A method, system, device and medium for carrying capacity assessment of a power distribution network

Abstract

The application discloses a kind of carrying capacity evaluation method, system, equipment and medium for distribution network, the method is by selecting the typical day of distribution network, and according to the historical load data of target line in the typical day and the historical capacity data when distributed photovoltaic and energy storage device access, with the minimum load standard deviation after energy storage device access as objective function, optimal power flow calculation is carried out under the operation constraint of distribution network, and the power flow calculation result is obtained;According to the relationship between power flow calculation result and distribution network operation constraint, the out-of-limit judgment is carried out, and the historical capacity data is updated according to the judgment result, and the power flow calculation link and the out-of-limit judgment link are repeated using the updated historical capacity data, until the cycle termination condition is reached, and the historical capacity data at the time of the penultimate power flow calculation is used as the final carrying capacity evaluation result;Fully consider the regulation of energy storage, improve the accuracy of carrying capacity evaluation result of distribution network with energy storage access and distributed photovoltaic access.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a carrying capacity evaluation method, system, device and medium for a power distribution network. BACKGROUND

[0002] As a kind of distributed power supply, large-scale access of distributed photovoltaic to power grid will cause power generation greater than load power consumption, resulting in a series of problems such as reverse power flow, reverse overload of equipment, voltage out-of-limit and other problems affecting the safe and stable operation of power grid. In order to promote the coordinated development of distributed photovoltaic and power grid, it is urgent to reasonably evaluate the carrying capacity of distributed photovoltaic access to power grid. At present, the evaluation of the carrying capacity of distributed power supply access to power grid is usually based on the power value flowing through the equipment at a certain typical moment for calculation, without considering the regulation effect of energy storage equipment resources, so that the evaluation result is small.

[0003] Therefore, how to improve the carrying capacity evaluation result of the power distribution network has become a technical problem to be solved by those skilled in the art. SUMMARY

[0004] The present application provides a carrying capacity evaluation method, system, device and medium for a power distribution network, which solves the problem of how to improve the carrying capacity evaluation result of the power distribution network.

[0005] To solve the above technical problems, the present application provides a carrying capacity evaluation method for a power distribution network, comprising:

[0006] selecting a typical day of the power distribution network, and obtaining historical load data of a target line in the power distribution network in the typical day and historical capacity data of distributed photovoltaic and energy storage equipment when accessing the target line;

[0007] In the power flow calculation link, the historical load data and the historical capacity data are taken as inputs, the minimum load standard deviation after the energy storage equipment accesses the target line is taken as the objective function, and the optimal power flow calculation is carried out based on the typical day under the operation constraint of the power distribution network to obtain the power flow calculation result;

[0008] In the out-of-limit judgment link, the out-of-limit judgment is carried out according to the relationship between the power flow calculation result and the operation constraint, and the historical capacity data is updated according to the judgment result;

[0009] The power flow calculation link and the out-of-limit judgment link are repeated according to the updated historical capacity data until the cycle termination condition is reached, and the historical capacity data at the second last power flow calculation is taken as the carrying capacity evaluation result of the power distribution network accessing the distributed photovoltaic.

[0010] As one of the preferred schemes, the target line includes a 10kV feeder and a connected distribution transformer.

[0011] As one of the preferred solutions, the typical day includes dates with minimum power flowing through the first section of the feeder selected from four seasons of a year respectively; the historical capacity data includes feeder access distributed photovoltaic capacity, distribution transformer access distributed photovoltaic capacity and distribution transformer access energy storage capacity.

[0012] As one of the preferred solutions, the historical load data and the historical capacity data are taken as inputs, the minimum load standard deviation after the energy storage device accesses the target line is taken as the objective function, the optimal power flow calculation is carried out based on the operation constraints of the power distribution network and the typical day to obtain the power flow calculation result, including:

[0013] The power generation of the distributed photovoltaic on the typical day is quantified according to the historical load data and the historical capacity data, and the power flow calculation boundary is constructed according to the power generation, the historical load data and the historical capacity data;

[0014] The minimum load standard deviation after the energy storage device accesses the target line is taken as the objective function, and the objective function is converted into a penalty objective function based on the operation constraints;

[0015] The penalty objective function is solved by a particle swarm algorithm within the power flow calculation boundary to obtain the power flow calculation result.

[0016] As one of the preferred solutions, the out-of-limit judgment is carried out according to the relationship between the power flow calculation result and the operation constraints, and the historical capacity data is updated according to the judgment result, including:

[0017] If the power flow calculation result meets the operation constraints, the power flow calculation result is not out of limit, and the feeder access distributed photovoltaic capacity is increased to a first preset multiple of the original capacity;

[0018] If the power flow calculation result does not meet the operation constraints, the power flow calculation result is out of limit, and the feeder access distributed photovoltaic capacity is reduced to a second preset multiple of the original capacity.

[0019] As one of the preferred solutions, the first preset multiple is 1.05 times, and the second preset multiple is 0.95 times.

[0020] As one of the preferred solutions, the cycle termination condition is that the judgment results of two consecutive out-of-limit judgments are opposite.

[0021] The second aspect of the present application provides a carrying capacity evaluation system for a power distribution network, including:

[0022] The data acquisition module is configured to select a typical day of the power distribution network, and acquire historical load data of a target line in the power distribution network in the typical day and historical capacity data of a distributed photovoltaic and energy storage device when the distributed photovoltaic and energy storage device is accessed to the target line.

[0023] The power flow calculation module is configured to, in a power flow calculation link, take the historical load data and the historical capacity data as inputs, take a load standard deviation after the energy storage device is accessed to the target line as an objective function, and perform optimal power flow calculation based on the typical day under operation constraints of the power distribution network to obtain a power flow calculation result.

[0024] The data updating module is configured to, in an out-of-limit judgment link, perform out-of-limit judgment according to a relationship between the power flow calculation result and the operation constraints, and update the historical capacity data according to a judgment result.

[0025] The carrying capacity evaluation module is configured to repeat the power flow calculation link and the out-of-limit judgment link according to the updated historical capacity data until a loop termination condition is reached, and take historical capacity data at a penultimate power flow calculation as a carrying capacity evaluation result of the power distribution network accessing the distributed photovoltaic.

[0026] The third aspect of the present application provides an electronic device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the carrying capacity evaluation method of the power distribution network when executing the computer program.

[0027] The fourth aspect of the present application provides a computer readable storage medium including a stored computer program, and the device where the computer readable storage medium is located implements the carrying capacity evaluation method of the power distribution network when executing the computer program.

[0028] Compared with the prior art, the beneficial effects of the embodiment of the present application are at least one of the following:

[0029] (1) By using the historical data based on the typical day and the optimal power flow calculation, and fully considering the energy storage regulation, the carrying capacity of the power distribution network after accessing the distributed photovoltaic and energy storage device can be more accurately evaluated, and a reliable basis can be provided for power grid planning and operation.

[0030] (2) Through the out-of-limit judgment and data updating link, dynamic adjustment of the access capacity of the distributed photovoltaic and energy storage device is realized, and it is ensured that the power distribution network can still maintain stable operation after accessing the new device; and taking the load standard deviation after the energy storage device is accessed as the objective function helps to balance the load fluctuation of the power distribution network and improve the stability and economy of the power grid.

[0031] (3) The accuracy of the calculation result of the carrying capacity of the distribution network after the distributed photovoltaic and energy storage devices are accessed is improved by the time sequence power flow calculation and the distribution network operation constraint, and the calculation scheme used in the application is safer, and the problem that the calculation result exists overvoltage risk caused by the fact that the existing evaluation method does not involve power flow calculation and cannot effectively count voltage deviation constraint is solved. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0033] Figure 1 is a flow chart of a carrying capacity evaluation method for a distribution network provided by an embodiment of the present application;

[0034] Figure 2 is a flow chart of a carrying capacity evaluation method for a distribution network provided by another embodiment of the present application;

[0035] Figure 3 is a structural diagram of a carrying capacity evaluation system for a distribution network provided by an embodiment of the present application;

[0036] Figure 4 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings and embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] In the description of the present application, the terms "first", "second", "third" and the like are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0039] In the description of the application, it should be noted that, unless otherwise explicitly defined and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; can be mechanical connection, or electrical connection; can be directly connected, or indirectly connected through intermediate medium, can be the communication inside two elements. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the application. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those skilled in the art, the specific meaning of the above terms in the application can be understood in specific circumstances.

[0040] In the description of the application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the application are the same as those commonly understood by those skilled in the art. The terms used in the specification of the application are only for the purpose of describing the specific embodiments, and are not intended to limit the application. For those skilled in the art, the specific meaning of the above terms in the application can be understood in specific circumstances.

[0041] In an embodiment, as shown in Figure 1 The first aspect of the application provides a carrying capacity evaluation method for a power distribution network, comprising:

[0042] S1, selecting a typical day of the power distribution network, and obtaining the historical load data of the target line in the power distribution network in the typical day and the historical capacity data of the distributed photovoltaic and energy storage device when accessing the target line;

[0043] Specifically, the target line includes a 10 kilovolt feeder line and a connected distribution transformer, that is, the scheme of the application is suitable for evaluating the carrying capacity of the distribution network after the 10 kilovolt feeder line and the 10 kilovolt distribution transformer are connected with distributed photovoltaic and energy storage devices; wherein, the typical day includes the day with the minimum power flowing through the first section of the feeder line selected from the four seasons of a year, in order to improve the calculation efficiency, the application selects the day with the minimum power flowing through the first section of the feeder line in spring, summer, autumn and winter as the typical day for calculation. The historical load data includes the hourly load curve of the four typical days and the hourly unit capacity photovoltaic output curve of the area where the feeder line is located, the historical capacity data includes the distributed photovoltaic capacity connected with the feeder line, the distributed photovoltaic capacity connected with the distribution transformer and the energy storage capacity connected with the distribution transformer; wherein, the initial value of the distributed photovoltaic capacity connected with the feeder line is set as 50% of the capacity of the feeder line, the initial value of the distributed photovoltaic capacity connected with each distribution transformer is set as 50% of the capacity of the feeder line divided by the total number of distribution transformers, and the energy storage capacity is the distributed photovoltaic capacity multiplied by the storage coefficient. The obtained data also includes the electrical relationship, rated capacity, impedance and power factor of the 10 kilovolt feeder line and the distribution transformer, and the charging and discharging parameters of the commonly used energy storage devices in the area where the feeder line is located.

[0044] S2, in the power flow calculation link, the historical load data and the historical capacity data are taken as inputs, the minimum load standard deviation after the energy storage device is connected with the target line is taken as the objective function, the optimal power flow calculation is carried out based on the typical day under the operation constraint of the distribution network, and a power flow calculation result is obtained;

[0045] In an embodiment, the historical load data and the historical capacity data are taken as inputs, the minimum load standard deviation after the energy storage device is connected with the target line is taken as the objective function, the optimal power flow calculation is carried out based on the typical day under the operation constraint of the distribution network, and a power flow calculation result is obtained, including:

[0046] The power generation of the distributed photovoltaic in the typical day is quantified according to the historical load data and the historical capacity data, and a power flow calculation boundary is constructed according to the power generation, the historical load data and the historical capacity data;

[0047] The minimum load standard deviation after the energy storage device is connected with the target line is taken as the objective function, and the objective function is converted into a penalty objective function based on the operation constraint;

[0048] In the power flow calculation boundary, the penalty objective function is solved by a particle swarm algorithm, and a power flow calculation result is obtained.

[0049] Specifically, the application adopts a time sequence power flow simulation method to calculate the carrying capacity of a target line (such as a 10-kilovolt feeder line and a distribution transformer connected thereto) in a distribution network to which a distributed photovoltaic device is connected, that is, based on a certain incremental input variable, in accordance with a predetermined distribution network operation target and operation constraint condition, the distribution network is simulated in time sequence for each hour of operation, and the key operation parameters of the power grid including power flow are calculated. However, in practice, the distributed photovoltaic device and the energy storage device are mainly connected to the distribution transformer of the target line, therefore, in this embodiment, the feeder line connected to the distributed photovoltaic device and the energy storage capacity is the sum of the distributed photovoltaic device and the energy storage device connected to all distribution transformers connected to the feeder line, and the distributed photovoltaic device is evenly connected to the distribution transformer connected to the feeder line, that is, the distributed photovoltaic device connected to each distribution transformer is the distributed photovoltaic device connected to the feeder line divided by the number of distribution transformers connected to the feeder line, and according to the differentiated distributed photovoltaic device and energy storage ratio specified by each region, the energy storage capacity connected to each distribution transformer is the product of the distributed photovoltaic device capacity and the distributed photovoltaic device and energy storage ratio. The distributed photovoltaic device does not accept grid regulation, that is, the distributed photovoltaic device power at each moment is equal to the distributed photovoltaic device installed capacity multiplied by the unit capacity photovoltaic output at that moment, and the unit capacity photovoltaic output at each moment can be obtained by collecting the photovoltaic output curve.

[0050] Since the optimal power flow is an optimization problem, it is to find the power flow distribution that satisfies all the operation constraints and makes the system performance index (such as generation cost or network loss) reach the optimal value by adjusting the power generation of each moment, the charging and discharging power of energy storage, the tap of adjustable transformer, etc. under the given boundary conditions of grid structure parameters, load conditions, etc. Then, the application constructs the power flow calculation boundary by taking the hourly load curve of 4 typical days, the generation power calculated by the distributed photovoltaic device installed capacity (which can also be considered as the distributed photovoltaic device connected to the feeder line), the hourly unit capacity photovoltaic output curve of the region where the feeder line is located, and the initial values of the distributed photovoltaic device connected to the feeder line, the distributed photovoltaic device connected to the distribution transformer, and the energy storage capacity connected to the distribution transformer as inputs, and the device parameters required for power flow calculation such as the electrical relationship between the 10-kilovolt feeder line and the distribution transformer, the rated capacity, the impedance, and the power factor as inputs. The decision variable is the charging and discharging power of the energy storage device at each hour of the day, and the optimization target is to minimize the load standard deviation of the distribution network after the energy storage device is connected to the target line, that is, the energy storage device has the best peak clipping and valley filling effect and the best load fluctuation smoothing effect, and its mathematical expression is:

[0051]

[0052] P b =[P b,1 ,P b,2 ,......P b,t ,......P b,T ] T

[0053]

[0054] wherein P b is a vector form of decision variables; P l,t is the power consumption load at time t; P b,t is the charge and discharge power of the energy storage device at time t, greater than 0 when discharging, less than 0 when charging, equal to 0 when floating, neither charging nor discharging; P a is the average load after peak shaving and valley filling by the energy storage in the load sampling period; T is the load sampling period, preferably 24.

[0055] The operation constraints include:

[0056] (1) Energy storage charge and discharge power constraint, expressed by the following formula:

[0057] P ch,max ≤ P b,t ≤ 0

[0058] 0 ≤ P b,t ≤ P dis,max

[0059] wherein P ch,max is the maximum charging power of the energy storage device, which is negative because the power is less than zero when charging; P dis,max is the maximum discharging power of the energy storage device, which is positive because the power is greater than zero when discharging.

[0060] (2) Energy storage remaining capacity constraint

[0061] Since the energy storage device has limited storage capacity, the size of its remaining capacity needs to be considered when charging and discharging. The amount of electricity stored by the energy storage device at any time needs to meet:

[0062]

[0063] wherein E t is the amount of electricity stored by the energy storage device at time t; ξ is the charging coefficient.

[0064] The state of charge (SOC) is introduced to evaluate the remaining capacity:

[0065]

[0066] wherein E is the amount of electricity stored by the current energy storage device; C is the rated capacity of the energy storage device; SOC t is the state of charge at time t; SOC min is the minimum value of the state of charge of the energy storage device; SOC max is the maximum value of the state of charge of the energy storage device.

[0067] Then, the remaining power constraint can be expressed as:

[0068]

[0069] where S t is the working state of the energy storage device at time t, 0 means the energy storage device is in charging state, and 1 means the energy storage device is in discharging state; P dc is the discharging power of the energy storage device at time t; S t = 0, P dc = 0 means that the energy storage device is not in discharging state; η c is the charging efficiency, which represents the efficiency loss of the energy storage device in the charging process, used when charging; P c is the charging power of the energy storage device at time t; S t = 1, P c = 0 means that the energy storage device is not in charging state; η dc is the discharging efficiency, which represents the efficiency loss of the energy storage device in the discharging process, used when discharging.

[0070] (3) Distribution transformer load rate constraint, that is, the distribution transformer cannot be overloaded when running, that is:

[0071]

[0072] where P bz,t is the rated capacity of the distribution transformer; S bz is the active power flowing through the distribution transformer.

[0073] (4) Node voltage constraint, expressed by:

[0074] V min ≤ V s,i,t ≤ V max

[0075] where V s,i,t is the voltage of node i at time t; V min and V max are the lower and upper limits of the node voltage, respectively.

[0076] (5) Power grid flow constraint, expressed by:

[0077] V i,t -V j,t = r ij P ij,t +x ij Q ij,t

[0078]

[0079] where, r ij is the resistance of branch ij; x ij is the reactance of branch ij; Q ij,t is the reactive power of branch ij at time t; P k,t is the active power flowing through branch jk at time t; is the active power of node j at time t; is the active power of node j belonging to distributed photovoltaic at time t; is the maximum active power of node j at time t; Q jk,t is the reactive power flowing through branch jk at time t; is the reactive power of node j at time t; is the maximum reactive power of node j at time t.

[0080] That is, the objective function is to make the load standard deviation minimum by adjusting the energy storage charging and discharging power while meeting the operation constraints, under the given grid structure parameters, energy storage installed capacity, power output, and load demand. Then, the particle swarm optimization algorithm is used to solve the typical day optimal power flow. Since the particle swarm optimization algorithm cannot directly process the constraint conditions, the objective function can be converted into a penalty objective function based on the operation constraints:

[0081]

[0082] where, f p (P b,t ) is the penalty function, which is 0 when the solution of the optimal power flow meets the aforementioned operation constraints, and is a very large number when it does not meet any of the constraints. Thus, the objective function value of the solution that does not meet the constraint condition will be much larger than that of the solution that meets the constraint condition, and the solution that does not meet the constraint condition will be gradually eliminated in the optimization iteration process, so that the solution that meets the condition is finally obtained.

[0083] Then, a number of groups of initial values of energy storage charging and discharging power for each hour are randomly generated within the boundary of power flow calculation, i.e. between the upper and lower limits of energy storage charging and discharging power, each group is called a particle, and the related parameters of the particle swarm optimization algorithm are set, such as the number of particles, inertia weight, learning factor, and iteration number. Then, based on the initial decision variable value, the particle swarm optimization algorithm is used for iterative optimization, the particle position is updated in each iteration, and the power flow of the grid and the penalty objective function of the new particle are calculated, and finally the calculation results of the typical day optimal power flow and the corresponding energy storage charging and discharging power are obtained.

[0084] The application quantifies the power generation of distributed photovoltaics on typical days by using historical data, and builds a power flow calculation boundary based on the data, and then solves the optimal configuration problem of the energy storage device after accessing the target line through the particle swarm algorithm. The scheme based on historical data can more accurately reflect the actual changes of photovoltaic power generation and improve the accuracy of power generation prediction. Not only the power of photovoltaic power generation is considered, but also historical load data and historical capacity data are combined to build a comprehensive power flow calculation boundary, which helps to more accurately simulate the actual operation of the power system and improve the accuracy of power flow calculation. It can be adjusted according to different typical days and different power system operating states, and has strong flexibility and adaptability. The accuracy of the calculation result of the carrying capacity of the distribution network after accessing the distributed photovoltaics and the energy storage device is improved through the time sequence power flow calculation and the distribution network operation constraint, and the calculation scheme used in the application is safer, which can solve the problem that the existing evaluation method does not involve power flow calculation and cannot effectively calculate the overvoltage risk caused by voltage deviation constraint.

[0085] S3、In the over-limit judgment link, the relationship between the power flow calculation result and the operation constraint is used to judge whether the over-limit, and the historical capacity data is updated according to the judgment result;

[0086] In an embodiment, the relationship between the power flow calculation result and the operation constraint is used to judge whether the over-limit, and the historical capacity data is updated according to the judgment result, comprising:

[0087] If the power flow calculation result meets the operation constraint, the power flow calculation result is not over-limit, and the distributed photovoltaic capacity connected to the feeder is increased to the first preset multiple of the original capacity;

[0088] If the power flow calculation result does not meet the operation constraint, the power flow calculation result is over-limit, and the distributed photovoltaic capacity connected to the feeder is reduced to the second preset multiple of the original capacity;

[0089] Wherein, the first preset multiple is 1.05 times, and the second preset multiple is 0.95 times.

[0090] Specifically, the over-limit judgment is to judge whether the power flow calculation result meets the operation constraint. If it meets, it means that it is not over-limit, and then the feeder access distributed photovoltaic capacity is increased to 1.05 times of the original capacity, that is, the result is increased by 5% each time in the iterative judgment, and the proportion coefficient is multiplied by the feeder access distributed photovoltaic capacity to update the distributed photovoltaic capacity of each distribution transformer according to the proportion of the rated capacity of each distribution transformer to the total rated capacity of all distribution transformers, and the energy storage capacity is updated according to the proportion of the distributed photovoltaic capacity. If it does not meet, it means that it is over-limit, and then the feeder access distributed photovoltaic capacity is increased to 0.95 times of the original capacity, that is, the result is decreased by 5% each time in the iterative judgment, and the proportion coefficient is multiplied by the feeder access distributed photovoltaic capacity to update the distributed photovoltaic capacity of each distribution transformer according to the proportion of the rated capacity of each distribution transformer to the total rated capacity of all distribution transformers, and the energy storage capacity is updated according to the proportion of the distributed photovoltaic capacity.

[0091] If the power flow calculation result under a certain distributed photovoltaic access capacity meets the grid operation constraint, it means that the distributed photovoltaic access capacity can make the grid run safely and stably. According to the definition of carrying capacity, "the carrying capacity of distributed photovoltaic access to the grid refers to the maximum installed capacity of distributed photovoltaic that the grid can accommodate under the premise of meeting the safe and stable operation of the grid", when the power flow calculation result meets the grid operation constraint, the carrying capacity should be greater than or equal to the capacity, then the distributed photovoltaic access capacity is continuously increased by a smaller proportion and the optimal power flow calculation is carried out, to judge whether the power flow calculation result can still meet the constraint condition, until the distributed photovoltaic access capacity can no longer meet the grid operation constraint, then the distributed photovoltaic access capacity can no longer meet the safe and stable operation of the grid, that is, the access capacity exceeds the carrying capacity, then the carrying capacity is between the distributed photovoltaic access capacity calculated this time and the last time, when the incremental proportion is small, it can be approximately considered that the carrying capacity is equal to the distributed photovoltaic access capacity calculated last time, then the carrying capacity can be obtained by such a way of continuously increasing or decreasing trial.

[0092] The application can quickly judge whether the power system is in an out-of-limit state through real-time comparison of the power flow calculation result and the operation constraint, and accurate out-of-limit judgment and dynamic adjustment help to discover and handle potential operation problems in time, and ensure the safe and stable operation of the power system; according to the out-of-limit judgment result, the scheme can dynamically adjust the capacity of the feeder connected to the distributed photovoltaic. When the power flow calculation result meets the operation constraint, the photovoltaic capacity is appropriately increased to fully utilize renewable energy; when the power flow calculation result does not meet the operation constraint, the photovoltaic capacity is appropriately reduced to avoid the out-of-limit risk, and this dynamic adjustment strategy helps to realize the optimal configuration of the photovoltaic capacity and the stable operation of the power system; the first preset multiple (1.05 times) and the second preset multiple (0.95 times) set in the scheme are reasonable adjustments under the premise of ensuring the safe operation of the power system, which not only considers the appropriate increase of the photovoltaic capacity to utilize renewable energy, but also avoids the out-of-limit risk caused by excessive photovoltaic capacity.

[0093] S4, repeating the power flow calculation link and the out-of-limit judgment link according to the updated historical capacity data until a loop termination condition is reached, and taking the historical capacity data at the second last power flow calculation as the carrying capacity evaluation result of the distribution network connected to the distributed photovoltaic; wherein the loop termination condition is that the judgment results of two consecutive out-of-limit judgments are opposite.

[0094] Specifically, the application repeats the power flow calculation link and the out-of-limit judgment link according to the updated historical capacity data until the calculation result changes from not out-of-limit to out-of-limit or from out-of-limit to not out-of-limit, and then takes the distributed photovoltaic access capacity of the feeder and the distribution transformer at the last time (i.e. the second last time) as the carrying capacity of the feeder and the distribution transformer connected to the distributed photovoltaic. Another carrying capacity evaluation method for a distribution network is shown in Figure 2 The application can obtain more actual calculation results by considering the new situation of distributed photovoltaic storage, and fully taking into account the regulation effect of energy storage; and the existing evaluation method is mostly based on a single typical time to calculate the carrying capacity in order to improve the calculation speed, but the load and the distributed photovoltaic output have strong seasonal variation characteristics, and a single time cannot effectively represent the variation characteristics, and the representativeness is insufficient, and the calculation result will change greatly with the selection of different time, and the calculation stability is poor, the application performs hourly time sequence power flow simulation calculation on four typical days of the four seasons, which can fully take into account the seasonal and daily variation characteristics of the load and the photovoltaic output while ensuring the calculation efficiency, and obtain calculation results with stronger representativeness and higher stability, so as to improve the accuracy of the carrying capacity evaluation result of the distribution network connected to the distributed photovoltaic and the energy storage.

[0095] The embodiment of the application designs a carrying capacity evaluation method for a power distribution network based on how to improve the evaluation result of the carrying capacity of the power distribution network, which realizes selecting a typical day of the power distribution network, and obtaining historical load data of a target line in the power distribution network in the typical day and historical capacity data of a distributed photovoltaic and energy storage device when the distributed photovoltaic and energy storage device accesses the target line; in a power flow calculation link, the historical load data and the historical capacity data are taken as inputs, a minimum load standard deviation after the energy storage device accesses the target line is taken as an objective function, optimal power flow calculation is performed based on the typical day under the operation constraint of the power distribution network, and a power flow calculation result is obtained; in an out-of-limit judgment link, out-of-limit judgment is performed according to the relationship between the power flow calculation result and the operation constraint, and the historical capacity data is updated according to a judgment result; the power flow calculation link and the out-of-limit judgment link are repeated according to the updated historical capacity data until a cycle termination condition is reached, and the historical capacity data at the second-to-last power flow calculation is taken as a carrying capacity evaluation result of the power distribution network accessing the distributed photovoltaic, and the technical scheme fully takes into account the regulation effect of the energy storage, and improves the accuracy of the carrying capacity evaluation result of the power distribution network with the energy storage and the distributed photovoltaic.

[0096] It should be noted that although each step in the above flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps has no strict order limitation, and these steps can be executed in other orders.

[0097] In another embodiment, as shown in Figure 3 the second aspect of the application provides a carrying capacity evaluation system for a power distribution network, comprising:

[0098] The data acquisition module 10 is configured to select a typical day of the power distribution network, and obtain historical load data of a target line in the power distribution network in the typical day and historical capacity data of a distributed photovoltaic and energy storage device when the distributed photovoltaic and energy storage device accesses the target line.

[0099] The power flow calculation module 20 is configured to, in a power flow calculation link, take the historical load data and the historical capacity data as inputs, take a minimum load standard deviation after the energy storage device accesses the target line as an objective function, perform optimal power flow calculation based on the typical day under the operation constraint of the power distribution network, and obtain a power flow calculation result.

[0100] The data update module 30 is configured to, in an out-of-limit judgment link, perform out-of-limit judgment according to the relationship between the power flow calculation result and the operation constraint, and update the historical capacity data according to a judgment result.

[0101] The carrying capacity evaluation module 40 is configured to repeatedly perform the power flow calculation and the over-limit determination until a loop termination condition is reached, and use the historical capacity data of the second last power flow calculation as the carrying capacity evaluation result of the distribution network to which the distributed photovoltaic is connected.

[0102] It should be noted that the modules in the above-mentioned carrying capacity evaluation system for a distribution network can be realized by software, hardware, or a combination thereof, in whole or in part. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the modules. For specific limitations of a carrying capacity evaluation system for a distribution network, refer to the limitations of a carrying capacity evaluation method for a distribution network, which have the same functions and effects, and will not be described here.

[0103] The third aspect of the application provides an electronic device, comprising:

[0104] a processor, a memory and a bus;

[0105] the bus is configured to connect the processor and the memory;

[0106] the memory is configured to store operation instructions;

[0107] the processor is configured to execute the operation corresponding to the carrying capacity evaluation method for a distribution network by calling the operation instructions.

[0108] In an optional embodiment, an electronic device is provided, as shown in Figure 4 The electronic device 5000 shown in Figure 4 The electronic device 5000 shown in

[0109] The processor 5001 can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor 5001 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0110] The bus 5002 can include a path that transmits information between the above-described components. The bus 5002 can be a PCI bus or an EISA bus, etc. The bus 5002 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 4 In the figure, only one thick line is used, but this does not mean that there is only one bus or one type of bus.

[0111] The memory 5003 can be a ROM, or other type of static storage device that can store static information and instructions; a RAM, or other type of dynamic storage device that can store information and instructions; an EEPROM, a CD-ROM or other optical disk storage, a magnetic disk storage or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0112] The memory 5003 is used to store application program codes for implementing the scheme of the present application, and is controlled by the processor 5001 to execute. The processor 5001 is used to execute the application program codes stored in the memory 5003 to realize the content shown in any of the foregoing method embodiments.

[0113] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a car terminal (e.g., a car navigation terminal), etc., and a fixed terminal such as a digital TV, a desktop computer, etc.

[0114] The fourth aspect of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the program is executed by a processor to implement the method for carrying capacity evaluation of a power distribution network according to the first aspect of the present application.

[0115] Another embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program is run on a computer, the computer can execute the corresponding content in the foregoing method embodiments.

[0116] In addition, an embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the program is executed by a processor to implement the steps of the foregoing method.

[0117] In summary, the present application relates to the technical field of data processing, and discloses a carrying capacity evaluation method, system, device and medium for a power distribution network, which selects a typical day of the power distribution network, and according to historical load data of a target line in the typical day and historical capacity data when a distributed photovoltaic and energy storage device is accessed, takes the minimum standard deviation of the load after the energy storage device is accessed as an objective function, performs optimal power flow calculation under the operation constraints of the power distribution network to obtain a power flow calculation result, performs out-of-limit judgment according to the relationship between the power flow calculation result and the operation constraints of the power distribution network, updates the historical capacity data according to the judgment result, repeats the power flow calculation and out-of-limit judgment steps using the updated historical capacity data until a cycle termination condition is reached, and takes the historical capacity data at the second last power flow calculation as the final carrying capacity evaluation result, thereby fully taking into account the energy storage regulation effect and improving the accuracy of the carrying capacity calculation result of the power distribution network with the access of the energy storage and the distributed photovoltaic.

[0118] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment. It should be noted that each technical feature of the above embodiments can be combined arbitrarily, in order to make the description simple, each technical feature of the above embodiments is not described all possible combinations, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the specification.

[0119] The above-described embodiments only express several preferred embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and replacements can be made, and these improvements and replacements should be considered as the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the protection scope of the claims.

Claims

1. A method for carrying capacity assessment of a power distribution network, characterized in that, The method comprises the following steps: selecting a typical day of a power distribution network, and obtaining historical load data of a target line in the power distribution network in the typical day and historical capacity data of a distributed photovoltaic and energy storage device when the distributed photovoltaic and energy storage device is connected to the target line; in a power flow calculation link, the historical load data and the historical capacity data are taken as inputs, a minimum load standard deviation after the energy storage device is connected to the target line is taken as an objective function, and optimal power flow calculation is performed based on the typical day under the operation constraint of the power distribution network to obtain a power flow calculation result; in an over-limit judgment link, over-limit judgment is performed according to the relationship between the power flow calculation result and the operation constraint, and the historical capacity data is updated according to the judgment result; the power flow calculation link and the over-limit judgment link are repeated according to the updated historical capacity data until a cycle termination condition is reached, and the historical capacity data at the time of the second-to-last power flow calculation is taken as a carrying capacity evaluation result of the power distribution network connected to the distributed photovoltaic.

2. The method for carrying capacity assessment of a power distribution network according to claim 1, wherein, The target line comprises a 10-kilovolt feeder and a connected distribution transformer.

3. The method for carrying capacity assessment of a power distribution network according to claim 2, wherein, The typical day comprises dates with minimum power flowing through the first section of the feeder selected from dates in each of the four seasons of a year; the historical capacity data comprises feeder connection distributed photovoltaic capacity, distribution transformer connection distributed photovoltaic capacity, and distribution transformer connection energy storage capacity.

4. The method for carrying capacity assessment of a power distribution network according to claim 1, wherein, The power flow calculation link comprises the following steps: quantifying power generation of the distributed photovoltaic in the typical day according to the historical load data and the historical capacity data, and constructing a power flow calculation boundary according to the power generation, the historical load data, and the historical capacity data; taking a minimum load standard deviation after the energy storage device is connected to the target line as an objective function, and converting the objective function into a penalty objective function based on the operation constraint; solving the penalty objective function in the power flow calculation boundary by a particle swarm algorithm to obtain a power flow calculation result.

5. The method for carrying capacity assessment of a power distribution network according to claim 3, wherein, The over-limit judgment link comprises the following steps: if the power flow calculation result satisfies the operation constraint, the power flow calculation result is not over-limit, and the feeder connection distributed photovoltaic capacity is increased to a first preset multiple of the original capacity; if the power flow calculation result does not satisfy the operation constraint, the power flow calculation result is over-limit, and the feeder connection distributed photovoltaic capacity is reduced to a second preset multiple of the original capacity.

6. The method for carrying capacity assessment of a power distribution network according to claim 5, wherein, The first preset multiple is 1.05 times, and the second preset multiple is 0.95 times.

7. The method for carrying capacity assessment of a power distribution network according to claim 1, wherein, The cycle termination condition is that the judgment results of two consecutive over-limit judgments are opposite.

8. A carrying capacity assessment system for a power distribution network, characterized by, The method comprises the following steps: a data acquisition module is configured to select a typical day of a power distribution network, and obtain historical load data of a target line in the power distribution network in the typical day and historical capacity data of a distributed photovoltaic and energy storage device when the distributed photovoltaic and energy storage device is connected to the target line; The power flow calculation module is configured to, in a power flow calculation link, take the historical load data and the historical capacity data as inputs, take a minimum load standard deviation of the energy storage device after being connected to the target line as an objective function, perform optimal power flow calculation based on the typical day under operation constraints of the power distribution network, and obtain a power flow calculation result. The data updating module is configured to, in an out-of-limit judgment link, perform out-of-limit judgment according to a relationship between the power flow calculation result and the operation constraints, and update the historical capacity data according to a judgment result. The carrying capacity evaluation module is configured to repeat the power flow calculation link and the out-of-limit judgment link according to the updated historical capacity data until a loop termination condition is reached, and take historical capacity data at a second-to-last power flow calculation as a carrying capacity evaluation result of the power distribution network connected to the distributed photovoltaic.

9. An electronic device, comprising: The computer readable storage medium comprises a stored computer program, wherein a device where the computer readable storage medium is located implements the carrying capacity evaluation method of the power distribution network according to any one of claims 1 to 7 when the computer program is executed.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein a device where the computer readable storage medium is located implements the carrying capacity evaluation method of the power distribution network according to any one of claims 1 to 7 when the computer program is executed.

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