Power distribution network affine optimization operation method considering adjustable potential of standby energy storage of base station

Abstract

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

Description

Technical Field

[0001] The present application relates to the technical field of power grids, and particularly to a method and device for affine optimal operation of a distribution network considering the adjustable potential of base station backup energy storage. Background Art

[0002] Solar energy and wind energy are highly favored due to their sustainability and environmental friendliness. Among them, the installed capacities of photovoltaic generation (PVG) and wind turbine generation (WTG) have also been continuously increasing. However, the outputs of PVG and WTG are random and volatile, posing severe challenges to the safe and economic operation of the distribution network. At the same time, with the rapid development of communication technology, the number of base station constructions has increased rapidly, and backup energy storage facilities have also been widely deployed.

[0003] Currently, for the method of evaluating the dispatchable capacity of base station backup energy storage, there is a problem of low evaluation accuracy. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method and device for affine optimal operation of a distribution network considering the adjustable potential of base station backup energy storage, which can improve the evaluation accuracy of the dispatchable capacity of base station backup energy storage.

[0005] In a first aspect, the present application provides a method for affine optimal operation of a distribution network considering the adjustable potential of base station backup energy storage, including: for each distribution network node in the distribution network, determining the dispatchable capacity of the 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 for characterizing the operating cost of the distribution network according to the cost information of the distribution network; constructing operating constraints for the base station backup energy storage according to the dispatchable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node; constructing an affine optimal operation model for the distribution network according to the affine objective function and the operating constraints of the base station backup energy storage; and solving the affine optimal operation model for the distribution network to obtain the affine optimal operation result of the distribution network.

[0006] Second aspect, the present application provides a distribution network affine optimization operation device considering the adjustable potential of base station backup energy storage. The device includes: a determination module, configured to determine the schedulable capacity of the base station backup energy storage at 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; a first construction module, configured to construct an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network; a processing module, configured to construct the operating constraints of the base station backup energy storage according to the schedulable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node; a second construction module, configured to construct a distribution network affine optimization operation model according to the affine objective function and the operating constraints of the base station backup energy storage; and an analysis module, configured to solve the distribution network affine optimization operation model to obtain the distribution network affine optimization operation result.

[0007] The above-mentioned distribution network affine optimization operation method and device considering the adjustable potential of base station backup energy storage determine the schedulable capacity of the base station backup energy storage at 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. Thus, on the basis of considering the node load level of the distribution network node, the evaluation accuracy of the schedulable capacity of the base station backup energy storage can be improved; construct an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network, construct the operating constraints of the base station backup energy storage according to the backup capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node, construct a distribution network affine optimization operation model according to the affine objective function and the operating constraints of the base station backup energy storage, and solve the distribution network affine optimization operation model to obtain the distribution network affine optimization operation result. Thus, on the basis of accurately evaluating the schedulable capacity of the base station backup energy storage, the accuracy of the affine optimization operation of the distribution network can be further improved. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0009] Figure 1 It is a schematic flowchart of a distribution network affine optimization operation method considering the adjustable potential of base station backup energy storage in an embodiment;

[0010] Figure 2 It is a schematic flowchart of a distribution network affine optimization operation method considering the adjustable potential of base station backup energy storage in another embodiment;

[0011] Figure 3 Schematic diagram of the distribution network topology structure in an embodiment;

[0012] Figure 4 Result schematic diagram of the load characteristics of 5G base stations in four functional areas in an embodiment;

[0013] Figure 5 Schematic diagram of the daily variation trends of the output of WTG and PVG and the load power factor in an embodiment;

[0014] Figure 6 Schematic diagram of the node load level weights and reliability indexes of the distribution network in an embodiment;

[0015] Figure 7 Result schematic diagram of the minimum backup power time of different models in an embodiment;

[0016] Figure 8 Result schematic diagram of the schedulable capacity of the 5G base station backup energy storage of different models in an embodiment;

[0017] Figure 9 Result schematic diagram of the real-time output of the 5G base station backup energy storage at each distribution network node in an embodiment;

[0018] Figure 10 Structure block diagram of the affine optimal operation device of the distribution network considering the adjustable potential of the base station backup energy storage in an embodiment;

[0019] Figure 11 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] Currently, for the evaluation method of the adjustable potential of 5G base station backup energy storage, the existing methods do not consider the influence of node load levels. For node loads of different levels, the power supply recovery times are quite different, and the backup power times of 5G base station backup energy storage will also vary. Therefore, by considering the influence of the node loads of the distribution network nodes, a more accurate evaluation model for the schedulable capacity of 5G base station backup energy storage can be constructed. Further, on the basis of accurately evaluating the schedulable capacity of 5G base station backup energy storage, the uncertainty optimal operation of the distribution network can be performed.

[0022] For the distribution network uncertainty optimal operation method considering the regulation of standby energy storage for 5G base stations, which includes probability methods. The existing probability methods take into account the probability characteristics of 5G base station loads, use the multi-scenario method to handle the uncertainties of new energy and 5G base station load powers in the distribution network, and construct a typical scenario set of "source-load". However, this requires a large amount of historical data as support to obtain the distribution characteristics of uncertainty variables, so the application scenarios are limited. The distribution network uncertainty optimal operation method considering the regulation of standby energy storage for 5G base stations also includes non-probability methods, such as robust optimization methods, interval optimization methods, etc., which can be modeled based on the boundary information of uncertainty variables and have higher engineering application value in the case of less historical data; however, the robust optimization method is too conservative in the optimization result in order to ensure the feasibility of the solution in the worst case; and the interval optimization method also faces the problems of conservative optimization results and even no feasible solutions due to the interval expansion phenomenon.

[0023] In view of this, as Figure 1 shown, an affine optimal operation method for a distribution network considering the adjustable potential of standby energy storage for base stations is provided. Taking the application of this method to a server in a distribution network affine optimal operation system as an example for illustration, where the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Specifically, it 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 schedulable capacity of the standby energy storage of the base station at the distribution network node.

[0025] Among them, a base station can be accessed at the distribution network node, and the base station power consumption can include the static power consumption and the maximum dynamic power of the base station at the distribution network node during the target period. For example, the base station can be a 5G base station.

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

[0027] S104. According to the cost information of the distribution network, construct an affine objective function for characterizing the operating cost of the distribution network.

[0028] As an improved type of interval algorithm, the affine algorithm locates the propagation trajectory of uncertain factors through noise elements and realizes the association between different variables in the form of sharing noise elements, which can solve the interval expansion problem caused by the interval algorithm's inability to consider the correlation between variables.

[0029] Specifically, the affine variables satisfy:

[0030]

[0031] Among them, is an affine variable, x0 is the affine center value, and ε k is the noise element k, and its value range is [-1, 1]; x k is the affine fluctuation value; n is the total number of noise elements.

[0032] Specifically, the upper and lower limits of the affine variable respectively satisfy:

[0033]

[0034] Among them, is the upper limit of the affine variable, and x is the lower limit of the affine variable.

[0035] According to the interval change ranges of the loads of each node in the distribution network or the outputs of WTG and PVG, an affine model of the uncertainty of the source-load output can be inversely deduced based on the formulas corresponding to the upper and lower limits of the affine variable, and the affine model of the uncertainty of the source-load output satisfies:

[0036]

[0037] Among them, are respectively the affine forms of the active power load, the active power output of WTG, and the active power output of PVG of the distribution network node i at the target time period t; are respectively the affine center values of the active power load, the active power output of WTG, and the active power output of PVG of the distribution network node i at the target time period t; are respectively the corresponding fluctuation values generated by the active power load, the active power output of WTG, and the active power output of PVG of the distribution network node i at the target time period t affected by the noise element k; ε k is the noise element k, and Ω e and Ω T and Ω ε are respectively the set of distribution network nodes, the set of time periods, and the set of noise elements.

[0038] Combining the above content and considering the influence of the "source-load" uncertainty, the present application can express the affine objective function as an affine form combining the center value and the fluctuation value based on the basic composition form of the affine number.

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

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

[0041] S106. Construct the operating constraints of the base station backup energy storage according to the dispatchable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node.

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

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

[0044] S108. Construct an affine optimal operation model of the distribution network according to the affine objective function and the operating constraints of the base station backup energy storage.

[0045] Among them, the affine objective function is used to characterize the operating cost of the distribution network. In one embodiment, an affine optimal operation model of the distribution network can be constructed according to the operating constraints of the base station backup energy storage under the condition of minimizing the affine objective function.

[0046] S110. Solve the affine optimal operation model of the distribution network to obtain the affine optimal operation result of the distribution network.

[0047] Among them, the constructed affine optimal operation model of the distribution network can be in Core TMSimulations were carried out on a computer with an i5-10500 1.8GHz CPU. Programming was done using the Matlab R2022a platform, and the CPLEX toolbox was called for solving.

[0048] Among them, the affine optimal operation results of the distribution network can include: the cost interval corresponding to the operation cost of the distribution network, the cost center value, the cost fluctuation value, and the actual output of the base station backup energy storage at each distribution network node in the distribution network, etc.

[0049] Based on Figure 1 The method shown, 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, determines the schedulable capacity of the base station backup energy storage at the distribution network node. Thus, on the basis of considering the node load level of the distribution network node, the evaluation accuracy of the schedulable capacity of the base station backup energy storage can be improved; according to the cost information of the distribution network, an affine objective function for characterizing the operation cost of the distribution network is constructed, and based on the backup capacity, the charging power and discharging power of the base station backup energy storage at the distribution network node, the operation constraints of the base station backup energy storage are constructed. According to the affine objective function and the operation constraints of the base station backup energy storage, an affine optimal operation model of the distribution network is constructed, and the affine optimal operation model of the distribution network is solved to obtain the affine optimal operation results of the distribution network. Thus, on the basis of accurately evaluating the schedulable capacity of the base station backup energy storage, the accuracy of the affine optimal 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 period. Specifically, based on the base station power consumption at the distribution network node and the node load level of the distribution network node, determining the schedulable capacity of the base station backup energy storage at the distribution network node (i.e., S102) includes the following steps:

[0051] Step 1: Determine the minimum backup time of the backup energy storage at the distribution network node according to 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 duration.

[0052] Among them, the minimum backup time is 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 time of the backup energy storage at the distribution network node satisfies:

[0054]

[0055] Among them, T i BSrepresents the minimum power backup time of the backup energy storage at distribution network node i, n represents the total number of distribution network nodes, λ i represents the power supply reliability of distribution network node i; w i represents the weight corresponding to the node load level of distribution network node i. In some cases, the weight values for first, second, and third-level loads can be 1, 0.5, and 0.3 respectively; represents the average power backup duration.

[0056] Among them, taking the base station as a 5G base station 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 currently only macro base stations have backup energy storage, therefore, this application can take macro base stations as an example for illustration.

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

[0058]

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

[0060] Among them, the implementation method for determining the reliability of distribution network nodes is not limited, and the following is an example in combination with possible methods.

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

[0062]

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

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

[0065] Specifically, the power backup capacity of the base station's backup energy storage at the distribution network node during the target period satisfies:

[0066]

[0067] where represents the power backup capacity of the backup energy storage at distribution network node i during the target period t.

[0068] Step 3: Determine the schedulable capacity of the base station's backup energy storage at the distribution network node according to the maximum capacity and the power backup capacity of the backup energy storage at the distribution network node.

[0069] Specifically, the schedulable capacity satisfies:

[0070]

[0071] where represents the schedulable capacity of the base station's backup energy storage at distribution network node i during the target period t, represents the maximum capacity of the backup energy storage at distribution network node i.

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

[0073] Taking the base station as a 5G base station as an example, the backup energy storage of the 5G base station can provide power when the power grid fails or the power supply is interrupted, ensuring the continuous operation of the base station and avoiding communication interruption. As the power supply reliability of the urban power grid gradually improves, it leads to the long-term idle of the backup energy storage. Therefore, the idle part of it can be scheduled to improve the operating economy of the distribution network.

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

[0075] Specifically, the first operating constraint is satisfied when the difference between the affine central value of the discharge power and the first operating statistic 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 central value of the discharge power and the first operating statistic 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 charge-discharge state of the backup energy storage at the distribution network node and the rated power of the backup energy storage at the distribution network node, and the first operating statistic value is the statistic value obtained by performing an absolute value operation on the affine fluctuation values of the discharge powers corresponding to each distribution network node.

[0076] For example, taking the first preset value as 0, the first operating constraint is satisfied when:

[0077]

[0078] Among them, Ω e is the set of noise elements, and Ω T is the set of time periods; represents the affine central value of the discharge power of the base station backup energy storage at the distribution network node i during the target time period t; represents the affine fluctuation value of the discharge power of the base station backup energy storage at the distribution network node i affected by the noise element k during the target time period t, that is, the affine fluctuation value of the discharge power corresponding to the distribution network node i; γ i,t characterizes the charge-discharge state of the backup energy storage at the distribution network node i during the target time period t, and γ i,t being 1 represents discharge, and γ i,t being 0 represents charge; P i BS represents the rated power of the backup energy storage at the distribution network node i.

[0079] Specifically, the second operating constraint is satisfied when the difference between the affine central value of the charge power and the second operating statistic 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 central value of the charge power and the second operating statistic 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 that is inversely correlated with the state value corresponding to the charge-discharge state and the rated power, and the second operating statistic value is the statistic value obtained by performing an absolute value operation on the affine fluctuation values of the charge powers corresponding to each distribution network node.

[0080] For example, taking the first preset value as 0, the second operating constraint is satisfied when:

[0081]

[0082] Among them, represents the affine central value of the charge power of the base station backup energy storage at the distribution network node i during the target time period t, Denote the affine fluctuation value of the charging power of the base station backup energy storage at the distribution network node \(i\) during the target period \(t\) affected by the noise element \(k\), that is, the charging power affine fluctuation value corresponding to the distribution network node \(i\).

[0083] Specifically, the third operation constraint is satisfied: the difference between the affine center value of the capacity of the backup energy storage at the distribution network node during the target period and the third operation statistical value is greater than or equal to the backup power capacity of the base station backup energy storage at the distribution network node during the target period and less than or equal to the maximum capacity of the backup energy storage at the distribution network node; the sum of the affine center value of the capacity and the third operation statistical value is greater than or equal to the backup power capacity and less than or equal to the maximum capacity of the backup energy storage; the third operation statistical value is the statistical value after taking the absolute value operation on the capacity affine fluctuation values corresponding to each distribution network node; the difference between the maximum capacity of the backup energy storage and the backup power capacity is equal to the dispatchable capacity.

[0084] Among them, the capacity affine fluctuation value corresponding to the distribution network node refers to the corresponding fluctuation value of the capacity of the backup energy storage at the distribution network node during the target period affected by the noise element. For example, the third operation constraint is satisfied:

[0085]

[0086] Among them, Denote the backup power capacity of the backup energy storage at the distribution network node \(i\) during the target period \(t\), Denote the affine center value of the capacity of the backup energy storage at the distribution network node \(i\) during the target period \(t\); Denote the corresponding fluctuation value of the capacity of the backup energy storage at the distribution network node \(i\) during the target period \(t\) affected by the noise element \(k\), that is, the capacity affine fluctuation value corresponding to the distribution network node; Denote the maximum capacity of the backup energy storage at the distribution network node \(i\), Denote the dispatchable capacity.

[0087] Specifically, the fourth operation constraint is satisfied: the sum of the affine center value of the capacity and the fourth operation statistical value is equal to the affine center value of the capacity of the backup energy storage at the distribution network node during the next period of the target period; the fourth operation statistical value is the difference between the first multiplication value and the first ratio value, the first multiplication value is the product of the charging efficiency of the backup energy storage at the distribution network node and the affine center value of the charging power, and the first ratio value is the ratio of the affine center value of the discharging power to the discharging efficiency.

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

[0089]

[0090] Among them, Denote the affine center value of the capacity of the backup energy storage at node \(i\) of the distribution network in the next time period \((t + 1)\) of the target time period \(t\). Denote the affine center value of the capacity of the backup energy storage at node \(i\) of the distribution network in the target time period \(t\); Denote the charging efficiency of the backup energy storage at node \(i\) of the distribution network.

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

[0092] For example, the fifth operation constraint is satisfied:

[0093]

[0094] Among them, Denote the affine fluctuation value of the capacity of the backup energy storage at node \(i\) of the distribution network in the next time period of the target time period \(t\) affected by the noise element \(k\).

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

[0096] Among them, the electricity purchase cost of the distribution network operator may refer to the cost paid by the distribution network operator for purchasing electric energy from power generation enterprises or other power market entities. The electricity purchase cost of the distribution network operator may refer to the total cost paid by the distribution network operator for purchasing electric energy from power generation enterprises or other power markets to meet the electricity consumption needs of users. The electricity purchase cost includes the electricity purchase cost; the operation subsidy cost of the base station backup energy storage may refer to the subsidy provided by the subsidy institution to support the installation and operation of the energy storage system (such as battery energy storage) in communication base stations; the operation subsidy cost of the base station backup energy storage may refer to the total cost involved in the subsidy provided by the subsidy institution to support the installation and operation of the backup energy storage system in communication base stations. The subsidy cost includes the subsidy cost.

[0097] Specifically, according to the cost information of the distribution network, the implementation method of constructing the affine objective function (i.e., S104) for characterizing the operation cost of the distribution network may include the following steps:

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

[0099] Step 2: Based on the weight coefficient, the first affine statistical value, and the second affine statistical value, construct an affine objective function for characterizing the operation 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 weight coefficient, which is used to achieve the balance between the affine central value and the interval value in the affine objective function; represents the affine central value of the power purchase cost, represents the affine central value of the operation subsidy cost of the base station backup energy storage, represents the affine fluctuation value of the power purchase cost, represents the affine fluctuation value of the operation subsidy cost of the base station backup energy storage, and characterizes the cost fluctuation range caused by the "source-load" uncertainty.

[0103] In one embodiment, the affine central value of the power purchase cost of the distribution network operator can be determined according to the unit power purchase cost of the distribution network in the target period and the affine central value of the active power of the substation in the target period.

[0104] Specifically, the affine central value of the power purchase cost satisfies:

[0105]

[0106] where, represents the affine central value of the power purchase cost, u Sub,t represents the unit power purchase cost of the distribution network in the target period, represents the affine central value of the active power of the substation in the target period, Δt represents the unit time period, and Ω T represents the time period set.

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

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

[0109]

[0110] Among them, represents the affine center value of the operation subsidy cost of the base station backup energy storage, u BS represents the unit subsidy cost of the base station backup energy storage, represents the affine center value of the charging power of the base station backup energy storage at the distribution network node i during the target period, represents the affine center value of the discharging power of the base station backup energy storage at the distribution network node i during the target period, Ω e represents the set of distribution network nodes.

[0111] In one embodiment, the affine fluctuation value of the power purchase cost of the distribution network can be determined according to the unit power purchase cost of the distribution network during the target period, the affine fluctuation value of the active power corresponding to the substation, and the unit time period. Among them, the affine fluctuation value of the active power corresponding to the substation refers to the corresponding fluctuation value of the active power of the substation affected by the noise element during the target period.

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

[0113]

[0114] Among them, represents the affine fluctuation value of the power purchase cost, represents the affine fluctuation value of the active power corresponding to the substation.

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

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

[0117]

[0118] Among them, represents the affine fluctuation value of the operation subsidy cost of the base station backup energy storage, Ω ε represents the set of noise elements.

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

[0120] Step 1: Construct the power balance constraint of the distribution network nodes based on the affine central value of the active power injected by the distribution network nodes during the target period, the affine fluctuation value of the active power affected by the noise elements, the affine fluctuation value of the reactive power injected by the distribution network nodes during the target period, and the corresponding affine fluctuation value of the reactive power.

[0121] Among them, the affine central value of the active power refers to the central value of the active power represented in affine form. Therefore, based on the basic composition form of affine numbers, the power balance constraint of the distribution network nodes can be expressed in an affine form combining the affine central value and the affine fluctuation value. The corresponding affine fluctuation value of the reactive power refers to the corresponding fluctuation value of the reactive power injected by the distribution network nodes during the target period affected by the noise elements.

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

[0123] Specifically, the first balance constraint is satisfied as follows: the difference between the sum of the affine central value of the substation active power injected by the distribution network nodes during the target period, the affine central value of the active power output of wind power generation injected by the distribution network nodes during the target period, the affine central value of the active power output of photovoltaic power generation, and the affine central value of the discharge power, and the first balance statistical value is equal to the affine central value of the active power injected by the distribution network nodes during the target period; the first balance statistical value is the sum of the affine central value of the total power consumption of the base station at the distribution network node during the target period, the affine central value of the charging power, and the affine central value of the active power demand of the load injected by the distribution network nodes during the target period.

[0124] For example, the first balance constraint is satisfied as follows:

[0125]

[0126] Among them, represents the affine central value of the active power injected by distribution network node i during target period t, represents the affine central value of the substation active power injected by distribution network node i during target period t, represents the affine central value of the active power output of wind power generation injected by distribution network node i during target period t, represents the affine central value of the active power output of photovoltaic power generation injected by distribution network node i during target period t, represents the affine central value of the total power consumption of the base station at distribution network node i during target period t, represents the affine central value of the active power demand of the load injected by distribution network node i during target period t.

[0127] Specifically, the second balance constraint is satisfied as follows: the difference between the sum of the affine fluctuation values corresponding to the active power of the substation, the affine fluctuation value corresponding to the active power output of wind power generation, the affine fluctuation value corresponding to the active power output of photovoltaic power generation, 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 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 generated by the active power of the substation injected at 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 generated by the active power output of wind power generation injected at the distribution network node in the target period affected by the noise element.

[0129] For example, the second balance constraint is satisfied as follows:

[0130]

[0131] Among them, represents the corresponding fluctuation value generated by the active power injected by the distribution network node i in the target period t affected by the noise element k, that is, the affine fluctuation value corresponding to the injected active power; represents the affine fluctuation value corresponding to the active power of the substation, represents the affine fluctuation value corresponding to the active power output of wind power generation, represents the affine fluctuation value corresponding to the active power output of photovoltaic power generation, represents the affine fluctuation value corresponding to the total power consumption, represents the affine fluctuation value corresponding to the active power demand of the load.

[0132] Specifically, the third balance constraint is satisfied as follows: the sum of the affine center value of the reactive power of the substation injected by the distribution network node in the target 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 balance constraint is satisfied as follows:

[0134]

[0135] Among them, respectively represent the affine center value of the reactive power injected by the distribution network node i in the target period t, the affine center value of the reactive power of the substation, and the affine center value of the reactive power demand of the load.

[0136] Specifically, the fourth balance constraint is satisfied as follows: 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 balance constraint is satisfied as follows:

[0138]

[0139] Wherein, represents the affine fluctuation value of the reactive power injected by the distribution network node i at the target time period t affected by the noise element k, that is, the affine fluctuation value corresponding to the injected reactive power; respectively represent the affine fluctuation values corresponding to the substation reactive power and the affine fluctuation values corresponding to the load reactive power demand.

[0140] Step 2: For the target distribution network nodes 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 and the affine fluctuation value of the voltage amplitude of the target distribution network node at the target time period, construct the distribution network linearized power flow equation constraint.

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

[0142] Wherein, the affine center value of the voltage amplitude refers to the center value of the voltage amplitude expressed in affine form, and the affine fluctuation value of the voltage amplitude refers to the corresponding fluctuation value of the voltage amplitude affected by the noise element.

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

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

[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 is satisfied as follows:

[0147]

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

[0149] Specifically, the second linearized power flow constraint is satisfied: the difference between the third power flow statistic value and the fourth power flow statistic value is equal to the affine fluctuation value corresponding to the injected active power; the third power flow statistic value is the statistic value of the third correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the third correlation value is the product of the conductance corresponding to the target distribution network node and the affine fluctuation value of the voltage amplitude; the fourth power flow statistic value is the statistic value of the fourth correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the fourth correlation value is the product of the conductance corresponding to the target distribution network node and the affine fluctuation value of the phase angle.

[0150] Among them, the affine fluctuation value of the voltage amplitude corresponding to the target distribution network node refers to the corresponding fluctuation value generated by the voltage amplitude of the target distribution network node being affected by the noise element. The affine fluctuation value of the phase angle corresponding to the target distribution network node refers to the corresponding fluctuation value generated by the phase angle of the target distribution network node being affected by the noise element.

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

[0152]

[0153] Among them, represents the corresponding fluctuation value generated by the active power injected by distribution network node i at the target time period t being affected by noise element k, that is, the affine fluctuation value corresponding to the injected active power; V j,t,k represents the affine fluctuation value of the voltage amplitude of target distribution network node j at the target time period t, that is, the affine fluctuation value of the voltage amplitude corresponding to the target distribution network node; θ j,t,k represents the affine fluctuation value of the phase angle of target distribution network node j at the target time period t, that is, the affine fluctuation value of the phase angle corresponding to the target distribution network node.

[0154] Specifically, the third linearized power flow constraint is satisfied as follows: the difference between the negative of the fifth power flow statistic value and the sixth power flow statistic value is equal to the affine center value of the injected reactive power; the fifth power flow statistic value is the statistic value of the fifth correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the fifth correlation value is the product of the susceptance corresponding to the target distribution network node and the affine center value of the voltage magnitude; the sixth power flow statistic value is the statistic value of the sixth correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the sixth correlation value is the product of the conductance corresponding to the target distribution network node and the affine center value of the phase angle.

[0155] For example, the third linearized power flow constraint is satisfied as follows:

[0156]

[0157] Among them, represents the affine center value of the reactive power injected by the distribution network node i at the target time period t.

[0158] Specifically, the fourth linearized power flow constraint is satisfied as follows: the difference between the negative of the seventh power flow statistic value and the eighth power flow statistic value is equal to the affine fluctuation value corresponding to the injected reactive power; the seventh power flow statistic value is the statistic value of the seventh correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the seventh correlation value is the product of the susceptance corresponding to the target distribution network node and the affine fluctuation value of the voltage magnitude; the eighth power flow statistic value is the statistic value of the eighth correlation values of the respective target distribution network nodes corresponding to the distribution network node, and the eighth correlation value is the product of the conductance corresponding to the target distribution network node and the affine fluctuation value of the phase angle.

[0159] For example, the fourth linearized power flow constraint is satisfied as follows:

[0160]

[0161] Among them, represents the corresponding fluctuation value of the reactive power injected by the distribution network node i at the target time period t affected by the noise element k, that is, the affine fluctuation value corresponding to the injected reactive power.

[0162] Step 3: Construct affine constraint conditions corresponding to the affine objective function according to the distribution network node power balance constraint, the distribution network linearized power flow equation constraint, and the standby energy storage operation constraint.

[0163] Step 4: Construct a distribution network affine optimal operation model according to the affine objective function and the affine constraint conditions.

[0164] In one embodiment, the distribution network affine optimal operation model can be constructed according to the affine constraint conditions under the condition of minimizing the affine objective function.

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

[0166] In one embodiment, according to the power balance constraint of the distribution network nodes, the constraint of the linearized power flow equation of the distribution network, and the operation constraint 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 the current affine fluctuation value of the line in the target period, and the current transmission range of the line, the line current constraint is constructed.

[0168] Among them, the current affine center value refers to the center value of the current of the line in the target period represented in an affine form, and the current affine fluctuation value refers to the corresponding fluctuation value of the current of the line affected by the noise element in the target period.

[0169] In one of the embodiments, the line current constraint satisfies that 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; among them, the current constraint statistical value is the statistical value after taking the absolute value operation of the current affine fluctuation values corresponding to each line.

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

[0171]

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

[0173] Step 2: According to the voltage amplitude affine center value and the voltage amplitude affine fluctuation value corresponding to the distribution network node, and the node voltage deviation range, the voltage deviation constraint is constructed.

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

[0175] In one embodiment, the difference between the affine center value of the voltage amplitude corresponding to the distribution network node and the statistical value of the voltage deviation 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 the distribution network node and the statistical value of the voltage deviation 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 statistical value of the voltage deviation is the statistical value after taking the absolute value operation of the voltage affine fluctuation values corresponding to each distribution network node.

[0176] For example, the voltage deviation constraint is satisfied as follows:

[0177]

[0178] wherein, V i represents the lower limit of the node voltage deviation range, represents the upper limit of the node voltage deviation range; V i,t,0 represents the affine center value of the voltage amplitude of the distribution network node i at the target time period t, that is, the affine center value corresponding to the distribution network node; V i,t,k represents the corresponding fluctuation value of the voltage amplitude of the distribution network node i at the target time period t affected by the noise element, that is, the affine fluctuation value corresponding to the distribution network node.

[0179] Step 3: Construct affine constraint conditions corresponding to the affine objective function according to the distribution network node power balance constraint, the distribution network linearized power flow equation constraint, the line current constraint, the voltage deviation constraint, and the base station backup energy storage operation constraint.

[0180] Among them, the affine constraint conditions include the distribution network node power balance constraint, the distribution network linearized power flow equation constraint, the line current constraint, the voltage deviation constraint, and the base station backup energy storage operation constraint.

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

[0182] Furthermore, a distribution network affine optimal operation model can be constructed according to the distribution network node power balance constraint, the distribution network linearized power flow equation constraint, the line current constraint, the voltage deviation constraint, the base station backup energy storage operation constraint, and the affine objective function.

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

[0184]

[0185] wherein, F0 and F εrespectively represent the cost affine center value and the cost affine fluctuation value. Specifically, the cost affine center value is the sum between the affine center value of the power purchase cost of the distribution network operator and the affine center value of the base station backup energy storage operation subsidy cost paid by the distribution network operator to the communication network operator; the cost affine fluctuation value is the sum between the affine fluctuation value of the power purchase cost of the distribution network operator and the affine fluctuation value of the base station backup energy storage operation subsidy cost paid by the distribution network operator to the communication network operator. Specifically, F0 and F ε respectively satisfy:

[0186]

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

[0188] It can be seen from the above formula that all variables (including decision variables and state variables) in the distribution network affine optimization operation model are composed of a center value and N fluctuation values. Through the inequality constraint h(u0,u k ,v0,v k ) it can be ensured that the affine value formed by the variable center value and the fluctuation value can meet the upper and lower limits constraints of the system operation; through the equality constraint g(u0,v0) = 0 it can be ensured that the center value of the variable meets the operation requirements of the system; through the equality constraint g(u k ,v k ) = 0 the kth fluctuation value generated by the noise element k on all variables of the system can be calculated, so as to trace the propagation trajectory of the uncertainty factor. Thus, by solving the distribution network affine optimization operation model, the distribution network affine optimization operation result can be obtained.

[0189] Combining the above content, taking the base station as a 5G base station as an example, as Figure 2 shown, a distribution network affine optimization operation method considering the adjustable potential of the base station backup energy storage is provided. Taking the application of this method to the server in the 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, determine the minimum power backup time of the backup energy storage of the distribution network node according to 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 power backup duration.

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

[0192] S206. According to the maximum capacity and the power backup capacity of the backup energy storage of the distribution network node, determine the schedulable capacity of the 5G base station backup energy storage of the distribution network node.

[0193] S208. Obtain the cost information of the distribution network, where the cost information includes: the power purchase cost of the distribution network operator, the operation subsidy cost paid by the distribution network operator to the communication network operator for the 5G base station backup energy storage, the power purchase cost of the distribution network operator, and the operation subsidy cost paid by the distribution network operator to the communication network operator for the 5G base station backup energy storage.

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

[0195] S212. Based on the weight coefficient, the first affine statistical value, and the second affine statistical value, construct an affine objective function for characterizing the operation cost of the distribution network.

[0196] S214. According to the affine center value of the active power injected by the distribution network node during the target period and the affine fluctuation value caused by the influence of the noise element on the active power, as well as the affine center value of the reactive power injected by the distribution network node during the target period and the corresponding affine fluctuation value of the reactive power, construct the power balance constraint of the distribution network node.

[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 and the affine fluctuation value of the voltage amplitude of the target distribution network node during the target period, construct the linearized power flow equation constraint of the distribution network.

[0198] S218. Based on the affine center value and the affine fluctuation value of the current of the line during the target period, and the current transmission range of the line, construct the line current constraint.

[0199] S220. According to the affine center value and the affine fluctuation value of the voltage amplitude corresponding to the distribution network node, and the node voltage deviation range, construct the voltage deviation constraint.

[0200] S222. Construct the operating constraints of the 5G base station backup energy storage according to the dispatchable capacity, the charging power and the discharging power of the 5G base station backup energy storage at the distribution network nodes.

[0201] S224. Construct the affine constraint conditions corresponding to the affine objective function according to the distribution network node power balance constraint, the distribution network linearized power flow equation constraint, the line current constraint, the voltage deviation constraint, and the operating constraints of the base station backup energy storage.

[0202] S226. Construct the affine optimal operation model of the distribution network according to the affine objective function and the affine constraint conditions.

[0203] S228. Solve the affine optimal operation model of the distribution network to obtain the affine optimal operation result of the distribution network.

[0204] Among them, the specific content of S202 - S228 can refer to the foregoing content for adaptation description and will not be elaborated here.

[0205] Based on the above content, a modified 33 - node distribution network example is used for testing to verify the effectiveness of the proposed model. The topological structure of the distribution network example is as Figure 3 shown, Figure 3 which shows the access situation of 5G base stations in the office area, commercial area, residential area, and boarding school area. Specifically, distribution network nodes 1 - 5, 14, 15, 19, 20, 23, 26, 29, 32 indicate the base stations (BS) in the office area, distribution network nodes 7, 8, 12, 30, 31 indicate the commercial area BS, distribution network nodes 18 and 25 indicate the boarding school area BS, and the remaining distribution network nodes indicate the residential area BS. W represents the wind turbine, and P represents the photovoltaic. Among them, 6 5G base stations are accessed at each node. Among them, the voltage amplitude range is 0.9 p.u. (per - unit value) to 1.1 p.u., and distribution network nodes 7, 17, 22 are respectively connected to 800 kW, 700 kW, 700 kW of PVG, and distribution network nodes 25, 32 are respectively connected to 800 kW, 900 kW of WTG.

[0206] The communication load of the 5G base station will affect the power consumption per unit duration of the backup energy storage, and the node power supply reliability and the node load level will affect the minimum power backup time of the backup energy storage. Moreover, the communication load fluctuation of the 5G base station has obvious regional characteristics. As Figure 4 shown, a schematic diagram of the daily change trend of the communication load of 5G base stations in four functional areas is provided. From Figure 4It can be seen that the peak-valley characteristics of communication loads in different regions have certain differences and complementarities. By comprehensively considering the impacts of communication loads in different functional areas, the dispatchable power of backup energy storage is increased and decreased during peak and valley load periods respectively, and more dispatch is carried out during valley load periods, so as to fully tap the dispatchable potential of backup energy storage and provide more flexible regulation capabilities for the distribution network. That is, the communication loads in different functional areas are seen Figure 4 , and the access situations of 5G base stations in the office area, commercial area, residential area, and boarding school area are seen Figure 3 .

[0207] As Figure 5 shown, a schematic diagram of the daily change trends of the output powers of WTG and PVG and the load power factor is provided, and Table 1 provides the relevant parameters of 5G base stations. The electricity purchase price refers to the time-of-use electricity price for industrial and commercial users, and the dispatching cost of energy storage in 5G base stations is 0.1 yuan / kWh.

[0208] Table 1 Equipment Parameters of 5G Base Stations

[0209]

[0210] The dispatchable potential of backup energy storage in 5G base stations is related to communication loads, node power supply reliability, and node load levels. As Figure 6 shown, a schematic diagram of the weight of node load levels and reliability indexes in the distribution network is provided. For nodes close to the substation, their reliability indexes are higher; while for nodes far from the substation, their reliability indexes are lower.

[0211] Combining the above content, an evaluation model for the dispatchable potential of backup energy storage in 5G base stations can be configured based on the calculation process of dispatchable capacity. To verify the effectiveness of the evaluation model for the dispatchable potential of backup energy storage in 5G base stations proposed in this application, the model proposed in the literature is used as a comparison. The literature name is Active Distribution Network Cooperative Optimal Dispatching Method Considering Dispatchable Backup Energy Storage and Intelligent Soft Switches in 5G Base Stations. Taking the average power backup time of backup energy storage in 5G base stations of the distribution network as 3 hours as an example, the minimum power backup time of different models is calculated according to relevant indexes as Figure 7 shown. From Figure 7 it can be seen that there are differences in the power backup times of the two models at some nodes. For the model proposed in this application: the higher the node load level and reliability index, the shorter the power backup time of the base station; the lower the node load level and reliability index, the longer the power backup time of the base station. The power backup times of 5G base stations calculated by the two models both fluctuate within 1 - 5h.

[0212] According to the power backup times of 5G base stations of different models and combined with the predicted values of communication loads, the following can be obtained as Figure 8The dispatchable capacity of the backup energy storage of 5G base stations for different models shown. It can be seen from the figure that the dispatchable capacity of 5G base stations changes continuously with the backup power supply time and communication load value of 5G base stations. Among them, the sum of the real-time dispatchable capacity of the model proposed in this application fluctuates between 2383 and 3567 kWh.

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

[0214] Scenario 1: The affine optimization of the distribution network without considering the dispatchable potential of the backup energy storage of 5G base stations.

[0215] Scenario 2: The affine optimization of the distribution network considering the dispatchable potential of the backup energy storage of 5G base stations.

[0216] Set the upper and lower fluctuation ranges of the output of WTG and PVG and the load power to 10%, the weight coefficient of the affine objective function to 0.5, and the correlation between variables to 0.5. The comparison results of the typical daily operation costs of different scenarios obtained through simulation operation are shown in Table 2.

[0217] It can be seen from Table 2 that without considering the dispatchable potential of the backup energy storage of 5G base stations, the central value of the typical daily operation cost of the distribution network is 67,736.3 yuan, and the fluctuation value is 4,982.2 yuan; after considering the dispatchable potential of the backup energy storage of 5G base stations, the central value of the typical daily operation cost 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 the backup energy storage of 5G base stations reduces the system's operation cost and cost fluctuation value through low-cost storage and high-price discharge arbitrage. The result shows that considering the dispatchable potential of the backup energy storage of 5G base stations can greatly improve the operation economy of the distribution network.

[0218] Table 2 Comparison results of typical daily operation costs of different scenarios

[0219] Scenario Scenario 1 Scenario 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] As Figure 9 shown, the real-time output of the backup energy storage of 5G base stations at each distribution network node is provided. Figure 9 The backup energy storage device charges during the low electricity price period (1:00 - 5:00, 22:00 - 24:00) and discharges during the flat and peak electricity price periods, and improves the operation economy of the system through low-cost storage and high-price discharge arbitrage. At the same time, the output of the backup energy storage in some periods is not affected by the uncertainty of "source-load", so the output fluctuation value of the energy storage in these periods is 0.

[0221] To verify the performance of the proposed affine optimization method, based on considering the schedulable potential of the backup energy storage of 5G base stations, the interval optimization method and the affine optimization method are respectively used to solve the system, and the comparison results of the calculation results of different methods are shown in Table 3.

[0222] Table 3 Comparison of calculation results of different methods

[0223] Scenario 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 results 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 can be seen from 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 increased by 0.75% and 60.90% respectively compared with the affine optimization method. This is because the affine optimization can consider the correlation between the "source-load" uncertainty variables, so it has lower conservatism. Among them, the calculation time of the affine optimization method is increased by 4.1 times compared with 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, resulting in a large number of constraint conditions being introduced into the model, so the calculation time increases.

[0227] To analyze the influence of the "source-load" uncertainty on the operation results of the distribution network, the upper and lower fluctuation ranges of the output of WTG and PVG and the load power are respectively set to 0%, 2%, 4%, 6%, 8%, 10%, and the comparison results of the typical daily operating costs under different fluctuation ranges are shown in Table 4. As can be seen from Table 4, as the fluctuation range increases, the cost center value of the distribution network operation also continuously increases. This is because the system needs to reserve more flexibility margins to cope with the "source-load" uncertainty, resulting in a decrease in its schedulable flexibility. This shows that the enhancement of the "source-load" uncertainty will lead to an increase in the typical daily operating cost of the system.

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

[0229] Table 5 Comparison results of typical daily operating costs under different weight coefficients

[0230] Weight 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 can be seen from Table 5, as the weight coefficient increases, the central value of the operation cost of the distribution network decreases, but the cost fluctuation value increases. This is because an increase in the weight coefficient will lead to an increase in the proportion of the central value of the system cost in the objective function, and reducing the central value of the cost is beneficial to minimizing the objective function. Thus, it can be seen that adjusting the weight coefficient can change the conservativeness of the operation mode. When the weight coefficient is 1, the uncertainty will be completely ignored and the optimization will be carried out according to the predicted value.

[0232] As can be seen from the above, the 5G base station standby energy storage schedulable capacity evaluation model constructed in this application can dynamically evaluate the schedulable potential of the standby energy storage of 5G base stations at different locations according to the communication load, node power supply reliability, and node load level at different times, realizing the efficient utilization of standby energy storage resources. Moreover, the affine optimization method provided in this application can effectively improve the operation economy under the "source-load" uncertainty. In addition, by adjusting the weight coefficient, the conservativeness of the operation mode obtained by this method can be changed, which has good adaptability and popularization value.

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

[0234] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed 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 executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0235] Based on the same inventive concept, an embodiment of the present application further provides a distribution network affine optimization operation device for considering the adjustable potential of base station backup energy storage to implement the above-mentioned distribution network affine optimization operation method considering the adjustable potential of base station backup energy storage. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the distribution network affine optimization operation device for considering the adjustable potential of base station backup energy storage provided below can refer to the limitations on the distribution network affine optimization operation method for considering the adjustable potential of base station backup energy storage in the above text, and will not be repeated here.

[0236] In an exemplary embodiment, as Figure 10 shown, a distribution network affine optimization operation device for considering the adjustable potential of base station backup energy storage is provided, including: a determination module 1002, a first construction module 1004, a processing module 1006, a second construction module 1008, and an analysis module 1010. Among them, the determination module 1002 is configured to determine the schedulable capacity of the base station backup energy storage of 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; the first construction module 1004 is configured to construct an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network; the processing module 1006 is configured to construct the operating constraints of the base station backup energy storage according to the schedulable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node; the second construction module 1008 is configured to construct a distribution network affine optimization operation model according to the affine objective function and the operating constraints of the base station backup energy storage; the analysis module 1010 is configured to solve the distribution network affine optimization operation model to obtain the distribution network affine optimization operation result.

[0237] In one of the embodiments, the second construction module 1008 is further configured to construct the power balance constraint of the distribution network node according to the affine central value of the active power injected by the distribution network node in the target period and the affine fluctuation value generated by the active power affected by the noise element, as well as the affine central value of the reactive power injected by the distribution network node in the target period and the affine fluctuation value corresponding to the reactive power; 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 central value of the voltage amplitude and the affine fluctuation value of the voltage amplitude of the target distribution network node in the target period; construct the affine constraint conditions corresponding to the affine objective function according to the power balance constraint of the distribution network node, the linearized power flow equation constraint of the distribution network, and the operating constraints of the backup energy storage; construct the distribution network affine optimization operation model according to the affine objective function and the affine constraint conditions.

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

[0239] In one embodiment, the cost information includes: the power 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 power 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 center value of the power 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 power purchase cost and the affine fluctuation value of the base station backup energy storage operation subsidy cost; construct an affine objective function for characterizing the operating cost of the distribution network based on the weight coefficient, the first affine statistical value, and the second affine statistical value.

[0240] 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 period; the determination module 1002 is further configured to determine the minimum backup time of the backup energy storage of the distribution network node according to 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 duration; perform an integration operation on the static power consumption and the maximum dynamic power consumption based on the minimum backup time to determine the backup capacity of the base station backup energy storage of the distribution network node during the target period; determine the schedulable capacity of the base station backup energy storage of the distribution network node according to the maximum capacity of the backup energy storage and the backup capacity of the distribution network node.

[0241] Each module in the above distribution network affine optimization operation device considering the adjustable potential of the base station backup energy storage can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0242] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 11As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as cost information of the distribution network. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it realizes an affine optimization operation method for a distribution network considering the adjustable potential of base station backup energy storage.

[0243] Those skilled in the art can understand that Figure 11 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0244] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0245] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

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

[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 for analysis, stored data, displayed data, 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 relevant data need to comply with relevant regulations.

[0248] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0249] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in the present application.

[0250] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An affine optimization operation method for a distribution network considering the adjustable potential of backup energy storage in base stations, characterized in that The method includes: For each distribution network node in the distribution network, determining the schedulable capacity of the base station backup energy storage of the distribution network node based on the base station power consumption at the distribution network node and the node load level of the distribution network node; Constructing an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network; Constructing operating constraints for the base station backup energy storage according to the schedulable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node; Constructing an affine optimal operation model for the distribution network according to the affine objective function and the operating constraints of the base station backup energy storage; Solving the affine optimal operation model for the distribution network to obtain the affine optimal operation result of the distribution network.

2. The method according to claim 1, wherein The constructing an affine optimal operation model for the distribution network according to the affine objective function and the operating constraints of the base station backup energy storage includes: Constructing power balance constraints for the distribution network nodes according to the affine central value of the active power injected by the distribution network nodes in the target period and the affine fluctuation value of the active power affected by the noise element, as well as the affine central value of the reactive power injected by the distribution network nodes in the target period and the affine fluctuation value corresponding to the reactive power; For the target distribution network nodes connected to each distribution network node in the distribution network, constructing linearized power flow equation constraints for the distribution network based on the electrical parameters of the line between the two distribution network nodes, the affine central value of the voltage amplitude and the affine fluctuation value of the voltage amplitude of the target distribution network node in the target period; Constructing affine constraint conditions corresponding to the affine objective function according to the power balance constraints of the distribution network nodes, the linearized power flow equation constraints of the distribution network, and the operating constraints of the backup energy storage; Constructing an affine optimal operation model for the distribution network according to the affine objective function and the affine constraint conditions.

3. The method according to claim 2, characterized in that, The constructing affine constraint conditions corresponding to the affine objective function according to the power balance constraints of the distribution network nodes, the linearized power flow equation constraints of the distribution network, and the operating constraints of the backup energy storage includes: Constructing line current constraints based on the affine central value and the affine fluctuation value of the line current in the target period, and the current transmission range of the line; Constructing voltage deviation constraints according to the affine central value and the affine fluctuation value of the voltage amplitude corresponding to the distribution network node, and the node voltage deviation range; Constructing affine constraint conditions corresponding to the affine objective function according to 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 operating constraints of the base station backup energy storage.

4. The method according to claim 2, wherein The power balance constraints of the distribution network nodes include at least one of the first balance constraint, the second balance constraint, the third balance constraint, and the fourth balance constraint; The first balance constraint is satisfied as follows: the difference between the sum of the affine central value of the active power of the substation injected by the distribution network node during the target period, the affine central value of the active power output of wind power generation injected by the distribution network node during the target period, the affine central value of the active power output of photovoltaic power generation, and the affine central value of the discharge power, and the first balance statistical value is equal to the affine central value of the active power injected by the distribution network node during the target period; the first balance statistical value is the sum of the affine central value of the total power consumption of the base station at the distribution network node during the target period, the affine central value of the charging power, and the affine central value of the active power demand of the load injected by the distribution network node during the target period; The second balance constraint is satisfied as follows: the difference between the sum of the affine fluctuation value corresponding to the active power of the substation, the affine fluctuation value corresponding to the active power output of wind power generation, the affine fluctuation value corresponding to the active power output of photovoltaic power generation, 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 active power demand of the load; The third balance constraint is satisfied as follows: the sum of the affine central value of the reactive power of the substation injected by the distribution network node during the target period and the affine central value of the reactive power demand of the load is equal to the affine central value of the reactive power injected by the distribution network node during the target period; The fourth balance constraint is satisfied as follows: 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.

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

6. The method according to claim 3, wherein The line current constraint is satisfied as follows: The difference between 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. The 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 the statistical value after taking the absolute value operation of the current affine fluctuation values corresponding to the respective lines. The voltage deviation constraint is satisfied as follows: The difference between the affine central value of the voltage amplitude corresponding to the 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 central value of the voltage amplitude corresponding to the 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 taking the absolute value operation of the voltage affine fluctuation values corresponding to the respective distribution network nodes.

7. The method according to claim 1, characterized in that, The operating constraints of the base station backup energy storage include at least one of the following constraints: the first operating constraint, the second operating constraint, the third operating constraint, the fourth operating constraint, and the fifth operating constraint; The first operating constraint is satisfied when: the difference between the affine center value of the discharge power and the first operating statistical 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 operating 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 the product of the state value corresponding to the charge-discharge state of the backup energy storage at the target time period of the distribution network node and the rated power of the backup energy storage of the distribution network node; the first operating statistical value is the statistical value obtained by performing an absolute value operation on the affine fluctuation values of the discharge power corresponding to each distribution network node; The second operating constraint is satisfied when: the difference between the affine center value of the charging power and the second operating statistical 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 operating 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 the product of the third target value, which is inversely correlated with the state value corresponding to the charge-discharge state, and the rated power; the second operating statistical value is the statistical value obtained by performing an absolute value operation on the affine fluctuation values of the charging power corresponding to each distribution network node; The third operating constraint is satisfied when: the difference between the affine center value of the capacity of the backup energy storage at the target time period of the distribution network node and the third operating statistical value is greater than or equal to the backup power capacity of the base station backup energy storage at the target time period of the distribution network node and less than or equal to the maximum capacity of the backup energy storage of the distribution network node; the sum of the affine center value of the capacity and the third operating statistical value is greater than or equal to the backup power capacity and less than or equal to the maximum capacity of the backup energy storage; the third operating statistical value is the statistical value obtained by performing an absolute value operation on the affine fluctuation values of the capacity corresponding to each distribution network node; the difference between the maximum capacity of the backup energy storage and the backup power capacity is equal to the dispatchable capacity; The fourth operating constraint is satisfied when: the sum of the affine center value of the capacity and the fourth operating statistical value is equal to the affine center value of the capacity of the backup energy storage at the next time period of the distribution network node at the target time period; the fourth operating statistical value is the difference between the first multiplication value and the first ratio value, the first multiplication value 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 value is the ratio of the affine center value of the discharge power to the discharge efficiency; The fifth operating constraint is satisfied when: the sum of the affine fluctuation value of the capacity corresponding to the distribution network node and the fifth operating statistical value is equal to the affine fluctuation value corresponding to the capacity at the next time period; the fifth operating statistical value is the difference between the second multiplication value and the second ratio value, the second multiplication value is the product of the charging efficiency and the affine fluctuation value corresponding to the charging power, and the second ratio value is the ratio of the affine fluctuation value corresponding to the discharge power to the discharge efficiency.

8. The method according to claim 1, characterized in that, The cost information includes: the electricity purchase cost of the distribution network operator, the operation subsidy cost of the 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 operation subsidy cost of the base station backup energy storage paid by the distribution network operator to the communication network operator; Constructing an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network includes: Determining a first affine statistical value between the affine central value of the electricity purchase cost and the affine central value of the operation subsidy cost of the base station backup energy storage, and a second affine statistical value between the affine fluctuation value of the electricity purchase cost and the affine fluctuation value of the operation subsidy cost of the base station backup energy storage; Based on the weight coefficient, the first affine statistical value and the second affine statistical value, constructing an affine objective function for characterizing the operating cost of the distribution network.

9. The method according to claim 1, characterized in that, 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 period; Determining the schedulable capacity of the base station backup energy storage of the distribution network node based on the base station power consumption at the distribution network node and the node load level of the distribution network node includes: Determining the minimum power backup time of the backup energy storage of the distribution network node according to 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 power backup duration; Based on the minimum power backup time, performing an integration operation on the static power consumption and the maximum dynamic power consumption to determine the power backup capacity of the base station backup energy storage of the distribution network node during the target period; Determining the schedulable capacity of the base station backup energy storage of the distribution network node according to the maximum capacity of the backup energy storage of the distribution network node and the power backup capacity.

10. A distribution network affine optimization operation device considering the adjustable potential of base station backup energy storage, characterized in that The device includes: A determination module, configured to determine the schedulable capacity of the base station backup energy storage of 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; A first construction module, configured to construct an affine objective function for characterizing the operating cost of the distribution network according to the cost information of the distribution network; A processing module, configured to construct an operation constraint of the base station backup energy storage according to the schedulable capacity, the charging power and the discharging power of the base station backup energy storage at the distribution network node; A second construction module, configured to construct an affine optimization operation model of the distribution network according to the affine objective function and the operation constraint of the base station backup energy storage; An analysis module, configured to solve the affine optimization operation model of the distribution network to obtain an affine optimization operation result of the distribution network.

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