Power transmission and distribution system scheduling method and device, storage medium and computer program product

Abstract

The invention discloses a power transmission and distribution system scheduling method and device, a storage medium and a computer program product. The method comprises the following steps: constructing a fuzzy set containing single peak-skewness information according to a new energy output historical data sample; determining a distribution robust scheduling model of the transmission and distribution cooperative system; based on the fuzzy set, carrying out solvability conversion on the distribution robust scheduling model of the transmission and distribution cooperative system to obtain a close model corresponding to the scheduling problem of the power transmission and distribution system; according to communication packet loss information between the power transmission system and the power distribution system, a distributed relaxation alternating direction multiplier algorithm is adopted to solve the compact model, a scheduling optimization result is obtained, and unit output of the power transmission system and the power distribution system is scheduled according to the scheduling optimization result. The technical problem that the actual scheduling optimization effect is poor due to the fact that a power transmission and distribution system scheduling method in the related technology lacks consideration on instability of a communication channel and processing on uncertainty of new energy is too conservative is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a method, device, storage medium and computer program product for dispatching a power transmission and distribution system. Background Art

[0002] With the widespread application of distributed energy in power systems, distribution networks can use their distributed generators to supply power to loads, forming active distribution networks. Power systems using active distribution networks can be more reliable, efficient, and cost-effective. Therefore, the coordinated dispatch of integrated transmission and distribution (ITD) systems has attracted widespread attention from researchers.

[0003] However, the power transmission and distribution system scheduling methods in related technologies lack consideration of the instability of communication channels, are overly conservative in handling the uncertainty of new energy, and have technical problems such as poor actual scheduling optimization effects.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a power transmission and distribution system scheduling method, device, storage medium and computer program product to at least solve the technical problem that the actual scheduling optimization effect is poor due to the lack of consideration of the instability of communication channels and the overly conservative handling of the uncertainty of new energy in the power transmission and distribution system scheduling method in the related technology.

[0006] According to one aspect of an embodiment of the present application, a method for dispatching a power transmission and distribution system is provided, comprising: constructing a fuzzy set containing unimodal-skewness information based on a sample of historical data on output of new energy, wherein the fuzzy set is used to characterize the uncertainty of the output of a new energy subsystem in the power transmission and distribution system; determining a distributed robust dispatching model for a transmission and distribution coordinated system, wherein the distributed robust dispatching model for a transmission and distribution coordinated system is determined by an optimal dispatching model for a transmission system and an optimal dispatching model for a distribution system, and the distributed robust dispatching model for a transmission and distribution coordinated system contains unimodal-distribution information; based on the fuzzy set, performing a solvability transformation on the distributed robust dispatching model for a transmission and distribution coordinated system to obtain a compact model corresponding to a scheduling problem of a power transmission and distribution system, wherein the scheduling problem of a power transmission and distribution system is used to describe the distribution relationship between the output of units of a transmission system and a distribution system; based on the communication packet loss information between the transmission system and the distribution system, using a distributed relaxed alternating direction multiplier algorithm to solve the compact model to obtain a scheduling optimization result, and scheduling the output of units of the transmission system and the distribution system based on the scheduling optimization result.

[0007] Optionally, the fuzzy set B containing the unimodal-skewness information is as follows: Where P represents a multivariate distribution, ξ is a random variable, γ1 and γ2 are adjustment coefficients for the mean and variance, respectively, M(Ξ) is the set of all distributions on the unbounded support set Ξ, and E P {·} is the expectation operator; (·) is the transposed matrix, U0 α is a distribution set that conforms to the α-unimodal property with a mode of 0, Σ0 is the empirical covariance matrix, and the empirical covariance matrix is ​​determined based on the standard deviation of the prediction error of the output of the new energy units and the correlation coefficients between different new energy units.

[0008] Optionally, determining the distributed robust scheduling model of the transmission and distribution coordinated system includes: determining a first objective function and a first constraint corresponding to the transmission system, and determining the transmission system optimization scheduling model based on the first objective function and the first constraint, wherein the first objective function is used to minimize the production cost of the transmission system; determining a second objective function and a second constraint corresponding to the distribution system, and determining the distribution system optimization scheduling model based on the second objective function and the second constraint, wherein the first objective function is used to minimize the production cost of the distribution system; establishing a transmission and distribution coupling constraint between the transmission system optimization scheduling model and the distribution system optimization scheduling model to obtain the distributed robust scheduling model of the transmission and distribution coordinated system, wherein the transmission and distribution coupling constraint is used to ensure that the output active power of the transmission system is equal to the input active power of the distribution system.

[0009] Optionally, the first objective function is as follows: in, is the actual output of thermal power units in the transmission system. is the spinning reserve factor, are cost coefficients, T G is the set of thermal power units in the transmission system, TD is the set of scheduling time; the first constraint condition includes:

[0010] in, are the predicted outputs of thermal power units and new energy units in the transmission system, that is, the outputs when there is no prediction error. P i G,T , are the lower and upper limits of active power of thermal power unit i in TS at time t, respectively; are the upward and downward rotation reserve capacities of thermal power unit i in TS at time t, is the adjustment coefficient for the forecast error of renewable energy in the transmission system, is the prediction error of the active power of the renewable energy units in the transmission system, The reduction in the number of new energy units in the transmission system, is the power in the transmission line of the power transmission system, is the active load in the transmission system, are the distribution transfer factors in the transmission system, is the maximum value of the permissible power flow of the line in the transmission system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the transmission system, T WD , T L , T T , T LD They are respectively the collection of new energy units, lines, transmission and distribution exchange lines, and loads in the transmission system.

[0011] Optionally, the second objective function is as follows: in, is the actual output of thermal power units in the power distribution system. is the spinning reserve factor; are cost coefficients, D G is the set of thermal power units in the distribution system, TD is the set of scheduling time; the second constraint includes:

[0012] in, They are the predicted active and reactive outputs of thermal power units and new energy units in the power distribution system, is the actual reactive power of the thermal power unit; They are the upward and downward rotation reserve capacities of thermal power units in DS respectively; They are the upper and lower limits of the upward and downward spinning reserve capacity of thermal power units in DS, respectively; P i G,D , They are the lower and upper limits of active power of thermal power units in DS respectively; are the active and reactive loads of the electric load in DS, is the adjustment coefficient for the prediction error of renewable energy in the distribution system, They are the prediction errors of active power and reactive power of renewable energy units in the power distribution system respectively; is the constant power factor in the distribution system, P i,t , Q i,t are the node voltages, are the predicted active and reactive power of the root node in the distribution system, are the predicted active and reactive power of the lines in the distribution system, P i,t, Q i,t are the actual active and reactive power of the lines in the distribution system, V i,t are the predicted voltage and actual voltage of the node, respectively. i 、x i are the resistance and reactance in the distribution system, R, X, B are the matrices of resistance, reactance, and susceptance, respectively, V0 is the reference voltage of the root node substation, are respectively the active and reactive power reduction of new energy units in the distribution system, is the power in the transmission line of the power distribution system, are the active load and reactive load in the distribution system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the distribution system, D WD D is a collection of new energy units. i C is the set of child nodes of node i in the power distribution system, A i is the set of parent nodes of node i in the power distribution system.

[0013] Optionally, based on fuzzy sets, the distributed robust scheduling model of the transmission and distribution coordinated system is transformed into a solvability model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, including: according to the duality theory, based on the fuzzy sets without considering the unimodal information, the objective function and constraints corresponding to the distributed robust scheduling model of the transmission and distribution coordinated system are transformed; based on the fuzzy sets after adding the unimodal information, the transformed objective function and constraints are further transformed, wherein the objective function after further transformation is a second-order cone programming problem; according to the objective function and constraints after further transformation, the compact model is determined, wherein the compact model is shown in the following formula: Among them, f(x), g j (y j ) are the objective functions of the transmission system and the jth distribution system, respectively, x, y j are the decision variables of the transmission system and the jth distribution system respectively; X, Y j are the feasible regions of the transmission system and the jth distribution system, A j , B j are coupling matrices, and J is the set of distribution systems.

[0014] Optionally, based on the communication packet loss information between the transmission system and the distribution system, a distributed relaxed alternating direction multiplier algorithm is used to solve the compact model, including: based on the PR (Peaceman-Rachford) splitting theory, constructing the Lagrangian function of the scheduling problem corresponding to the compact model, and determining the sub-problems corresponding to the transmission system and the distribution system; using the relaxed PR splitting method to solve the Lagrangian function, and transforming the sub-problems corresponding to the transmission system and the distribution system, wherein the transformed sub-problems are to find the fixed points of the operator; determining the packet loss probability of the communication channel between the transmission system and the distribution system, and solving the sub-problems taking into account the packet loss probability.

[0015] According to another aspect of the embodiment of the present application, a transmission and distribution system scheduling device is also provided, including: a fuzzy set construction module, which is used to construct a fuzzy set containing unimodal-skewness information based on a sample of historical data on the output of new energy, wherein the fuzzy set is used to characterize the uncertainty of the output of the new energy subsystem in the transmission and distribution system; a scheduling model construction module, which is used to determine the distributed robust scheduling model of the transmission and distribution coordination system, wherein the distributed robust scheduling model of the transmission and distribution coordination system is determined by the transmission system optimization scheduling model and the distribution system optimization scheduling model, and the distributed robust scheduling model of the transmission and distribution coordination system contains unimodal-distribution information; a solvability transformation module, which is used to perform solvability transformation on the distributed robust scheduling model of the transmission and distribution coordination system based on fuzzy sets, and obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system; a scheduling optimization solution module, which is used to solve the compact model using a distributed relaxed alternating direction multiplier algorithm based on the communication packet loss information between the transmission system and the distribution system, obtain a scheduling optimization result, and schedule the unit outputs of the transmission system and the distribution system based on the scheduling optimization result.

[0016] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the power transmission and distribution system scheduling method by running the computer program.

[0017] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, which implements the steps of the power transmission and distribution system scheduling method when the computer program is executed by a processor.

[0018] In an embodiment of the present application, a fuzzy set containing unimodal-skewness information is constructed based on a sample of historical data on new energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of the new energy subsystem in the transmission and distribution system; a distributionally robust scheduling model of the transmission and distribution coordination system is determined, wherein the distributionally robust scheduling model of the transmission and distribution coordination system is determined by the transmission system optimization scheduling model and the distribution system optimization scheduling model, and the distributionally robust scheduling model of the transmission and distribution coordination system contains unimodal-distribution information; based on the fuzzy set, the distributionally robust scheduling model of the transmission and distribution coordination system is transformed into a solvable model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system; based on the communication packet loss information between the transmission system and the distribution system, a distributed relaxed alternating direction multiplier algorithm is used to solve the compact model to obtain a scheduling optimization result, and a method of scheduling the unit outputs of the transmission system and the distribution system based on the scheduling optimization result is used to provide a transmission and distribution scheduling method that considers uncertain unimodal-skewness information, thereby alleviating the distributionally robust optimization based on moment information (distributionally robust optimization) The conservatism of the scheduling results in the distributed robust optimization (DRO) method is improved, and the purpose of distributed collaborative scheduling of the ITD system is achieved under the premise of considering lossy communication, thereby solving the technical problems of poor actual scheduling optimization effect caused by the lack of consideration of the instability of communication channels in the transmission and distribution system scheduling method in related technologies and the overly conservative treatment of the uncertainty of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for dispatching a power transmission and distribution system according to an embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of a method flow for scheduling a power transmission and distribution system provided according to an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of a method flow for transmission and distribution scheduling considering uncertain unimodal-skewness information provided in accordance with an embodiment of the present application;

[0023] Figure 4 It is a structural schematic diagram of a power transmission and distribution system scheduling device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] In actual scheduling, the transmission system (TS) and distribution system (DS) in the ITD system are independently managed by the DS operator and the TS operator respectively. Due to the need for privacy protection, the operators cannot exchange detailed operating status and topology information, so it is necessary to implement distributed collaborative scheduling for the ITD system. However, most of the current research focuses on distributed collaboration under ideal communication conditions, and does not pay attention to the actual communication process. In actual cases, most of the power system outages are caused by program and communication failures. Therefore, it is particularly important to consider the instability or potential failures of the communication channel in the distributed algorithm, but the classic distributed algorithm relies on the limited available data provided by the neighboring area. If the communication channel fails, it may lead to failure to converge.

[0027] In ITD, another key issue is the uncertainty of renewable energy. Due to the inherent uncertainty of renewable energy, the safe operation of ITD may be endangered. For the uncertainty of renewable energy, current research can be divided into stochastic optimization, robust optimization, and distributionally robust optimization. However, when the distribution of uncertainty is unknown, stochastic optimization may lead to large deviations, and the scheduling results of robust optimization are too conservative to meet the actual scheduling needs. In the case of limitations of the above two optimization methods, distributionally robust optimization (DRO) can strike a balance between economy and conservatism. DRO assumes that the true distribution of uncertainty belongs to a fuzzy set, which is achieved by using certain statistical properties, such as moment information or statistical distance. However, the fuzzy sets based on first-order moment information and second-order moment information, which are widely used at present, contain too many extreme distributions and cannot guarantee the economy of distributionally robust optimization.

[0028] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0029] According to an embodiment of the present application, a method embodiment of a power transmission and distribution system scheduling is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or electronic device) for implementing the power transmission and distribution system scheduling method is shown. Figure 1 As shown, the computer terminal 10 (or electronic device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power transmission and distribution system scheduling method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned power transmission and distribution system scheduling method is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0035] In the above operating environment, the embodiment of the present application provides a method for dispatching a power transmission and distribution system. Figure 2 is a schematic diagram of a method flow for dispatching a power transmission and distribution system provided according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0036] Step S202, constructing a fuzzy set containing unimodal-skewness information based on the historical data sample of renewable energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of renewable energy subsystems in the power transmission and distribution system;

[0037] Step S204, determining a distributed robust dispatching model for the transmission and distribution coordinated system, wherein the distributed robust dispatching model for the transmission and distribution coordinated system is determined by an optimal dispatching model for the transmission system and an optimal dispatching model for the distribution system, and the distributed robust dispatching model for the transmission and distribution coordinated system includes unimodal distribution information;

[0038] Step S206, based on fuzzy sets, the transmission and distribution coordinated system distributed robust scheduling model is transformed into a solvable model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system;

[0039] Step S208, based on the communication packet loss information between the transmission system and the distribution system, a distributed relaxed alternating direction multiplier algorithm is used to solve the compact model to obtain a scheduling optimization result, and the unit output of the transmission system and the distribution system is scheduled based on the scheduling optimization result.

[0040] Through the above steps, by providing a transmission and distribution scheduling method considering uncertain unimodal-skewness information, the conservatism of the scheduling results in the distributed robust optimization (DRO) method based on moment information is reduced, and the purpose of distributed collaborative scheduling of the ITD system is achieved under the premise of considering lossy communication, thereby solving the technical problem of poor actual scheduling optimization effect caused by the lack of consideration of the instability of communication channels and overly conservative handling of new energy uncertainties in the transmission and distribution system scheduling methods in related technologies.

[0041] The following further introduces the power transmission and distribution system scheduling method in steps S202 to S208 of the embodiment of the present application.

[0042] Figure 3 is a schematic diagram of a method flow for transmission and distribution scheduling considering uncertain unimodal-skewness information provided in an embodiment of the present application, such as Figure 3 As shown, the method comprises the following steps:

[0043] Step 1: Based on the historical data samples of renewable energy output, a fuzzy set containing unimodal-skewness information is constructed, as follows.

[0044] First, in order to introduce unimodality information into the fuzzy set, α-unimodality is first defined in the embodiment of the present application: for any α>0, if for each Borel set R∈B, z α P(R / z) is non-decreasing in z∈(0,+∞), then the multivariate distribution P∈R dIt is called an α-unimodal distribution with a mode of 0.

[0045] It should be noted that the above definition generalizes the concept of classical unimodality to R d , in the definition of α-unimodality, the parameter α is crucial, which represents the degree of unimodality. When α=d=1, the distribution P has classical unimodality. As α increases, the unimodality requirement will be gradually relaxed. If α→∞, the unimodality will eventually disappear. In addition, the mode of the random distribution can be assumed to be 0 without loss of generality, because it is always possible to convert the mode of the distribution to 0 by a simple transformation.

[0046] Then, in addition to the unimodality, skewness information is also introduced in the embodiment of the present application to include more statistical characteristics in the fuzzy set, so as to more accurately describe the distribution in reality. In actual scheduling, uncertainty may reflect symmetry to some extent, and this characteristic can be described by skewness information. Under multivariate distribution, Isogai's skewness is a common choice, and its specific definition is shown in the following formula (1):

[0047] S1=(μ-m)g -1 (Σ)(μ-m) (1)

[0048] Where μ and m are the mean and mode in vector form, respectively; g(Σ) is a function of the covariance Σ, and its common choice is g(Σ) = Σ. S1 is an effective statistical measure used to specify the skewness information of the constructed fuzzy set, because it can always be assumed that the mode of P is 0 and μ = E P {ξ}, the empirical covariance matrix Σ0 is used to approximate the true covariance matrix Σ, so (1) can be written as follows:

[0049]

[0050] Therefore, based on the above definition, the embodiment of the present application proposes a novel fuzzy set considering unimodal-skewness information, as shown in the following formula:

[0051]

[0052] Where P represents a multivariate distribution, ξ is a random variable, γ1 and γ2 are adjustment coefficients for the mean and variance, respectively, M(Ξ) is the set of all distributions on the unbounded support set Ξ, and E P {·} is the expectation operator; (·) is the transposed matrix, U0 α is a distribution set that conforms to the α-unimodal property with a mode of 0, Σ0 is the empirical covariance matrix, which is determined based on the standard deviation of the prediction error of the output of the new energy unit and the correlation coefficient between different new energy units. Specifically, the empirical covariance matrix Σ0 is defined as shown in the following formula (taking two new energy units as an example):

[0053]

[0054] σ w,m =δ w,m p w (5)

[0055] Among them, σ w,1 , σ w,2 are the standard deviations of the prediction errors of new energy units 1 and 2 respectively; p w Predict output for new energy units; ρ m,n is the correlation coefficient between new energy unit m and new energy unit n; δ w,m is the standard deviation σ w,m The ratio to the predicted output of new energy units.

[0056] In the fuzzy set (3), the first constraint is based on Σ0 and γ1 to limit the skewness information of the distribution in the fuzzy set. When γ1 = 0, it is assumed that the mean and the mode completely coincide, that is, μ0 = 0. When γ1 > 0, the constraint on the skewness of the distribution is relaxed to describe the possible asymmetric distribution. The second constraint is based on Σ0 and γ2 to describe the uncertainty of the second-order moment information. The third constraint assumes that the distribution in the fuzzy set conforms to α-unimodality to eliminate the extreme distribution information in the moment information fuzzy set.

[0057] It should be noted that the conservatism of the fuzzy set proposed in the embodiment of the present application can be controlled not only by the parameter α, but also by the parameters γ1 and γ2, which provides a flexible method for describing the uncertainty of new energy.

[0058] Step 2: Establish a distributed robust dispatch model for the transmission and distribution coordination system considering unimodal-distributed information. In actual dispatch, since the ITD systems are managed by different operators, distributed coordinated dispatch is required. In distributed coordinated dispatch, TS and DS operators calculate TS and DS locally respectively, and coordinate by exchanging limited data information with adjacent power grids. The specific steps are as follows.

[0059] In some embodiments of the present application, determining a distributed robust scheduling model for a transmission and distribution coordinated system includes the following steps: determining a first objective function and a first constraint corresponding to the transmission system, and determining an optimal scheduling model for the transmission system based on the first objective function and the first constraint, wherein the first objective function is used to minimize the production cost of the transmission system; determining a second objective function and a second constraint corresponding to the distribution system, and determining an optimal scheduling model for the distribution system based on the second objective function and the second constraint, wherein the first objective function is used to minimize the production cost of the distribution system; establishing a transmission and distribution coupling constraint between the transmission system optimal scheduling model and the distribution system optimal scheduling model to obtain a distributed robust scheduling model for the transmission and distribution coordinated system, wherein the transmission and distribution coupling constraint is used to ensure that the output active power of the transmission system is equal to the input active power of the distribution system.

[0060] A detailed introduction is given below.

[0061] Firstly, the process of establishing the transmission system optimization scheduling model is introduced.

[0062] The first objective function corresponding to the transmission system is established to minimize the generation cost, as shown in the following formula:

[0063]

[0064] in, is the actual output of thermal power units in the transmission system. is the spinning reserve factor, are cost coefficients, T G is the set of thermal power units in the transmission system, TD is the set of dispatching time;

[0065] Establish the first constraint condition corresponding to the transmission system, including:

[0066]

[0067] in, are the predicted outputs of thermal power units and new energy units in the transmission system, that is, the outputs when there is no prediction error. P i G,T , are the lower and upper limits of active power of thermal power unit i in TS at time t, respectively; are the upward and downward rotation reserve capacities of thermal power unit i in TS at time t, respectively; is the adjustment coefficient for the forecast error of renewable energy in the transmission system, is the prediction error of the active power of the renewable energy units in the transmission system, The reduction in the number of new energy units in the transmission system, is the power in the transmission line of the power transmission system, is the active load in the transmission system, are the distribution transfer factors in the transmission system, is the maximum value of the permissible power flow of the line in the transmission system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the transmission system, T WD , T L , T T , T LD They are respectively the collection of new energy units, lines, transmission and distribution exchange lines, and loads in the transmission system.

[0068] The following is an introduction to the process of establishing an optimal scheduling model for the distribution system.

[0069] The second objective function corresponding to the power distribution system is established to minimize the generation cost, as shown in the following formula:

[0070]

[0071] in, is the actual output of thermal power units in the power distribution system. is the spinning reserve factor; are cost coefficients, D G is the set of thermal power units in the distribution system, and TD is the set of scheduling times.

[0072] The second constraint condition corresponding to the power distribution system is established, including:

[0073]

[0074]

[0075] in, They are the predicted active and reactive outputs of thermal power units and new energy units in the power distribution system, is the actual reactive power of the thermal power unit; They are the upward and downward rotation reserve capacities of thermal power units in DS respectively; They are the upper and lower limits of the upward and downward spinning reserve capacity of thermal power units in DS, respectively; P i G ,D , They are the lower and upper limits of active power of thermal power units in DS respectively; are the active and reactive loads of the electric load in DS, is the adjustment coefficient for the prediction error of renewable energy in the distribution system, They are the prediction errors of active power and reactive power of renewable energy units in the power distribution system respectively; is the constant power factor in the distribution system, P i,t , Q i,t are the node voltages, are the predicted active and reactive power of the root node in the distribution system, are the predicted active and reactive power of the lines in the distribution system, P i,t , Q i,t are the actual active and reactive power of the lines in the distribution system, V i,t are the predicted voltage and actual voltage of the node, respectively. i 、x i are the resistance and reactance in the distribution system, R, X, B are the matrices of resistance, reactance, and susceptance, respectively, V0 is the reference voltage of the root node substation, are respectively the active and reactive power reduction of new energy units in the distribution system, is the power in the transmission line of the power distribution system, are the active load and reactive load in the distribution system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the distribution system, D WD D is a collection of new energy units. i C is the set of child nodes of node i in the power distribution system, A i is the set of parent nodes of node i in the power distribution system.

[0076] In establishing the transmission system optimization scheduling model and the distribution system optimization scheduling model, it is also necessary to establish the transmission and distribution coupling constraint conditions between the two, so as to obtain the transmission and distribution coordinated system distributed robust scheduling model. In this embodiment, the transmission and distribution coupling constraint conditions are used to ensure that the output active power P of TS i Tie,T The input active power P of DS i Tie,D are equal, as shown below:

[0077] P i Tie,T =P i Tie,D . (34)

[0078] Step 3, after the distributed robust dispatching model of the transmission and distribution coordinated system is established, the distributed robust dispatching model of the transmission and distribution coordinated system containing unimodal-skewness information can be transformed into a solvable model. The specific steps are as follows.

[0079] In some embodiments of the present application, based on fuzzy sets, a solvability transformation is performed on the distributed robust scheduling model of the transmission and distribution coordinated system to obtain a compact model corresponding to the transmission and distribution system scheduling problem, including the following steps: according to the duality theory, based on fuzzy sets that do not consider unimodal information, the objective function and constraints corresponding to the distributed robust scheduling model of the transmission and distribution coordinated system are transformed; based on the fuzzy sets after adding unimodal information, the transformed objective function and constraints are further transformed, wherein the objective function after further transformation is a second-order cone programming problem; based on the objective function and constraints after further transformation, the compact model is determined.

[0080] Specifically, in the embodiment of the present application, the objective function can be first converted into a linear form, as shown in the following formula:

[0081]

[0082] in, is the optimal electric power of thermal power unit i when the prediction error of renewable energy is zero. h i (x) = -c i0 γ i , then the expectation term in formula (35) can be transformed into:

[0083]

[0084] Next, the objective function shown in formula (36) can be transformed in two steps: first, the objective function is transformed based on the fuzzy set D' without unimodal information, and then the unimodal information is added to the fuzzy set to obtain the final deterministic model, which is described in detail below.

[0085] First, without considering the unimodal information in the fuzzy set, the objective function of distribution robustness is transformed into a deterministic model. When the unimodal information is not considered, the fuzzy set D' can be expressed as:

[0086]

[0087] Let w i =h i The true mean and variance of (x)s,w are h(x)μ t and Therefore, the integral form of the distributed robust optimization is shown as the objective function (38) and constraints (39)-(41):

[0088]

[0089] ∫ Ξ P(dw i )=1:δ0 (39)

[0090] ∫ Ξ w i P(dw i )=h(x)μ t :δ1 (40)

[0091]

[0092] Among them, δ0, δ1, and δ2 are all dual variables;

[0093] According to the duality theory, the optimization model (38)-(41) can be equivalent to:

[0094]

[0095] In addition, to ensure the feasibility of model (42)-(43), it is necessary to ensure that δ2 ≥ 0. Therefore, constraint (43) can be replaced by:

[0096]

[0097] Since constraint (44) requires Always holds true, so equation (44) should satisfy that the minimum value on the left side is greater than or equal to 0, that is:

[0098]

[0099] In (45), auxiliary variables θ0, θ1, θ2 are introduced, and their mapping relationship with δ0, δ1, δ2 is:

[0100]

[0101] After introducing auxiliary variables, the optimization problems (42)-(44) can be equivalent to:

[0102]

[0103]

[0104] θ0+θ1-max(h(x)μ t )-θ2≥0 (49)

[0105] In constraint (48), due to the existence of Therefore, we can get In constraint (49), max(h(x)μ t) can be further transformed by introducing auxiliary variables τ1 and τ2. Therefore, constraints (48)-(49) can be transformed into the following second-order cone constraints:

[0106]

[0107] τ1-τ2=h(x), (52)

[0108] θ2>0,τ1≥0,τ2≥0 (53)

[0109] Afterwards, the unimodal information can be further added to the fuzzy set. When the unimodal information is further utilized in the fuzzy set D1, the constraints (50)-(51) will be further transformed into:

[0110]

[0111] Finally, the distributed robust objective function containing unimodal-skewness information can be transformed into objective function (54) and constraints (52)-(53), (55)-(56), which is a second-order cone programming problem that can be efficiently solved by the solver.

[0112] In addition, opportunity constraint transformation is also required. First, the joint opportunity constraint can be transformed into a compact form:

[0113]

[0114] Among them, C(x) and D(x) are coefficients of chance constraints, which are linear mappings of the variable x.

[0115] Under the fuzzy set containing unimodal-skewness information, constraint (57) can be equivalent to:

[0116]

[0117] Where c is a constant, which is calculated as:

[0118]

[0119] Among them, η1, η2, κ1, The calculation method is:

[0120]

[0121] After the above transformation, the chance constraint is also transformed into a second-order cone constraint, which can be efficiently solved by the solver.

[0122] After the above transformation, a compact model can be constructed. The compact form of the ITD optimization scheduling problem is as follows:

[0123]

[0124] Among them, f(x), g j (y j ) are the objective functions of the transmission system and the jth distribution system, respectively, x, y j are the decision variables of the transmission system and the jth distribution system respectively; X, Y j are the feasible regions of the transmission system and the jth distribution system, A j , B j are coupling matrices, and J is the set of distribution systems.

[0125] Step 4, after the distributed robust scheduling model of the transmission and distribution coordination system is transformed into a solvable one, the distributed coordination solution of the transmission and distribution system considering packet loss can be performed based on the Relaxed Alternating Direction Method of Multipliers (R-ADMM) algorithm. The specific steps are as follows.

[0126] In some embodiments of the present application, based on the communication packet loss information between the transmission system and the distribution system, a distributed relaxed alternating direction multiplier algorithm is used to solve the compact model, including the following steps: based on the PR (Peaceman-Rachford) splitting theory, a Lagrangian function of the scheduling problem corresponding to the compact model is constructed, and the sub-problems corresponding to the transmission system and the distribution system are determined; the Lagrangian function is solved by a relaxed PR splitting method, and the sub-problems corresponding to the transmission system and the distribution system are converted, wherein the converted sub-problems are to find the fixed points of the operator; the packet loss probability of the communication channel between the transmission system and the distribution system is determined, and the sub-problems are solved taking into account the packet loss probability.

[0127] Specifically, in order to apply the PR splitting theory, it is necessary to construct the Lagrangian function of the original problem (i.e., the scheduling problem corresponding to the above compact model), as shown in the following formula:

[0128]

[0129] Among them, λ k is the Lagrange multiplier vector; ρ is the penalty factor.

[0130] In the classic ADMM algorithm, the TS and DS subproblems are:

[0131]

[0132] In the embodiment of the present application, the Lagrangian dual problem can be solved based on the relaxed PR splitting method, and the sub-problems of TS and DS can be reformulated as follows:

[0133]

[0134] in, is the dual multiplier vector in the R-ADMM algorithm, which is related to the Lagrange multiplier λ k The dimensions are the same.

[0135] The relaxed PR splitting theory can transform the original optimization problem into finding the fixed point of the operator. In fact, the vector z is the fixed point of the relaxed PR splitting operator TPRS. By applying PR splitting, the dual multipliers in equations (68) and (69) can be calculated as follows:

[0136]

[0137] Among them, η is a parameter in the R-ADMM algorithm, and its value range is η∈[0,1].

[0138] Assuming that the communication link between TS and DS is connected, this is reasonable in practice. It can be noted that for TS, and Updated and received by DS operator, also for DS, and A j x k Updated and received by the TS operator. Based on this, (70) and (71) can be restated as:

[0139]

[0140] in, is the boundary information transmitted from TS to DS in the kth iteration; It is the boundary information transmitted from the jth DS to the TS in the kth iteration.

[0141] Similar to common distributed algorithms, the R-ADMM algorithm proposed in the embodiment of the present application can exchange limited boundary information between TS and its DS to achieve distributed collaborative scheduling of ITD. The specific steps of R-ADMM are as follows:

[0142] First, for TS, solve the subproblem shown in equation (68). Then, update the boundary information through equation (75) And transmit it to the jth DS. After receiving the boundary information from the DS, the TS updates the auxiliary multiplier through equation (72).

[0143] For each DS, solve the subproblem shown in equation (69). Then, update the boundary information through equation (73) And transmit it to TS. After receiving the boundary information from EPS, DHS updates the auxiliary multiplier through equation (74).

[0144] R-ADMM can maintain the splitting framework of the PR splitting method and decompose the ITD coordination problem into smaller separable subproblems (72) and (74) by exchanging only a small amount of data.

[0145] It should be noted that when η=0.5, the R-ADMM algorithm will degenerate into the classic ADMM algorithm. It can be proved that when η∈[0,1] and ρ>0, for closed convex functions f(x) and g(y j ), the R-ADMM algorithm can converge to the global optimal solution.

[0146] The above R-ADMM algorithm implements the distributed collaborative scheduling of ITD based on the assumption of a reliable communication channel. However, in an actual lossy communication channel, due to signal attenuation and noise in transmission, TS and DS may not be able to receive correct boundary information from each other, that is, packet loss events may occur randomly in the collaboration. Therefore, an R-ADMM algorithm considering communication packet loss is established in the embodiment of the present application, as follows.

[0147] In the embodiment of the present application, it is assumed that the packet loss probability is p j , based on the binary probability distribution to describe the packet loss event in collaboration, that is:

[0148]

[0149] in, are the packet loss events and occurrence probabilities transmitted from TS to DS, respectively; are the packet loss events and occurrence probabilities transmitted from DS to TS, respectively.

[0150] Under the premise of considering the packet loss event, if packet loss occurs, The auxiliary multiplier remains unchanged; otherwise, let And use equations (72) and (74) to update the auxiliary multiplier. Therefore, equations (72) and (74) will be modified as follows:

[0151]

[0152] In the R-ADMM algorithm, the iteration termination criterion is set according to the original residual and the dual residual as follows:

[0153]

[0154] Among them, r k 、s k are the primal residual and dual residual in the kth iteration respectively; ε P , ε D are the convergence criteria of the primal residual and the dual residual, respectively, and both are set small positive constants.

[0155] It can be shown that when packet loss is considered, for η∈[0,1] and ρ>0, the distributed R-ADMM algorithm will still converge to the global optimal solution.

[0156] Step 5: After the above distributed solution converges, the unit output of the transmission system and the distribution system can be dispatched according to the dispatch optimization results. Specifically, the ITD system can obtain the global optimal unit output, and TS and DS issue instructions according to the optimization results to dispatch the unit output.

[0157] This application proposes a distributed distributed robust optimization scheduling method for the transmission and distribution system considering the unimodal-skewness information of new energy uncertainty. It can construct a transmission and distribution collaborative scheduling model considering the unimodal-skewness information of new energy based on the fuzzy set of unimodal information, skewness information and second-order moment information to exclude extreme distributions in the fuzzy set, and reduce the conservatism of the distributed robust scheduling results compared with the fuzzy set of commonly used moment information; at the same time, since the distributed robust scheduling model is difficult to solve, this application is based on the duality theory to convert the objective function and the joint chance constraint into a deterministic reconstruction model for subsequent solution. In addition, based on the Peaceman-Rachford (PR) splitting theory and binary probability distribution, this application proposes an R-ADMM distributed algorithm that considers communication packet loss, which can realize distributed collaborative scheduling of the transmission and distribution system under actual communication conditions.

[0158] According to an embodiment of the present application, an embodiment of a power transmission and distribution system scheduling device is also provided. Figure 4 Schematic diagram of a power transmission and distribution system dispatching device provided according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0159] A fuzzy set construction module 40 is used to construct a fuzzy set containing unimodal-skewness information based on the historical data samples of renewable energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of renewable energy subsystems in the power transmission and distribution system;

[0160] A dispatch model building module 42 is used to determine a distributed robust dispatch model for a transmission and distribution coordinated system, wherein the distributed robust dispatch model for a transmission and distribution coordinated system is determined by an optimal dispatch model for a transmission system and an optimal dispatch model for a distribution system, and the distributed robust dispatch model for a transmission and distribution coordinated system includes unimodal distribution information;

[0161] A solvability conversion module 44 is used to perform solvability conversion on the distributed robust scheduling model of the transmission and distribution coordination system based on fuzzy sets to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system;

[0162] The scheduling optimization solution module 46 is used to solve the compact model based on the communication packet loss information between the transmission system and the distribution system by using a distributed relaxed alternating direction multiplier algorithm to obtain a scheduling optimization result, and schedule the unit output of the transmission system and the distribution system based on the scheduling optimization result.

[0163] Optionally, the fuzzy set B containing the unimodal-skewness information is as follows: Where P represents a multivariate distribution, ξ is a random variable, γ1 and γ2 are adjustment coefficients for the mean and variance, respectively, M(Ξ) is the set of all distributions on the unbounded support set Ξ, and E P {·} is the expectation operator; (·) is the transposed matrix, U0 α is a distribution set that conforms to the α-unimodal property with a mode of 0, Σ0 is the empirical covariance matrix, and the empirical covariance matrix is ​​determined based on the standard deviation of the prediction error of the output of the new energy units and the correlation coefficients between different new energy units.

[0164] Optionally, determining the distributed robust scheduling model of the transmission and distribution coordinated system includes: determining a first objective function and a first constraint corresponding to the transmission system, and determining the transmission system optimization scheduling model based on the first objective function and the first constraint, wherein the first objective function is used to minimize the production cost of the transmission system; determining a second objective function and a second constraint corresponding to the distribution system, and determining the distribution system optimization scheduling model based on the second objective function and the second constraint, wherein the first objective function is used to minimize the production cost of the distribution system; establishing a transmission and distribution coupling constraint between the transmission system optimization scheduling model and the distribution system optimization scheduling model to obtain the distributed robust scheduling model of the transmission and distribution coordinated system, wherein the transmission and distribution coupling constraint is used to ensure that the output active power of the transmission system is equal to the input active power of the distribution system.

[0165] Optionally, the first objective function is as follows: in, is the actual output of thermal power units in the transmission system. is the spinning reserve factor, are cost coefficients, T G is the set of thermal power units in the transmission system, TD is the set of scheduling time; the first constraint condition includes:

[0166] in, are the predicted outputs of thermal power units and new energy units in the transmission system, that is, the outputs when there is no prediction error. P i G,T , are the lower and upper limits of active power of thermal power unit i in TS at time t, respectively; are the upward and downward rotation reserve capacities of thermal power unit i in TS at time t, is the adjustment coefficient for the forecast error of renewable energy in the transmission system, is the prediction error of the active power of the renewable energy units in the transmission system, The reduction in the number of new energy units in the transmission system, is the power in the transmission line of the power transmission system, is the active load in the transmission system, are the distribution transfer factors in the transmission system, is the maximum value of the permissible power flow of the line in the transmission system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the transmission system, T WD , T L , T T , T LD They are respectively the collection of new energy units, lines, transmission and distribution exchange lines, and loads in the transmission system.

[0167] Optionally, the second objective function is as follows: in, is the actual output of thermal power units in the power distribution system. is the spinning reserve factor; are cost coefficients, D G is the set of thermal power units in the distribution system, TD is the set of scheduling time; the second constraint includes:

[0168] in, They are the predicted active and reactive outputs of thermal power units and new energy units in the power distribution system, is the actual reactive power of the thermal power unit; They are the upward and downward rotation reserve capacities of thermal power units in DS respectively; They are the upper and lower limits of the upward and downward spinning reserve capacity of thermal power units in DS, respectively; P i G,D , They are the lower and upper limits of active power of thermal power units in DS respectively; are the active and reactive loads of the electric load in DS, is the adjustment coefficient for the prediction error of renewable energy in the distribution system, They are the prediction errors of active power and reactive power of renewable energy units in the power distribution system respectively; is the constant power factor in the distribution system, P i,t , Q i,t are the node voltages, are the predicted active and reactive power of the root node in the distribution system, are the predicted active and reactive power of the lines in the distribution system, P i,t , Q i,t are the actual active and reactive power of the lines in the distribution system, V i,t are the predicted voltage and actual voltage of the node, respectively. i 、x i are the resistance and reactance in the distribution system, R, X, B are the matrices of resistance, reactance, and susceptance, respectively, V0 is the reference voltage of the root node substation, are respectively the active and reactive power reduction of new energy units in the distribution system, is the power in the transmission line of the power distribution system, are the active load and reactive load in the distribution system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the distribution system, D WD D is a collection of new energy units. i C is the set of child nodes of node i in the power distribution system, A i is the set of parent nodes of node i in the power distribution system.

[0169] Optionally, based on fuzzy sets, the distributed robust scheduling model of the transmission and distribution coordinated system is transformed into a solvability model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, including: according to the duality theory, based on the fuzzy sets without considering the unimodal information, the objective function and constraints corresponding to the distributed robust scheduling model of the transmission and distribution coordinated system are transformed; based on the fuzzy sets after adding the unimodal information, the transformed objective function and constraints are further transformed, wherein the objective function after further transformation is a second-order cone programming problem; according to the objective function and constraints after further transformation, the compact model is determined, wherein the compact model is shown in the following formula: Among them, f(x), g j (y j ) are the objective functions of the transmission system and the jth distribution system, respectively, x, y j are the decision variables of the transmission system and the jth distribution system respectively; X, Y j are the feasible regions of the transmission system and the jth distribution system, A j , B jare coupling matrices, and J is the set of distribution systems.

[0170] Optionally, based on the communication packet loss information between the transmission system and the distribution system, a distributed relaxed alternating direction multiplier algorithm is used to solve the compact model, including: based on the PR (Peaceman-Rachford) splitting theory, constructing the Lagrangian function of the scheduling problem corresponding to the compact model, and determining the sub-problems corresponding to the transmission system and the distribution system; using the relaxed PR splitting method to solve the Lagrangian function, and transforming the sub-problems corresponding to the transmission system and the distribution system, wherein the transformed sub-problems are to find the fixed points of the operator; determining the packet loss probability of the communication channel between the transmission system and the distribution system, and solving the sub-problems taking into account the packet loss probability.

[0171] It should be noted that the various modules in the above-mentioned power transmission and distribution system dispatching device can be program modules (for example, a set of program instructions to implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0172] It should be noted that the power transmission and distribution system dispatching device provided in this embodiment can be used to execute Figure 2 The power transmission and distribution system scheduling method shown, therefore, the relevant explanations and descriptions of the above-mentioned power transmission and distribution system scheduling method are also applicable to the embodiments of the present application and will not be repeated here.

[0173] The embodiment of the present application also provides a non-volatile storage medium, the non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following transmission and distribution system scheduling method by running the computer program: constructing a fuzzy set containing unimodal-skewness information based on a sample of historical data of new energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of the new energy subsystem in the transmission and distribution system; determining a distributed robust scheduling model for the transmission and distribution coordination system, wherein the distributed robust scheduling model for the transmission and distribution coordination system is composed of a transmission system optimization scheduling model and a distribution system optimization scheduling model It is determined that the distributed robust scheduling model of the transmission and distribution coordinated system contains unimodal distribution information; based on fuzzy sets, the distributed robust scheduling model of the transmission and distribution coordinated system is transformed into a solvable model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit output of the transmission system and the distribution system; according to the communication packet loss information between the transmission system and the distribution system, the distributed relaxed alternating direction multiplier algorithm is used to solve the compact model to obtain the scheduling optimization result, and the unit output of the transmission system and the distribution system is scheduled according to the scheduling optimization result.

[0174] The embodiments of the present application also provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of the power transmission and distribution system scheduling method described in each embodiment of the present application: constructing a fuzzy set containing unimodal-skewness information based on a sample of historical data on new energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of the new energy subsystem in the power transmission and distribution system; determining a distributed robust scheduling model for the transmission and distribution coordination system, wherein the distributed robust scheduling model for the transmission and distribution coordination system is determined by an optimal scheduling model for the transmission system and an optimal scheduling model for the distribution system, and the distributed robust scheduling model for the transmission and distribution coordination system contains unimodal-distribution information; based on the fuzzy set, performing a solvability transformation on the distributed robust scheduling model for the transmission and distribution coordination system to obtain a compact model corresponding to the scheduling problem of the transmission and distribution system, wherein the scheduling problem of the transmission and distribution system is used to describe the distribution relationship between the output of the units of the transmission system and the distribution system; based on the communication packet loss information between the transmission system and the distribution system, using a distributed relaxed alternating direction multiplier algorithm to solve the compact model to obtain a scheduling optimization result, and scheduling the unit output of the transmission system and the distribution system based on the scheduling optimization result.

[0175] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0176] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0178] 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 on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0179] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0180] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0181] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for dispatching a power transmission and distribution system, characterized in that: include: According to the historical data samples of renewable energy output, a fuzzy set containing unimodal-skewness information is constructed, wherein the fuzzy set is used to characterize the uncertainty of the output of the renewable energy subsystem in the power transmission and distribution system; Determine a distributed robust dispatching model for a transmission and distribution coordinated system, wherein the distributed robust dispatching model for the transmission and distribution coordinated system is determined by an optimal dispatching model for a transmission system and an optimal dispatching model for a distribution system, and the distributed robust dispatching model for the transmission and distribution coordinated system contains unimodal distribution information; Based on the fuzzy set, the transmission and distribution coordinated system distributed robust scheduling model is transformed into a solvable model to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system; According to the communication packet loss information between the transmission system and the distribution system, the compact model is solved by using a distributed relaxed alternating direction multiplier algorithm to obtain a scheduling optimization result, and the unit output of the transmission system and the distribution system is scheduled according to the scheduling optimization result.

2. The power transmission and distribution system scheduling method according to claim 1, characterized in that: The fuzzy set B containing unimodal-skewness information is shown as follows: Where P represents a multivariate distribution, ξ is a random variable, γ1 and γ2 are adjustment coefficients for the mean and variance, respectively, M(Ξ) is the set of all distributions on the unbounded support set Ξ, and E P {·} is the expectation operator; (·) is the transposed matrix, U0 α is a distribution set that conforms to α-unimodality with a mode of 0, Σ0 is an empirical covariance matrix, and the empirical covariance matrix is ​​determined based on the standard deviation of the prediction error of the output of the new energy unit and the correlation coefficients between different new energy units.

3. The power transmission and distribution system dispatching method according to claim 2, characterized in that: Determine the robust dispatch model of the transmission and distribution coordination system including: Determine a first objective function and a first constraint condition corresponding to the power transmission system, and determine an optimization scheduling model of the power transmission system according to the first objective function and the first constraint condition, wherein the first objective function is used to minimize the production cost of the power transmission system; Determine a second objective function and a second constraint condition corresponding to the power distribution system, and determine an optimization scheduling model for the power distribution system according to the second objective function and the second constraint condition, wherein the first objective function is used to minimize the production cost of the power distribution system; A transmission and distribution coupling constraint condition is established between the transmission system optimization scheduling model and the distribution system optimization scheduling model to obtain the transmission and distribution coordinated system distributed robust scheduling model, wherein the transmission and distribution coupling constraint condition is used to ensure that the output active power of the transmission system is equal to the input active power of the distribution system.

4. The power transmission and distribution system dispatching method according to claim 3, characterized in that: The first objective function is shown as follows: in, is the actual output of thermal power units in the transmission system. is the spinning reserve factor, are cost coefficients, T G is the set of thermal power units in the transmission system, TD is the set of dispatching time; The first constraint condition includes: in, are the predicted outputs of thermal power units and new energy units in the transmission system, that is, the outputs when there is no prediction error. P i G,T , are the lower and upper limits of active power of thermal power unit i in TS at time t, respectively; are the upward and downward rotation reserve capacities of thermal power unit i in TS at time t, is the adjustment coefficient for the forecast error of renewable energy in the transmission system, is the prediction error of the active power of the renewable energy units in the transmission system, The reduction in the number of new energy units in the transmission system, is the power in the transmission line of the power transmission system, is the active load in the transmission system, are the distribution transfer factors in the transmission system, is the maximum value of the permissible power flow of the line in the transmission system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the transmission system, T WD , T L , T T , T LD They are respectively the collection of new energy units, lines, transmission and distribution exchange lines, and loads in the transmission system.

5. The power transmission and distribution system dispatching method according to claim 3, characterized in that: The second objective function is shown as follows: in, is the actual output of thermal power units in the power distribution system. is the spinning reserve factor; are cost coefficients, D G is the set of thermal power units in the distribution system, TD is the set of dispatching time; The second constraint condition includes: in, They are the predicted active and reactive outputs of thermal power units and new energy units in the power distribution system, is the actual reactive power of the thermal power unit; They are the upward and downward rotation reserve capacities of thermal power units in DS respectively; They are the upper and lower rotation reserve capacities of thermal power units in DS respectively; P i G,D , They are the lower and upper limits of active power of thermal power units in DS respectively; are the active and reactive loads of the electric load in DS, is the adjustment coefficient for the prediction error of renewable energy in the distribution system, They are the prediction errors of active power and reactive power of renewable energy units in the power distribution system respectively; is the constant power factor in the distribution system, P i,t , Q i,t are the node voltages, are the predicted active and reactive power of the root node in the distribution system, are the predicted active and reactive power of the lines in the distribution system, P i,t , Q i,t are the actual active and reactive power of the lines in the distribution system, V i,t are the predicted voltage and actual voltage of the node, respectively. i 、x i are the resistance and reactance in the distribution system, R, X, B are the matrices of resistance, reactance, and susceptance, respectively, V0 is the reference voltage of the root node substation, are respectively the active and reactive power reduction of new energy units in the distribution system, is the power in the transmission line of the power distribution system, are the active load and reactive load in the distribution system, are the upper limits of the corresponding variables. are the risk coefficients set in the opportunity constraints of the distribution system, D WD D is a collection of new energy units. i C is the set of child nodes of node i in the power distribution system, A i is the set of parent nodes of node i in the power distribution system.

6. The power transmission and distribution system dispatching method according to claim 3, characterized in that: Based on the fuzzy set, the transmission and distribution coordinated system distributed robust scheduling model is transformed into a solvable model, and the compact model corresponding to the transmission and distribution system scheduling problem is obtained, including: According to the duality theory, based on the fuzzy set without considering the single peak information, the objective function and constraint conditions corresponding to the distributed robust dispatching model of the transmission and distribution coordinated system are transformed; Based on the fuzzy set after adding the unimodal information, further transforming the transformed objective function and constraint conditions, wherein the objective function after further transformation is a second-order cone programming problem; According to the objective function and constraint conditions after further transformation, the compact model is determined, wherein the compact model is shown in the following formula: s.t.A j x-B j y j =0,x∈X,y j ∈Y j ,j∈J Among them, f(x), g j (y j ) are the objective functions of the transmission system and the jth distribution system, respectively, x, y j are the decision variables of the transmission system and the jth distribution system respectively; X, Y j are the feasible regions of the transmission system and the jth distribution system, A j , B j are coupling matrices, and J is the set of distribution systems.

7. The power transmission and distribution system dispatching method according to claim 1, characterized in that: According to the communication packet loss information between the power transmission system and the power distribution system, solving the compact model by using a distributed relaxed alternating direction multiplier algorithm includes: Based on the PR (Peaceman-Rachford) splitting theory, the Lagrangian function of the scheduling problem corresponding to the compact model is constructed, and the sub-problems corresponding to the transmission system and the distribution system are determined; The Lagrangian function is solved by using a relaxed PR splitting method, and the sub-problems corresponding to the transmission system and the distribution system are converted, wherein the converted sub-problems are to find the fixed points of the operator; A packet loss probability of a communication channel between the power transmission system and the power distribution system is determined, and the subproblems are solved taking into account the packet loss probability.

8. A power transmission and distribution system dispatching device, characterized in that: include: A fuzzy set construction module, used to construct a fuzzy set containing unimodal-skewness information based on the historical data samples of renewable energy output, wherein the fuzzy set is used to characterize the uncertainty of the output of the renewable energy subsystem in the power transmission and distribution system; A dispatch model building module, used to determine a distributed robust dispatch model for a transmission and distribution coordinated system, wherein the distributed robust dispatch model for the transmission and distribution coordinated system is determined by an optimal dispatch model for a transmission system and an optimal dispatch model for a distribution system, and the distributed robust dispatch model for the transmission and distribution coordinated system contains unimodal distribution information; A solvability conversion module is used to perform solvability conversion on the distributed robust scheduling model of the transmission and distribution coordinated system based on the fuzzy set to obtain a compact model corresponding to the transmission and distribution system scheduling problem, wherein the transmission and distribution system scheduling problem is used to describe the distribution relationship between the unit outputs of the transmission system and the distribution system; The scheduling optimization solution module is used to solve the compact model based on the communication packet loss information between the transmission system and the distribution system by using a distributed relaxed alternating direction multiplier algorithm to obtain a scheduling optimization result, and schedule the unit output of the transmission system and the distribution system based on the scheduling optimization result.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the power transmission and distribution system scheduling method according to any one of claims 1 to 7 by running the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the power transmission and distribution system scheduling method described in any one of claims 1 to 7 are implemented.

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