Power distribution network emergency self-organizing power supply recovery method based on 5G base station energy storage and related device

Abstract

The invention provides a power distribution network emergency self-organizing power supply recovery method based on 5G base station energy storage and a related device. The method comprises the steps of obtaining a predicted value and an output interval of uncertain energy in a power distribution network, a load predicted value and an output interval of base station energy storage; based on the obtained parameters, solving a pre-constructed two-stage robust optimization model according to a set target, and obtaining an optimal scheduling scheme for the base station energy storage to participate in the power supply recovery of the power distribution network; wherein in the solving stage, the model is divided into a main problem and a sub-problem for solving; the main problem is used for determining an initial scene and a corresponding set target lower bound; the sub-problem is used for determining the worst scene and the corresponding set target upper bound in the initial scene; and when the upper and lower bounds of the set target meet set conditions, finishing solving. According to the method, a two-stage robust optimization model is utilized, prediction information of uncertain energy and loads is combined, an optimal scheduling scheme for participation of base station energy storage in power supply recovery of the power distribution network is obtained, and stable and reliable operation of the power distribution network is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and specifically relates to a distribution network emergency self-organized power supply recovery method and related devices based on 5G base station energy storage. Background Art

[0002] The increase in 5G base station communication capacity has increased base station power consumption by three to four times that of existing 4G base stations, significantly increasing the energy storage capacity required for these base stations. The continuous improvement in grid reliability has left a significant amount of 5G base station energy storage often idle, resulting in a waste of storage resources. Existing research on the application of 5G base station energy storage has primarily focused on enabling it to participate in demand response under normal grid conditions. However, limited research has examined the use of base station energy storage as a resource for powering fault loads during grid fault conditions. Base stations are often deployed at load centers, and their energy storage is closer to the grid load. When a grid fails and loses power, while ensuring the base station's backup energy storage capacity, the base station's available capacity can be used to provide emergency power to the feeders connected to the base station. However, given the temporal and spatial variations in 5G base station communication volume, research is urgently needed to quantify the differences in backup energy storage capacity, thereby fully leveraging its role in grid emergency power supply and assessing its feasibility. This research aims to increase the economic value of base station energy storage while minimizing grid power losses.

[0003] Existing research on modeling the available storage capacity for 5G base station energy storage primarily focuses on the fixed standby storage time, without considering the impact of regional base station load variations and power supply reliability on the base station standby storage time. Therefore, the determination of standby time in base station energy storage models should consider the temporal and spatial variability of base station loads and the power supply reliability of different grid load nodes. During distribution network emergency restoration, distribution network load and base station load demand are not constant but fluctuate. Therefore, this volatility needs to be considered when developing strategies for utilizing 5G base station energy storage in distribution network power supply restoration. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a distribution network emergency self-organized power supply recovery method and related devices based on 5G base station energy storage, aiming to solve the problem that current research has not considered the impact of differences in base station load changes in different regions and power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in distribution network load and base station load demand.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0006] In a first aspect, the present invention provides a method for emergency self-organized power supply restoration of a distribution network based on 5G base station energy storage, which is applied to scenarios where base station energy storage participates in power supply restoration of the distribution network, and includes the following steps:

[0007] Obtain the predicted value and output range of uncertain energy in the distribution network, the load forecast value, and the output range of base station energy storage;

[0008] Within each output range, based on the forecast values of uncertain energy and load, a pre-built two-stage robust optimization model is solved according to the set objectives to obtain the optimal scheduling plan for base station energy storage to participate in distribution network power restoration.

[0009] Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of uncertain energy and the predicted value of load; the sub-problem is used to consider the differences in base station load changes in different regions and the impact of power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in distribution network load and base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

[0010] Furthermore, a two-stage robust optimization model is developed for 5G base stations that can call upon energy storage and distribution network emergency power supply. This model sets the goal of minimizing the operating cost of the fault area. The corresponding objective function and constraints are as follows:

[0011] Objective function:

[0012]

[0013]

[0014]

[0015]

[0016] Constraints:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] Where, Refers to the load loss cost within the fault area; Refers to the base station operating cost; 、 refers to the power generation cost of photovoltaic and wind turbines; T refers to the duration of the distribution network fault; W refers to the load level in the distribution network; Refers to the load loss cost when the load level is w at time t; Refers to the load loss amount at load level w at time t; Refers to the unit electricity price for charging compensation; Refers to the charging capacity of the base station at time t; Refers to the base station energy storage charging coefficient; Refers to the unit price of compensation paid to mobile operators for base station energy storage discharge; Refers to the discharged amount of energy stored in the base station at time t; , Refers to the unit power generation cost of photovoltaic and wind power; The maximum charging and discharging power of the base station energy storage; Refers to the upper limit of base station energy storage charging; Refers to the lower limit of energy storage discharge capacity; 、 are the maximum values of photovoltaic and wind power output respectively; Refers to the load loss of load level w at time t; Refers to the power consumption of the 5G base station at the grid node j; N is the number of nodes in the distribution network; Refers to the base station energy storage charging power of energy storage type i at time t; Refers to the energy storage discharge power of the base station of energy storage type i at time t; Refers to the load of the distribution network at time t; Refers to the photovoltaic output at time t; Refers to the wind power output at time t.

[0023] Furthermore, within the constraints of the two-stage robust optimization model for 5G base station-available energy storage and distribution network emergency power supply, the lower limit of energy storage discharge capacity is determined based on a refined model of the 5G base station-available energy storage capacity. The model is expressed as follows:

[0024]

[0025] Where, Refers to the available energy storage capacity of base station i at time t; Refers to the energy storage backup time of base station i; Refers to the power consumption of base station i at time t.

[0026] Furthermore, the base station power consumption is determined according to a 5G base station power consumption calculation model that takes into account the traffic volume and the distance between the user and the base station. The expression of this model is as follows:

[0027]

[0028] Where, Refers to the benchmark power consumption of 5G base stations, Refers to the base number of variable power consumption of the base station; Refers to the variable power consumption of 5G base stations; Refers to the working status of base station i at time t. Its value equal to 1 indicates that the base station is in normal working state, and its value equal to 0 indicates that the base station is in dormant state; Refers to the power consumption of the base station in sleep mode.

[0029] Furthermore, the calculation expression of variable power consumption is as follows:

[0030]

[0031] Where; Refers to the traffic volume of 5G base stations; is a constant; is the channel bandwidth; Refers to the distance between user i and base station j; and is the channel attenuation coefficient.

[0032] Furthermore, the calculation expression of energy storage backup time is as follows:

[0033]

[0034] Where, Refers to the load level of grid node i, i=1,2,3; refers to the comprehensive node vulnerability of grid node i; Refers to the base station communication capacity factor at grid node i.

[0035] Furthermore, the calculation expression of comprehensive node vulnerability is as follows:

[0036]

[0037] Where, Refers to the modified Gini coefficient, which is used to characterize the topological vulnerability of the system; Refers to the modified Gini coefficient weight; Refers to Theil entropy, which is used to characterize the fragility of the system state; Refers to the weight of Theil entropy; modified Gini coefficient The calculation is as follows:

[0038]

[0039] Where, Refers to the weighted average node degree; Refers to the raw Gini coefficient result; 、 are the influence rate indicators of grid node degree and grid node betweenness, respectively.

[0040] Furthermore, under the constraints, a column-based and constraint-generated C&CG algorithm is used to solve the two-stage robust optimization model of 5G base station available energy storage and distribution network emergency power supply, including:

[0041] Determine the uncertainty set as follows:

[0042]

[0043] Where, Refers to a set of uncertainties; , , , are the actual output values of photovoltaic power, wind power, power grid and base station at time t respectively; , , , are the predicted values of photovoltaic, wind power, grid load and base station power consumption at time t respectively; , , , These are the maximum fluctuation ranges of photovoltaic, wind power, grid load and base station power consumption respectively;

[0044] The compact form of the deterministic model is as follows:

[0045]

[0046] Where c refers to the coefficient vector of the original deterministic model; B, D, X, and Y refer to the coefficient matrices of the inequality constraint variables and equation constraint variables, respectively; x and y are optimization variables, as shown below:

[0047]

[0048]

[0049] Where, and are the optimization variable sets of x and y respectively; , are the charging and discharging states of the energy storage of the nth type of base station in the distribution network fault area at time t, with values ranging from [0, 1]; , , are the distribution network load, photovoltaic power output and wind power output at time t in the fault area respectively; Refers to the total power consumption of the base station at time t; , Refers to the amount of charge and discharge power of the energy storage of type n base station;

[0050] The two-stage robust model is as follows:

[0051]

[0052] The two-stage original problem is decomposed into a main problem and sub-problems through the column and constraint generation algorithm. The main problem after decomposition is as follows:

[0053]

[0054] in, is the solution to the subproblem after iteration; is the value of the uncertainty variable u after iteration;

[0055] The decomposed sub-problems are as follows:

[0056]

[0057] The sub-problems after dualization are as follows:

[0058]

[0059] in, , , , are the dual variables in the constraints of the original objective function.

[0060] In a second aspect, the present invention provides a distribution network emergency self-organizing power supply restoration device based on 5G base station energy storage, which is applied to scenarios where base station energy storage participates in distribution network power supply restoration, including:

[0061] The initial parameter acquisition module is used to obtain the predicted value and output range of uncertain energy in the distribution network, the load prediction value, and the output range of base station energy storage;

[0062] The solution solving module is used to solve the pre-built two-stage robust optimization model based on the predicted values of uncertain energy and load in each output range according to the set objectives, and obtain the optimal scheduling plan for base station energy storage to participate in distribution network power restoration;

[0063] Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of uncertain energy and the predicted value of load; the sub-problem is used to consider the differences in base station load changes in different regions and the impact of power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in distribution network load and base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

[0064] In a third aspect, the present invention provides a computer device, comprising a processor and a memory:

[0065] The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0066] The processor executes a distribution network emergency self-organized power supply recovery method based on 5G base station energy storage as described in the first aspect according to the instructions of the computer program.

[0067] In summary, the present invention provides a distribution network emergency self-organized power supply restoration method and related devices based on 5G base station energy storage, which are applied to scenarios where base station energy storage participates in distribution network power supply restoration, including obtaining the predicted value and output range of uncertain energy in the distribution network, the load predicted value and the output range of the base station energy storage; within each output range, based on the predicted value of the uncertain energy and the load predicted value, a pre-constructed two-stage robust optimization model is solved according to the set target to obtain the optimal scheduling scheme for the base station energy storage to participate in the distribution network power supply restoration; wherein, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower bound based on the predicted value of the uncertain energy and the load predicted value; the sub-problem is used to determine the worst scenario of the distribution network operation and the corresponding set target upper bound under the initial scenario; the solution ends when the set target lower bound and the set target upper bound meet the set conditions. In response to existing problems, the present invention utilizes a two-stage robust optimization model and combines the forecast information of uncertain energy and load to derive the optimal scheduling scheme for base station energy storage to participate in distribution network power supply restoration, thereby ensuring stable and reliable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0069] Figure 1 A flow chart of a distribution network emergency self-organized power supply restoration method based on 5G base station energy storage provided by an embodiment of the present invention;

[0070] Figure 2 A technical roadmap for a distribution network emergency self-organized power supply restoration method based on 5G base station energy storage provided by an embodiment of the present invention;

[0071] Figure 3 A flow chart showing base station energy storage participating in power distribution network power supply according to an embodiment of the present invention;

[0072] Figure 4 Energy storage output diagrams for various types of base stations provided in embodiments of the present invention;

[0073] Figure 5 A block diagram of a distribution network emergency self-organizing power supply restoration device based on 5G base station energy storage provided by an embodiment of the present invention;

[0074] Figure 6 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] See also Figure 1 The embodiment of the present invention provides a distribution network emergency self-organized power supply restoration method based on 5G base station energy storage, which is applied to the scenario where base station energy storage participates in the distribution network power supply restoration, and includes the following steps:

[0077] S11: Obtain the predicted value and output range of the uncertain energy in the distribution network, the load prediction value, and the output range of the base station energy storage.

[0078] It should be noted that uncertain energy sources, primarily wind power and photovoltaic power generation, are energy sources with uncertain output and are difficult to accurately predict. Due to random variations in natural factors such as wind speed and sunlight intensity, the actual power generated by wind and photovoltaic power generation fluctuates continuously. The forecast value is an estimate of the future power generation, load demand, and the charging and discharging power of base station energy storage. The output range accounts for these uncertainties. The power generation, load, and charging and discharging power of uncertain energy sources fluctuate within a certain range; this range is the output range. For example, the output range of wind power can be expressed as a range of upper and lower power limits.

[0079] S12: Within each output range, based on the predicted values of uncertain energy resources and load, a pre-built two-stage robust optimization model is solved according to the set objectives to obtain the optimal scheduling plan for base station energy storage to participate in distribution network power restoration;

[0080] Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of uncertain energy and the predicted value of load; the sub-problem is used to consider the differences in base station load changes in different regions and the impact of power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in distribution network load and base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

[0081] It should be noted that the two-stage robust optimization model is an optimization model for handling uncertain problems. It is solved in two stages: a main problem and a sub-problem. It can find a relatively optimal solution in an uncertain environment, ensuring that the system maintains good performance under various possible scenarios. The main problem determines the initial scenario for the distribution network operation and the corresponding lower bound of the target based on the uncertain energy and load forecasts. The initial scenario is a relatively ideal or typical system operation condition given the current forecast information. The lower bound of the target is the minimum value of the objective function (such as system operating cost and power supply reliability) achieved under the initial scenario. The sub-problem is to determine the worst-case scenario for the distribution network operation and the corresponding upper bound of the target under the initial scenario, taking into account the impact of regional base station load variations and power supply reliability on base station backup energy storage time, as well as fluctuations in distribution network load and base station load demand. The worst-case scenario is the combination of scenarios that has the most adverse impact on system performance. The upper bound of the target is the maximum value of the objective function achieved under the worst-case scenario. The target can be, for example, minimizing system operating cost or maximizing power supply reliability, which is the goal of the optimization model.

[0082] The method provided in this embodiment is to transform the problem of emergency self-organized power supply restoration of the distribution network into a two-stage robust optimization problem. First, by obtaining relevant predicted values and output ranges, a comprehensive understanding of the system's operating conditions and uncertainty factors is obtained. Then, using the two-stage robust optimization model, an optimal scheduling solution is found that can not only meet the system's operating requirements but also maintain good performance under various possible circumstances, taking into account uncertainty. Specifically, the main problem provides an initial scheduling solution based on the prediction information, and the sub-problem evaluates and adjusts this solution to cope with the worst case scenario. Through iterative solution, the scheduling solution is continuously optimized, and ultimately the emergency self-organized power supply restoration of the distribution network is achieved.

[0083] The method provided in this embodiment can more accurately determine the base station backup energy storage time by considering the differences in base station load changes in different regions and the impact of power supply reliability on the base station backup energy storage time in the sub-problem. For example, for areas with large load changes and high power supply reliability requirements, the base station backup energy storage time is appropriately increased to ensure that the base station can continue to operate in the event of a failure. At the same time, the fluctuations in distribution network load and base station load demand are also considered in the sub-problem, and the scheduling plan can be adjusted according to different load conditions. For example, when the load demand increases, the discharge power of the base station energy storage is increased; when the load demand decreases, the discharge power of the base station energy storage is reduced, thereby achieving an effective response to load fluctuations.

[0084] See also Figure 2 , Figure 2 The technical route of a distribution network emergency self-organized power supply restoration method based on 5G base station energy storage proposed based on the above embodiment is demonstrated, including:

[0085] (1) Obtain the spatiotemporal characteristics of 5G traffic distribution and construct a 5G base station power consumption calculation model that takes into account the traffic volume and the distance between users and base stations;

[0086] (2) Establish a refined model of the energy storage capacity that can be called upon by 5G base stations, including the calculation of the backup time of base stations at different grid nodes and the vulnerability of the integrated grid nodes;

[0087] (3) Construct a two-stage robust optimization model for 5G base station-based energy storage and distribution network emergency power supply;

[0088] (4) Taking the minimum operating cost of the minimum fault area as the objective function, the column-based and constraint-generated C&CG algorithm is used under the constraints to solve the two-stage robust optimization model of the 5G base station's callable energy storage and distribution network emergency power supply;

[0089] (5) According to the solution results of the column-based and constraint-generated C&CG algorithm, the base station backup energy storage capacity determination scheme and the base station energy storage participation in the distribution network power supply restoration operation scheme are obtained.

[0090] The above technical route is further introduced below in conjunction with some other embodiments of the present invention.

[0091] In one embodiment of the present invention, a two-stage robust optimization model is a two-stage robust optimization model for 5G base station-callable energy storage and distribution network emergency power supply. The model sets the goal of minimizing the operating cost of the fault area. The corresponding objective function and constraints are as follows:

[0092] Objective function:

[0093]

[0094]

[0095]

[0096]

[0097] Constraints:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Where, Refers to the load loss cost within the fault area; Refers to the base station operating cost; 、 refers to the power generation cost of photovoltaic and wind turbines; T refers to the duration of the distribution network fault; W refers to the load level in the distribution network; Refers to the load loss cost when the load level is w at time t; Refers to the load loss amount at load level w at time t; Refers to the unit electricity price for charging compensation; Refers to the charging capacity of the base station at time t; Refers to the base station energy storage charging coefficient; Refers to the unit price of compensation paid to mobile operators for base station energy storage discharge; Refers to the discharged amount of energy stored in the base station at time t; , Refers to the unit power generation cost of photovoltaic and wind power; The maximum charging and discharging power of the base station energy storage; Refers to the upper limit of base station energy storage charging; Refers to the lower limit of energy storage discharge capacity; 、 are the maximum values of photovoltaic and wind power output respectively; Refers to the load loss of load level w at time t; Refers to the power consumption of the 5G base station at the grid node j; N is the number of nodes in the distribution network; Refers to the base station energy storage charging power of energy storage type i at time t; Refers to the energy storage discharge power of the base station of energy storage type i at time t; Refers to the load of the distribution network at time t; Refers to the photovoltaic output at time t; Refers to the wind power output at time t.

[0104] In a further embodiment of the present invention, within the constraints of the two-stage robust optimization model for 5G base station callable energy storage and distribution network emergency power supply, the lower limit of the energy storage discharge capacity is determined based on a refined model of the 5G base station callable energy storage capacity. The expression of the model is as follows:

[0105]

[0106] Where, Refers to the available energy storage capacity of base station i at time t; Refers to the energy storage backup time of base station i; Refers to the power consumption of base station i at time t.

[0107] In a further embodiment of the present invention, the power consumption of the base station is determined according to a 5G base station power consumption calculation model that takes into account the traffic volume and the distance between the user and the base station. The expression of the model is as follows:

[0108]

[0109] Where, Refers to the benchmark power consumption of 5G base stations, Refers to the base number of variable power consumption of the base station; Refers to the variable power consumption of 5G base stations; Refers to the working status of base station i at time t. Its value equal to 1 indicates that the base station is in normal working state, and its value equal to 0 indicates that the base station is in dormant state; Refers to the power consumption of the base station in sleep mode.

[0110] In a further embodiment of the present invention, the calculation expression of the variable power consumption is as follows:

[0111]

[0112] Where; Refers to the traffic volume of 5G base stations; is a constant; is the channel bandwidth; Refers to the distance between user i and base station j; and is the channel attenuation coefficient.

[0113] In a further embodiment of the present invention, the calculation expression of the energy storage backup time is as follows:

[0114]

[0115] Where, Refers to the load level of grid node i, i=1,2,3; refers to the comprehensive node vulnerability of grid node i; Refers to the base station communication capacity factor at grid node i.

[0116] In a further embodiment of the present invention, the calculation expression of the comprehensive node vulnerability is as follows:

[0117]

[0118] Where, Refers to the modified Gini coefficient, which is used to characterize the topological vulnerability of the system; Refers to the modified Gini coefficient weight; Refers to Theil entropy, which is used to characterize the fragility of the system state; Refers to the weight of Theil entropy; modified Gini coefficient The calculation is as follows:

[0119]

[0120] Where, Refers to the weighted average node degree; Refers to the raw Gini coefficient result; 、 are the influence rate indicators of grid node degree and grid node betweenness, respectively.

[0121] In one embodiment of the present invention, a two-stage robust optimization model of 5G base station callable energy storage and distribution network emergency power supply is solved using a column-based and constraint-generated C&CG algorithm under constraints, including:

[0122] Determine the uncertainty set as follows:

[0123]

[0124] Where, Refers to a set of uncertainties; , , , are the actual output values of photovoltaic power, wind power, power grid and base station at time t respectively; , , , are the predicted values of photovoltaic, wind power, grid load and base station power consumption at time t respectively; , , , These are the maximum fluctuation ranges of photovoltaic, wind power, grid load and base station power consumption respectively.

[0125] The compact form of the deterministic model is as follows:

[0126]

[0127] Where c refers to the coefficient vector of the original deterministic model; B, D, X, and Y refer to the coefficient matrices of the inequality constraint variables and equation constraint variables, respectively; x and y are optimization variables, as shown below:

[0128]

[0129]

[0130] Where, and are the optimization variable sets of x and y respectively; , are the charging and discharging states of the energy storage of the nth type of base station in the distribution network fault area at time t, with values ranging from [0, 1]; , , are the load size, photovoltaic power output and wind power output at time t in the fault area respectively; Refers to the total power consumption of the base station at time t; , Refers to the amount of charge and discharge power of the energy storage of type n base station;

[0131] The two-stage robust model is as follows:

[0132]

[0133] The model consists of two phases. The first phase determines some decision variables y. In the second phase, after determining the uncertainty factors u (such as actual energy output and load), the decision variables x (such as the specific charging and discharging strategy) are determined. By maximizing the minimum target value under the worst-case scenario, the model is made robust.

[0134] The two-stage original problem is decomposed into a main problem and sub-problems through the column and constraint generation algorithm. The main problem after decomposition is as follows:

[0135]

[0136] in, is the solution to the subproblem after iteration; is the value of the uncertainty variable u after iteration;

[0137] The goal of the main problem is to minimize the objective function while satisfying the constraints. By combining the solutions to the subproblems and the uncertainty scenario, the range of feasible solutions is gradually narrowed down to approach the optimal solution.

[0138] The decomposed sub-problems are as follows:

[0139]

[0140] The sub-problems after dualization are as follows:

[0141]

[0142] in, , , , are the dual variables in the constraints of the original objective function.

[0143] The goal of the subproblem is to find the worst uncertainty scenario u within a given uncertainty set and to determine the optimal decision variable y under that scenario. By continuously iteratively solving the subproblems, more accurate information is provided to improve the solution to the main problem.

[0144] This embodiment uses the C&CG algorithm to decompose the two-stage robust optimization model into a main problem and subproblems for iterative solution. First, the uncertainty set is determined to provide a foundation for the model to account for uncertainty. A deterministic model and a two-stage robust model are then established to describe the problem's objectives and constraints. The robust model is then decomposed using the C&CG algorithm, and the main problem and subproblems are solved alternately, with the solution continuously updated iteratively until convergence conditions are met. During this iterative process, the subproblems search for the worst-case scenario, and the main problem adjusts the decision variables based on the subproblem's results, ultimately resulting in the optimal scheduling solution under uncertainty.

[0145] See also Figure 3 , Figure 3The demonstration of base station energy storage participating in the power distribution network includes two parts: initial parameter acquisition and second-order robust optimization. In the initial parameter acquisition stage, through wind-solar-base station power consumption and output fitting sampling, load analysis, base station backup energy storage solution, etc., the uncertain parameters and wind-solar-base station power consumption-load forecast values and output ranges, and base station energy storage output ranges are obtained. In the second-order robust optimization stage, the iterative convergence threshold r is determined at the beginning, k=1 is set, and then the main problem is solved according to the wind-solar load forecast value to obtain the initial scenario a and determine the lower bound LB. k ; Then solve the sub-problem based on the initial scenario a and get the worst scenario s k Incorporate into the worst scenario S and determine the upper bound UB k ; Then judge (|UB k -LB k | / LB k ) is less than r. If it is satisfied, end. If not, set (k=k+1) and repeat the above process of solving the main problem, sub-problems and judgment.

[0146] The following simulation is performed using the improved IEEE-33 node topology as the research object to verify the feasibility of the distribution network emergency self-organized power supply restoration method based on 5G base station energy storage proposed in an embodiment of the present invention.

[0147] The system base voltage in this example is 12.66kV. PV systems with a rated capacity of 200kW are connected to nodes 8, 12, 16, and 20, respectively. Wind power systems with rated capacities of 450kW and 400kW are connected to nodes 24 and 28, respectively. The entire grid area is divided into residential and office areas, with weights of 10, 1, and 0.1 for primary, secondary, and tertiary loads, respectively. The base station backup energy storage is determined and used as the lower limit for energy storage charging and discharging during base station energy storage scheduling. Based on the number of base stations at each grid node, the total backup and available capacity of each type of energy storage is calculated as follows:

[0148] Table 1 Backup energy storage capacity and available capacity

[0149]

[0150] Regarding the participation of base station energy storage in distribution network power restoration, a set of wind-photovoltaic combined output scenarios was selected. A wind-photovoltaic-base power consumption-load output sequence was established based on the base station power consumption and load variation curves. Wind-photovoltaic output fluctuations were assumed to be 15% of the original output, and base station power consumption and load output fluctuations were assumed to be 10% of the original output. These fluctuations were used as inputs in a two-stage robust optimization model to determine the amount of load loss in this scenario when base station energy storage participates in system power restoration.

[0151] See also Figure 4It can be seen that the base station energy storage is charged at 2~3h, 20h and 24h. At this time, the load in the system is at a low level and the wind power generation is at a high level. This not only reduces the energy storage charging cost under the background of time-of-use electricity prices, but also improves the energy storage's absorption of wind power; at 8h~11h, 19h, and 21~23h, the energy storage is discharged, thereby reducing the system load loss and reducing the load power loss loss.

[0152] Based on the same inventive concept, the embodiment of the present application also provides a distribution network emergency self-organized power supply recovery device based on 5G base station energy storage for realizing the above-mentioned distribution network emergency self-organized power supply recovery method based on 5G base station energy storage. The implementation solution provided by the device is similar to the implementation solution recorded in the above-mentioned method. Therefore, the specific limitations in the embodiment of the distribution network emergency self-organized power supply recovery device based on 5G base station energy storage provided below can be found in the above-mentioned limitations on the distribution network emergency self-organized power supply recovery method based on 5G base station energy storage, and will not be repeated here.

[0153] See also Figure 5 The embodiment of the present invention further provides a distribution network emergency self-organizing power supply restoration device based on 5G base station energy storage, which is applied to the scenario where base station energy storage participates in the distribution network power supply restoration, including:

[0154] The initial parameter acquisition module is used to obtain the predicted value and output range of uncertain energy in the distribution network, the load prediction value, and the output range of base station energy storage;

[0155] The solution solving module is used to solve the pre-built two-stage robust optimization model based on the predicted values of uncertain energy and load in each output range according to the set objectives, and obtain the optimal scheduling plan for base station energy storage to participate in distribution network power restoration;

[0156] Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of uncertain energy and the predicted value of load; the sub-problem is used to consider the differences in base station load changes in different regions and the impact of power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in distribution network load and base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0158] Reference Figure 6 An embodiment of the present invention also provides a computer device, including: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, it implements the distribution network emergency self-organized power supply recovery method based on 5G base station energy storage as described in any one of the above methods.

[0159] The computer device may be a desktop computer, notebook computer, PDA, cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 6 The computer device is merely an example and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, etc.

[0160] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0161] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped with the computer device. Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0162] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for emergency self-organized power supply restoration of a distribution network based on 5G base station energy storage as described in any one of the above methods is implemented.

[0163] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0164] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0165] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0166] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0167] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distribution network emergency self-organized power supply restoration method based on 5G base station energy storage, applied to the scenario where base station energy storage participates in the distribution network power supply restoration, characterized in that: The steps include: Obtain the predicted value and output range of uncertain energy in the distribution network, the load forecast value, and the output range of base station energy storage; In each output range, based on the predicted value of the uncertain energy source and the predicted load value, a pre-built two-stage robust optimization model is solved according to the set objectives to obtain the optimal scheduling plan for base station energy storage to participate in distribution network power supply restoration; Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of the uncertain energy and the predicted value of the load; the sub-problem is used to consider the impact of the load change differences of base stations in different regions and the power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in the distribution network load and the base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

2. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 1 is characterized in that: The two-stage robust optimization model is a two-stage robust optimization model for 5G base station-callable energy storage and distribution network emergency power supply. The model takes minimizing the operating cost of the fault area as the set goal. The corresponding objective function and constraints are as follows: Objective function: Constraints: Where, Refers to the load loss cost within the fault area; Refers to the base station operating cost; 、 refers to the power generation cost of photovoltaic and wind turbines; T refers to the duration of the distribution network fault; W refers to the load level in the distribution network; Refers to the load loss cost when the load level is w at time t; Refers to the load loss amount at load level w at time t; Refers to the unit electricity price for charging compensation; Refers to the charging capacity of the base station at time t; Refers to the base station energy storage charging coefficient; Refers to the unit price of compensation paid to mobile operators for base station energy storage discharge; Refers to the discharged amount of energy stored in the base station at time t; , Refers to the unit power generation cost of photovoltaic and wind power; The maximum charging and discharging power of the base station energy storage; Refers to the upper limit of base station energy storage charging; Refers to the lower limit of energy storage discharge capacity; 、 are the maximum values of photovoltaic and wind power output respectively; Refers to the load loss of load level w at time t; Refers to the power consumption of the 5G base station at the grid node j; N is the number of nodes in the distribution network; Refers to the base station energy storage charging power of energy storage type i at time t; Refers to the energy storage discharge power of the base station of energy storage type i at time t; Refers to the load of the distribution network at time t; Refers to the photovoltaic output at time t; Refers to the wind power output at time t.

3. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 2 is characterized in that: In the constraints of the two-stage robust optimization model of the 5G base station's callable energy storage and distribution network emergency power supply, the lower limit of the energy storage discharge capacity is determined based on a refined model of the 5G base station's callable energy storage capacity. The expression of the model is as follows: Where, Refers to the available energy storage capacity of base station i at time t; Refers to the energy storage backup time of base station i; Refers to the power consumption of base station i at time t.

4. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 3 is characterized in that: The base station power consumption is determined based on the 5G base station power consumption calculation model that takes into account the traffic volume and the distance between the user and the base station. The expression of this model is as follows: Where, Refers to the benchmark power consumption of 5G base stations, Refers to the base number of variable power consumption of the base station; Refers to the variable power consumption of 5G base stations; Refers to the working status of base station i at time t. Its value equal to 1 indicates that the base station is in normal working state, and its value equal to 0 indicates that the base station is in dormant state; Refers to the power consumption of the base station in sleep mode.

5. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 4 is characterized in that: The calculation expression of the variable power consumption is as follows: Where, Refers to the traffic volume of 5G base stations; is a constant; is the channel bandwidth; Refers to the distance between user i and base station j; and is the channel attenuation coefficient.

6. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 3 is characterized in that: The calculation expression of the energy storage backup time is as follows: Where, Refers to the load level of grid node i, i=1,2,3; refers to the comprehensive node vulnerability of grid node i; Refers to the maximum value of comprehensive node vulnerability; Refers to the base station communication capacity factor at grid node i; Refers to the energy storage backup time benchmark value.

7. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 5, characterized in that: The calculation expression of comprehensive node vulnerability is as follows: Where, Refers to the modified Gini coefficient, which is used to characterize the topological vulnerability of the system; Refers to the modified Gini coefficient weight; Refers to Theil entropy, which is used to characterize the fragility of the system state; Refers to the weight of Theil entropy; Corrected Gini coefficient The calculation is as follows: Where, Refers to the weighted average node degree; Refers to the raw Gini coefficient result; 、 are the influence rate indicators of grid node degree and grid node betweenness, respectively.

8. The method for emergency self-organized power supply restoration of distribution network based on 5G base station energy storage according to claim 2, characterized in that: Under the constraints, the column-based and constraint-generated C&CG algorithm is used to solve the two-stage robust optimization model of the 5G base station's callable energy storage and distribution network emergency power supply, including: Determine the uncertainty set as follows: Where, refers to the set of uncertainties; , , , are the actual output values of photovoltaic power, wind power, power grid and base station at time t respectively; , , , are the predicted values of photovoltaic, wind power, grid load and base station power consumption at time t respectively; , , , These are the maximum fluctuation ranges of photovoltaic, wind power, grid load and base station power consumption respectively; The compact form of the deterministic model is as follows: Where c refers to the coefficient vector of the original deterministic model; B, D, X, and Y refer to the coefficient matrices of the inequality constraint variables and equation constraint variables, respectively; x and y are optimization variables, as shown below: Where, and are the optimization variable sets of x and y respectively; , are the charging and discharging states of the energy storage of the nth type of base station in the distribution network fault area at time t, with values ranging from [0, 1]; , , are the distribution network load, photovoltaic power output and wind power output at time t in the fault area respectively; Refers to the total power consumption of the base station at time t; , Refers to the amount of charge and discharge power of the energy storage of type n base station; The two-stage robust model is as follows: The two-stage original problem is decomposed into a main problem and sub-problems through the column and constraint generation algorithm. The main problem after decomposition is as follows: in, is the solution to the subproblem after iteration; is the value of the uncertainty variable u after iteration; The decomposed sub-problems are as follows: The sub-problems after dualization are as follows: in, , , , are the dual variables in the constraints of the original objective function.

9. A distribution network emergency self-organizing power supply restoration device based on 5G base station energy storage, which is applied to the scenario where base station energy storage participates in the distribution network power supply restoration, characterized in that: include: The initial parameter acquisition module is used to obtain the predicted value and output range of uncertain energy in the distribution network, the load prediction value, and the output range of base station energy storage; A solution solving module is used to solve a pre-built two-stage robust optimization model in accordance with set objectives within each output interval based on the predicted value of the uncertain energy source and the predicted load value, to obtain an optimal scheduling solution for base station energy storage to participate in distribution network power restoration; Among them, the two-stage robust optimization model is divided into a main problem and a sub-problem for solution in the two stages of the solution; the main problem is used to determine the initial scenario of the distribution network operation and the corresponding set target lower limit based on the predicted value of the uncertain energy and the predicted value of the load; the sub-problem is used to consider the impact of the load change differences of base stations in different regions and the power supply reliability on the backup energy storage time of the base station, as well as the fluctuations in the distribution network load and the base station load demand under the initial scenario, to determine the worst scenario of the distribution network operation and the corresponding set target upper limit; the solution ends when the set target lower limit and the set target upper limit meet the set conditions.

10. A computer device, characterized in that: The device includes a processor and a memory: The memory is used to store the computer program and send instructions of the computer program to the processor; The processor executes a distribution network emergency self-organized power supply recovery method based on 5G base station energy storage as described in any one of claims 1-8 according to the instructions of the computer program.

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