A method and device for cross-period collaborative safety assessment of photovoltaic power distribution systems

Abstract

The present invention provides a method and device for collaborative safety assessment of photovoltaic distribution systems across time periods, which can be used in the field of power distribution system technology. The method comprises analyzing the photovoltaic distribution system according to a martingale process table to obtain a photovoltaic inter-period prediction error and a load inter-period prediction error; quantifying the photovoltaic inter-period prediction error and the load inter-period prediction error to obtain a first probability density function and a second probability density function; constructing a safety assessment model for each subject using the photovoltaic inter-period prediction error, the load inter-period prediction error, the first probability density function, and the second probability density function; constructing an inter-period collaborative safety assessment model based on each safety assessment model; solving the inter-period collaborative safety assessment model using the augmented Benders decomposition method to obtain a safety assessment result; and identifying the operating status of the photovoltaic distribution system based on the safety assessment result. In this solution, the purpose of performing inter-period safety assessment on a high-proportion photovoltaic distribution system is achieved while protecting privacy and considering the inter-period uncertainty of photovoltaics and loads.

Description

Technical Field

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

[0002] Photovoltaic (PV), a widely distributed renewable energy source, is rapidly penetrating distribution systems to reduce carbon emissions, improve economic efficiency, and support autonomous operation. However, in high-PV distribution systems, the complex interplay of PV output uncertainty and load demand uncertainty poses a significant challenge to operational safety. Furthermore, distribution systems typically include not only public distribution but also autonomous distribution components such as microgrids and industrial parks. In practical projects, these public and autonomous distribution components belong to different entities, each unwilling to disclose private details of local models through collaborative security assessments. Therefore, it is necessary to collaboratively assess the security of high-PV distribution systems using multiple entities while also considering privacy protection. Furthermore, in traditional distribution systems with low or no PV penetration, the value of inter-period security assessments is limited due to the weak coupling of system operating states between adjacent cycles. These assessments are limited to single periods, such as day-ahead and intra-day. In contrast, high-PV penetration distribution systems, as typical weather-sensitive systems, are subject to significant inter-period coupling in system operating states, often affected by persistent abnormal weather conditions such as cold snaps and rainy conditions. As the uncertainty of photovoltaic and load forecasts increases significantly during intertemporal evolution, the likelihood of the system falling into an unsafe state gradually increases, and the uncertainty representation methods in corresponding safety assessment models need to be adjusted accordingly. Therefore, the safety assessment of high-proportion photovoltaic distribution systems must not only ensure privacy protection during multi-agent collaborative solution but also consider the local quantitative representation of intertemporal uncertainty, making centralized methods no longer applicable.

[0003] Existing distributed methods are primarily divided into dual decomposition methods, exemplified by the Lagrangian method, and primal decomposition methods based on column-sum constraints. While dual decomposition methods perform well in convex models, high-volume photovoltaic systems often incorporate a large amount of distributed energy storage. The binary variables of the charge / discharge states result in non-convex mixed-integer models, making convergence difficult. While primal decomposition methods can handle mixed-integer models, they require copying some information during iterations, making them incapable of fully protecting privacy among multiple agents.

[0004] In summary, how to conduct inter-period security assessment of high-proportion photovoltaic distribution systems while protecting privacy and considering the inter-period uncertainty of photovoltaics and loads is an issue that needs to be addressed urgently. Summary of the Invention

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

[0006] To achieve the above objectives, 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 safety assessment of a photovoltaic power distribution system, the method comprising:

[0008] According to the stochastic process characterized by the martingale process, the inter-period evolution of photovoltaic power and load power in the photovoltaic distribution system is analyzed respectively, and the inter-period prediction error of photovoltaic power and load power is obtained.

[0009] Quantifying the photovoltaic inter-period prediction error to obtain a first probability density function, and quantifying the load inter-period prediction error to obtain a second probability density function;

[0010] Constructing 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;

[0011] Based on the security assessment models corresponding to the respective subjects, a cross-period collaborative security assessment model conforming to the pessimistic-optimistic framework is constructed;

[0012] The augmented Benders decomposition method is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances, respectively, to obtain pessimistic and optimistic safety assessment results.

[0013] Based on the pessimistic safety assessment result and the optimistic safety assessment result, an operating state of the photovoltaic power distribution system is identified.

[0014] Preferably, the quantifying the photovoltaic inter-period forecast error to obtain a first probability density function, and the quantifying the load inter-period forecast error to obtain a second probability density function, include:

[0015] The photovoltaic inter-period prediction error and the load inter-period prediction error are quantified using the probability density function of the cumulative mixed Gaussian mixture distribution to obtain a first probability density function corresponding to the photovoltaic inter-period prediction error and a second probability density function corresponding to the load inter-period prediction error.

[0016] Preferably, the use of 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 power distribution system includes:

[0017] A corresponding objective function is set for each subject in the photovoltaic power distribution system; the objective function includes non-negative slack variables corresponding to each preset evaluation item and a preset weight corresponding to each non-negative slack variable; the optimization goal 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, setting the constraint conditions of the objective function;

[0019] Using the first probability density function to set a first uncertainty interval for the photovoltaic inter-period forecast error, and using the second probability density function to set a second uncertainty interval for the load inter-period forecast error;

[0020] A safety assessment model is constructed 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.

[0021] Preferably, the augmented Benders decomposition method is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances, respectively, to obtain a pessimistic safety assessment result and an optimistic safety assessment result, including:

[0022] Using the augmented Benders decomposition method, the inter-period collaborative safety assessment model is decoupled to obtain the optimistic main problem and optimistic sub-problems corresponding to the optimistic case, and the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic case;

[0023] Performing iterative interactive solutions between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each of the subjects, and performing iterative interactive solutions between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a 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 objective function corresponding to the subject;

[0024] The optimistic safety assessment value and the optimistic value of the target vector corresponding to each of the subjects are used as an optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each of the subjects are used as a pessimistic safety assessment result.

[0025] Preferably, the identifying the operating status of the photovoltaic power distribution system based on the pessimistic safety assessment result and the optimistic safety assessment result includes:

[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 operating 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 unsafe 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 operating state.

[0029] Preferably, the method further comprises:

[0030] 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, when the value of the target vector is optimistic, find a first non-negative slack variable greater than 0 from each of the non-negative slack variables constituting the target vector, and determine whether a preset evaluation item corresponding to the first non-negative slack variable has an unsafe operation problem under the optimistic scenario;

[0031] 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 when the value of the target vector is a pessimistic value, a second non-negative slack variable greater than 0 is found from the various non-negative slack variables constituting the target vector, and it is determined that a preset evaluation item corresponding to the second non-negative slack variable has an unsafe operation problem under the pessimistic situation.

[0032] A second aspect of an embodiment of the present invention discloses a device for collaborative safety assessment of a photovoltaic power distribution system over a period of time, the device comprising:

[0033] An analysis unit is used to analyze the inter-period evolution laws of photovoltaic power and load power in the photovoltaic distribution system according to the random process characterized by the martingale process, and obtain the inter-period prediction error of photovoltaic power and the inter-period prediction error of load power;

[0034] a quantization unit, configured to quantify the photovoltaic inter-period prediction error to obtain a first probability density function, and to quantify the load inter-period prediction error to obtain a second probability density function;

[0035] A first construction unit is configured 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;

[0036] A second construction unit is configured to construct an inter-period collaborative security assessment model that conforms to a pessimistic-optimistic architecture based on the security assessment models corresponding to the respective subjects;

[0037] A solving unit is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances respectively by using an augmented Benders decomposition method to obtain a pessimistic safety assessment result and an optimistic safety assessment result;

[0038] An identification unit is configured to identify an operating state of the photovoltaic power distribution system based on the pessimistic safety assessment result and the optimistic safety assessment result.

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

[0040] The photovoltaic inter-period prediction error and the load inter-period prediction error are quantified using the probability density function of the cumulative mixed Gaussian mixture distribution to obtain a first probability density function corresponding to the photovoltaic inter-period prediction error and a second probability density function corresponding to the load inter-period prediction error.

[0041] Preferably, the first building block is specifically used for:

[0042] A corresponding objective function is set for each subject in the photovoltaic power distribution system; the objective function includes non-negative slack variables corresponding to each preset evaluation item and a preset weight corresponding to each non-negative slack variable; the optimization goal of the objective function is to minimize the sum of the products of each non-negative slack variable and the corresponding preset weight;

[0043] Using the photovoltaic inter-period prediction error and the load inter-period prediction error, setting the constraint conditions of the objective function;

[0044] Using the first probability density function to set a first uncertainty interval for the photovoltaic inter-period forecast error, and using the second probability density function to set a second uncertainty interval for the load inter-period forecast error;

[0045] A safety assessment model is constructed 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.

[0046] Preferably, the solving unit is specifically used to:

[0047] Using the augmented Benders decomposition method, the inter-period collaborative safety assessment model is decoupled to obtain the optimistic main problem and optimistic sub-problems corresponding to the optimistic case, and the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic case;

[0048] Performing iterative interactive solutions between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each of the subjects, and performing iterative interactive solutions between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a 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 objective function corresponding to the subject;

[0049] The optimistic safety assessment value and the optimistic value of the target vector corresponding to each of the subjects are used as an optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each of the subjects are used as a pessimistic safety assessment result.

[0050] Based on the above-mentioned embodiment of the present invention, a method and device for inter-period collaborative safety assessment of a photovoltaic distribution system are provided. According to the random process characterized by the martingale process, the inter-period evolution laws of the photovoltaic power and the load power in the photovoltaic distribution system are analyzed respectively to obtain the photovoltaic inter-period prediction error and the load inter-period prediction error; the photovoltaic inter-period prediction error is quantified to obtain a first probability density function, and the load inter-period prediction error is quantified to obtain a second probability density function; the photovoltaic inter-period prediction error, the load inter-period prediction error, the first probability density function and the second probability density function are used to construct a safety assessment model corresponding to each subject in the photovoltaic distribution system; based on the safety assessment models corresponding to each of the subjects, an inter-period collaborative safety assessment model that conforms to the pessimistic-optimistic architecture is constructed; the augmented Benders decomposition method is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances respectively to obtain pessimistic safety assessment results and optimistic safety assessment results; based on the pessimistic safety assessment results and the optimistic safety assessment results, the operating status of the photovoltaic distribution system is identified. In this scheme, the inter-temporal evolution law of photovoltaic power and load power is analyzed by analogy with the martingale process, and the inter-temporal uncertainty of photovoltaic power and load power is quantified. Under the premise of considering the inter-temporal uncertainty of photovoltaic and load, an inter-temporal collaborative security assessment model that conforms to the pessimistic-optimistic architecture is constructed, and the augmented Benders decomposition method is used to solve it to obtain the operating status of the photovoltaic distribution system. This achieves the purpose of inter-temporal security assessment of high-proportion photovoltaic distribution systems under the premise of protecting privacy and considering the inter-temporal uncertainty of photovoltaic and load. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

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

[0054] Figure 3 This is a diagram of a collaborative safety assessment architecture for a photovoltaic power distribution system disclosed in an embodiment of the present invention;

[0055] Figure 4 This is a structural diagram of a photovoltaic distribution system inter-period collaborative safety assessment device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0058] As can be seen from the background technology, how to perform inter-period security assessment of high-proportion photovoltaic distribution systems while protecting privacy and considering the inter-period uncertainty of photovoltaics and loads is an urgent problem that needs to be solved.

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

[0060] like Figure 1 FIG. 1 is a flow chart of a method for collaborative safety assessment of a photovoltaic power distribution system across periods disclosed in an embodiment of the present invention, comprising the following steps:

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

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

[0063] It's important to note that a martingale process is a concept in probability theory that describes a special type of random process. Its core concept is that the expected value in the future is equal to the current value. In other words, if a process is a martingale, then regardless of future random variations, its average outcome will not deviate from its current state.

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

[0065] The vertical axis represents predicted power, reflecting the power output of the photovoltaic system and the load. The horizontal axis represents time, with five time periods, T1 to T5, marked from left to right, showing the inter-period power changes.

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

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

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

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

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

[0072] Uncertainty changes: As time goes by, the uncertainty of the predicted power of photovoltaic and load gradually increases.

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

[0074] (1)

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

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

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

[0078] According to the expression of the inter-period evolution law obtained in step S101, the probability density function of the cumulative mixed Gaussian mixture distribution is used to quantitatively characterize the photovoltaic inter-period prediction error. and load intertemporal forecast error The probability density function is obtained as follows:

[0079] (2)

[0080] in, is the photovoltaic inter-period forecast error The first probability density function of the cumulative mixture Gaussian mixture distribution obeys, is the load intertemporal forecast error The second probability density function of the cumulative mixed Gaussian mixture distribution is obeyed. The meanings of the parameters in formula (1) and formula (2) can be referred to each other and will not be repeated here.

[0081] It should be noted that the inter-period evolution of the uncertainty of photovoltaic power and load power is analyzed by analogy with the martingale process, and the nonlinear inter-period growth of the prediction error is quantitatively characterized as a cumulative mixed Gaussian mixture distribution, which is not only limited to the uncertainty characterization within the cycle, but also characterizes the inter-period uncertainty of photovoltaic power and load power.

[0082] Step S103: constructing a safety assessment model corresponding to each entity in the photovoltaic 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.

[0083] The specific implementation process of step S103 includes the following steps:

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

[0085] 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 goal of the objective function is to minimize the sum of the products of each non-negative slack variable and the corresponding preset weight.

[0086] For any subject i in the photovoltaic power 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 cycles, T is the length of a single cycle, t is the time identifier, which is the time set in the kth cycle, α and β are node identifiers, and αβ are line identifiers. 、 、 and are the weights of each preset evaluation item, and are non-negative slack variables for detecting whether the interactive active power between the subjects exceeds the lower limit and the upper limit, respectively. and are non-negative slack variables for detecting whether the interactive reactive power between entities exceeds the lower limit and the upper limit, respectively. and are non-negative slack variables for detecting whether the line active power exceeds the lower limit and the upper limit, respectively. and are non-negative slack variables for detecting whether the node voltage is below or above the upper limit, and are the injected active power and reactive power of the root node of subject i, and are the amplitude and phase angle of the node voltage respectively, and are the active power and reactive power of line αβ (i.e. the line between node α and node β), and are the discharge power and charging power of energy storage respectively, and are charging state and discharging state respectively, is the energy storage charge state, is the photovoltaic reactive power.

[0089] Step S202: using the photovoltaic inter-period prediction error and the load inter-period prediction error, 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 forecast error as follows:

[0091] (4)

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

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

[0094] (5)

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

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

[0097] The interaction power constraints of the common connection points between the entities are as follows:

[0098] (6)

[0099] (7)

[0100] in, and are the lower and upper limits of the interactive active power at the common connection point between the entities, and are the lower and upper limits of the interactive reactive power at the common connection point between the entities, and are the active power and reactive power of the line at the common connection point, is the set of all feedforward agents of agent i.

[0101] To smooth out photovoltaic fluctuations, some nodes will be equipped with distributed energy storage, and the constraints are set as follows:

[0102] (8)

[0103] (9)

[0104] (10)

[0105] (11)

[0106] (12)

[0107] in, and are the upper limit of charging power and the upper limit of discharging power of energy storage at node α, and are the charging and discharging states of the energy storage at node α, respectively. and are the discharge power and charging power of the energy storage at node α, and are the charging power coefficient and discharging power coefficient of energy storage at node α, and is the state of charge of the energy storage at node α in different cycles, and are the upper and lower limits of the state of charge of the energy storage at node α, respectively.

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

[0109] (13)

[0110] (14)

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

[0112] The coupling constraints of node voltage and line power for each subject’s objective function are set as follows:

[0113] (15)

[0114] (16)

[0115] in, and are the active power and reactive power of lines αβ respectively, and are the mutual conductance and mutual susceptance between node α and node β, and are the voltage amplitudes of node α and node β respectively, and are the voltage phase angles at node α and node β respectively.

[0116] The operating constraints of node voltage and line power for each subject’s objective function are as follows:

[0117] (17)

[0118] (18)

[0119] in, and are the node voltage lower limit and node voltage upper limit respectively, The meanings of the other parameters have been explained above and will not be repeated here.

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

[0121] (19)

[0122] (20)

[0123] (twenty one)

[0124] (twenty two)

[0125] in, 、 and are respectively the vector of non-negative slack variables, the vector of continuous variables, and the vector of binary variables used by subject i for security assessment at time t in the kth period. 、 and The values of the vector of non-negative slack variables, the vector of continuous variables, and the vector of binary variables corresponding to the time t=T of the kth period, respectively, 、 and The values of the vector of non-negative slack variables, the vector of continuous variables, and the vector of binary variables corresponding to time t = 0 of the k + 1th period, respectively. The meanings of the remaining parameters have been explained above and will not be repeated here.

[0126] Step S203: using the first probability density function to set a first uncertainty interval for the photovoltaic inter-period forecast error, and using the second probability density function to set a second uncertainty interval for the load inter-period forecast error.

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

[0128] (twenty three)

[0129] in, is the total uncertainty interval including the first uncertainty interval and the second uncertainty interval, is the first probability density function, is the second probability density function, and are the confidence levels for the inter-period forecast error of PV and the inter-period forecast error of load, respectively. The meanings of the remaining parameters have been explained above and will not be repeated here.

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

[0131] In step S204, a security assessment model corresponding to each subject is constructed.

[0132] Step S104: Based on the security assessment models corresponding to the various entities, a cross-period collaborative security assessment model that conforms to the pessimistic-optimistic architecture is constructed.

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

[0134] The expression of the inter-period collaborative safety assessment model is as follows:

[0135] (twenty four)

[0136] in, 、 、 、 and is the coefficient matrix or vector of subject i, is the coefficient matrix of agent 0, agent 0 and agent i correspond to the public distribution part and autonomous distribution part in the photovoltaic distribution system respectively. is the subject set, Indicates that i is the identifier of other entities after removing entity 0. is the vector of the uncertain power of photovoltaic and load at all times in all cycles within subject i, is a vector of non-negative slack variables for all periods and moments in agent i, To correspond The weight coefficient of and are the continuous variable vector and binary variable vector of all periods and moments in subject i, respectively. for Dimensions, is the coupling variable at the common connection point between agent 0 and agent i, covering all periods and all moments.

[0137] It should be noted that two consecutive mins represent the minimum value under an optimistic scenario, where the first min represents the most optimistic uncertainty situation and the second min represents minimizing the objective function; one max and one min represent the minimum value under a pessimistic scenario.

[0138] Step S105: using the augmented Benders decomposition method to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances, respectively, to obtain a pessimistic safety assessment result and an optimistic safety assessment result.

[0139] Among them, the augmented Benders decomposition method is used to solve the inter-period collaborative security assessment model under pessimistic and optimistic scenarios respectively, effectively realizing privacy protection in the inter-period collaborative security assessment process of photovoltaic distribution systems.

[0140] The specific implementation process of step S105 includes the following steps:

[0141] Step S301: using the augmented Benders decomposition method, decoupling the inter-period collaborative safety assessment model, obtaining the optimistic main problem and optimistic sub-problems corresponding to the optimistic situation, and obtaining the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic situation.

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

[0143] Under the optimistic scenario, the inter-period coordination security assessment model of high-proportion photovoltaic distribution systems is decomposed into the following optimistic main problem and optimistic sub-problems:

[0144] (25)

[0145] (26)

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

[0147] and is the cutting plane identifier, 、 、 、 and is the coefficient matrix or vector of principal 0, is the vector of non-negative slack variables in agent 0 for all periods and all times, is a vector of non-negative slack variables for all periods and moments in agent i, and are the continuous variable vector and binary variable vector containing all periods and all moments in subject 0, respectively. for Dimensions, is the coupling variable at the common connection point between agent 0 and agent i, including all cycles and all moments, is the vector of the uncertain power of photovoltaic and load at all times in all cycles in subject 0, and They are The lower and upper limits of is the vector of the uncertain power of photovoltaic and load at all times in all cycles within subject i, and They are The lower and upper limits of is the estimated value of the objective function of the optimistic main problem for the optimistic subproblem, for The tentative solution of is the augmented Benders cut plane under the optimistic situation, is the feasibility recovery cutting plane under the optimistic scenario, is the set of augmented Benders cutting planes under optimistic conditions, Recover the set of cuts for optimistic feasibility.

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

[0149] (27)

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

[0151] (28)

[0152] in, is the function of augmenting the Benders cutting plane, is the function of feasibility recovery cutting plane, 、 、 、 and is the strong dual variable of the continuous variable part of the optimistic subproblem, is the weak dual variable of the discrete variable part of the optimistic subproblem, for The minimum value of for A tentative solution of .

[0153] Under the pessimistic scenario, the inter-period coordination security assessment model of high-proportion photovoltaic distribution system is decomposed into the following pessimistic main problem and pessimistic sub-problems:

[0154] (29)

[0155] (30)

[0156] in, The uncertainty interval The number of vertices in The uncertainty interval The number of vertices in and is the vertex identifier, that is and They are and the number of is an auxiliary variable used to estimate the objective function of the pessimistic main problem under pessimistic circumstances, is an auxiliary variable used to estimate the objective function of the pessimistic subproblem under pessimistic circumstances, is the estimated value of the objective function of the pessimistic main problem to the pessimistic sub-problem under the pessimistic situation, The corresponding vertex in body 0 A vector of non-negative slack variables, and They are the corresponding vertices in body 0 Continuous variable vectors and binary variable vectors, The uncertainty interval The inner vertex, The uncertainty interval The inner vertex, is the set of augmented Benders cutting planes under the pessimistic case, The set of cutting planes for pessimistic feasibility recovery, is the corresponding vertex in subject i A vector of non-negative slack variables, and are the corresponding vertices in subject i Continuous variable vectors and binary variable vectors, for The tentative solution of is the augmented Benders cutting plane under the pessimistic case, is the feasibility recovery cutting plane under the pessimistic case, is the function that characterizes the augmented Benders cutting plane under the pessimistic situation, is a function that characterizes the feasibility recovery cutting plane under pessimistic circumstances, for The minimum value of .

[0157] Step S302: Perform iterative interactive solutions between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each subject, and perform iterative interactive solutions between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a pessimistic value of the target vector corresponding to each subject.

[0158] Among them, the target vector corresponding to each subject is composed of the non-negative slack variables in the objective function corresponding to the subject.

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

[0160] Preferably, the augmented Benders cut and feasibility recovery cut are formulated in pessimistic and optimistic situations respectively, which can enable multiple subjects in a high-proportion photovoltaic distribution system to make collaborative decisions on their respective mixed integer inter-period security verification models without leaking local privacy.

[0161] like Figure 3FIG. 1 shows a collaborative safety assessment architecture diagram of a photovoltaic power distribution system disclosed in an embodiment of the present invention.

[0162] Among them, the photovoltaic distribution system consists of a public distribution part (subject 0) and multiple autonomous distribution parts (subject i, i is a positive integer).

[0163] based on Figure 3 The specific implementation process of step S302 of the collaborative safety assessment architecture of the photovoltaic power distribution system shown is as follows:

[0164] In the optimistic case, the optimistic main problem and the optimistic subproblem shown in S301 are solved alternately. First, the optimistic main problem is solved to obtain a trial solution, and the trial solution is sent to the optimistic subproblem. The optimistic subproblem is solved based on the trial solution to generate a cutting plane. In this way, the iterative interaction continuously generates trial solutions and cutting planes. When convergence, the vector of non-negative slack variables in the best case can be obtained. and The value of , that is, the optimistic value of the target vector corresponding to each subject;

[0165] In the pessimistic case, the pessimistic main problem and the pessimistic sub-problem shown in S301 are solved alternately. First, the pessimistic main problem is solved to obtain a trial solution, and the trial solution is sent to the pessimistic sub-problem. The pessimistic sub-problem is solved based on the trial solution to generate a cutting plane. In this way, the iterative interaction continuously generates trial solutions and cutting planes. When convergence, the vector of non-negative slack variables in the best case can be obtained. and The value of is the pessimistic value of the target vector corresponding to each subject.

[0166] It should be noted that since the trial solution in the interactive iterative process only contains information at the common connection points, and the cutting plane masks the original information in a dual form, the augmented Benders method effectively achieves privacy protection in the inter-period collaborative security assessment process of high-proportion photovoltaic distribution systems.

[0167] Step S303: The optimistic safety assessment value and the optimistic value of the target vector corresponding to each subject are taken as the optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each subject are taken as the pessimistic safety assessment result.

[0168] Step S106: Based on the pessimistic safety assessment result and the optimistic safety assessment result, the operating status of the photovoltaic power distribution system is identified.

[0169] In the specific implementation process of step S106, 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 operating state;

[0170] 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 distribution system is in a warning operation state with an unsafe operation risk;

[0171] 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 distribution system is in an abnormal operating state.

[0172] It should be noted that for non-negative slack variable vector 、 、 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 in the equation, if the objective functions in both the pessimistic and optimistic scenarios are less than 0, it means that the corresponding evaluation item will not have unsafe operation problems, and the photovoltaic distribution system is in normal operation; if the objective function in the pessimistic scenario is greater than 0, and equal to 0 in the optimistic scenario, it means that the corresponding evaluation item may have unsafe operation problems, and the photovoltaic distribution system is in a warning operation state; if the objective functions in both the pessimistic and optimistic scenarios are greater than 0, it means that the corresponding evaluation item will definitely have unsafe operation problems, and the photovoltaic distribution system is in an abnormal operation state.

[0173] In one embodiment, 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 when the value of the target vector is the optimistic value, a first non-negative slack variable greater than 0 is found from each non-negative slack variable constituting the target vector, and it is determined that a preset evaluation item corresponding to the first non-negative slack variable has an unsafe operation problem under the optimistic scenario;

[0174] 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 a pessimistic value, a second non-negative slack variable greater than 0 is found from the various non-negative slack variables that constitute the target vector, and it is determined that the preset evaluation item corresponding to the second non-negative slack variable under the pessimistic situation has an unsafe operation problem.

[0175] It should be noted that according to the non-negative slack variable vector 、 、 and The values of each non-negative slack variable in the is used to locate the common connection points, nodes, and lines of unsafe operation. That is, a non-negative slack variable of 0 indicates that there is no unsafe operation problem, and a non-negative slack variable greater than 0 indicates that the evaluation item corresponding to the non-negative slack variable has an unsafe operation problem. The pre-evaluation item includes multiple evaluation items for common connection points, nodes, and lines respectively. For example, in a certain subject, if there is an unsafe operation problem in the evaluation item preset for the common connection point, it is determined that the common connection point of the subject has an unsafe operation problem.

[0176] Based on the above-mentioned embodiment of the present invention, a method for inter-period collaborative safety assessment of a photovoltaic distribution system is disclosed. In this scheme, the inter-period evolution law of photovoltaic power and load power is analyzed by analogy with the martingale process, and the inter-period uncertainty of photovoltaic power and load power is quantified. Under the premise of considering the inter-period uncertainty of photovoltaic and load, an inter-period collaborative safety assessment model that conforms to the pessimistic-optimistic architecture is constructed, and the augmented Benders decomposition method is used to solve it to obtain the operating status of the photovoltaic distribution system, so as to achieve the purpose of inter-period safety assessment of a high-proportion photovoltaic distribution system under the premise of protecting privacy and considering the inter-period uncertainty of photovoltaic and load.

[0177] Corresponding to the above-mentioned embodiment of the present invention disclosed a method for cross-period collaborative safety assessment of photovoltaic power distribution system, such as Figure 4 , which is a structural diagram of a photovoltaic distribution system inter-period collaborative safety assessment device 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 used to analyze the inter-period evolution laws of photovoltaic power and load power in the photovoltaic distribution system according to the random process characterized by the martingale process, and obtain the photovoltaic inter-period prediction error and the load inter-period prediction error.

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

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

[0181] The probability density function of the cumulative mixed Gaussian mixture distribution is used to quantify the photovoltaic inter-period forecast error and the load inter-period forecast error, and the first probability density function corresponding to the photovoltaic inter-period forecast error and the second probability density function corresponding to the load inter-period forecast error are obtained.

[0182] The first construction unit 403 is used to construct a safety assessment model corresponding to each entity in the photovoltaic 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 configured to:

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

[0185] Using the inter-period forecast error of photovoltaic power and the inter-period forecast error of load, the constraints of the objective function are set;

[0186] A first uncertainty interval for the photovoltaic inter-period forecast error is set using a first probability density function, and a second uncertainty interval for the load inter-period forecast error is set using a second probability density function;

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

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

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

[0190] In one embodiment, the solving unit 405 is specifically configured to:

[0191] Using the augmented Benders decomposition method, the inter-period collaborative safety assessment model is decoupled to obtain the optimistic main problem and optimistic sub-problems corresponding to the optimistic scenario, and the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic scenario;

[0192] An iterative interactive solution is performed between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each subject. An iterative interactive solution is performed between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a pessimistic value of the target vector corresponding to each subject. The target vector corresponding to each subject is composed of the non-negative slack variables in the objective function corresponding to the subject.

[0193] The optimistic safety assessment value and the optimistic value of the target vector corresponding to each subject are taken as the optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each subject are taken as the pessimistic safety assessment result.

[0194] The identification unit 406 is configured to identify the operating status of the photovoltaic power distribution system based on the pessimistic safety assessment result and the optimistic safety assessment result.

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

[0196] 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 normal operation;

[0197] 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 distribution system is in a warning operation state with an unsafe operation risk;

[0198] 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 distribution system is in an abnormal operating state.

[0199] In one embodiment, the photovoltaic power distribution system inter-period collaborative safety assessment device further includes:

[0200] a positioning unit configured to, for each subject in the photovoltaic power distribution system, find a first non-negative slack variable greater than 0 from among the non-negative slack variables constituting the target vector if the optimistic value of the target vector corresponding to the subject is greater than 0, and determine whether a preset evaluation item corresponding to the first non-negative slack variable has an unsafe operation problem under the optimistic scenario;

[0201] 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 a pessimistic value, a second non-negative slack variable greater than 0 is found from the various non-negative slack variables that constitute the target vector, and it is determined that the preset evaluation item corresponding to the second non-negative slack variable under the pessimistic situation has an unsafe operation problem.

[0202] Based on the above-mentioned embodiment of the present invention, a photovoltaic distribution system inter-period collaborative safety assessment device is disclosed. In this scheme, the inter-period evolution law of photovoltaic power and load power is analyzed by analogy with the martingale process, and the inter-period uncertainty of photovoltaic power and load power is quantified. Under the premise of considering the inter-period uncertainty of photovoltaic and load, an inter-period collaborative safety assessment model that conforms to the pessimistic-optimistic architecture is constructed, and the augmented Benders decomposition method is used to solve it to obtain the operating status of the photovoltaic distribution system, so as to achieve the purpose of inter-period safety assessment of a high-proportion photovoltaic distribution system under the premise of protecting privacy and considering the inter-period uncertainty of photovoltaic and load.

[0203] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on 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 expending creative work.

[0204] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0205] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for inter-period collaborative safety assessment of photovoltaic distribution systems, characterized in that: The method comprises: According to the stochastic process characterized by the martingale process, the inter-period evolution of photovoltaic power and load power in the photovoltaic distribution system is analyzed respectively, and the inter-period prediction error of photovoltaic power and load power is obtained. Quantifying the photovoltaic inter-period prediction error to obtain a first probability density function, and quantifying the load inter-period prediction error to obtain a second probability density function; Constructing 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; Based on the security assessment models corresponding to the respective subjects, a cross-period collaborative security assessment model conforming to the pessimistic-optimistic framework is constructed; The augmented Benders decomposition method is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances, respectively, to obtain pessimistic and optimistic safety assessment results. Based on the pessimistic safety assessment result and the optimistic safety assessment result, identifying the operating status of the photovoltaic power distribution system; The step of constructing 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 includes: A corresponding objective function is set for each subject in the photovoltaic power distribution system; the objective function includes non-negative slack variables corresponding to each preset evaluation item and a preset weight corresponding to each non-negative slack variable; the optimization goal of the objective function is to minimize the sum of the products of each non-negative slack variable and the corresponding preset weight; Using the photovoltaic inter-period prediction error and the load inter-period prediction error, setting the constraint conditions of the objective function; Using the first probability density function to set a first uncertainty interval for the photovoltaic inter-period forecast error, and using the second probability density function to set a second uncertainty interval for the load inter-period forecast error; A safety assessment model is constructed 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.

2. The method according to claim 1, characterized in that The quantifying the photovoltaic inter-period prediction error to obtain a first probability density function, and the quantifying the load inter-period prediction error to obtain a second probability density function, includes: The photovoltaic inter-period prediction error and the load inter-period prediction error are quantified using the probability density function of the cumulative mixed Gaussian mixture distribution to obtain a first probability density function corresponding to the photovoltaic inter-period prediction error and a second probability density function corresponding to the load inter-period prediction error.

3. The method according to claim 1, characterized in that The augmented Benders decomposition method is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances, respectively, to obtain pessimistic and optimistic safety assessment results, including: Using the augmented Benders decomposition method, the inter-period collaborative safety assessment model is decoupled to obtain the optimistic main problem and optimistic sub-problems corresponding to the optimistic case, and the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic case; Performing iterative interactive solutions between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each of the subjects, and performing iterative interactive solutions between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a 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 objective function corresponding to the subject; The optimistic safety assessment value and the optimistic value of the target vector corresponding to each of the subjects are used as an optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each of the subjects are used as a pessimistic safety assessment result.

4. The method according to claim 3, characterized in that The identifying the operating state of the photovoltaic power distribution system based on the pessimistic safety assessment result and the optimistic safety assessment result includes: 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 operating state; 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 unsafe operation risk; 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 operating state.

5. The method according to claim 3, characterized in that The method further comprises: 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, when the value of the target vector is optimistic, find a first non-negative slack variable greater than 0 from each of the non-negative slack variables constituting the target vector, and determine whether a preset evaluation item corresponding to the first non-negative slack variable has an unsafe operation problem under the optimistic scenario; 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 when the value of the target vector is a pessimistic value, a second non-negative slack variable greater than 0 is found from the various non-negative slack variables constituting the target vector, and it is determined that a preset evaluation item corresponding to the second non-negative slack variable has an unsafe operation problem under the pessimistic situation.

6. A photovoltaic power distribution system inter-period collaborative safety assessment device, characterized in that: The device comprises: An analysis unit is used to analyze the inter-period evolution laws of photovoltaic power and load power in the photovoltaic distribution system according to the random process characterized by the martingale process, and obtain the inter-period prediction error of photovoltaic power and the inter-period prediction error of load power; a quantization unit, configured to quantify the photovoltaic inter-period prediction error to obtain a first probability density function, and to quantify the load inter-period prediction error to obtain a second probability density function; A first construction unit is configured 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; A second construction unit is configured to construct an inter-period collaborative security assessment model that conforms to a pessimistic-optimistic architecture based on the security assessment models corresponding to the respective subjects; A solving unit is used to solve the inter-period collaborative safety assessment model under pessimistic and optimistic circumstances respectively by using an augmented Benders decomposition method to obtain a pessimistic safety assessment result and an optimistic safety assessment result; an identification unit, configured to identify an operating state of the photovoltaic power distribution system based on the pessimistic safety assessment result and the optimistic safety assessment result; Wherein, the first building block is specifically used for: A corresponding objective function is set for each subject in the photovoltaic power distribution system; the objective function includes non-negative slack variables corresponding to each preset evaluation item and a preset weight corresponding to each non-negative slack variable; the optimization goal of the objective function is to minimize the sum of the products of each non-negative slack variable and the corresponding preset weight; Using the photovoltaic inter-period prediction error and the load inter-period prediction error, setting the constraint conditions of the objective function; Using the first probability density function to set a first uncertainty interval for the photovoltaic inter-period forecast error, and using the second probability density function to set a second uncertainty interval for the load inter-period forecast error; A safety assessment model is constructed 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.

7. The device according to claim 6, characterized in that The quantization unit is specifically used for: The photovoltaic inter-period prediction error and the load inter-period prediction error are quantified using the probability density function of the cumulative mixed Gaussian mixture distribution to obtain a first probability density function corresponding to the photovoltaic inter-period prediction error and a second probability density function corresponding to the load inter-period prediction error.

8. The device according to claim 6, characterized in that The solving unit is specifically used for: Using the augmented Benders decomposition method, the inter-period collaborative safety assessment model is decoupled to obtain the optimistic main problem and optimistic sub-problems corresponding to the optimistic case, and the pessimistic main problem and pessimistic sub-problems corresponding to the pessimistic case; Performing iterative interactive solutions between the optimistic main problem and the optimistic subproblems to obtain an optimistic safety assessment value and an optimistic value of the target vector corresponding to each of the subjects, and performing iterative interactive solutions between the pessimistic main problem and the pessimistic subproblems to obtain a pessimistic safety assessment value and a 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 objective function corresponding to the subject; The optimistic safety assessment value and the optimistic value of the target vector corresponding to each of the subjects are used as an optimistic safety assessment result, and the pessimistic safety assessment value and the pessimistic value of the target vector corresponding to each of the subjects are used as a pessimistic safety assessment result.

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