5G base station backup energy storage optimal utilization method considering communication load uncertainty

Abstract

The invention provides a 5G base station backup energy storage optimal utilization method considering communication load uncertainty. The method comprises the following steps: S1, constructing a 5G base station load model by considering the communication load uncertainty; s2, constructing a 5G base station backup energy storage power control model in combination with an opportunity constraint method; step S3, utilizing controllable idle resources to construct a 5G base station backup energy storage optimal utilization model considering communication load uncertainty, the model takes minimization of network loss cost as a target, constraint conditions comprise a power system power flow constraint, a power safety constraint, a 5G base station constraint, a distributed photovoltaic constraint and a power balance constraint, and the constraint conditions comprise a power system power flow constraint, a power safety constraint, a 5G base station constraint, a distributed photovoltaic constraint and a power balance constraint. The 5G base station constraint is determined according to a 5G base station load model and a 5G base station backup energy storage power control model, and the model is solved to perform power control on the 5G base station. The backup energy storage idle capacity of the 5G base station can be fully utilized, and voltage fluctuation of the power distribution network can be stabilized.

Description

Technical Field

[0001] The present invention relates to the field of power grid control technology, and in particular to a method for optimizing the utilization of 5G base station backup energy storage taking into account the uncertainty of communication load. Background Art

[0002] The distribution network is connected to a large number of distributed photovoltaic (DPV) and 5G base stations. The high penetration rate of DPV improves the flexible and low-carbon power supply capacity of the distribution network, but its random fluctuation characteristics also bring huge impacts on the voltage of the distribution network. At the same time, the load uncertainty and high energy consumption of 5G base stations are prone to cause problems such as uneven load distribution in the distribution network and high idle rate of backup energy storage. The above problems have brought new challenges to the economic dispatch and power quality of the distribution network. Therefore, it is urgent to develop an optimal utilization method for 5G base station backup energy storage that takes into account the uncertainty of communication load, which is of great significance for improving the utilization value of distributed power sources and 5G base stations and improving voltage quality.

[0003] 5G base station backup energy storage can be deeply integrated into the power demand response system as a flexible resource, and actively promote the balance and efficient operation of the power system through intelligent management and optimized scheduling strategies. However, most existing studies have not considered the impact of communication load uncertainty on the utilization of idle backup energy storage of 5G base stations, resulting in the distribution network's incomplete and conservative utilization of base station backup energy storage systems, making it difficult to fully regulate the idle capacity of 5G base station backup energy storage. Summary of the invention

[0004] In order to solve the problems of voltage fluctuation, source-load mismatch and difficulty in sufficient regulation when a high proportion of distributed power sources and 5G base stations are connected to the distribution network, the present invention provides a method for optimizing the utilization of 5G base station backup energy storage taking into account the uncertainty of communication load.

[0005] In order to solve the above technical problems, the present invention adopts the following technical method: a method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load, comprising:

[0006] Step S1, constructing a 5G base station load model considering the uncertainty of communication load;

[0007] Step S2, constructing a 5G base station backup energy storage power control model in combination with the opportunity constraint method;

[0008] Step S3, using controllable idle resources to build a 5G base station backup energy storage optimization utilization model that takes into account the uncertainty of communication load, and performing power control on the 5G base station;

[0009] The objective function of the 5G base station backup energy storage optimization utilization model is as follows:

[0010] minC=C LIN (1)

[0011]

[0012] In the formula, minC represents the minimum cost; C LIN is the distribution network loss cost; N BUS Indicates the number of nodes in the distribution network; c LIN , is the unit network loss price at time t; I ij is the branch current between node i and node j; r ij is the resistance of the branch between node i and node j;

[0013] The constraints of the 5G base station backup energy storage optimization utilization model include: power system flow constraints, power safety constraints, 5G base station constraints, distributed photovoltaic constraints, and power balance constraints; wherein, the 5G base station constraints are determined based on the 5G base station load model and the 5G base station backup energy storage power control model.

[0014] Furthermore, the expression of the 5G base station load model is as follows:

[0015]

[0016] P D,i,t =βP D,max (4)

[0017] Where P 5G,BS,t is the total load of the 5G base station at time t; ε represents the working status of the 5G base station. When ε is 1, the 5G base station is in an active state, and when it is 0, the 5G base station is in a sleep state; P ACT,i,t , P SLE,i,t are the activation state load and sleep state load of the i-th 5G base station at time t respectively; P S,i,t is the static load of the i-th 5G base station at time t; P D,i,t represents the communication load of the i-th 5G base station at time t, which is a dynamic load; α is the load scale factor of the i-th 5G base station; β is the coefficient reflecting the communication data of mobile users; P D,max It represents the predicted maximum value of the dynamic load of the 5G base station, which is the RF output power corresponding to the active antenna unit in the 5G base station communication device when the mobile user communication load is predicted.

[0018] Furthermore, in step S2, the communication load samples are first processed by the opportunity constraint method, and then the capacity is configured with reference to the load under full load to determine the 5G base station backup energy storage capacity model. Finally, the active power is constrained by the maximum charge and discharge capacity of the converter of the 5G base station backup energy storage device, and the 5G base station backup energy storage capacity is constrained not to exceed the limit in the entire cycle and the backup energy storage capacity is equal in the first and last time periods of the cycle to determine the 5G base station backup energy storage power control model.

[0019] Furthermore, in step S2, the communication load sample is processed as follows using the opportunity constraint method:

[0020] 1) Input the original data of 5G base station communication load and use the Latin hypercube sampling method to generate N communication load uncertainty samples;

[0021] 2) Using the synchronous back-substitution method of probability distance, the uncertainty samples generated in 1) are reduced and superimposed to n, and a set of uncertainty sample values ​​of 5G base station communication load is obtained;

[0022] 3) The chance constraint method based on sample mean approximation is used to relax the upper and lower soft constraints of the communication load into the following formula (5), and then the actual distribution of the uncertainty is approximated into the following formula (6) using the empirical distribution of the uncertainty;

[0023] Pr{0≤P D,i,t ≤P D,i}≥ε L (5)

[0024]

[0025] In the formula, Pr{} represents the probability of a random event; P D,i represents the uncertainty sample value of the communication load of the i-th 5G base station; ε L represents the confidence level that the communication load constraint needs to meet; D,i,t represents the opportunity constraint failure indicator function, when z D,i,t When it is 0, it means that the opportunity constraint fails. D,i,t When it is 1, it means that the opportunity constraint has not failed; M represents a very large number, which is 10 4 ; N represents the number of 5G base station communication load samples.

[0026] Furthermore, in step S2, the capacity is configured with reference to the load under full load, and the 5G base station backup energy storage capacity is divided into two parts, one part is used to ensure high reliability power supply of the 5G base station, and the other part is the dispatchable capacity that can participate in the distribution network demand response, thereby determining the expression of the 5G base station backup energy storage capacity model as follows:

[0027]

[0028] In the formula, S SUR,t The safe backup power capacity required by the 5G base station in period t; T res,min The shortest backup time for 5G base stations; S REM,t S is the idle capacity of the 5G base station backup energy storage in period t; 5G It is the total capacity of backup energy storage for 5G base stations.

[0029] Furthermore, in step S2, the process of determining the 5G base station backup energy storage power control model is as follows:

[0030] First, the maximum charge and discharge capacity of the converter of the 5G base station backup energy storage device is used to constrain the active power, and the following is obtained:

[0031]

[0032] In the formula, express or It is the active charging flag of the backup energy storage of the 5G base station, which is a variable of 0 or 1. When the flag is 0, no charging is performed, and when the flag is 1, charging is performed; is the active discharge flag of the 5G base station backup energy storage, which is a variable of 0 or 1. When the flag is 0, no discharge is performed, and when the flag is 1, discharge is performed; P t 5G,ch / dis is the active power absorbed or released by the energy storage battery at time t; P t 5G,ch / dis The minimum value of P t 5G,ch / dis The maximum value of

[0033] Then, assuming that the backup energy storage capacity of the 5G base station does not exceed the limit during the entire cycle and the backup energy storage capacity at the beginning and end of the cycle is equal, we get:

[0034]

[0035] is the actual operating capacity of the 5G base station backup energy storage in period t; δ is the self-discharge rate of the 5G base station backup energy storage; They are the charging and discharging efficiency of 5G base station backup energy storage; They are the maximum and minimum actual operating capacities of 5G base station backup energy storage; They are the actual operating capacity of 5G base station backup energy in the initial and final periods of the scheduling cycle.

[0036] Furthermore, the power system flow constraints are as follows:

[0037]

[0038] In the formula, p j ,q j are the active and reactive injected powers of node j respectively; P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from the previous node i to node j; I ij is the branch current between node i and node j; r ij 、x ij is the resistance and reactance of the branch between node i and node j, which is a constant; g j 、b j is the conductance and susceptance of node j to ground, which are constants; V i 、V j are the voltages of nodes i and j respectively;

[0039] The power security constraints are as follows:

[0040]

[0041] In the formula, I ij.max ,I ij.min are the upper and lower limits of the current in the branch between node i and node j respectively; P t grid , They are the interactive active and reactive power of the upper power grid respectively; They are the maximum and minimum interactive active powers allowed to pass through the connecting branch between the distribution network and the upper-level power grid; are the maximum and minimum reactive interaction powers allowed to pass through the distribution network and the upper grid connection branch; V j.max 、V j.min are the upper and lower limits of the voltage at node j respectively; is the square of the branch current between node i and node j; is the square of the voltage at node j;

[0042] The 5G base station constraints are as shown in formulas (3)-(10);

[0043] The distributed photovoltaic constraints are as follows:

[0044] P DPV,j,t =P DPV,PRE,j,t (13)

[0045] Where P DPV,j,t , P DPV,PRE,j,tare the actual and predicted outputs of the PV power station installed at node j in period t, respectively;

[0046] The power balance constraints are as follows:

[0047]

[0048] Where P IN , Q IN are the sum of active power and reactive power injected into each node of the distribution network; P load , Q load are respectively the total active load and the total reactive load; P 5G.BS represents the sum of the total load of 5G base stations at each time; P GRI Represents the total amount of active power purchased by the distribution network, which is determined by P in each period. t grid Add together to get; Q GRI is the total amount of reactive power purchased by the distribution network, which is determined by the Add together to get; P 5G is the total charge and discharge amount of the 5G base station backup energy storage battery, which is the active power P absorbed in each period t 5G,ch And the released active power P t 5G,dis Add together.

[0049] Preferably, in step S3, a CPLEX solver is used to solve the 5G base station backup energy storage optimization utilization model, and power control of the 5G base station is performed according to the solution result.

[0050] The 5G base station backup energy storage optimization utilization method considering the uncertainty of communication load proposed in the present invention can make full use of the idle capacity of the 5G base station backup energy storage and smooth the voltage fluctuation of the distribution network. Specifically, the present invention takes into account the uncertainty factor of the communication load, and adjusts the accuracy of the 5G base station backup energy storage power control decision by increasing the opportunity constraints related to the communication load and according to the parameters such as the probability of failure of the communication load, so that the risk preference of the decision maker can be better reflected, and at the same time, the actual available capacity of the backup energy storage can be more fully and accurately excavated, so that more idle energy storage can be fully utilized and participate in the distribution network regulation to smooth the voltage fluctuation of the distribution network. In addition, the present invention uses the 5G base station backup energy storage to absorb active power at noon when the distributed power source is high, and emits active power at night when the load demand is high, so as to realize the distributed power supply to other time periods, thereby solving the source-load mismatch problem at the time level. Not only that, the present invention has significant loss reduction and economic optimization effects, and can effectively ensure the safety and economy of the distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1It is a flow chart of a method for optimizing the utilization of backup energy storage of a 5G base station considering the uncertainty of communication load of the present invention;

[0052] Figure 2 is a flow chart of communication load sample processing in the present invention;

[0053] Figure 3 is a topological diagram of an IEEE 33-node distribution network in an embodiment of the present invention;

[0054] Figure 4 is a load and DPV output prediction data diagram in an embodiment of the present invention;

[0055] Figure 5 is a schematic diagram of a communication load uncertainty sample in an embodiment of the present invention;

[0056] Figure 6 Schematic diagram of node voltage distribution in scenario 1 according to an embodiment of the present invention;

[0057] Figure 7 Schematic diagram of node voltage distribution in scenario 2 according to an embodiment of the present invention;

[0058] Figure 8 Schematic diagram of node voltage distribution in scenario 3 in an embodiment of the present invention;

[0059] Fig. 9 This is a comparison chart of the remaining capacity of the 5G base station backup energy storage of node 6 in various scenarios in an implementation manner of the present invention. DETAILED DESCRIPTION

[0060] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0061] like Figure 1 As shown, a method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load includes:

[0062] Step S1, construct a 5G base station load model considering the uncertainty of communication load.

[0063] The 5G base station communication device mainly includes the active antenna unit (Active Antenna System, AAU) and the indoor baseband processing unit (Building Base band Unite, BBU). More than 80% of the communication load comes from the AAU, which is very sensitive to the intensity of the communication load, and its power consumption is directly proportional to the number of connected mobile users. The BBU load mainly comes from the baseband board, which is less affected by mobile users and is relatively stable. In view of this, the basic load of the 5G base station can be roughly divided into static load and dynamic load. Among them, the static power consumption is the baseline power consumption of the BBU, transmission equipment and AAU, which is unrelated to the access mobile signal load and is the basic load to maintain the normal operation of the 5G base station. The dynamic power consumption is the incremental signal power consumption of the AAU, and the base station load is linearly related to the business traffic. The linear equation of the 5G base station communication load is approximately expressed as:

[0064]

[0065] P D,i,t =βP D,max (4)

[0066] Where P 5G,BS,t is the total load of the 5G base station at time t; ε represents the working status of the 5G base station. When ε is 1, the 5G base station is in an active state, and when it is 0, the 5G base station is in a sleep state; P ACT,i,t , P SLE,i,t are the activation state load and sleep state load of the i-th 5G base station at time t, respectively, S,i,t is the static load of the i-th 5G base station at time t, P D,i,t represents the communication load of the i-th 5G base station at time t, which is a dynamic load. The active state load is calculated from the mobile communication load. The sleep state load is a standard value. The sleep state load of a 5G base station is 3-5W. P S,i,t For 100W, P D,i,t Calculated by formula (4); α is the load scale factor of the i-th 5G base station; β is the coefficient reflecting the communication data of mobile users; P D,max It represents the predicted maximum value of the dynamic load of the 5G base station, which is the RF output power corresponding to the active antenna unit in the 5G base station communication device when the mobile user communication load is predicted.

[0067] Step S2, construct a 5G base station backup energy storage power control model in combination with the opportunity constraint method.

[0068] S201, such as Figure 2 As shown, the opportunity constraint method is used to process the communication load samples to reduce the failure probability of communication data, improve the accuracy of decision-making risks, and ensure the accuracy of subsequent capacity calculations.

[0069] Due to the volatility and randomness of mobile user access, it is assumed that the error of 5G base station communication load samples obeys the normal distribution. The original data of 5G base station communication load is input, and the Latin hypercube sampling method (LHS) is used to generate N uncertainty samples.

[0070] In order to improve the solution efficiency, the probabilistic distance synchronous regression method (PD-SRM) is used to reduce and superimpose the generated uncertainty samples to n. Specifically, the Euclidean distance is first used to calculate the probability distance between N communication load uncertainty samples, and then backward elimination is performed to determine and eliminate the shortest distance error samples, accumulate the sample probability distance, and then update the probability distance to determine whether the reduction superposition number n is met. If not, jump to the step of calculating the probability distance between each sample and repeat the above operation. If it is satisfied, the representative sample set can be determined. In this embodiment, n is 100, that is, the obtained 5G base station communication load uncertainty sample value set is P. D = {P D,1 ,P D,,2 ,…,P D,100}.

[0071] The chance constraint method based on sample mean approximation is used to relax the upper and lower soft constraints of the communication load into equation (5), and then the actual distribution of the uncertainty is approximated into equation (6) using the empirical distribution of the uncertainty.

[0072] Pr{0≤P D,i,t ≤P D,i}≥ε L (5)

[0073]

[0074] In the formula, Pr{} represents the probability of a random event; P D,i represents the uncertainty sample value of the communication load of the i-th 5G base station; ε L represents the confidence level that the communication load constraint needs to meet; D,i,t represents the opportunity constraint failure indicator function, when z D,i,t When it is 0, it means that the opportunity constraint fails. D,i,t When it is 1, it means that the opportunity constraint has not failed; M represents a very large number, which is 10 4 ; N represents the number of 5G base station communication load samples.

[0075] S202, determine the 5G base station backup energy storage safety backup capacity model.

[0076] The capacity is configured with reference to the load under full load, and the 5G base station backup energy storage capacity is divided into two parts. One part is used to ensure the high reliability power supply of the 5G base station, and the other part is the dispatchable capacity that can participate in the distribution network demand response. The expression of the 5G base station backup energy storage capacity model is determined as follows:

[0077]

[0078] In the formula, S SUR,t The safe backup power capacity required by the 5G base station in period t; T res,min The shortest backup time for 5G base stations; S REM,t S is the idle capacity of the 5G base station backup energy storage in period t; 5G It is the total capacity of backup energy storage for 5G base stations.

[0079] S203, determine the 5G base station backup energy storage power control model.

[0080] In order to fully reflect the flexibility of the backup energy storage device, the active power is constrained by the maximum charge and discharge capacity of the converter, and the following is obtained:

[0081]

[0082] In the formula, express or It is the active charging flag of the backup energy storage of the 5G base station, which is a variable of 0 or 1. When the flag is 0, no charging is performed, and when the flag is 1, charging is performed; is the active discharge flag of the 5G base station backup energy storage, which is a variable of 0 or 1. When the flag is 0, no discharge is performed, and when the flag is 1, discharge is performed; P t 5G,ch / dis is the active power absorbed or released by the energy storage battery at time t; P t 5G,ch / dis The minimum value of P t 5G,ch / dis The maximum value of .

[0083] The backup energy storage capacity of 5G base stations is directly related to the charging and discharging power. At the same time, it is necessary to ensure that the backup energy storage capacity does not exceed the limit during the entire cycle, and the backup energy storage capacity at the beginning and end of the cycle is equal, resulting in:

[0084]

[0085] is the actual operating capacity of the 5G base station backup energy storage in period t; δ is the self-discharge rate of the 5G base station backup energy storage; They are the charging and discharging efficiency of 5G base station backup energy storage; They are the maximum and minimum actual operating capacities of 5G base station backup energy storage; They are the actual operating capacity of 5G base station backup energy in the initial and final periods of the scheduling cycle.

[0086] Step S3, using controllable idle resources to build a 5G base station backup energy storage optimization utilization model that takes into account the uncertainty of communication load, and perform power control on the 5G base station.

[0087] The objective function of the 5G base station backup energy storage optimization utilization model is determined as follows:

[0088] minC=C LIN (1)

[0089]

[0090] In the formula, minC represents the minimum cost; C LIN is the distribution network loss cost; N BUS Indicates the number of nodes in the distribution network; c LIN , is the unit network loss price at time t; I ij is the branch current between node i and node j; r ij is the resistance of the branch between node i and node j;

[0091] Determine the constraints of the optimal utilization model of 5G base station backup energy storage.

[0092] 1) Power system flow constraints are as follows:

[0093]

[0094] In the formula, p j ,q j are the active and reactive injected powers of node j respectively; P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from the previous node i to node j; I ij is the branch current between node i and node j; r ij 、x ij is the resistance and reactance of the branch between node i and node j, which is a constant; g j 、b j is the conductance and susceptance of node j to ground, which are constants; V i 、V j are the voltages at nodes i and j respectively.

[0095] 2) Power safety constraints, as follows:

[0096]

[0097] In the formula, I ij.max ,I ij.min are the upper and lower limits of the current in the branch between node i and node j respectively; P t grid , They are the interactive active and reactive power of the upper power grid respectively; They are the maximum and minimum interactive active powers allowed to pass through the connecting branch between the distribution network and the upper-level power grid; are the maximum and minimum reactive interaction powers allowed to pass through the distribution network and the upper grid connection branch; V j.max 、V j.min are the upper and lower limits of the voltage at node j respectively; is the square of the branch current between node i and node j; is the square of the voltage at node j;

[0098] 3) 5G base station constraints

[0099] The distribution network dispatching model takes into account the volatility and randomness of mobile user access, and uses the opportunity constraint method to constrain the base station communication load. The 5G base station communication load model is as follows: (3) to (6). The dispatchable capacity of the 5G base station backup energy storage calculated by equation (7) enables full utilization of the flexible resources of the base station. Therefore, the constraints that need to be considered in the 5G base station backup energy storage modeling are equations (3) to (10).

[0100] 4) Distributed photovoltaic (DPV) constraints

[0101] In actual power grids, most DPVs are uncontrollable active power and controllable reactive power resources, and the active output is the predicted output of DPV.

[0102] P DPV,j,t =P DPV,PRE,j,t (13)

[0103] Where P DPV,j,t , P DPV,PRE,j,t are the actual and predicted outputs of the PV power station installed at node j in period t, respectively.

[0104] 5) Power balance constraints

[0105]

[0106] Where P IN , Q INare the sum of active power and reactive power injected into each node of the distribution network; P load , Q load are respectively the total active load and the total reactive load; P 5G.BS represents the sum of the total load of 5G base stations at each time; P GRI Represents the total amount of active power purchased by the distribution network, which is determined by P in each period. t grid Add together to get; Q GRI is the total amount of reactive power purchased by the distribution network, which is determined by the Add together to get; P 5G is the total charge and discharge amount of the 5G base station backup energy storage battery, which is the active power P absorbed in each period t 5G,ch And the released active power P t 5G,dis Add together.

[0107] The 5G base station backup energy storage optimization utilization model involved in the present invention is a distribution network economic dispatch model, which is a mixed integer second-order cone programming. The CPLEX solver is directly used to solve the model, and the power control of the 5G base station is implemented according to the solution result to make full use of the idle capacity of the 5G base station backup energy storage and smooth the voltage fluctuations of the distribution network.

[0108] In order to verify the effectiveness and superiority of the method involved in the present invention, this embodiment is based on Figure 3 Taking the distribution network system shown in the figure as an example, three distribution network control methods are set to formulate 5G base station backup energy storage power control strategy and distribution network flow control.

[0109] Scenario 1: Using the traditional distribution network coordination optimization method that does not consider the optimal utilization of 5G base station backup energy storage.

[0110] Scenario 2: Using the traditional method of optimizing the utilization of 5G base station backup energy storage without considering the uncertainty of communication load.

[0111] Scenario 3: Using the method of the present invention, a method for optimizing the backup energy storage utilization of 5G base stations considering the uncertainty of communication load.

[0112] like Figure 3 As shown in Figure 1, nodes 4, 11, 16, 22, and 32 are connected to DPV, and nodes 6, 15, 19, and 28 are connected to the 5G base station system. Each device is numbered with Arabic numerals according to the order of access. The 24-hour load and DPV predicted total output curve is shown in Figure 1. Figure 4 The parameters involved in this implementation are shown in Tables 1 and 2. The uncertainty sample of 5G base station communication load is shown in Figure 5 shown.

[0113] Table 1 DPV grid-connected inverter capacity at each node

[0114] Access node number Grid-connected inverter capacity / (kVA) 4 1250 11 800 16 1320 22 1050 32 750

[0115] Table 2 Test parameters

[0116]

[0117] Table 3 is a comparison of the various costs and comprehensive costs of the three scenarios under IEEE 33 nodes. The comprehensive cost is composed of network loss costs and base station subsidies. It is worth mentioning that base station subsidies can reflect the degree of participation of 5G base stations in scheduling. Therefore, the comprehensive costs of each scenario are compared in this embodiment. In terms of network loss optimization, the network loss value under the action of the method of the present invention is the smallest, which is reduced by 34.65% and 11.76% compared with scenario 1 and scenario 2, respectively; in terms of comprehensive operating costs, the comprehensive cost of the method of the present invention is reduced by 7.83% and 3.77% compared with scenario 1 and scenario 2, respectively. Therefore, the method proposed in the present invention has obvious loss reduction and economic optimization effects, and can effectively ensure the safe and economic operation of the distribution network.

[0118] Table 3 Results of each scenario

[0119]

[0120]

[0121] Table 4 shows the calculation results of voltage distribution under various scenarios. Figures 6 to 8 The voltage distribution diagram of each scenario. In scenario 1, the maximum value of the distribution network voltage is 1.1, the minimum value is 0.981, and the overall voltage deviation is 35.84, which does not meet the national standard "Allowable Deviation of Power Quality Supply Voltage" GB 12325-2008: The allowable deviation of the power supply voltage of 10kV and below is ±7% of the rated voltage. Therefore, some voltages are over-limit in scenario 1, and the voltage deviation rate has exceeded the allowable range of the national standard. In the traditional method, a more conservative power coordination control is performed on DPV and 5G base stations, and the maximum value of the distribution network voltage is reduced to 1.024, the minimum value is 0.982, and the overall voltage deviation is reduced by 72.74% compared with scenario 1. In the method involved in the present invention, the backup energy storage of the 5G base station is used to absorb part of the DPV output and buffer the increase in the distribution network voltage during the high-incidence stage of DPV. At the same time, the remaining capacity of the backup energy storage of the DPV and 5G base station is fully tapped for reactive power compensation. The overall voltage deviation of the distribution network in this method is 5.97, which is 83.34% and 38.89% lower than that in scenario 1 and scenario 2, respectively, and meets the ±7% range specified by the national standard.

[0122] Table 4 Voltage distribution in each scenario

[0123] Scenario Maximum voltage / pu Minimum voltage / pu Overall voltage deviation / pu 1 1.1 0.981 35.84 2 1.024 0.981 9.77 3 1.012 0.982 5.97

[0124] Fig. 9 This is the comparison of the remaining capacity of the 5G base station backup energy storage at node 6 in various scenarios. Fig. 9 It can be seen that the coordinated optimization of the distribution network in scenario 1 does not use the backup energy storage of 5G base stations. The calculated remaining capacity is the idle capacity of the backup energy storage of 5G base stations under the premise of safe backup power, that is, the dispatchable capacity. Scenario 2 conservatively uses the remaining capacity of the backup energy storage of 5G base stations, and the remaining capacity is not fully utilized in some periods. The remaining capacity result of scenario 3 is the lowest. This is because scenario 3 takes into account the uncertainty of communication load and can obtain a more accurate dispatchable capacity of the backup energy storage of 5G base stations. More idle energy storage is involved in the regulation of the distribution network, so it can be fully and effectively utilized.

[0125] The above embodiments are preferred implementation schemes of the present invention. In addition, the present invention may also be implemented in other ways. Any obvious replacement without departing from the concept of the present technical solution is within the protection scope of the present invention.

[0126] In order to make it easier for ordinary technicians in the field to understand the improvements of the present invention over the prior art, some drawings and descriptions of the present invention have been simplified, and for the sake of clarity, some other elements are omitted in this application document. Ordinary technicians in the field should realize that these omitted elements may also constitute the content of the present invention.

Claims

1. A method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load, characterized in that: include: Step S1, constructing a 5G base station load model considering the uncertainty of communication load; Step S2, constructing a 5G base station backup energy storage power control model in combination with the opportunity constraint method; Step S3, using controllable idle resources to build a 5G base station backup energy storage optimization utilization model that takes into account the uncertainty of communication load, and performing power control on the 5G base station; The objective function of the 5G base station backup energy storage optimization utilization model is as follows: minC=C LIN (1) In the formula, minC represents the minimum cost; C LIN is the distribution network loss cost; N BUS Indicates the number of nodes in the distribution network; c LIN , is the unit network loss price at time t; I ij is the branch current between node i and node j; r ij is the resistance of the branch between node i and node j; The constraints of the 5G base station backup energy storage optimization utilization model include: power system flow constraints, power safety constraints, 5G base station constraints, distributed photovoltaic constraints, and power balance constraints; wherein, the 5G base station constraints are determined based on the 5G base station load model and the 5G base station backup energy storage power control model.

2. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 1 is characterized in that: The expression of the 5G base station load model is as follows: P D,i,t =βP D,max (4) Where P 5G,BS,t is the total load of 5G base stations at time t; ε represents the working status of the 5G base station. When ε is 1, the 5G base station is in an active state; when it is 0, the 5G base station is in a sleep state; P ACT,i,t , P SLE,i,t are the activation state load and sleep state load of the i-th 5G base station at time t respectively; P S,i,t is the static load of the i-th 5G base station at time t; P D,i,t represents the communication load of the i-th 5G base station at time t, which is a dynamic load; α is the load scaling factor of the i-th 5G base station; β is the coefficient reflecting the communication data of mobile users; P D,max It represents the predicted maximum value of the dynamic load of the 5G base station, which is the RF output power corresponding to the active antenna unit in the 5G base station communication device when the mobile user communication load is predicted.

3. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 2 is characterized in that: In step S2, the communication load samples are first processed by the opportunity constraint method, and then the capacity is configured with reference to the load under full load to determine the 5G base station backup energy storage capacity model. Finally, the active power is constrained by the maximum charge and discharge capacity of the converter of the 5G base station backup energy storage device, and the 5G base station backup energy storage capacity is constrained not to exceed the limit in the entire cycle and the backup energy storage capacity at the beginning and end of the cycle is equal. The 5G base station backup energy storage power control model is determined.

4. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 3 is characterized in that: In step S2, the communication load sample is processed as follows using the opportunity constraint method: 1) Input the original data of 5G base station communication load and use the Latin hypercube sampling method to generate N communication load uncertainty samples; 2) Using the synchronous back-substitution method of probability distance, the uncertainty samples generated in 1) are reduced and superimposed to n, and a set of uncertainty sample values ​​of 5G base station communication load is obtained; 3) The chance constraint method based on sample mean approximation is used to relax the upper and lower soft constraints of the communication load into the following formula (5), and then the actual distribution of the uncertainty is approximated into the following formula (6) using the empirical distribution of the uncertainty; Pr{0≤P D,i,t ≤P D,i }≥ε L (5) In the formula, Pr{} represents the probability of a random event; P D,i represents the uncertainty sample value of the communication load of the i-th 5G base station; ε L represents the confidence level that the communication load constraint needs to meet; D,i,t represents the opportunity constraint failure indicator function, when z D,i,t When it is 0, it means that the opportunity constraint fails. D,i,t When it is 1, it means that the opportunity constraint has not failed; M represents a very large number, which is 10 4 ; N represents the number of 5G base station communication load samples.

5. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 4 is characterized in that: In step S2, the capacity is configured with reference to the load under full load, and the 5G base station backup energy storage capacity is divided into two parts, one part is used to ensure high reliability power supply of the 5G base station, and the other part is the dispatchable capacity that can participate in the distribution network demand response, thereby determining the expression of the 5G base station backup energy storage capacity model as follows: In the formula, S SUR,t The safe backup power capacity required by the 5G base station in period t; T res,min The shortest backup time for 5G base stations; S REM,t S is the idle capacity of the 5G base station backup energy storage in period t; 5G It is the total capacity of backup energy storage for 5G base stations.

6. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 5 is characterized in that: The process of determining the 5G base station backup energy storage power control model in step S2 is as follows: First, the maximum charge and discharge capacity of the converter of the 5G base station backup energy storage device is used to constrain the active power, and the following is obtained: In the formula, express or It is the active charging flag of the backup energy storage of the 5G base station, which is a variable of 0 or 1. When the flag is 0, no charging is performed, and when the flag is 1, charging is performed; It is the active discharge flag of the backup energy storage of the 5G base station, which is a variable of 0 or 1. When it is 0, it does not discharge, and when it is 1, it discharges. is the active power absorbed or released by the energy storage battery at time t; for The minimum value of for The maximum value of Then, assuming that the backup energy storage capacity of the 5G base station does not exceed the limit during the entire cycle and the backup energy storage capacity at the beginning and end of the cycle is equal, we get: is the actual operating capacity of the 5G base station backup energy storage in period t; δ is the self-discharge rate of the 5G base station backup energy storage; They are the charging and discharging efficiency of 5G base station backup energy storage; They are the maximum and minimum actual operating capacities of 5G base station backup energy storage; They are the actual operating capacity of 5G base station backup energy in the initial and final periods of the scheduling cycle.

7. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 6 is characterized in that: The power system flow constraints are as follows: In the formula, p j ,q j are the active and reactive injected powers of node j respectively; P jk , Q jk are the active and reactive power flowing from node j to the next node k; P ij , Q ij are the active and reactive power flowing from the previous node i to node j respectively; I ij is the branch current between node i and node j; r ij 、x ij is the resistance and reactance of the branch between node i and node j, which is a constant; g j , b j is the conductance and susceptance of node j to ground, which are constants; V i 、V j are the voltages of nodes i and j respectively; The power security constraints are as follows: In the formula, I ij.max ,I ij.min are the upper and lower limits of the current in the branch between node i and node j respectively; They are the interactive active and reactive power of the upper power grid respectively; They are the maximum and minimum interactive active powers allowed to pass through the connecting branch between the distribution network and the upper-level power grid; are the maximum and minimum reactive interaction powers allowed to pass through the connection branch between the distribution network and the upper power grid; V j.max 、V j.min are the upper and lower limits of the voltage at node j respectively; is the square of the branch current between node i and node j; is the square of the voltage at node j; The 5G base station constraints are as shown in formulas (3)-(10); The distributed photovoltaic constraints are as follows: P DPV,j,t =P DPV,PRE,j,t (13) Where P DPV,j,t , P DPV,PRE,j,t are the actual and predicted outputs of the PV power station installed at node j in period t, respectively; The power balance constraints are as follows: Where P IN , Q IN are the sum of active power and reactive power injected into each node of the distribution network; P load , Q load are respectively the total active load and the total reactive load; P 5G.BS represents the sum of the total load of 5G base stations at each time; P GRI Indicates the total amount of active power purchased by the distribution network, which is determined by the Add together to get; Q GRI is the total amount of reactive power purchased by the distribution network, which is determined by the Add together to get; P 5G The total amount of charge and discharge of the backup energy storage battery of the 5G base station, which is the active power absorbed in each period and the released active power Add together.

8. The method for optimizing the utilization of 5G base station backup energy storage considering the uncertainty of communication load according to claim 7 is characterized in that: In step S3, a CPLEX solver is used to solve the 5G base station backup energy storage optimization utilization model, and power control of the 5G base station is performed according to the solution result.

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