Photovoltaic power distribution system cross-period collaborative security assessment method and device

Abstract

The invention provides a photovoltaic power distribution system cross-period collaborative safety assessment method and device, which can be applied to the technical field of power distribution systems, and can be used for analyzing a photovoltaic power distribution system according to a yoke process table to obtain a photovoltaic cross-period prediction error and a load cross-period prediction error; quantifying the photovoltaic cross-period prediction error and the load cross-period prediction error to obtain a first probability density function and a second probability density function; constructing a safety evaluation model of each subject by using the photovoltaic cross-period prediction error, the load cross-period prediction error, the first probability density function and the second probability density function; constructing a cross-period collaborative security assessment model based on each security assessment model; using an augmented Benders decomposition method to solve the cross-period collaborative security assessment model to obtain a security assessment result; and identifying the operation state of the photovoltaic power distribution system based on the safety evaluation result. According to the scheme, the purpose of performing cross-period safety evaluation on the high-proportion photovoltaic power distribution system on the premise of protecting privacy and considering photovoltaic and load cross-period uncertainty is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution systems, and in particular to a cross-period collaborative security assessment method and device for a photovoltaic distribution system. Background Art

[0002] Photovoltaic, as a widely distributed renewable energy source, is accelerating its penetration into distribution systems to reduce carbon emissions, improve economic efficiency, and support the autonomous operation of distribution systems. However, in a high-proportion photovoltaic distribution system, there is a complex superposition of the uncertainty of photovoltaic power output and the uncertainty of load demand, posing a severe challenge to operation safety. At the same time, a distribution system usually not only includes the public distribution part but also autonomous distribution parts such as microgrids and industrial parks. In actual engineering, the public distribution part and the autonomous distribution parts belong to different entities, and each entity is reluctant to disclose the privacy details of its local model due to collaborative security assessment. Therefore, it is necessary to collaboratively conduct the security assessment of a high-proportion photovoltaic distribution system among multiple entities while considering privacy protection. More critically, in traditional distribution systems with low or no photovoltaic penetration, the coupling of system operation states between adjacent periods is weak, and the value of cross-period security assessment is limited. The security assessment is limited to a single period such as day-ahead and intra-day. In contrast, a high-proportion photovoltaic distribution system, as a typical weather-sensitive system, is affected by continuous abnormal weather such as cold snaps and rainy days, and there is a relatively obvious cross-period coupling in the system operation state. With the significant increase in the uncertainty of photovoltaic and load forecasts during cross-period evolution, the possibility of the system falling into an unsafe state gradually increases, and the uncertainty characterization method in the corresponding security assessment model needs to be adjusted accordingly. Therefore, the security assessment of a high-proportion photovoltaic distribution system not only needs to ensure privacy protection during collaborative solution by multiple entities but also needs to consider the local quantification and characterization of cross-period uncertainty, making the centralized method no longer applicable.

[0003] Existing distributed methods are mainly divided into dual decomposition methods represented by the Lagrangian method and primal decomposition methods generated by column sum constraints. Although the dual decomposition method performs excellently in convex models, a high-proportion photovoltaic system is often equipped with a large number of distributed energy storages, and the binary variables of their charge / discharge states result in a non-convex mixed-integer form of the model, making it difficult to guarantee the convergence of the dual decomposition method. Although the primal decomposition method can handle mixed-integer models, it needs to copy some information during iteration and cannot fully achieve privacy protection among multiple entities.

[0004] In summary, how to conduct cross-period security assessment of a high-proportion photovoltaic distribution system while protecting privacy and considering the cross-period uncertainty of photovoltaic and load is an urgent problem to be solved at present. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a method and device for cross-period collaborative security assessment of a photovoltaic power distribution system, so as to achieve the purpose of cross-period security assessment of a high-proportion photovoltaic power distribution system on the premise of protecting privacy and considering the cross-period uncertainty of photovoltaic and load.

[0006] To achieve the above object, the embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of an embodiment of the present invention discloses a method for cross-period collaborative security assessment of a photovoltaic power distribution system, the method comprising:

[0008] According to the stochastic process characterized by the martingale process, respectively analyze the cross-period evolution laws of the photovoltaic power and the load power in the photovoltaic power distribution system, and obtain the cross-period prediction error of the photovoltaic and the cross-period prediction error of the load;

[0009] Quantify the cross-period prediction error of the photovoltaic to obtain a first probability density function, and quantify the cross-period prediction error of the load to obtain a second probability density function;

[0010] Utilize the cross-period prediction error of the photovoltaic, the cross-period prediction error of the load, the first probability density function and the second probability density function to construct a security assessment model corresponding to each entity in the photovoltaic power distribution system;

[0011] Based on the security assessment models corresponding to each entity, construct a cross-period collaborative security assessment model that conforms to the pessimistic-optimistic architecture;

[0012] Use the augmented Benders decomposition method to solve the cross-period collaborative security assessment model in the pessimistic case and the optimistic case respectively, and obtain the pessimistic security assessment result and the optimistic security assessment result;

[0013] Based on the pessimistic security assessment result and the optimistic security assessment result, identify the operating state of the photovoltaic power distribution system.

[0014] Preferably, the quantifying the cross-period prediction error of the photovoltaic to obtain a first probability density function, and quantifying the cross-period prediction error of the load to obtain a second probability density function includes:

[0015] Utilize the probability density function of the cumulative mixed Gaussian mixture distribution to quantify the cross-period prediction error of the photovoltaic and the cross-period prediction error of the load, and obtain the first probability density function corresponding to the cross-period prediction error of the photovoltaic and the second probability density function corresponding to the cross-period prediction error of the load.

[0016] Preferably, the utilizing the cross-period prediction error of the photovoltaic, the cross-period prediction error of the load, the first probability density function and the second probability density function to construct a security assessment model corresponding to each entity in the photovoltaic power distribution system includes:

[0017] A corresponding objective function is set for each entity in the photovoltaic power distribution system; the objective function includes non - negative slack variables corresponding to each preset evaluation item and preset weights corresponding to each non - negative slack variable; the optimization objective of the objective function is to minimize the sum of the products of each non - negative slack variable and the corresponding preset weight;

[0018] Using the photovoltaic inter - period prediction error and the load inter - period prediction error, the constraint conditions of the objective function are set;

[0019] Using the first probability density function to set the first uncertainty interval where the photovoltaic inter - period prediction error is located, and using the second probability density function to set the second uncertainty interval where the load inter - period prediction error is located;

[0020] Based on each objective function, the constraint conditions corresponding to each objective function, the first uncertainty interval, and the second uncertainty interval, a security assessment model is constructed.

[0021] Preferably, using the augmented Benders decomposition method to solve the inter - period collaborative security assessment model respectively in the pessimistic scenario and the optimistic scenario to obtain the pessimistic security assessment result and the optimistic security assessment result, including:

[0022] Using the augmented Benders decomposition method to decouple the inter - period collaborative security assessment model, obtaining an optimistic master problem and an optimistic sub - problem corresponding to the optimistic scenario, and obtaining a pessimistic master problem and a pessimistic sub - problem corresponding to the pessimistic scenario;

[0023] Performing iterative interaction and solution between the optimistic master problem and the optimistic sub - problem to obtain an optimistic security assessment value and the optimistic value of the objective vector corresponding to each entity, and performing iterative interaction and solution between the pessimistic master problem and the pessimistic sub - problem to obtain a pessimistic security assessment value and the pessimistic value of the objective vector corresponding to each entity; the objective vector corresponding to each entity is composed of each non - negative slack variable in the objective function corresponding to the entity;

[0024] Taking the optimistic security assessment value and the optimistic value of the objective vector corresponding to each entity as the optimistic security assessment result, and taking the pessimistic security assessment value and the pessimistic value of the objective vector corresponding to each entity as the pessimistic security assessment result.

[0025] Preferably, based on the pessimistic security assessment result and the optimistic security assessment result, identifying the operating state of the photovoltaic power distribution system, including:

[0026] If the optimistic safety assessment value is less than 0 and the pessimistic safety assessment value is less than 0, it is determined that the photovoltaic power distribution system is in a normal operation state;

[0027] If the optimistic safety assessment value is equal to 0 and the pessimistic safety assessment value is greater than 0, it is determined that the photovoltaic power distribution system is in a warning operation state with an insecure operation risk;

[0028] If the optimistic safety assessment value is greater than 0 and the pessimistic safety assessment value is greater than 0, it is determined that the photovoltaic power distribution system is in an abnormal operation state.

[0029] Preferably, the method further includes:

[0030] For each entity in the photovoltaic power distribution system, if the optimistic value of the target vector corresponding to the entity is greater than 0, then when the value of the target vector is the optimistic value, from each of the non - negative slack variables that make up the target vector, find the first non - negative slack variable greater than 0, and determine that there is an insecure operation problem in the preset evaluation item corresponding to the first non - negative slack variable in the optimistic scenario;

[0031] For each entity in the photovoltaic power distribution system, if the pessimistic value of the target vector corresponding to the entity is greater than 0, then when the value of the target vector is the pessimistic value, from each of the non - negative slack variables that make up the target vector, find the second non - negative slack variable greater than 0, and determine that there is an insecure operation problem in the preset evaluation item corresponding to the second non - negative slack variable in the pessimistic scenario.

[0032] A second aspect of the embodiments of the present invention discloses a cross - period collaborative safety assessment device for a photovoltaic power distribution system, the device includes:

[0033] An analysis unit, configured to respectively analyze the cross - period evolution laws of the photovoltaic power and the load power in the photovoltaic power distribution system according to the stochastic process characterized by the martingale process, and obtain the cross - period prediction error of the photovoltaic power and the cross - period prediction error of the load power;

[0034] A quantization unit, configured to quantize the cross - period prediction error of the photovoltaic power to obtain a first probability density function, and quantize the cross - period prediction error of the load power to obtain a second probability density function;

[0035] A first construction unit, configured to use the cross - period prediction error of the photovoltaic power, the cross - period prediction error of the load power, the first probability density function, and the second probability density function to construct a safety assessment model corresponding to each entity in the photovoltaic power distribution system;

[0036] A second construction unit, configured to construct a cross - period collaborative safety assessment model that conforms to the pessimistic - optimistic architecture based on the safety assessment models corresponding to each entity;

[0037] A solution unit, which is used to solve the intertemporal collaborative security assessment model respectively in the pessimistic scenario and the optimistic scenario by using the augmented Benders decomposition method, and obtain a pessimistic security assessment result and an optimistic security assessment result;

[0038] An identification unit, which is used to identify the operating state of the photovoltaic power distribution system based on the pessimistic security assessment result and the optimistic security assessment result.

[0039] Preferably, the quantization unit is specifically used for:

[0040] Quantize the photovoltaic intertemporal prediction error and the load intertemporal prediction error by using the probability density function of the cumulative mixed Gaussian mixture distribution, and obtain a first probability density function corresponding to the photovoltaic intertemporal prediction error and a second probability density function corresponding to the load intertemporal prediction error.

[0041] Preferably, the first construction unit is specifically used for:

[0042] Set corresponding objective functions for each entity in the photovoltaic power distribution system; the objective functions include non-negative slack variables corresponding to each preset evaluation item and preset weights corresponding to each non-negative slack variable; the optimization objective of the objective function is: minimize the sum value of the products of each non-negative slack variable and the corresponding preset weight;

[0043] Use the photovoltaic intertemporal prediction error and the load intertemporal prediction error to set the constraint conditions of the objective function;

[0044] Use the first probability density function to set a first uncertainty interval where the photovoltaic intertemporal prediction error is located, and use the second probability density function to set a second uncertainty interval where the load intertemporal prediction error is located;

[0045] Based on each objective function, the constraint conditions corresponding to each objective function, the first uncertainty interval and the second uncertainty interval, construct a security assessment model.

[0046] Preferably, the solution unit is specifically used for:

[0047] Use the augmented Benders decomposition method to decouple the intertemporal collaborative security assessment model, and obtain an optimistic master problem and an optimistic sub-problem corresponding to the optimistic scenario, and obtain a pessimistic master problem and a pessimistic sub-problem corresponding to the pessimistic scenario;

[0048] Iteratively interactively solve between the optimistic master problem and the optimistic sub-problem to obtain the optimistic safety evaluation value and the optimistic value of the target vector corresponding to each of the subjects, and iteratively interactively solve between the pessimistic master problem and the pessimistic sub-problem to obtain the pessimistic safety evaluation value and the pessimistic value of the target vector corresponding to each of the subjects; the target vector corresponding to each of the subjects is composed of each of the non-negative slack variables in the target function corresponding to the subject.

[0049] Take the optimistic safety evaluation value and the optimistic value of the target vector corresponding to each of the subjects as the optimistic safety evaluation result, and take the pessimistic safety evaluation value and the pessimistic value of the target vector corresponding to each of the subjects as the pessimistic safety evaluation result.

[0050] Based on the cross-period collaborative safety evaluation method and device provided in the embodiment of the present invention above, according to the stochastic process characterized by the martingale process, respectively analyze the cross-period evolution laws of the photovoltaic power and the load power in the photovoltaic distribution system to obtain the cross-period prediction error of the photovoltaic power and the cross-period prediction error of the load power; quantify the cross-period prediction error of the photovoltaic power to obtain the first probability density function, and quantify the cross-period prediction error of the load power to obtain the second probability density function; use the cross-period prediction error of the photovoltaic power, the cross-period prediction error of the load power, the first probability density function and the second probability density function to construct a safety evaluation model corresponding to each subject in the photovoltaic distribution system; based on the safety evaluation models corresponding to each of the subjects, construct a cross-period collaborative safety evaluation model that conforms to the pessimistic-optimistic architecture; use the augmented Benders decomposition method to solve the cross-period collaborative safety evaluation model in the pessimistic case and the optimistic case respectively to obtain the pessimistic safety evaluation result and the optimistic safety evaluation result; based on the pessimistic safety evaluation result and the optimistic safety evaluation result, identify the operating state of the photovoltaic distribution system. In this solution, analogize the martingale process to analyze the cross-period evolution laws of the photovoltaic power and the load power, and quantify the cross-period uncertainty of the photovoltaic power and the load power. On the premise of considering the cross-period uncertainty of the photovoltaic and the load, construct a cross-period collaborative safety evaluation model that conforms to the pessimistic-optimistic architecture, and use the augmented Benders decomposition method to solve it to obtain the operating state of the photovoltaic distribution system, so as to achieve the purpose of cross-period safety evaluation of a high-proportion photovoltaic distribution system on the premise of protecting privacy and considering the cross-period uncertainty of the photovoltaic and the load. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0052] Figure 1 Flow chart of a cross - period collaborative security assessment method for a photovoltaic power distribution system disclosed in an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the cross - period evolution law of photovoltaic power and load power disclosed in an embodiment of the present invention;

[0054] Figure 3 Architecture diagram of a collaborative security assessment for a photovoltaic power distribution system disclosed in an embodiment of the present invention;

[0055] Figure 4 Structure diagram of a cross - period collaborative security assessment device for a photovoltaic power distribution system disclosed in an embodiment of the present invention. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] In this application, the term "including", "comprising" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0058] As can be seen from the background art, how to perform cross - period security assessment on a high - proportion photovoltaic power distribution system on the premise of protecting privacy and considering the cross - period uncertainty of photovoltaic and load is an urgent problem to be solved at present.

[0059] Therefore, an embodiment of the present invention discloses a cross - period collaborative security assessment method and device for a photovoltaic power distribution system. In this solution, the cross - period evolution law of photovoltaic power and load power is analyzed by analogy with the martingale process, and the cross - period uncertainty of photovoltaic power and load power is quantified. On the premise of considering the cross - period uncertainty of photovoltaic and load, a cross - period collaborative security assessment model that conforms to the pessimistic - optimistic architecture is constructed, and the augmented Benders decomposition method is used for solving to obtain the operating state of the photovoltaic power distribution system, so as to achieve the purpose of performing cross - period security assessment on a high - proportion photovoltaic power distribution system on the premise of protecting privacy and considering the cross - period uncertainty of photovoltaic and load.

[0060] As Figure 1 shown, the figure is a flowchart of a cross - period collaborative security assessment method for a photovoltaic power distribution system disclosed in an embodiment of the present invention, including the following steps:

[0061] Step S101: According to the stochastic process characterized by the martingale process, analyze the cross - period evolution laws of the photovoltaic power and the load power in the photovoltaic power distribution system respectively, and obtain the cross - period prediction error of the photovoltaic power and the cross - period prediction error of the load power.

[0062] In step S101, the photovoltaic power distribution system refers to a high - proportion photovoltaic power distribution system where the photovoltaic proportion reaches a certain threshold.

[0063] It should be noted that the martingale process is a concept in probability theory used to describe a special stochastic process. Its core idea is that the future expected value is equal to the current value. In other words, if a process is a martingale, then regardless of future random changes, its average result will not deviate from the current state.

[0064] As Figure 2 shown, the figure is a schematic diagram of the cross - period evolution laws of the photovoltaic power and the load power disclosed in an embodiment of the present invention. It shows the cross - period evolution of the photovoltaic power and the load power at different time periods, as well as their uncertainty ranges, and the specific analysis is as follows:

[0065] The vertical axis represents the predicted power, reflecting the magnitude of the power output of the photovoltaic and the load. The horizontal axis represents time, and five time periods from T1 to T5 are marked in sequence from left to right, showing the cross - period change of the power.

[0066] The photovoltaic curve represents the actual value or predicted value of the photovoltaic power, showing obvious periodic fluctuations with multiple peaks, reflecting the law that the photovoltaic power is affected by natural factors such as sunlight.

[0067] The upper and lower bounds of photovoltaic uncertainty represent the uncertainty range of the photovoltaic power, showing the volatility and uncertainty of the photovoltaic power prediction.

[0068] The load curve represents the actual value or predicted value of the load power. Compared with the photovoltaic curve, it is relatively stable, but there are also certain fluctuations.

[0069] The upper and lower bounds of load uncertainty represent the uncertainty range of the load power, which is relatively narrow, indicating that the prediction of the load power is relatively stable.

[0070] Power surplus means that the photovoltaic power is higher than the load power, and the photovoltaic power distribution system is in a power surplus state.

[0071] Power shortage means that the photovoltaic power is lower than the load power, and the photovoltaic power distribution system is in a power shortage state.

[0072] Uncertainty variation: Over time, the uncertainty of the predicted power of photovoltaic and load gradually increases.

[0073] In the specific implementation process of step S101, considering that both the photovoltaic power and the load power have obvious periodic variations, after normalizing the photovoltaic power and the load power, the cumulative hybrid Gaussian mixture distribution is used to describe the inter-period evolution law of the uncertainty of the photovoltaic power and the load power as follows:

[0074] (1)

[0075] where T is the length of a single period, k and k′ are period identifiers, and t is the time identifier. is the cumulative inter-period prediction error of photovoltaic, is the cumulative inter-period prediction error of load, and its numerical change at different times t reveals the inter-period evolution law. J is the number of Gaussian components, is the increment of the inter-period prediction error of photovoltaic, is the increment of the inter-period prediction error of load, and are respectively the weights of the j-th Gaussian component of the increment of the inter-period prediction error of photovoltaic and the increment of the inter-period prediction error of load, and are respectively the mean of the inter-period prediction error of photovoltaic of the j-th Gaussian component and the mean of the inter-period prediction error of load of the j-th Gaussian component, and are respectively the variance of the inter-period prediction error of photovoltaic of the j-th Gaussian component and the variance of the inter-period prediction error of load of the j-th Gaussian component.

[0076] Step S102: Quantify the inter-period prediction error of photovoltaic to obtain the first probability density function, and quantify the inter-period prediction error of load to obtain the second probability density function.

[0077] In the specific implementation process of step S102, the probability density function of the cumulative hybrid Gaussian mixture distribution is used to quantify the inter-period prediction error of photovoltaic and the inter-period prediction error of load, and the first probability density function corresponding to the inter-period prediction error of photovoltaic and the second probability density function corresponding to the inter-period prediction error of load are obtained.

[0078] According to the expression of the inter-period evolution law obtained in step S101, the probability density function of the cumulative hybrid Gaussian mixture distribution is used to quantify and represent the inter-period prediction error of photovoltaic and the inter-period prediction error of load to obtain the probability density function as follows:

[0079] (2)

[0080] where, Is the photovoltaic inter - period prediction error The first probability density function of the cumulative mixed Gaussian mixture distribution that it follows Is the load inter - period prediction error The second probability density function of the cumulative mixed Gaussian mixture distribution that it follows. The meanings of the parameters in Equation (1) and Equation (2) can be referred to each other, and will not be elaborated here.

[0081] It should be noted that by analogy with the martingale process to analyze the inter - period evolution of the uncertainties of photovoltaic power and load power, and quantifying the non - linear inter - period growth of the prediction error as a cumulative mixed Gaussian mixture distribution, it is not only limited to the description of the uncertainty within the period, but also represents the inter - period uncertainty of photovoltaic power and load power.

[0082] Step S103: Use the photovoltaic inter - period prediction error, the load inter - period prediction error, the first probability density function, and the second probability density function to construct a safety assessment model corresponding to each entity in the photovoltaic distribution system.

[0083] In the specific implementation process of step S103, it includes the following steps:

[0084] Step S201: Set a corresponding objective function for each entity in the photovoltaic distribution system.

[0085] Among them, the objective function contains non - negative slack variables corresponding to each preset evaluation item and preset weights corresponding to each non - negative slack variable; the optimization objective of the objective function is: minimize the sum of the products of each non - negative slack variable and the corresponding preset weight.

[0086] For any entity i in the photovoltaic distribution system, its objective function is set as follows:

[0087] (3)

[0088] Among them, i is the identifier of each entity in the photovoltaic distribution system, K is the number of periods, T is the length of a single period, t is the time identifier, is the set of times in the k - th period, α, β are node identifiers, αβ is the line identifier 、 、 and Are the weights of each preset evaluation item respectively and Are non - negative slack variables for detecting whether the interactive active power between entities exceeds the lower and upper limits respectively and Are non - negative slack variables for detecting whether the interactive reactive power between entities exceeds the lower and upper limits respectively and Are non - negative slack variables for detecting whether the active power of the line exceeds the lower and upper limits respectively and are non - negative slack variables for detecting whether the node voltage exceeds the lower and upper limits respectively, and are the active power injection and reactive power injection at the root node of the main body i respectively, and are the amplitude and phase angle of the node voltage respectively, and are the active power and reactive power of the line αβ (i.e., the line between node α and node β) respectively, and are the discharge power and charge power of the energy storage respectively, and are the charge state and discharge state respectively, is the state of charge of the energy storage, is the reactive power of the photovoltaic.

[0089] Step S202: Use the photovoltaic inter - period prediction error and the load inter - period prediction error to set the constraint conditions of the objective function.

[0090] In step S202, the photovoltaic operation constraints of the objective function are set using the photovoltaic inter - period prediction error as follows:

[0091] (4)

[0092] Wherein, is the photovoltaic inter - period predicted power at node α in the main body, is the photovoltaic inter - period prediction error, is the power factor angle of the photovoltaic at node α in the main body.

[0093] The load operation constraints of the objective function are set using the load inter - period prediction error as follows:

[0094] (5)

[0095] Wherein, is the load inter - period predicted power at node α in the main body, is the load inter - period prediction error, is the power factor angle of the load at node α in the main body.

[0096] In addition to the above - mentioned photovoltaic operation constraints and load operation constraints, the conditional constraints set for the objective function also include:

[0097] The interactive power constraints at the common connection points between the main bodies are as follows:

[0098] (6)

[0099] (7)

[0100] Among them, and are respectively the lower limit and upper limit of the interactive active power at the common connection point between each entity. and are respectively the lower limit and upper limit of the interactive reactive power at the common connection point between each entity. and are respectively the active power and reactive power of the line at the common connection point. is the set of all feed-forward entities of entity i.

[0101] To suppress the fluctuations of photovoltaic power, distributed energy storage will be installed at some nodes, and its constraints are set as follows:

[0102] (8)

[0103] (9)

[0104] (10)

[0105] (11)

[0106] (12)

[0107] Among them, and are respectively the upper limit of the charging power and the upper limit of the discharging power of the energy storage at node α. and are respectively the charging state and the discharging state of the energy storage at node α. and are respectively the discharging power and the charging power of the energy storage at node α. and are respectively the charging power coefficient and the discharging power coefficient of the energy storage at node α. and are the state of charge of the energy storage at node α in different periods. and are respectively the upper limit and the lower limit of the state of charge of the energy storage at node α.

[0108] The power flow constraints for the objective functions of each entity are set as follows:

[0109] (13)

[0110] (14)

[0111] Among them, and are the mutual conductance and susceptance between nodes α and β respectively, is the set of nodes within node i. The meanings of the remaining parameters have been described above and will not be elaborated here.

[0112] The coupling constraints for the node voltages and line powers in each subject objective function are set as follows:

[0113] (15)

[0114] (16)

[0115] where, and are the active power and reactive power of line αβ respectively, and are the mutual conductance and susceptance between nodes α and β respectively, and are the voltage magnitudes of nodes α and β respectively, and are the voltage phase angles of nodes α and β respectively.

[0116] The operating constraints for the node voltages and line powers in each subject objective function are as follows:

[0117] (17)

[0118] (18)

[0119] where, and are the lower and upper limits of the node voltage respectively, is the upper limit of the line voltage. The meanings of the remaining parameters have been described above and will not be elaborated here.

[0120] The intertemporal coupling constraints for each non - negative slack variable in each subject objective function are as follows:

[0121] (19)

[0122] (20)

[0123] (21)

[0124] (22)

[0125] where, , and They are the vectors of non - negative slack variables, continuous variables, and binary variables used by agent \(i\) for security assessment at time \(t\) in the \(k\) - th period, respectively. 、 and correspond to the values of the vectors of non - negative slack variables, continuous variables, and binary variables at \(t = T\) in the \(k\) - th period, respectively. 、 and correspond to the values of the vectors of non - negative slack variables, continuous variables, and binary variables at \(t = 0\) in the \((k + 1)\) - th period, respectively. The meanings of the remaining parameters have been described above and will not be elaborated here.

[0126] Step S203: Set the first uncertainty interval where the photovoltaic inter - period prediction error is located using the first probability density function, and set the second uncertainty interval where the load inter - period prediction error is located using the second probability density function.

[0127] In step S20, for each agent, the first uncertainty interval containing all photovoltaic inter - period prediction errors and the second uncertainty interval containing all load inter - period prediction errors are set as:

[0128] (23)

[0129] where is the total uncertainty interval including the first and second uncertainty intervals, is the first probability density function, is the second probability density function, and are the confidence levels characterized by the photovoltaic inter - period prediction error and the load inter - period prediction error, respectively. The meanings of the remaining parameters have been described above and will not be elaborated here.

[0130] Step S204: Based on each objective function, the constraint conditions corresponding to each objective function, the first uncertainty interval, and the second uncertainty interval, construct a security assessment model.

[0131] In step S204, construct a security assessment model corresponding to each agent.

[0132] Step S104: Based on the security assessment models corresponding to each agent, construct an inter - period collaborative security assessment model that conforms to the pessimistic - optimistic framework.

[0133] In step S104, under the pessimistic - optimistic framework, combine the security assessment models corresponding to each agent constructed in steps S201 to S204 to construct an inter - period collaborative security assessment model.

[0134] Among them, the expression of the intertemporal collaborative security assessment model is as follows:

[0135] (24)

[0136] Among them, 、 、 、 and are the coefficient matrix or vector of entity i, is the coefficient matrix of entity 0. Entity 0 and entity i correspond to the common power distribution part and the autonomous power distribution part in the photovoltaic power distribution system respectively, is the set of entities, indicates that i is the identification of other entities except entity 0, is the vector of the uncertain power of photovoltaic and load at all times in all cycles within entity i, is the vector of non - negative slack variables at all times in all cycles within entity i, is corresponding to 's weight coefficient, and are the continuous variable vector and binary variable vector at all times in all cycles within entity i respectively, is for 's dimension, is the coupling variable at all times in all cycles at the common connection point between entity 0 and entity i.

[0137] It should be noted that two consecutive mins represent the minimum value in the optimistic case. The first min represents the most optimistic situation of uncertainty, and the second min represents minimizing the objective function; one max and one min represent the minimum value in the pessimistic case.

[0138] Step S105: Use the augmented Benders decomposition method to solve the intertemporal collaborative security assessment model under the pessimistic case and the optimistic case respectively, and obtain the pessimistic security assessment result and the optimistic security assessment result.

[0139] Among them, using the augmented Benders decomposition method to solve the intertemporal collaborative security assessment model under the pessimistic case and the optimistic case respectively can effectively achieve privacy protection in the process of intertemporal collaborative security assessment of the photovoltaic power distribution system.

[0140] In the specific implementation process of step S105, it includes the following steps:

[0141] Step S301: Use the augmented Benders decomposition method to decouple the intertemporal collaborative security assessment model, and obtain the optimistic master problem and optimistic sub - problem corresponding to the optimistic case, and the pessimistic master problem and pessimistic sub - problem corresponding to the pessimistic case.

[0142] In step S301, considering the privacy protection issues among various entities, the collaborative intertemporal security assessment model is decoupled as follows:

[0143] In the optimistic scenario, the security assessment model for the collaborative intertemporal of the high - proportion photovoltaic power distribution system is decomposed into the following optimistic master problem and optimistic sub - problem:

[0144] (25)

[0145] (26)

[0146] Among them, equation (25) is the expression of the optimistic master problem, and equation (26) is the expression of the optimistic sub - problem.

[0147] and are the cut - plane identifiers, 、 、 、 and are the coefficient matrix or vector of entity 0, is the vector containing all non - negative slack variables at all times in all periods within entity 0, is the vector containing all non - negative slack variables at all times in all periods within entity i, and are respectively the continuous variable vector and binary variable vector containing all times in all periods within entity 0, is the dimension of, is the coupling variable containing all times in all periods at the common connection point between entity 0 and entity i, is the vector containing the uncertain power of photovoltaic and load at all times in all periods within entity 0, and are respectively the lower and upper limits of, is the vector containing the uncertain power of photovoltaic and load at all times in all periods within entity i, and are respectively the lower and upper limits of, is the estimated value of the objective function of the optimistic master problem for the optimistic sub - problem, is the trial solution of, is the augmented Benders cut - plane in the optimistic scenario, is the feasibility recovery cut - plane in the optimistic scenario, is the set of augmented Benders cut - planes in the optimistic scenario, The set of feasibility recovery cutting planes for the optimistic scenario.

[0148] The specific construction method of the augmented Benders cutting plane in the optimistic scenario is as follows:

[0149] (27)

[0150] The specific construction method of the feasibility recovery cutting plane in the optimistic scenario is as follows:

[0151] (28)

[0152] Among them, is the function of the augmented Benders cutting plane, is the function of the feasibility recovery cutting plane, 、 、 、 and are the strong dual variables of the continuous variable part of the optimistic subproblem, is the weak dual variable of the discrete variable part of the optimistic subproblem, is the minimum value of, is the trial solution of.

[0153] In the pessimistic scenario, the security assessment model for the inter-temporal coordination of the high-proportion photovoltaic power distribution system is decomposed into the following pessimistic master problem and pessimistic subproblem:

[0154] (29)

[0155] (30)

[0156] Among them, is the number of vertices in the uncertainty interval , is the number of vertices in the uncertainty interval , and are vertex identifiers, that is, and are respectively and the number of, is an auxiliary variable used to estimate the objective function of the pessimistic master problem in the pessimistic scenario, is an auxiliary variable used to estimate the objective function of the pessimistic subproblem in the pessimistic scenario, is the estimated value of the objective function of the pessimistic subproblem by the pessimistic master problem in the pessimistic scenario, is the vector of non-negative slack variables corresponding to vertex in body 0 and are the continuous variable vector and the binary variable vector corresponding to the vertices within the main body 0 respectively, is the uncertainty interval within the vertex, is the uncertainty interval within the vertex, is the set of augmented Benders cut planes in the pessimistic scenario, is the set of feasibility restoration cut planes in the pessimistic scenario, is the vector of non - negative slack variables corresponding to the vertices within the main body i respectively, and are the continuous variable vector and the binary variable vector corresponding to the vertices within the main body i respectively, is the trial solution of, is the augmented Benders cut plane in the pessimistic scenario, is the feasibility restoration cut plane in the pessimistic scenario, is the function representing the augmented Benders cut plane in the pessimistic scenario, is the function representing the feasibility restoration cut plane in the pessimistic scenario, is the minimum value of.

[0157] Step S302: Iteratively interactively solve between the optimistic master problem and the optimistic sub - problem to obtain the optimistic safety assessment value and the optimistic values of the objective vectors corresponding to each main body, and iteratively interactively solve between the pessimistic master problem and the pessimistic sub - problem to obtain the pessimistic safety assessment value and the pessimistic values of the objective vectors corresponding to each main body.

[0158] Among them, the objective vector corresponding to each main body is composed of each non - negative slack variable in the objective function corresponding to the main body.

[0159] The optimistic safety assessment value is the value of the objective function obtained by iteratively interactively solving between the optimistic master problem and the optimistic sub - problem, and the pessimistic safety assessment value is the value of the objective function obtained by iteratively interactively solving between the pessimistic master problem and the pessimistic sub - problem.

[0160] Preferably, formulating the augmented Benders cut and the feasibility restoration cut respectively in the pessimistic and optimistic scenarios in parallel can enable multiple agents in a high - proportion photovoltaic power distribution system to make collaborative decisions on their respective mixed - integer inter - temporal security verification models without disclosing local privacy.

[0161] Such as Figure 3As shown in the figure, it is an architecture diagram of collaborative security assessment for a photovoltaic power distribution system disclosed in an embodiment of the present invention.

[0162] Among them, the photovoltaic power distribution system consists of a public power distribution part (main body 0) and multiple independent power distribution parts (main body i, where i is a positive integer).

[0163] Based on Figure 3 the collaborative security assessment architecture of the photovoltaic power distribution system shown in the figure, the specific implementation process of step S302 is as follows:

[0164] In the optimistic case, alternately solve the optimistic master problem and the optimistic subproblem shown in S301. Among them, first solve the optimistic master problem to obtain a trial solution, and send the trial solution to the optimistic subproblem. The optimistic subproblem solves to generate a cutting plane based on this trial solution. In this way, iterative interaction continuously generates trial solutions and cutting planes. When converging, the vector of non - negative slack variables in the best - case scenario can be obtained and the values of, that is, the optimistic values of the target vectors corresponding to each main body;

[0165] In the pessimistic case, alternately solve the pessimistic master problem and the pessimistic subproblem shown in S301. Among them, first solve the pessimistic master problem to obtain a trial solution, and send the trial solution to the pessimistic subproblem. The pessimistic subproblem solves to generate a cutting plane based on this trial solution. In this way, iterative interaction continuously generates trial solutions and cutting planes. When converging, the vector of non - negative slack variables in the best - case scenario can be obtained and the values of, that is, the pessimistic values of the target vectors corresponding to each main body.

[0166] It should be noted that since the trial solutions in the interactive iteration process only contain information at the common connection point, and the cutting plane masks the original information in the dual form, the augmented Benders method effectively realizes privacy protection in the process of cross - period collaborative security assessment of a high - proportion photovoltaic power distribution system.

[0167] Step S303: Take the optimistic security assessment value and the optimistic values of the target vectors corresponding to each main body as the optimistic security assessment results, and take the pessimistic security assessment value and the pessimistic values of the target vectors corresponding to each main body as the pessimistic security assessment results.

[0168] Step S106: Based on the pessimistic security assessment results and the optimistic security assessment results, identify the operating state of the photovoltaic power distribution system.

[0169] In the specific implementation process of step S106, if the optimistic security assessment value is less than 0 and the pessimistic security assessment value is less than 0, it is determined that the photovoltaic power distribution system is in a normal operating state;

[0170] If the optimistic safety evaluation value is equal to 0 and the pessimistic safety evaluation value is greater than 0, it is determined that the photovoltaic power distribution system is in a warning operation state with the risk of unsafe operation;

[0171] If the optimistic safety evaluation value is greater than 0 and the pessimistic safety evaluation value is greater than 0, it is determined that the photovoltaic power distribution system is in an abnormal operation state.

[0172] It should be noted that for the non - negative slack variable vectors 、 、 and For the three evaluation items of the common connection point interaction power, line power, and node voltage corresponding to each non - negative slack variable among them, if the objective functions in both the pessimistic and optimistic cases are less than 0, it indicates that there will be no unsafe operation problems for the corresponding evaluation items, and the photovoltaic power distribution system is in a normal operation state; if the objective function in the pessimistic case is greater than 0 and equal to 0 in the optimistic case, it indicates that there may be unsafe operation problems for the corresponding evaluation items, and the photovoltaic power distribution system is in a warning operation state; if the objective functions in both the pessimistic and optimistic cases are greater than 0, it indicates that there will definitely be unsafe operation problems for the corresponding evaluation items, and the photovoltaic power distribution system is in an abnormal operation state.

[0173] In one embodiment, for each entity in the photovoltaic power distribution system, if the optimistic value of the target vector corresponding to the entity is greater than 0, then when the value of the target vector is the optimistic value, among the non - negative slack variables that make up the target vector, find the first non - negative slack variable greater than 0, and determine that there is an unsafe operation problem for the preset evaluation item corresponding to the first non - negative slack variable in the optimistic case;

[0174] For each entity in the photovoltaic power distribution system, if the pessimistic value of the target vector corresponding to the entity is greater than 0, then when the value of the target vector is the pessimistic value, among the non - negative slack variables that make up the target vector, find the second non - negative slack variable greater than 0, and determine that there is an unsafe operation problem for the preset evaluation item corresponding to the second non - negative slack variable in the pessimistic case.

[0175] It should be noted that according to the non - negative slack variable vectors 、 、 and The values of each non - negative slack variable are used to locate the common connection points, nodes, and lines with unsafe operation. That is, when the non - negative slack variable is 0, it indicates that there is no problem of unsafe operation. When the non - negative slack variable is greater than 0, it indicates that there is a problem of unsafe operation in the evaluation item corresponding to the non - negative slack variable. Among them, the pre - evaluation items include multiple evaluation items respectively for common connection points, nodes, and lines. For example, in a certain entity, if there is a problem of unsafe operation in the evaluation item preset for the common connection point, it is determined that there is a problem of unsafe operation at the common connection point of this entity.

[0176] Based on the cross - period collaborative security assessment method for a photovoltaic power distribution system disclosed in the above - mentioned embodiments of the present invention, in this solution, the cross - period evolution laws of photovoltaic power and load power are analyzed by analogy with the martingale process, and the cross - period uncertainties of photovoltaic power and load power are quantified. On the premise of considering the cross - period uncertainties of photovoltaic and load, a cross - period collaborative security assessment model conforming to the pessimistic - optimistic architecture is constructed, and the augmented Benders decomposition method is used for solution to obtain the operating state of the photovoltaic power distribution system, so as to achieve the purpose of cross - period security assessment of a high - proportion photovoltaic power distribution system on the premise of protecting privacy and considering the cross - period uncertainties of photovoltaic and load.

[0177] Corresponding to the cross - period collaborative security assessment method for a photovoltaic power distribution system disclosed in the above - mentioned embodiments of the present invention, as Figure 4 shown, it is a structural diagram of a cross - period collaborative security assessment device for a photovoltaic power distribution system disclosed in an embodiment of the present invention, including: an analysis unit 401, a quantification unit 402, a first construction unit 403, a second construction unit 404, a solution unit 405, and an identification unit 406.

[0178] The analysis unit 401 is configured to analyze the cross - period evolution laws of photovoltaic power and load power in the photovoltaic power distribution system respectively according to the stochastic process characterized by the martingale process, and obtain the photovoltaic cross - period prediction error and the load cross - period prediction error.

[0179] The quantification unit 402 is configured to quantify the photovoltaic cross - period prediction error to obtain a first probability density function, and quantify the load cross - period prediction error to obtain a second probability density function.

[0180] In one embodiment, the quantification unit 402 is specifically configured to:

[0181] Use the probability density function of the cumulative mixture of Gaussian mixtures to quantify the photovoltaic cross - period prediction error and the load cross - period prediction error, and obtain the first probability density function corresponding to the photovoltaic cross - period prediction error and the second probability density function corresponding to the load cross - period prediction error.

[0182] The first construction unit 403 is used to construct a safety assessment model corresponding to each entity in the photovoltaic power distribution system by using the photovoltaic inter - period prediction error, the load inter - period prediction error, the first probability density function, and the second probability density function.

[0183] In one embodiment, the first construction unit 403 is specifically used for:

[0184] Set a corresponding objective function for each entity in the photovoltaic power distribution system; the objective function includes non - negative slack variables corresponding to each preset evaluation item and preset weights corresponding to each non - negative slack variable; the optimization objective of the objective function is to minimize the sum of the products of each non - negative slack variable and its corresponding preset weight;

[0185] Use the photovoltaic inter - period prediction error and the load inter - period prediction error to set the constraint conditions of the objective function;

[0186] Use the first probability density function to set the first uncertainty interval in which the photovoltaic inter - period prediction error is located, and use the second probability density function to set the second uncertainty interval in which the load inter - period prediction error is located;

[0187] Based on each objective function, the constraint conditions corresponding to each objective function, the first uncertainty interval, and the second uncertainty interval, construct a safety assessment model.

[0188] The second construction unit 404 is used to construct an inter - period collaborative safety assessment model that conforms to the pessimistic - optimistic architecture based on the safety assessment models corresponding to each entity.

[0189] The solving unit 405 is used to solve the inter - period collaborative safety assessment model in the pessimistic case and the optimistic case respectively by using the augmented Benders decomposition method, and obtain the pessimistic safety assessment result and the optimistic safety assessment result.

[0190] In one embodiment, the solving unit 405 is specifically used for:

[0191] Use the augmented Benders decomposition method to decouple the inter - period collaborative safety assessment model, obtain the optimistic master problem and the optimistic sub - problem corresponding to the optimistic case, and obtain the pessimistic master problem and the pessimistic sub - problem corresponding to the pessimistic case;

[0192] Perform iterative interactive solution between the optimistic master problem and the optimistic sub - problem to obtain the optimistic safety assessment value and the optimistic value of the objective vector corresponding to each entity, and perform iterative interactive solution between the pessimistic master problem and the pessimistic sub - problem to obtain the pessimistic safety assessment value and the pessimistic value of the objective vector corresponding to each entity; the objective vector corresponding to each entity is composed of non - negative slack variables in the objective function corresponding to the entity.

[0193] Take the optimistic security evaluation value and the optimistic value of the target vector corresponding to each subject as the optimistic security evaluation result, and take the pessimistic security evaluation value and the pessimistic value of the target vector corresponding to each subject as the pessimistic security evaluation result.

[0194] An identification unit 406, configured to identify the operating state of the photovoltaic power distribution system based on the pessimistic security evaluation result and the optimistic security evaluation result.

[0195] In one embodiment, the identification unit 406 is specifically configured to:

[0196] If the optimistic security evaluation value is less than 0 and the pessimistic security evaluation value is less than 0, it is determined that the photovoltaic power distribution system is in a normal operating state;

[0197] If the optimistic security evaluation value is equal to 0 and the pessimistic security evaluation value is greater than 0, it is determined that the photovoltaic power distribution system is in a warning operating state with insecure operating risks;

[0198] If the optimistic security evaluation value is greater than 0 and the pessimistic security evaluation value is greater than 0, it is determined that the photovoltaic power distribution system is in an abnormal operating state.

[0199] In one embodiment, the photovoltaic power distribution system intertemporal collaborative security evaluation device further includes:

[0200] A positioning unit, for each subject in the photovoltaic power distribution system, if the optimistic value of the target vector corresponding to the subject is greater than 0, then in the case where the value of the target vector is the optimistic value, find out the first non-negative slack variable greater than 0 from each non-negative slack variable that makes up the target vector, and determine that there is an insecure operation problem in the preset evaluation item corresponding to the first non-negative slack variable in the optimistic case;

[0201] For each subject in the photovoltaic power distribution system, if the pessimistic value of the target vector corresponding to the subject is greater than 0, then in the case where the value of the target vector is the pessimistic value, find out the second non-negative slack variable greater than 0 from each non-negative slack variable that makes up the target vector, and determine that there is an insecure operation problem in the preset evaluation item corresponding to the second non-negative slack variable in the pessimistic case.

[0202] Based on the above-mentioned photovoltaic power distribution system intertemporal collaborative security evaluation device disclosed in the embodiments of the present invention, in this solution, the intertemporal evolution law of photovoltaic power and load power is analyzed by analogy with the martingale process, and the intertemporal uncertainty of photovoltaic power and load power is quantified. On the premise of considering the intertemporal uncertainty of photovoltaic and load, an intertemporal collaborative security evaluation model that conforms to the pessimistic-optimistic architecture is constructed, and the augmented Benders decomposition method is used for solution to obtain the operating state of the photovoltaic power distribution system, so as to achieve the purpose of intertemporal security evaluation of a high-proportion photovoltaic power distribution system on the premise of protecting privacy and considering the intertemporal uncertainty of photovoltaic and load.

[0203] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for a system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the corresponding part of the method embodiment for the relevant content. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0204] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0205] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cross - period collaborative security assessment method for a photovoltaic power distribution system, characterized in that, The method includes: According to the stochastic process characterized by the martingale process, respectively analyze the intertemporal evolution laws of the photovoltaic power and the load power in the photovoltaic distribution system to obtain the photovoltaic intertemporal prediction error and the load intertemporal prediction error; Quantify the photovoltaic intertemporal prediction error to obtain a first probability density function, and quantify the load intertemporal prediction error to obtain a second probability density function; Utilize the photovoltaic intertemporal prediction error, the load intertemporal prediction error, the first probability density function, and the second probability density function to construct a safety assessment model corresponding to each entity in the photovoltaic distribution system; Based on the safety assessment models corresponding to each of the entities, construct an intertemporal collaborative safety assessment model that conforms to the pessimistic-optimistic architecture; Utilize the augmented Benders decomposition method to solve the intertemporal collaborative safety assessment model in the pessimistic scenario and the optimistic scenario respectively to obtain the pessimistic safety assessment result and the optimistic safety assessment result; Based on the pessimistic safety assessment result and the optimistic safety assessment result, identify the operating state of the photovoltaic distribution system.

2. The method according to claim 1, characterized in that, The quantifying the photovoltaic intertemporal prediction error to obtain a first probability density function, and quantifying the load intertemporal prediction error to obtain a second probability density function includes: Utilize the probability density function of the cumulative mixture Gaussian mixture distribution to quantify the photovoltaic intertemporal prediction error and the load intertemporal prediction error to obtain the first probability density function corresponding to the photovoltaic intertemporal prediction error and the second probability density function corresponding to the load intertemporal prediction error.

3. The method according to claim 1, wherein The utilizing the photovoltaic intertemporal prediction error, the load intertemporal prediction error, the first probability density function, and the second probability density function to construct a safety assessment model corresponding to each entity in the photovoltaic distribution system includes: Set a corresponding objective function for each entity in the photovoltaic distribution system; the objective function includes non-negative slack variables corresponding to each preset assessment item and preset weights corresponding to each non-negative slack variable; the optimization objective of the objective function is: minimize the sum of the products of each non-negative slack variable and the corresponding preset weight; Utilize the photovoltaic intertemporal prediction error and the load intertemporal prediction error to set the constraint conditions of the objective function; Utilize the first probability density function to set a first uncertainty interval where the photovoltaic intertemporal prediction error is located, and utilize the second probability density function to set a second uncertainty interval where the load intertemporal prediction error is located; Based on each of the objective functions, the constraint conditions corresponding to each of the objective functions, the first uncertainty interval, and the second uncertainty interval, construct a safety assessment model.

4. The method according to claim 3, wherein The utilizing the augmented Benders decomposition method to solve the intertemporal collaborative safety assessment model in the pessimistic scenario and the optimistic scenario respectively to obtain the pessimistic safety assessment result and the optimistic safety assessment result includes: Utilize the augmented Benders decomposition method to decouple the intertemporal collaborative safety assessment model to obtain an optimistic master problem and an optimistic sub-problem corresponding to the optimistic scenario, and obtain a pessimistic master problem and a pessimistic sub-problem corresponding to the pessimistic scenario; Iteratively interactively solve between the optimistic master problem and the optimistic sub-problem to obtain the optimistic safety evaluation value and the optimistic value of the target vector corresponding to each subject, and iteratively interactively solve between the pessimistic master problem and the pessimistic sub-problem to obtain the pessimistic safety evaluation value and the pessimistic value of the target vector corresponding to each subject; the target vector corresponding to each subject consists of each non-negative slack variable in the target function corresponding to the subject. Take the optimistic safety evaluation value and the optimistic value of the target vector corresponding to each subject as the optimistic safety evaluation result, and take the pessimistic safety evaluation value and the pessimistic value of the target vector corresponding to each subject as the pessimistic safety evaluation result.

5. The method according to claim 4, wherein Based on the pessimistic safety evaluation result and the optimistic safety evaluation result, identify the operating state of the photovoltaic distribution system, including: If the optimistic safety evaluation value is less than 0 and the pessimistic safety evaluation value is less than 0, determine that the photovoltaic distribution system is in a normal operating state; If the optimistic safety evaluation value is equal to 0 and the pessimistic safety evaluation value is greater than 0, determine that the photovoltaic distribution system is in a warning operating state with unsafe operating risks; If the optimistic safety evaluation value is greater than 0 and the pessimistic safety evaluation value is greater than 0, determine that the photovoltaic distribution system is in an abnormal operating state.

6. The method according to claim 4, characterized in that The method further includes: For each subject in the photovoltaic distribution system, if the optimistic value of the target vector corresponding to the subject is greater than 0, then when the value of the target vector is the optimistic value, find the first non-negative slack variable greater than 0 from each non-negative slack variable that makes up the target vector, and determine that there is an unsafe operating problem in the preset evaluation item corresponding to the first non-negative slack variable in the optimistic case; For each subject in the photovoltaic distribution system, if the pessimistic value of the target vector corresponding to the subject is greater than 0, then when the value of the target vector is the pessimistic value, find the second non-negative slack variable greater than 0 from each non-negative slack variable that makes up the target vector, and determine that there is an unsafe operating problem in the preset evaluation item corresponding to the second non-negative slack variable in the pessimistic case.

7. A cross-period collaborative safety assessment device for a photovoltaic power distribution system, characterized in that The device includes: An analysis unit for respectively analyzing the intertemporal evolution laws of the photovoltaic power and the load power in the photovoltaic distribution system according to the stochastic process characterized by the martingale process to obtain the photovoltaic intertemporal prediction error and the load intertemporal prediction error; A quantization unit for quantifying the photovoltaic intertemporal prediction error to obtain a first probability density function and quantifying the load intertemporal prediction error to obtain a second probability density function; A first construction unit for constructing a safety evaluation model corresponding to each subject in the photovoltaic distribution system by using the photovoltaic intertemporal prediction error, the load intertemporal prediction error, the first probability density function, and the second probability density function; A second construction unit for constructing an intertemporal collaborative safety evaluation model that conforms to the pessimistic-optimistic architecture based on the safety evaluation models corresponding to each subject. A solution unit, which is used to solve the intertemporal collaborative security assessment model respectively in the pessimistic scenario and the optimistic scenario by using the augmented Benders decomposition method, so as to obtain a pessimistic security assessment result and an optimistic security assessment result; An identification unit, which is used to identify the operating state of the photovoltaic power distribution system based on the pessimistic security assessment result and the optimistic security assessment result.

8. The device according to claim 7, characterized in that, The quantification unit is specifically used for: Quantifying the photovoltaic intertemporal prediction error and the load intertemporal prediction error by using the probability density function of the cumulative mixture Gaussian mixture distribution, so as to obtain a first probability density function corresponding to the photovoltaic intertemporal prediction error and a second probability density function corresponding to the load intertemporal prediction error.

9. The device according to claim 7, characterized in that, The first construction unit is specifically used for: Setting a corresponding objective function for each entity in the photovoltaic power distribution system; the objective function includes non-negative slack variables corresponding to each preset evaluation item and preset weights corresponding to each non-negative slack variable; the optimization objective of the objective function is to minimize the sum of the products of each non-negative slack variable and the corresponding preset weight; Setting the constraint conditions of the objective function by using the photovoltaic intertemporal prediction error and the load intertemporal prediction error; Setting a first uncertainty interval in which the photovoltaic intertemporal prediction error is located by using the first probability density function, and setting a second uncertainty interval in which the load intertemporal prediction error is located by using the second probability density function; Based on each objective function, the constraint conditions corresponding to each objective function, the first uncertainty interval and the second uncertainty interval, constructing a security assessment model.

10. The device according to claim 9, wherein, The solution unit is specifically used for: Using the augmented Benders decomposition method to decouple the intertemporal collaborative security assessment model, so as to obtain an optimistic master problem and an optimistic sub-problem corresponding to the optimistic scenario, and obtaining a pessimistic master problem and a pessimistic sub-problem corresponding to the pessimistic scenario; Performing iterative interactive solution between the optimistic master problem and the optimistic sub-problem to obtain an optimistic security assessment value and an optimistic value of the target vector corresponding to each entity, and performing iterative interactive solution between the pessimistic master problem and the pessimistic sub-problem to obtain a pessimistic security assessment value and a pessimistic value of the target vector corresponding to each entity; the target vector corresponding to each entity is composed of each non-negative slack variable in the objective function corresponding to the entity; Taking the optimistic security assessment value and the optimistic value of the target vector corresponding to each entity as the optimistic security assessment result, and taking the pessimistic security assessment value and the pessimistic value of the target vector corresponding to each entity as the pessimistic security assessment result.

Images