfies the system's operational requirements; the equality constraint g(u0,v0)=0 ensures that the central value of the variables satisfies the system's operational requirements; k ,v k The value of ) = 0 can be used to calculate the k-th fluctuation value generated by noise element k on all variables of the system, thereby tracing the propagation trajectory of uncertainty factors. Therefore, by solving the affine optimization operation model of the distribution network, the affine optimization operation results of the distribution network can be obtained.

[0189] Based on the above, taking a 5G base station as an example, such as Figure 2 As shown, an affine optimization operation method for distribution networks considering the adjustable potential of base station backup energy storage is provided. Taking the application of this method to a server in a distribution network affine optimization operation system as an example, the method includes the following steps:

[0190] S202. For each distribution network node in the distribution network, the minimum backup time for the backup energy storage of the distribution network node is determined based on the total number of distribution network nodes in the distribution network, the power supply reliability of the distribution network node, the weight corresponding to the node load level of the distribution network node, and the average backup power duration.

[0191] S204, based on the minimum backup power time, performs integral calculations on the static power consumption and maximum dynamic power consumption of the 5G base station to determine the backup power capacity of the 5G base station backup energy storage at the distribution network node during the target period.

[0192] S206. Determine the dispatchable capacity of the 5G base station backup energy storage of the distribution network node based on the maximum backup energy storage capacity and backup power capacity of the distribution network node.

[0193] S208, Obtain cost information of the distribution network, including: electricity purchase cost of the distribution network operator, operating subsidy cost of 5G base station backup energy storage paid by the distribution network operator to the communication network operator, electricity purchase cost of the distribution network operator, and operating subsidy cost of 5G base station backup energy storage paid by the distribution network operator to the communication network operator.

[0194] S210, determine a first affine statistical value between the affine center value of the electricity purchase cost and the affine center value of the base station backup energy storage operation subsidy cost, and a second affine statistical value between the affine fluctuation value of the electricity purchase cost and the affine fluctuation value of the base station backup energy storage operation subsidy cost.

[0195] S212, based on weighting coefficients, the first affine statistical value, and the second affine statistical value, constructs an affine objective function to characterize the operating cost of the distribution network.

[0196] S214. Based on the affine center value of the active power injected by the distribution network node in the target time period and the affine fluctuation value of the active power affected by the noise element, as well as the affine center value of the reactive power injected by the distribution network node in the target time period and the corresponding affine fluctuation value of the reactive power, power balance constraints of the distribution network node are constructed.

[0197] S216, for the target distribution network node connected to each distribution network node in the distribution network, based on the electrical parameters of the line between the two distribution network nodes, the affine center value of the voltage amplitude of the target distribution network node in the target time period, and the affine fluctuation value of the voltage amplitude, the linearized power flow equation constraint of the distribution network is constructed.

[0198] S218. Based on the current affine center value and current affine fluctuation value of the line during the target time period, as well as the current transmission range of the line, a line current constraint is constructed.

[0199] S220: Based on the affine center value and affine fluctuation value of the voltage amplitude corresponding to the distribution network node, and the voltage deviation range of the node, a voltage deviation constraint is constructed.

[0200] S222, based on the dispatchable capacity, the charging power and discharging power of the 5G base station backup energy storage at the distribution network node, construct the operation constraints of the 5G base station backup energy storage.

[0201] S224. Based on the power balance constraints of distribution network nodes, the linearized power flow equation constraints of distribution network, the line current constraints, the voltage deviation constraints, and the standby energy storage operation constraints of base stations, construct the affine constraint conditions corresponding to the affine objective function.

[0202] S226. Based on the affine objective function and affine constraints, construct an affine optimization operation model for the distribution network.

[0203] S228, solve the affine optimization operation model of the distribution network to obtain the affine optimization operation results of the distribution network.

[0204] The specific content of S202-S228 can be found in the aforementioned description and will not be repeated here.

[0205] Based on the above, a modified 33-node distribution network example was used for testing to verify the effectiveness of the proposed model. The distribution network example topology is as follows: Figure 3 As shown, Figure 3 This indicates the 5G base station access status in office areas, commercial areas, residential areas, and boarding school areas. Specifically, distribution network nodes 1-5, 14, 15, 19, 20, 23, 26, 29, and 32 indicate base stations (BS) in the office area; distribution network nodes 7, 8, 12, 30, and 31 indicate BS in the commercial area; distribution network nodes 18 and 25 indicate BS in the boarding school area; and the remaining distribution network nodes indicate BS in the residential area. W represents wind turbines, and P represents photovoltaics. Each node connects to 6 5G base stations. The voltage amplitude ranges from 0.9 pu (per unit) to 1.1 pu. Distribution network nodes 7, 17, and 22 connect to 800kW, 700kW, and 700kW PVGs, respectively, while distribution network nodes 25 and 32 connect to 800kW and 900kW WTGs, respectively.

[0206] The communication load of 5G base stations affects the power consumption of backup energy storage per unit time, while the reliability of node power supply and the node load level affect the minimum backup power time of backup energy storage. Furthermore, the communication load fluctuations of 5G base stations exhibit significant regional variations, such as... Figure 4 As shown, this diagram illustrates the daily communication load variation trends of 5G base stations in four functional areas. Figure 4As can be seen, the peak-valley characteristics of communication load in different regions exhibit certain differences and complementarities. By comprehensively considering the impact of communication load in different functional areas, the dispatchable power of backup energy storage can be adjusted upwards and downwards during peak and off-peak periods, respectively, while dispatching can be increased during off-peak periods. This allows for the full exploitation of the dispatchable potential of backup energy storage, providing the distribution network with more flexible adjustment capabilities. In other words, the communication load in different functional areas is shown in the figure. Figure 4 For information on 5G base station access in office areas, commercial areas, residential areas, and boarding school areas, please see [link / reference]. Figure 3 .

[0207] like Figure 5 As shown, a schematic diagram illustrating the daily variation trends of WTG and PVG output and load power coefficient is provided. Table 1 provides relevant parameters for 5G base stations. The electricity purchase price is based on the industrial and commercial time-of-use electricity price, and the dispatch cost of 5G base station energy storage is 0.1 yuan / kWh.

[0208] Table 1 5G Base Station Equipment Parameters

[0209]

[0210] The dispatchable potential of backup energy storage for 5G base stations is related to communication load, node power supply reliability, and node load level. For example... Figure 6 The diagram illustrates the load level weighting and reliability index of a distribution network node. Nodes closer to the substation have higher reliability indices, while nodes farther from the substation have lower reliability indices.

[0211] Based on the above, a 5G base station backup energy storage dispatchable potential assessment model can be configured based on the dispatchable capacity calculation process. To verify the effectiveness of the 5G base station backup energy storage dispatchable potential assessment model proposed in this application, a model proposed in the literature is used for comparison. The literature title is "Active Distribution Network Cooperative Optimization Scheduling Method Considering 5G Base Station Dispatchable Backup Energy Storage and Smart Soft Switching." The average backup time of 5G base station backup energy storage in the distribution network is used as the benchmark. Taking 3 hours as an example, the minimum backup power time for different models is calculated based on relevant indicators, such as... Figure 7 As shown. From Figure 7 As can be seen, the two models differ in the backup power time for some nodes. For the model proposed in this application: the higher the node load level and reliability index, the shorter the backup power time of the base station; the lower the node load level and reliability index, the longer the backup power time of the base station. The backup power time of 5G base stations calculated by both models fluctuates within 1-5 hours.

[0212] Based on the 5G base station backup time from different models, combined with the predicted communication load, we can obtain, as follows: Figure 8The figure shows the schedulable capacity of backup energy storage for 5G base stations under different models. As can be seen from the figure, the schedulable capacity of a 5G base station changes continuously with the backup power time and communication load. Specifically, the total real-time schedulable capacity of the model proposed in this application fluctuates between 2383 and 3567 kWh.

[0213] To investigate the impact of the dispatchable potential of backup energy storage for 5G base stations on the optimized operation of the distribution network, the following two scenarios are set up for comparative analysis:

[0214] Scenario 1: Affine optimization of distribution networks without considering the dispatchable potential of backup energy storage for 5G base stations.

[0215] Scenario 2: Affine optimization of distribution networks considering the dispatchable potential of backup energy storage for 5G base stations.

[0216] The fluctuation range of WTG and PVG output and load power was set to 10%, the weight coefficient of the affine objective function was set to 0.5, and the correlation between variables was set to 0.5. The results of the simulation operation and the comparison of typical daily operating costs for different scenarios are shown in Table 2.

[0217] Table 2 shows that, without considering the dispatchable potential of 5G base station backup energy storage, the typical daily operating cost center value of the distribution network is 67,736.3 yuan, and the fluctuation value is 4,982.2 yuan. Considering the dispatchable potential of 5G base station backup energy storage, the typical daily operating cost center value of the distribution network is 53,488.5 yuan, and the cost fluctuation value is 3,981.9 yuan. Compared with scenario 1, these two indicators in scenario 2 are reduced by 21.03% and 20.08%, respectively. This is because 5G base station backup energy storage reduces the system's operating cost and cost fluctuation value through low-storage-high-generation arbitrage. This result indicates that considering the dispatchable potential of 5G base station backup energy storage can significantly improve the operational economy of the distribution network.

[0218] Table 2 Comparison of typical daily operating costs in different scenarios

[0219] Scene Scene 1 Scene 2 Cost range / yuan [62754.1,72718.4] [49506.6,57470.3] Cost center value / yuan 67736.3 53488.5 Cost fluctuation value / yuan 4982.2 3981.9

[0220] like Figure 9 As shown, this provides a real-time output of backup energy storage for 5G base stations at various distribution network nodes. Figure 9 The backup energy storage device charges during periods of low electricity prices (1:00-5:00 and 22:00-24:00) and discharges during periods of flat and high electricity prices, improving the system's operational economy through low-storage-high-output arbitrage. Meanwhile, the output of the backup energy storage is unaffected by the uncertainty of the "source-load" relationship during certain periods, therefore the output fluctuation during these periods is zero.

[0221] To verify the performance of the proposed affine optimization method, considering the schedulable potential of backup energy storage for 5G base stations, the system was solved using both interval optimization and affine optimization methods. The results of the different methods are shown in Table 3.

[0222] Table 3 Comparison of Calculation Results by Different Methods

[0223] Scene Affine optimization method Interval optimization method Cost range / yuan [49506.6,57470.3] [47481.3,60295.2] Cost center value / yuan 53488.5 53888.3 Cost fluctuation value / yuan 3981.9 6407.0 Calculation time / s 1179 229

[0224] Table 4 Comparison of Typical Daily Operating Costs under Different Fluctuation Ranges

[0225] Fluctuation range / % Cost range / yuan Cost center value / yuan Cost fluctuation value / yuan 0 - 52434.6 - 2 [51818.3,53490.7] 52654.5 836.2 4 [51311.1,54337.3] 52824.2 1513.1 6 [50609.3,55467.3] 53038.2 2429.0 8 [50117.0,56408.4] 53262.7 3145.7 10 [49506.6,57470.3] 53488.5 3981.9

[0226] As shown in Table 3, the cost center value of the interval optimization method is 53,888.3 yuan, and the cost fluctuation value is 8,407.0 yuan, which are 0.75% and 60.90% higher than those of the affine optimization method, respectively. This is because affine optimization can consider the correlation between uncertain variables of the "source-load" relationship, and therefore has lower conservatism. The computation time of the affine optimization method is 4.1 times longer than that of the interval optimization method. This is because the affine optimization method considers the need to locate the propagation trajectory of uncertain factors through noise elements, leading to the introduction of a large number of constraints in the model, thus increasing the computation time.

[0227] To analyze the impact of source-load uncertainty on distribution network operation, the fluctuation ranges of WTG and PVG output and load power were set to 0%, 2%, 4%, 6%, 8%, and 10%, respectively. Table 4 shows the comparison of typical daily operating costs under different fluctuation ranges. As can be seen from Table 4, the cost center value of the distribution network operation increases with the increase of the fluctuation range. This is because the system needs to reserve more flexibility margin to cope with source-load uncertainty, leading to a decrease in its dispatchable flexibility. This indicates that increased source-load uncertainty leads to an increase in the typical daily operating cost of the system.

[0228] To analyze the impact of the selection of weight coefficients of the objective function on the operation results of the distribution network, the weight coefficient values ​​were set to 0.1, 0.3, 0.5, 0.7 and 0.9 respectively. The comparison results of typical daily operating costs under different weight coefficients are shown in Table 5.

[0229] Table 5 Comparison of Typical Daily Operating Costs under Different Weighting Coefficients

[0230] Weighting coefficient Cost range / yuan Cost center value / yuan Cost fluctuation value / yuan 0.1 [49784.2,57495.4] 53639.8 3855.6 0.3 [49659.2,57475.6] 53567.4 3908.2 0.5 [49506.6,57470.3] 53488.5 3981.9 0.7 [48978.9,57507.7] 53243.3 4264.4 0.9 [48696.4,57582.8] 53139.6 4443.2

[0231] As shown in Table 5, with the increase of the weighting coefficient, the cost center value of the distribution network operation decreases, but the cost fluctuation value increases. This is because increasing the weighting coefficient leads to an increase in the proportion of the system cost center value in the objective function, while reducing the cost center value is beneficial to reducing the objective function. Therefore, adjusting the weighting coefficient can change the conservatism of the operation mode. When the weighting coefficient is 1, uncertainty is completely ignored, and optimization is performed according to the predicted value.

[0232] As shown above, the schedulable capacity assessment model for 5G base station backup energy storage constructed in this application can dynamically assess the schedulable potential of 5G base station backup energy storage at different locations based on communication load, node power supply reliability, and node load level at different times, thereby achieving efficient utilization of backup energy storage resources. Furthermore, the affine optimization method provided in this application can effectively improve operational economics under source-load uncertainty. In addition, by adjusting the weighting coefficients, the conservatism of the operational mode obtained by this method can be altered, demonstrating good adaptability and promotional value.

[0233] In summary, this application establishes a backup energy storage dispatchable capacity assessment model that comprehensively considers communication load, node power supply reliability, and load level. Compared with existing models, it can more accurately assess the dispatchable capacity of 5G base station backup energy storage. Moreover, it establishes a distribution network affine optimization operation model that considers the dispatchable potential of 5G base station backup energy storage. By reusing noise elements to characterize the correlation between different source load powers, it can solve the problem that the traditional interval optimization algorithm is too conservative in distribution network operation mode due to interval expansion.

[0234] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0235] Based on the same inventive concept, this application also provides an affine optimization operation device for distribution networks considering the adjustable potential of base station backup energy storage, for implementing the aforementioned distribution network affine optimization operation method considering the adjustable potential of base station backup energy storage. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the distribution network affine optimization operation device considering the adjustable potential of base station backup energy storage provided below can be found in the limitations of the distribution network affine optimization operation method considering the adjustable potential of base station backup energy storage described above, and will not be repeated here.

[0236] In one exemplary embodiment, such as Figure 10 As shown, an affine optimization operation device for a distribution network considering the adjustable potential of base station backup energy storage is provided, comprising: a determination module 1002, a first construction module 1004, a processing module 1006, a second construction module 1008, and an analysis module 1010. Specifically, the determination module 1002 is used to determine the dispatchable capacity of base station backup energy storage at each distribution network node based on the base station power consumption and node load level at the distribution network node; the first construction module 1004 is used to construct an affine objective function characterizing the operating cost of the distribution network based on the cost information of the distribution network; the processing module 1006 is used to construct operating constraints for base station backup energy storage based on the dispatchable capacity, the charging power and discharging power of the base station backup energy storage at the distribution network node; the second construction module 1008 is used to construct an affine optimization operation model for the distribution network based on the affine objective function and the base station backup energy storage operating constraints; and the analysis module 1010 is used to solve the affine optimization operation model for the distribution network to obtain the affine optimization operation results.

[0237] In one embodiment, the second construction module 1008 is further configured to: construct power balance constraints for distribution network nodes based on the affine center value of active power injected by distribution network nodes during the target time period and the affine fluctuation value of active power affected by noise elements, as well as the affine center value of reactive power injected by distribution network nodes during the target time period and the corresponding affine fluctuation value of reactive power; construct power flow equation constraints for linearized distribution network based on the electrical parameters of the lines between the two distribution network nodes, the affine center value of voltage amplitude of the target distribution network node during the target time period, and the affine fluctuation value of voltage amplitude; construct affine constraint conditions corresponding to the affine objective function based on the power balance constraints of distribution network nodes, the power flow equation constraints of linearized distribution network, and the standby energy storage operation constraints; and construct an affine optimization operation model for the distribution network based on the affine objective function and the affine constraint conditions.

[0238] In one embodiment, the second construction module 1008 is further configured to construct line current constraints based on the current affine center value and current affine fluctuation value of the line during the target time period and the current transmission range of the line; construct voltage deviation constraints based on the voltage amplitude affine center value and voltage amplitude affine fluctuation value corresponding to the distribution network node and the node voltage deviation range; and construct affine constraint conditions corresponding to the affine objective function based on the power balance constraints of the distribution network node, the linearized power flow equation constraints of the distribution network, the line current constraints, the voltage deviation constraints, and the base station backup energy storage operation constraints.

[0239] In one embodiment, the cost information includes: the electricity purchase cost of the distribution network operator, the base station backup energy storage operation subsidy cost paid by the distribution network operator to the communication network operator, the electricity purchase cost of the distribution network operator, and the base station backup energy storage operation subsidy cost paid by the distribution network operator to the communication network operator; the first construction module 1004 is further configured to determine a first affine statistical value between the affine central value of the electricity purchase cost and the affine central value of the base station backup energy storage operation subsidy cost, and a second affine statistical value between the affine fluctuation value of the electricity purchase cost and the affine fluctuation value of the base station backup energy storage operation subsidy cost; and based on the weighting coefficient, the first affine statistical value, and the second affine statistical value, construct an affine objective function to characterize the operating cost of the distribution network.

[0240] In one embodiment, the power consumption of the base station at the distribution network node includes: the static power consumption and the maximum dynamic power consumption of the base station at the distribution network node during the target time period; the determining module 1002 is further configured to determine the minimum backup power time of the backup energy storage of the distribution network node based on the total number of distribution network nodes in the distribution network, the power supply reliability of the distribution network nodes, the weight corresponding to the node load level of the distribution network nodes, and the average backup power duration; based on the minimum backup power time, perform an integral calculation on the static power consumption and the maximum dynamic power consumption to determine the backup power capacity of the base station backup energy storage of the distribution network node during the target time period; and determine the dispatchable capacity of the base station backup energy storage of the distribution network node based on the maximum backup energy storage capacity and the backup power capacity of the distribution network node.

[0241] The modules in the aforementioned distribution network affine optimization operation device, which considers the adjustable potential of base station backup energy storage, can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0242] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data such as distribution network cost information. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an affine optimization operation method for the distribution network that considers the adjustable potential of base station backup energy storage.

[0243] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0244] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0245] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0246] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0247] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0248] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0249] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0250] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power distribution network affine optimization operation method considering adjustable potential of base station backup energy storage, characterized in that, The method comprises: For each power distribution network node in the power distribution network, determining a minimum standby power time of standby energy storage of the power distribution network node according to a total number of power distribution network nodes in the power distribution network, power supply reliability of the power distribution network node, a weight corresponding to a node load level of the power distribution network node, and an average standby power duration; Based on the minimum standby power time, performing integral operation on static power consumption and maximum dynamic power consumption of a base station at the power distribution network node in a target period to determine a standby power capacity of the base station standby energy storage of the power distribution network node in the target period; According to the maximum capacity of the standby energy storage of the power distribution network node and the standby power capacity, determining a schedulable capacity of the base station standby energy storage of the power distribution network node; Determining a first affine statistical value between an affine center value of a power purchase cost of a power distribution network operator and an affine center value of a base station standby energy storage operation subsidy cost paid by the power distribution network operator to a communication network operator, and a second affine statistical value between an affine fluctuation value of the power purchase cost of the power distribution network operator and an affine fluctuation value of a base station standby energy storage operation subsidy cost paid by the power distribution network operator to the communication network operator; Based on a weight coefficient, the first affine statistical value and the second affine statistical value, constructing an affine objective function for representing an operation cost of the power distribution network; According to the schedulable capacity, charging power and discharging power of the base station standby energy storage at the power distribution network node, constructing a base station standby energy storage operation constraint; According to the affine objective function and the base station standby energy storage operation constraint, constructing a power distribution network affine optimization operation model; Solving the power distribution network affine optimization operation model to obtain a power distribution network affine optimization operation result.

2. The method of claim 1, wherein, The affine objective function and the base station standby energy storage operation constraint, constructing a power distribution network affine optimization operation model, comprises: According to an affine center value of active power injected by the power distribution network node in a target period and an affine fluctuation value of the active power affected by a noise element, and an affine center value of reactive power injected by the power distribution network node in the target period and an affine fluctuation value corresponding to the reactive power, constructing a power distribution network node power balance constraint; For each target power distribution network node connected to each power distribution network node in the power distribution network, based on electrical parameters of a line between the two power distribution network nodes, an affine center value of a voltage amplitude of the target power distribution network node in a target period and an affine fluctuation value of the voltage amplitude, constructing a power distribution network linearized power flow equation constraint; According to the power distribution network node power balance constraint, the power distribution network linearized power flow equation constraint and the standby energy storage operation constraint, constructing an affine constraint condition corresponding to the affine objective function; According to the affine objective function and the affine constraint condition, constructing a power distribution network affine optimization operation model.

3. The method of claim 2, wherein, The affine objective function and the base station standby energy storage operation constraint, constructing an affine constraint condition corresponding to the affine objective function, comprises: Based on an affine center value of a current of the line in a target period and an affine fluctuation value of the current, and a current transmission range of the line, constructing a line current constraint; constructing a voltage deviation constraint according to the voltage amplitude affine center value and the voltage amplitude affine fluctuation value corresponding to the power distribution network node, and a node voltage deviation range; constructing an affine constraint condition corresponding to the affine objective function according to the power distribution network node power balance constraint, the power distribution network linearized power flow equation constraint, the line current constraint, the voltage deviation constraint, and the base station standby energy storage operation constraint.

4. The method of claim 2, wherein, The power distribution network node power balance constraint includes at least one of a first balance constraint, a second balance constraint, a third balance constraint, and a fourth balance constraint; The first balance constraint satisfies: the difference between the sum of the affine center value of the substation active power injected by the power distribution network node in a target period, the affine center value of the wind power active output and the affine center value of the photovoltaic active output injected by the power distribution network node in the target period, and the affine center value of the discharge power, and the first balance statistical value, is equal to the affine center value of the active power injected by the power distribution network node in the target period; the first balance statistical value is the sum of the affine center value of the total power consumption of the base station at the power distribution network node in the target period, the affine center value of the charging power, and the affine center value of the load active demand injected by the power distribution network node in the target period; The second balance constraint satisfies: the difference between the sum of the affine fluctuation value corresponding to the substation active power, the affine fluctuation value corresponding to the wind power active output, the affine fluctuation value corresponding to the photovoltaic active output, the affine fluctuation value corresponding to the discharge power, and the affine fluctuation value corresponding to the total power consumption, and the second balance statistical value, is equal to the affine fluctuation value corresponding to the injected active power; the second balance statistical value is the sum of the affine fluctuation value corresponding to the charging power and the affine fluctuation value corresponding to the load active demand; The third balance constraint satisfies: the sum of the affine center value of the substation reactive power injected by the power distribution network node in a target period and the affine center value of the load reactive demand is equal to the affine center value of the reactive power injected by the power distribution network node in the target period; The fourth balance constraint satisfies: the difference between the affine fluctuation value corresponding to the substation reactive power and the affine fluctuation value corresponding to the load reactive demand is equal to the affine fluctuation value corresponding to the injected reactive power.

5. The method of claim 2, wherein, The electrical parameters include conductance and susceptance; the power distribution network linearized power flow equation constraint includes at least one of a first linearized power flow constraint, a second linearized power flow constraint, a third linearized power flow constraint, and a fourth linearized power flow constraint; The first linearized power flow constraint satisfies: a difference between a first power flow statistical value and a second power flow statistical value is equal to an affine central value of the injected active power; the first power flow statistical value is a statistical value of a first correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the first correlation value is a product of a conductance and a voltage amplitude affine central value corresponding to the target power distribution network node; and the second power flow statistical value is a statistical value of a second correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the second correlation value is a product of a susceptance and a phase angle affine central value corresponding to the target power distribution network node; The second linearized power flow constraint satisfies: a difference between a third power flow statistical value and a fourth power flow statistical value is equal to an affine fluctuation value corresponding to the injected active power; the third power flow statistical value is a statistical value of a third correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the third correlation value is a product of a conductance and a voltage amplitude affine fluctuation value corresponding to the target power distribution network node; and the fourth power flow statistical value is a statistical value of a fourth correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the fourth correlation value is a product of a conductance and a phase angle affine fluctuation value corresponding to the target power distribution network node; The third linearized power flow constraint satisfies: a difference between a negative of a fifth power flow statistical value and a sixth power flow statistical value is equal to an affine central value of the injected reactive power; the fifth power flow statistical value is a statistical value of a fifth correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the fifth correlation value is a product of a susceptance and a voltage amplitude affine central value corresponding to the target power distribution network node; and the sixth power flow statistical value is a statistical value of a sixth correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the sixth correlation value is a product of a conductance and a phase angle affine central value corresponding to the target power distribution network node; The fourth linearized power flow constraint satisfies: a difference between a negative of a seventh power flow statistical value and an eighth power flow statistical value is equal to an affine fluctuation value corresponding to the injected reactive power; the seventh power flow statistical value is a statistical value of a seventh correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the seventh correlation value is a product of a susceptance and a voltage amplitude affine fluctuation value corresponding to the target power distribution network node; and the eighth power flow statistical value is a statistical value of an eighth correlation value of each of the target power distribution network nodes corresponding to the power distribution network node, and the eighth correlation value is a product of a conductance and a phase angle affine fluctuation value corresponding to the target power distribution network node.

6. The method of claim 3, wherein The line current constraint satisfies: a difference between the current affine central value and a current constraint statistical value is greater than or equal to a lower limit of the current transmission range and less than or equal to an upper limit of the current transmission range; and a sum of the current affine central value and the current constraint statistical value is greater than or equal to the lower limit of the current transmission range and less than or equal to the upper limit of the current transmission range; wherein the current constraint statistical value is a statistical value of an absolute value of a current affine fluctuation value corresponding to each of the lines. ​ The voltage deviation constraint satisfies: a difference between the voltage amplitude affine center value corresponding to the power distribution network node and a voltage deviation statistical value is greater than or equal to a lower limit of the node voltage deviation range and less than or equal to an upper limit of the node voltage deviation range; a sum of the voltage amplitude affine center value corresponding to the power distribution network node and the voltage deviation statistical value is greater than or equal to the lower limit of the node voltage deviation range and less than or equal to the upper limit of the node voltage deviation range; wherein the voltage deviation statistical value is a statistical value after an absolute value operation is performed on the voltage affine fluctuation value corresponding to each of the power distribution network nodes.

7. The method of claim 1, wherein, The base station standby energy storage operation constraint includes at least one of a first operation constraint, a second operation constraint, a third operation constraint, a fourth operation constraint, and a fifth operation constraint. The first operation constraint satisfies: a difference between the affine center value of the discharge power and a first operation statistical value is greater than or equal to a first preset value and less than or equal to a first target value; a sum of the affine center value of the discharge power and the first operation statistical value is greater than or equal to the first preset value and less than or equal to the first target value; the first target value is a product of a state value corresponding to a charge-discharge state of the standby energy storage of the power distribution network node in a target period and a rated power of the standby energy storage of the power distribution network node; and the first operation statistical value is a statistical value after an absolute value operation is performed on the discharge power affine fluctuation value corresponding to each of the power distribution network nodes. The second operation constraint satisfies: a difference between the affine center value of the charge power and a second operation statistical value is greater than or equal to the first preset value and less than or equal to a second target value; a sum of the affine center value of the charge power and the second operation statistical value is greater than or equal to the first preset value and less than or equal to the second target value; the second target value is a product of a third target value that is inversely related to the state value corresponding to the charge-discharge state and the rated power; and the second operation statistical value is a statistical value after an absolute value operation is performed on the charge power affine fluctuation value corresponding to each of the power distribution network nodes. The third operation constraint satisfies: a difference between the affine center value of the capacity of the standby energy storage of the power distribution network node in the target period and a third operation statistical value is greater than or equal to a standby power capacity of the base station standby energy storage of the power distribution network node in the target period and less than or equal to a maximum capacity of the standby energy storage of the power distribution network node; a sum of the affine center value of the capacity and the third operation statistical value is greater than or equal to the standby power capacity and less than or equal to the maximum capacity of the standby energy storage; the third operation statistical value is a statistical value after an absolute value operation is performed on the capacity affine fluctuation value corresponding to each of the power distribution network nodes; and a difference between the maximum capacity of the standby energy storage and the standby power capacity is equal to the schedulable capacity. The fourth operation constraint satisfies that a sum of an affine center value of the capacity and a fourth operation statistical value is equal to an affine center value of a capacity of the backup energy storage of the power distribution network node in a next time period of the target time period; the fourth operation statistical value is a difference between a first multiplication value and a first ratio value, the first multiplication value is a product of a charging efficiency of the backup energy storage of the base station and an affine center value of the charging power, and the first ratio value is a ratio of an affine center value of the discharging power to a discharging efficiency; The fifth operation constraint satisfies that a sum of an affine fluctuation value of the capacity corresponding to the power distribution network node and a fifth operation statistical value is equal to an affine fluctuation value of the capacity in the next time period; the fifth operation statistical value is a difference between a second multiplication value and a second ratio value, the second multiplication value is a product of the charging efficiency and an affine fluctuation value corresponding to the charging power, and the second ratio value is a ratio of an affine fluctuation value corresponding to the discharging power to the discharging efficiency.

8. A power distribution network affine optimization operation device considering adjustable potential of base station backup energy storage, characterized in that, The apparatus comprises: A determination module is configured to determine, for each power distribution network node in a power distribution network, a minimum backup power time of backup energy storage of the power distribution network node according to a total number of power distribution network nodes in the power distribution network, power supply reliability of the power distribution network node, a weight corresponding to a node load level of the power distribution network node, and an average backup power duration; perform integral operation on static power consumption and maximum dynamic power consumption of a base station at the power distribution network node in a target time period based on the minimum backup power time, to determine backup power capacity of backup energy storage of the base station of the power distribution network node in the target time period; and determine a schedulable capacity of backup energy storage of the base station of the power distribution network node according to a maximum capacity of backup energy storage of the power distribution network node and the backup power capacity. A first construction module is configured to determine a first affine statistical value between an affine center value of a power purchase cost of a power distribution network operator and an affine center value of a base station backup energy storage operation subsidy cost paid by the power distribution network operator to a communication network operator, and a second affine statistical value between an affine fluctuation value of the power purchase cost of the power distribution network operator and an affine fluctuation value of the base station backup energy storage operation subsidy cost paid by the power distribution network operator to the communication network operator; and construct an affine objective function for representing an operation cost of the power distribution network based on a weight coefficient, the first affine statistical value, and the second affine statistical value. A processing module is configured to construct a base station backup energy storage operation constraint according to the schedulable capacity, charging power, and discharging power of the backup energy storage of the base station at the power distribution network node. A second construction module is configured to construct a power distribution network affine optimization operation model according to the affine objective function and the base station backup energy storage operation constraint. An analysis module is configured to solve the power distribution network affine optimization operation model to obtain a power distribution network affine optimization operation result.

9. The apparatus of claim 8, wherein, The second construction module is further configured to: construct a power distribution network node power balance constraint according to an affine center value of active power injected by the power distribution network node in the target time period, an affine fluctuation value of the active power affected by a noise element, an affine center value of reactive power injected by the power distribution network node in the target time period, and an affine fluctuation value corresponding to the reactive power. For each target power grid node connected with each power grid node in the power grid, a power grid linearized power flow equation constraint is constructed based on electrical parameters of a line between two power grid nodes, a voltage amplitude affine center value and a voltage amplitude affine fluctuation value of the target power grid node in a target period; An affine constraint condition corresponding to the affine objective function is constructed according to the power grid node power balance constraint, the power grid linearized power flow equation constraint and the backup energy storage operation constraint; An affine optimization operation model of the power grid is constructed according to the affine objective function and the affine constraint condition.

10. The apparatus of claim 9, wherein, The second construction module is further configured to: A line current constraint is constructed based on a current affine center value and a current affine fluctuation value of the line in the target period and a current transmission range of the line; A voltage deviation constraint is constructed according to the voltage amplitude affine center value and the voltage amplitude affine fluctuation value corresponding to the power grid node and a node voltage deviation range; The affine constraint condition corresponding to the affine objective function is constructed according to the power grid node power balance constraint, the power grid linearized power flow equation constraint, the line current constraint, the voltage deviation constraint and the base station backup energy storage operation constraint.

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