Bearing capacity evaluation method, system and equipment for power distribution network and medium

Abstract

The invention discloses a bearing capacity evaluation method, system and equipment for a power distribution network and a medium, and the method comprises the steps: selecting a typical day of the power distribution network, and obtaining a bearing capacity evaluation result according to the historical load data of a target line in the typical day and the historical capacity data of distributed photovoltaic and energy storage equipment when the distributed photovoltaic and energy storage equipment is connected; taking the minimum load standard deviation after the energy storage equipment is connected as an objective function, and performing optimal power flow calculation under the operation constraint of the power distribution network to obtain a power flow calculation result; and performing out-of-limit judgment according to the relationship between the load flow calculation result and the operation constraint of the power distribution network, updating the historical capacity data according to the judgment result, and repeating the load flow calculation link and the out-of-limit judgment link by using the updated historical capacity data until a cycle termination condition is reached. And taking historical capacity data in the last but one load flow calculation as a final bearing capacity evaluation result. And the energy storage adjustment effect is fully considered, and the accuracy of the bearing capacity evaluation result of the power distribution network with energy storage access and distributed photovoltaic access is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, device and medium for evaluating the carrying capacity of a distribution network. Background Art

[0002] Distributed photovoltaic power is a type of distributed power source. Its large-scale access to the grid will cause the power generation to be greater than the load power consumption, resulting in a series of problems that affect the safe and stable operation of the power grid, such as reverse flow, reverse overload of equipment, and voltage over-limit. In order to promote the coordinated development of distributed photovoltaic power and the power grid, it is urgent to reasonably evaluate the carrying capacity of distributed photovoltaic power access to the grid. At present, the evaluation of the carrying capacity of distributed power sources connected to the grid is usually calculated by using the power value flowing through the equipment at a typical moment, without considering the regulating role of energy storage equipment resources, which makes the evaluation result smaller.

[0003] It can be seen that how to improve the carrying capacity assessment results of the distribution network has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

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

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a method for evaluating the carrying capacity of a distribution network, comprising:

[0006] Select a typical day of the distribution network, and obtain historical load data of a target line in the distribution network on the typical day and historical capacity data of distributed photovoltaic and energy storage equipment when connected to the target line;

[0007] In the power flow calculation link, the historical load data and the historical capacity data are used as inputs, the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result;

[0008] In the over-limit judgment link, an 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;

[0009] The power flow calculation link and the over-limit judgment link are repeated according to the updated historical capacity data until the loop termination condition is reached, and the historical capacity data during the penultimate power flow calculation is used as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic.

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

[0011] As one of the preferred schemes, the typical day includes the date with the minimum power when flowing through the first section of the feeder selected from the four seasons of the year; the historical capacity data includes the feeder connected to the distributed photovoltaic capacity, the distribution transformer connected to the distributed photovoltaic capacity and the distribution transformer connected to the energy storage capacity.

[0012] As one of the preferred solutions, the historical load data and the historical capacity data are used as input, the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result, including:

[0013] quantifying the power generation of the distributed photovoltaic on 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;

[0014] Taking the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, and based on the operation constraints, converting the objective function into a penalty objective function;

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

[0016] As one of the preferred solutions, the step of making an over-limit judgment based on the relationship between the power flow calculation result and the operation constraint, and updating the historical capacity data based on the judgment result, includes:

[0017] If the power flow calculation result satisfies the operation constraint, the power flow calculation result is within the limit, and the distributed photovoltaic capacity connected to the feeder is increased to a first preset multiple of the original capacity;

[0018] If the power flow calculation result does not meet the operation constraint, the power flow calculation result exceeds the limit, and the feeder-connected 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 loop termination condition is that there are two consecutive limit crossing judgments with opposite judgment results.

[0021] A second aspect of the present invention provides a carrying capacity assessment system for a distribution network, comprising:

[0022] A data acquisition module is used to select a typical day of the distribution network and obtain the historical load data of the target line in the distribution network on the typical day and the historical capacity data of the distributed photovoltaic and energy storage equipment when connected to the target line;

[0023] A power flow calculation module, used to take the historical load data and the historical capacity data as input in the power flow calculation link, take the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, perform optimal power flow calculation based on the typical day under the operation constraints of the distribution network, and obtain a power flow calculation result;

[0024] A data updating module, used to make an over-limit judgment according to the relationship between the power flow calculation result and the operation constraint in the over-limit judgment link, and update the historical capacity data according to the judgment result;

[0025] The carrying capacity assessment module is used to repeat the power flow calculation link and the over-limit judgment link according to the updated historical capacity data until the loop termination condition is reached, and use the historical capacity data during the penultimate power flow calculation as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic.

[0026] A third aspect of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the distribution network carrying capacity assessment method as described above when executing the computer program.

[0027] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the method for evaluating the carrying capacity of the distribution network as described above is implemented.

[0028] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0029] (1) Based on historical data of typical days and optimal power flow calculations, and taking into account the regulation effect of energy storage, the carrying capacity of the distribution network after connecting to distributed photovoltaic and energy storage equipment can be more accurately evaluated, providing a reliable basis for grid planning and operation;

[0030] (2) Through the over-limit judgment and data update process, the access capacity of distributed photovoltaic and energy storage equipment is dynamically adjusted, ensuring that the distribution network can maintain stable operation after the access of new equipment. The objective function is to minimize the load standard deviation after the energy storage equipment is connected, which helps to balance the load fluctuations of the distribution network and improve the stability and economy of the power grid.

[0031] (3) The accuracy of the calculation results of the carrying capacity of the distribution network after connecting to the distributed photovoltaic and energy storage equipment is improved through time-series power flow calculation and distribution network operation constraints. The calculation scheme used in the present invention is more secure and can solve the problem that the existing evaluation method does not involve power flow calculation and cannot effectively calculate the voltage deviation constraint, resulting in the risk of overvoltage in the calculation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 is a flow chart of a method for evaluating the carrying capacity of a distribution network provided by a certain embodiment of the present invention;

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

[0035] Figure 3 is a structural diagram of a carrying capacity assessment system for a distribution network provided by a certain embodiment of the present invention;

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

[0037] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0039] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

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

[0041] In one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a method for evaluating the carrying capacity of a distribution network, comprising:

[0042] S1. Select a typical day of the distribution network, and obtain the historical load data of the target line in the distribution network on the typical day and the historical capacity data of the distributed photovoltaic and energy storage equipment when connected to the target line;

[0043] Specifically, the target line includes a 10 kV feeder and a connected distribution transformer, that is, the scheme of the present invention is suitable for evaluating the carrying capacity of the distribution network after the 10 kV feeder line and the 10 kV distribution transformer are connected to distributed photovoltaic and energy storage equipment; wherein, the typical day includes the date with the minimum power when flowing through the first section of the feeder selected from the four seasons of the year. In order to improve the calculation efficiency, the present invention selects the day with the minimum power flowing through the first section of the feeder from the four seasons of spring, summer, autumn and winter as the typical day for calculation. The historical load data includes the hourly load curves of 4 typical days and the hourly unit capacity photovoltaic output curve of the area where the feeder is located. The historical capacity data includes the feeder access to distributed photovoltaic capacity, the distribution transformer access to distributed photovoltaic capacity and the distribution transformer access to energy storage capacity; wherein, the initial value of the feeder access to distributed photovoltaic capacity is set to 50% of the feeder capacity, the initial value of the distributed photovoltaic capacity connected to each distribution transformer is 50% of the feeder capacity divided by the total number of distribution transformers, and the energy storage capacity is the distributed photovoltaic capacity multiplied by the distribution storage coefficient. The acquired data also includes the electrical relationship between the 10 kV feeder and the distribution transformer, the rated capacity, impedance, power factor and other equipment parameters required to calculate the grid flow, as well as the charging and discharging parameters of common energy storage equipment in the area where the feeder is located.

[0044] S2. In the power flow calculation link, the historical load data and the historical capacity data are used as inputs, the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result;

[0045] In one embodiment, the historical load data and the historical capacity data are used as inputs, the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result, including:

[0046] quantifying the power generation of the distributed photovoltaic on 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;

[0047] Taking the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, and based on the operation constraints, converting the objective function into a penalty objective function;

[0048] Within the power flow calculation boundary, the penalty objective function is solved by a particle swarm algorithm to obtain a power flow calculation result.

[0049] Specifically, the present invention adopts a time-series power flow simulation method to calculate the carrying capacity of the target line (such as a 10 kV feeder and the connected distribution transformer) in the distribution network connected to the distributed photovoltaic, that is, based on a certain increasing input variable, according to the specified distribution network operation target and operation constraints, the distribution network is simulated hourly in time sequence to calculate the key operation parameters of the power grid including the power grid power flow. However, in fact, the distributed photovoltaic and energy storage equipment are mainly connected to the distribution transformer of the target line. Therefore, in this embodiment, the feeder connected to the distributed photovoltaic and energy storage capacity is the sum of the distributed photovoltaic and energy storage connected to all the distribution transformers connected to the feeder, and the distributed photovoltaic is set to be evenly connected to the distribution transformer connected to the feeder, that is, the distributed photovoltaic capacity connected to each distribution transformer is the feeder connected distributed photovoltaic capacity divided by the number of distribution transformers connected to the feeder, and at the same time, according to the differentiated distributed photovoltaic storage ratio stipulated in various places, the energy storage capacity connected to each distribution transformer is set to be the product of the distributed photovoltaic capacity and the storage ratio. Distributed photovoltaics are not subject to grid regulation, that is, the distributed photovoltaic power generation power at each moment is equal to the product of the distributed photovoltaic installed capacity and the photovoltaic output per unit capacity at that moment. The photovoltaic output per unit capacity at each moment can be obtained by collecting the photovoltaic output curve.

[0050] Since the optimal power flow is a kind of optimization problem, it is to find the power flow distribution that satisfies all operation constraints and makes a certain performance indicator of the system (such as power generation cost or network loss) reach the optimal value by adjusting the power generation of power supply, energy storage charging and discharging power, adjustable transformer tap, etc. at each moment under given boundary conditions such as grid structure parameters and load conditions. Then, the present invention uses the hourly load curves of 4 typical days and the power generation power calculated with the distributed photovoltaic installed capacity (which can also be considered as the feeder access to distributed photovoltaic capacity), the hourly unit capacity photovoltaic output curve of the area where the feeder is located, and the initial values ​​of various capacity data such as the feeder access to distributed photovoltaic capacity, the distribution transformer access to distributed photovoltaic capacity, and the distribution transformer access to energy storage capacity, as well as the electrical relationship between the 10 kV feeder and the distribution transformer, the rated capacity, impedance, power factor, etc., required for calculating the power flow of the power grid as input to construct the power flow calculation boundary, 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 peak shaving and valley filling, and the best effect of smoothing load fluctuations, and its mathematical expression is:

[0051]

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

[0053]

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

[0055] Operational constraints include:

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

[0057] P ch,max ≤P b,t ≤0

[0058] 0≤P b,t ≤P dis,max

[0059] Where P ch,max is the maximum charging power of the energy storage device. Since the power is less than zero during charging, it is a negative value; P dis,max It is the maximum discharge power of the energy storage device. Since the power is greater than zero during discharge, it is a positive value.

[0060] (2) Constraints on remaining energy storage capacity

[0061] Since the energy storage capacity of the energy storage device is limited, the remaining power must be considered during charging and discharging. The amount of power stored in the energy storage device at any time must meet the following requirements:

[0062]

[0063] In the formula, E t is the amount of electricity stored in the energy storage device at time t; ξ is the charging coefficient.

[0064] Introduce the state of charge (SOC) to evaluate the remaining power:

[0065]

[0066] In the formula, E is the amount of electricity currently stored in the 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 SOC is the minimum value of the state of charge of the energy storage device; max It is the maximum value of the state of charge of the energy storage device.

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

[0068]

[0069] In the formula, S t is the working state of the energy storage device at time t, 0 means the energy storage device is in the charging state, and 1 means the energy storage device is in the discharging state; P dc is the discharge power of the energy storage device at time t; S t =0,P dc =0 indicates that the energy storage device is not in a discharging state; η c P is the charging efficiency, which indicates the efficiency loss of the energy storage device during the charging process and is used during charging; c is the charging power of the energy storage device at time t; S t =1,P c =0 indicates that the energy storage device is not in a charging state; η dc It is the discharge efficiency, which indicates the efficiency loss of the energy storage device during the discharge process and is used during discharge.

[0070] (3) Distribution transformer load factor constraint, that is, the distribution transformer cannot be overloaded during operation, namely:

[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 as follows:

[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) Grid flow constraint, expressed as follows:

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

[0078]

[0079] In the formula, 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 distributed photovoltaic node j 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] In other words, the objective function is that the distribution network, under given grid structure parameters, energy storage installed capacity, power output, and load demand, adjusts the energy storage charging and discharging power to minimize the load standard deviation while satisfying the operation constraints. Then the particle swarm algorithm is used to solve the typical day optimal power flow. Since the particle swarm algorithm cannot directly handle the constraints, the objective function can be converted into a penalty objective function based on the operation constraints:

[0081]

[0082] In the formula, f p (P b,t ) is a penalty function. It is 0 when the optimal power flow solution meets the aforementioned operating constraints, and a large value when it does not meet any of them. In this way, the objective function value of the solution that does not meet the constraints will be much larger than the solution that meets the constraints. The solution that does not meet the constraint adjustment will be gradually eliminated during the optimization iteration process, and finally a solution that meets the conditions will be obtained.

[0083] Then, within the boundary of the power flow calculation, that is, between the upper and lower limits of the energy storage charging and discharging power, several groups of initial values ​​of the energy storage charging and discharging power for each hour are randomly generated. Each group is called a particle, and the relevant parameters of the particle swarm algorithm are set, such as the number of particles, inertia weight, learning factor, number of iterations, etc.; then based on the initial decision variable value, the particle swarm algorithm is used to iteratively search for the best, the particle position is updated in each iteration, and the power grid power flow and penalty objective function under the new particle are calculated, and finally the optimal power flow calculation results of a typical day and the corresponding energy storage charging and discharging power are obtained.

[0084] The present invention uses historical data to quantify the power generation of distributed photovoltaics on typical days, and constructs a flow calculation boundary based on these data, and then solves the optimal configuration problem after the energy storage device is connected to the target line through a particle swarm algorithm. The scheme is based on historical data and can more accurately reflect the actual changes in photovoltaic power generation and improve the accuracy of power generation prediction; it not only takes into account the power of photovoltaic power generation, but also combines historical load data and historical capacity data to construct a comprehensive flow calculation boundary, which helps to more accurately simulate the actual operation of the power system and improve the accuracy of the flow calculation, and it can be adjusted according to different typical days and different power system operation states, with strong flexibility and adaptability; through time-series flow calculation and distribution network operation constraints to improve the accuracy of the distribution network carrying capacity calculation results after connecting to distributed photovoltaic and energy storage devices, and the calculation scheme used in the present invention is more secure, which can solve the problem that the existing evaluation method does not involve flow calculation and cannot effectively calculate the voltage deviation constraint, resulting in overvoltage risk in the calculation results.

[0085] S3. In the over-limit judgment link, an over-limit judgment is performed based on the relationship between the power flow calculation result and the operation constraint, and the historical capacity data is updated based on the judgment result;

[0086] In one embodiment, performing a limit crossing judgment based on a relationship between the power flow calculation result and the operation constraint, and updating the historical capacity data based on the judgment result, includes:

[0087] If the power flow calculation result satisfies the operation constraint, the power flow calculation result is within the limit, and the distributed photovoltaic capacity connected to the feeder is increased to a 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 exceeds the limit, and the distributed photovoltaic capacity connected to the feeder is reduced to a second preset multiple of the original capacity;

[0089] Among them, 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 is satisfied, it means that it is not over-limited, and the distributed photovoltaic capacity connected to the feeder is increased to 1.05 times of the original capacity, that is, it is increased by 5% each time under the result during iterative judgment. The proportional coefficient is updated by multiplying the distributed photovoltaic capacity connected to the feeder according to the ratio of the rated capacity of each distribution transformer to the total rated capacity of all distribution transformers, and the energy storage capacity is updated by multiplying the distributed photovoltaic capacity by the distribution-storage ratio; if it is not satisfied, it means that it is over-limited, and the distributed photovoltaic capacity connected to the feeder is increased to 0.95 times of the original capacity, that is, it is decreased by 5% each time under the result during iterative judgment, and the distributed photovoltaic capacity connected to the feeder is updated by multiplying the rated capacity of each distribution transformer to the total rated capacity of all distribution transformers by the distributed photovoltaic capacity, and the energy storage capacity is updated by multiplying the distributed photovoltaic capacity by the distribution-storage ratio.

[0091] If the power flow calculation results under a certain distributed photovoltaic access capacity meet the grid operation constraints, it means that the distributed photovoltaic access capacity can make the grid operate safely and stably. According to the definition of carrying capacity, "the distributed photovoltaic access grid carrying capacity refers to the maximum installed capacity of distributed photovoltaics that the grid can accept under the premise of meeting the safe and stable operation of the grid", when the power flow calculation results meet the grid operation constraints, the carrying capacity should be greater than or equal to this capacity, and then the distributed photovoltaic access capacity is continuously increased in a small proportion and the optimal power flow calculation is performed to determine whether the power flow calculation results can still meet the constraints, until a certain distributed photovoltaic access capacity can no longer meet the grid operation constraints. At this time, 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 input in this calculation and the last calculation. When the increasing ratio is small, it can be approximately considered that the carrying capacity is equal to the distributed photovoltaic access capacity input in the last calculation. Then the carrying capacity can be calculated by such a continuous increasing or decreasing trial method.

[0092] The present invention can quickly determine whether the power system is in an over-limit state through real-time comparison of the flow calculation results and the operation constraints. Accurate over-limit judgment and dynamic adjustment help to timely discover and deal with potential operation problems, ensuring the safe and stable operation of the power system; according to the over-limit judgment results, the scheme can dynamically adjust the capacity of the feeder connected to the distributed photovoltaic. When the flow calculation results meet the operation constraints, the photovoltaic capacity is appropriately increased to make full use of renewable energy; when the flow calculation results do not meet the operation constraints, the photovoltaic capacity is appropriately reduced to avoid the risk of over-limit. This dynamic adjustment strategy helps to achieve the optimal configuration of 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 made under the premise of ensuring the safe operation of the power system, which not only takes into account the appropriate increase of photovoltaic capacity to utilize renewable energy, but also avoids the risk of over-limit caused by excessive photovoltaic capacity.

[0093] S4. Repeat the power flow calculation step and the over-limit judgment step according to the updated historical capacity data until the loop termination condition is reached, and use the historical capacity data during the penultimate power flow calculation as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic power generation; wherein, the loop termination condition is that the results of two consecutive over-limit judgments are opposite.

[0094] Specifically, the present invention repeats the power flow calculation link and the over-limit judgment link according to the updated historical capacity data until a certain calculation result changes from not over-limit to over-limit or from over-limit to not over-limit, then the last (that is, the second to last) distributed photovoltaic access capacity of the feeder and distribution transformer is used as the feeder and distribution transformer access distributed photovoltaic carrying capacity. Another method for evaluating the carrying capacity of a distribution network is as follows: Figure 2 As shown, the present invention takes into account the new situation of distributed photovoltaic storage and fully accounts for the regulating effect of energy storage, and can obtain calculation results that are more in line with actual needs; and the existing evaluation methods are mostly based on a single typical moment to calculate the carrying capacity in order to improve the calculation speed, but the load and distributed photovoltaic output have strong seasonal variation characteristics, and it is difficult to effectively express the variation characteristics at a single moment, and its representativeness is insufficient. The calculation results will change greatly with the selection of different moments, and the calculation stability is poor. The present invention selects typical days of the four seasons to perform hourly time series flow simulation calculations, while ensuring the calculation efficiency, it can fully account for the seasonal changes and intraday changes of load and photovoltaic output, and obtain calculation results with stronger representativeness and higher stability, so as to improve the accuracy of the carrying capacity evaluation results of the distribution network with energy storage access and distributed photovoltaic access.

[0095] In the embodiment of the present application, based on the problem of how to improve the carrying capacity assessment result of the distribution network, a carrying capacity assessment method for the distribution network is designed, which realizes the selection of a typical day of the distribution network, and obtains the historical load data of the target line in the distribution network during the typical day and the historical capacity data when the distributed photovoltaic and energy storage devices are connected to the target line; in the power flow calculation link, the historical load data and the historical capacity data are used as input, and the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result; in the over-limit judgment link, the 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 the loop termination condition is reached, and the historical capacity data at the penultimate power flow calculation is used as the technical solution of the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic; it fully takes into account the regulation effect of energy storage, and improves the accuracy of the carrying capacity assessment result of the distribution network with energy storage access and distributed photovoltaic access.

[0096] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0097] In another embodiment, if Figure 3 As shown, the second aspect of the present invention provides a carrying capacity assessment system for a distribution network, comprising:

[0098] The data acquisition module 10 is used to select a typical day of the distribution network and obtain the historical load data of the target line in the distribution network on the typical day and the historical capacity data of the distributed photovoltaic and energy storage equipment when connected to the target line;

[0099] The power flow calculation module 20 is used to take the historical load data and the historical capacity data as input in the power flow calculation link, take the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, perform optimal power flow calculation based on the typical day under the operation constraints of the distribution network, and obtain the power flow calculation result;

[0100] The data updating module 30 is used to make an over-limit judgment according to the relationship between the power flow calculation result and the operation constraint in the over-limit judgment link, and update the historical capacity data according to the judgment result;

[0101] The carrying capacity assessment module 40 is used to repeat the power flow calculation link and the over-limit judgment link according to the updated historical capacity data until the loop termination condition is reached, and use the historical capacity data during the penultimate power flow calculation as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic.

[0102] It should be noted that each module in the above-mentioned carrying capacity assessment system based on the distribution network can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a carrying capacity assessment system for a distribution network, please refer to the definition of a carrying capacity assessment method for a distribution network above. The two have the same functions and effects, which will not be repeated here.

[0103] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0104] processor, memory, and bus;

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

[0106] The memory is used to store operation instructions;

[0107] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to a method for evaluating the carrying capacity of a distribution network as shown in the first aspect of the present application.

[0108] In an alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0109] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0110] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] The memory 5003 may 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, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0112] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0113] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0114] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for evaluating the carrying capacity of a distribution network as shown in the first aspect of the present application is implemented.

[0115] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0116] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0117] In summary, the present invention relates to the field of data processing technology, and discloses a method, system, device and medium for evaluating the carrying capacity of a distribution network. The method selects a typical day of the distribution network, and based on the historical load data of the target line on the typical day and the historical capacity data when the distributed photovoltaic and energy storage devices are connected, the method takes the minimum standard deviation of the load after the energy storage device is connected as the objective function, and performs optimal power flow calculation under the operation constraints of the distribution network to obtain the power flow calculation result; an over-limit judgment is performed according to the relationship between the power flow calculation result and the operation constraints of the distribution network, and the historical capacity data is updated according to the judgment result, and the power flow calculation link and the over-limit judgment link are repeated using the updated historical capacity data until the loop termination condition is reached, and the historical capacity data at the penultimate power flow calculation is used as the final carrying capacity evaluation result; the regulation effect of energy storage is fully taken into account, and the accuracy of the carrying capacity calculation result of the distribution network with energy storage access and distributed photovoltaic access is improved.

[0118] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences 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 relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for evaluating the carrying capacity of a distribution network, characterized in that: include: Select a typical day of the distribution network, and obtain historical load data of a target line in the distribution network on the typical day and historical capacity data of distributed photovoltaic and energy storage equipment when connected to the target line; In the power flow calculation link, the historical load data and the historical capacity data are used as inputs, the load standard deviation after the energy storage device is connected to the target line is minimized as the objective function, and the optimal power flow calculation is performed based on the typical day under the operation constraints of the distribution network to obtain the power flow calculation result; In the over-limit judgment link, an 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 the loop termination condition is reached, and the historical capacity data during the penultimate power flow calculation is used as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic.

2. A method for evaluating the carrying capacity of a distribution network according to claim 1, characterized in that: The target line includes a 10 kV feeder and a connected distribution transformer.

3. A method for evaluating the carrying capacity of a distribution network according to claim 2, characterized in that: The typical day includes the date with the minimum power when flowing through the first section of the feeder selected from the four seasons of the year; the historical capacity data includes the feeder connected to the distributed photovoltaic capacity, the distribution transformer connected to the distributed photovoltaic capacity and the distribution transformer connected to the energy storage capacity.

4. A method for evaluating the carrying capacity of a distribution network according to claim 1, characterized in that: The method uses the historical load data and the historical capacity data as input, takes the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, performs optimal power flow calculation based on the typical day under the operation constraints of the distribution network, and obtains the power flow calculation result, including: quantifying the power generation of the distributed photovoltaic on 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 the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, and based on the operation constraints, converting the objective function into a penalty objective function; Within the power flow calculation boundary, the penalty objective function is solved by a particle swarm algorithm to obtain a power flow calculation result.

5. A method for evaluating the carrying capacity of a distribution network according to claim 3, characterized in that: The step of performing over-limit judgment based on the relationship between the power flow calculation result and the operation constraint, and updating the historical capacity data based on the judgment result, includes: If the power flow calculation result satisfies the operation constraint, the power flow calculation result is within the limit, and the distributed photovoltaic capacity connected to the feeder is increased to a first preset multiple of the original capacity; If the power flow calculation result does not meet the operation constraint, the power flow calculation result exceeds the limit, and the feeder-connected distributed photovoltaic capacity is reduced to a second preset multiple of the original capacity.

6. A method for evaluating the carrying capacity of a distribution network according to claim 5, characterized in that: The first preset multiple is 1.05 times, and the second preset multiple is 0.95 times.

7. A method for evaluating the carrying capacity of a distribution network according to claim 1, characterized in that: The loop termination condition is that the judgment results of two consecutive limit crossing judgments are opposite.

8. A load-carrying capacity assessment system for a distribution network, characterized in that: include: A data acquisition module is used to select a typical day of the distribution network and obtain the historical load data of the target line in the distribution network on the typical day and the historical capacity data of the distributed photovoltaic and energy storage equipment when connected to the target line; A power flow calculation module, used to take the historical load data and the historical capacity data as input in the power flow calculation link, take the minimum load standard deviation after the energy storage device is connected to the target line as the objective function, perform optimal power flow calculation based on the typical day under the operation constraints of the distribution network, and obtain a power flow calculation result; A data updating module, used to make an over-limit judgment according to the relationship between the power flow calculation result and the operation constraint in the over-limit judgment link, and update the historical capacity data according to the judgment result; The carrying capacity assessment module is used to repeat the power flow calculation link and the over-limit judgment link according to the updated historical capacity data until the loop termination condition is reached, and use the historical capacity data during the penultimate power flow calculation as the carrying capacity assessment result of the distribution network connected to the distributed photovoltaic.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the carrying capacity of a distribution network as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the method for evaluating the carrying capacity of the distribution network as described in any one of claims 1 to 7 is implemented.

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