Power distribution network collaborative target planning method, system, equipment and medium

Abstract

The invention belongs to the technical field of power distribution network planning, and discloses a power distribution network collaborative target planning method, system and device and a medium, and the target planning method comprises the steps: building an error fuzzy set based on the historical collaborative planning data of a power distribution network through employing a probability distribution measurement technology and a uniform empirical distribution strategy; constructing a multi-objective collaborative function based on the operation information of the power distribution network, and resuming the multi-objective collaborative function as a single-objective quadratic programming function by using the risk condition value; and performing rolling optimization processing on the single-target quadratic programming function based on a pre-configured model control strategy prediction and error fuzzy set, and determining a power distribution network programming form based on a processing result. The method can effectively improve the economical efficiency and robustness of the power distribution network planning for the uncertainty of renewable energy sources, and inhibits the influence of the uncertainty of large-scale distributed photovoltaic output on the power distribution network planning.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distribution network collaborative target planning method, system, equipment and medium. Background Art

[0002] The distribution network refers to an electric power grid that receives electric energy from the transmission network or regional power plants and distributes it locally or step by step according to voltage to various types of users through distribution facilities. It is composed of overhead lines, cables, poles and towers, distribution transformers, disconnectors, reactive power compensators and some ancillary facilities.

[0003] With the advancement of the construction of new power systems, distribution networks are shifting towards the direction of source-grid-load-storage integration and coordinated interaction. After large-scale distributed photovoltaics with uncertain output are connected to the distribution network, the distribution system changes from a radial passive structure to a multi-power structure, which makes the line flow more complicated than the traditional distribution network and also seriously affects the operating characteristics of the distribution network, causing planning difficulties. Therefore, how to scientifically address and size distributed power sources and energy storage devices has become a research hotspot.

[0004] Therefore, how to provide a distribution network collaborative target planning method, system, equipment and medium is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a distribution network collaborative target planning method, system, device and medium to solve the problem in the prior art that complex line flow seriously affects the operating characteristics of the distribution network and causes planning difficulties.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a distribution network collaborative target planning method is provided.

[0008] In one embodiment, a distribution network collaborative target planning method includes:

[0009] Based on the historical collaborative planning data of distribution networks, the error fuzzy set is established by using the probability distribution measurement technology and the uniform empirical distribution strategy.

[0010] A multi-objective coordination function is constructed based on the distribution network operation information, and the multi-objective coordination function is restated as a single-objective quadratic programming function using the risk conditional value.

[0011] Based on the pre-configured model control strategy prediction and error fuzzy set, the single-objective quadratic programming function is subjected to rolling optimization processing, and the distribution network planning form is determined based on the processing results.

[0012] In one embodiment, constructing a multi-objective coordination function based on distribution network operation information and restating the multi-objective coordination function into a single-objective quadratic programming function using risk condition value includes:

[0013] Based on the distribution network operation information, the distribution network operation cost function is defined, and the device constraint function set and the network constraint function set are obtained according to the charging and discharging power information corresponding to the distribution network equipment;

[0014] The constraint violation risk function is calculated based on the device constraint function set and the network constraint function set, and a multi-objective coordination function is defined based on the constraint violation risk function and the distribution network operation cost function;

[0015] Based on the linearized power flow technology, linearization processing is performed to determine the distribution network equipment constraints and voltage constraints at different times and nodes, and the voltage constraints are restated using the risk condition value.

[0016] Based on the expression results, an affine constraint set is defined, the risk condition value under the confidence level is determined, and the multi-objective collaborative function is equivalently restated as a single-objective quadratic programming function according to the risk condition value.

[0017] In one embodiment, defining a distribution network operation cost function based on distribution network operation information includes:

[0018] Based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus are obtained, and the purchase and sale electricity prices corresponding to the active and reactive power are obtained, and the economic value of electricity is defined according to the active and reactive power loads and the purchase and sale electricity prices;

[0019] Obtain the maintenance cost and charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network, and obtain the available active power of the photovoltaic device based on the distribution network operation information;

[0020] The distribution network operation and maintenance cost, abandoned solar power cost and subsidy cost are calculated based on the maintenance cost and available active power, and the distribution network operation cost function is determined according to the economic value of electricity, the distribution network operation and maintenance cost, abandoned solar power cost and subsidy cost.

[0021] In one embodiment, the calculation formula of the power economic value K is:

[0022]

[0023] In the formula, K represents the economic value of electricity, C buy With C sellThey represent the electricity purchase cost and electricity sales income respectively, N represents the busbar set, n represents the number of busbar sets, Indicates the active power purchase price, represents the active power load of bus n at time t, Indicates the charging and discharging power of the energy storage device, represents the power reduction factor, Indicates the available active power generated by photovoltaics. Display reactive power purchase price, represents the reactive power load of the bus at time t, Indicates the available reactive power generated by photovoltaics, Indicates the active power selling price.

[0024] In one embodiment, the constraint violation risk function is expressed as:

[0025]

[0026] In the formula, represents the constraint violation risk value corresponding to time t, N l represents the constraint set, i represents the busbar node of the distribution network, CvaR represents the conditional value at risk, and β represents the random variable ξ t exist The confidence level of the conditional VaR under the distribution, represents the error fuzzy set, x t represents the state variable of the distribution network equipment at time t, u t Represents the control variable of the distribution network equipment at time t.

[0027] In one embodiment, defining an affine constraint set based on the expression result, determining a risk condition value at a confidence level, and restating the multi-objective coordination function as a single-objective quadratic programming function equivalently according to the risk condition value includes:

[0028] Based on the expression results, several groups of affine constraints including photovoltaic curtailment variables and controllable device setting points are defined, and the several groups of affine constraints are integrated into an affine constraint set;

[0029] Determine the risk condition value of the affine constraint set at the confidence level, restate the expectation operation according to the risk condition value, and perform distributed robustness optimization operations on the constraint violation risk function based on the restatement result;

[0030] The multi-objective coordination function is updated based on the optimized constraint violation risk function, and the multi-objective coordination function is equivalently restated as a single-objective quadratic programming function.

[0031] In one embodiment, based on the pre-configured model control strategy prediction and error fuzzy set, a rolling optimization process is performed on the single-objective quadratic programming function, and the distribution network planning form is determined based on the processing result, including:

[0032] The framework definition of the model-based control strategy prediction includes energy storage equipment and distributed power grid-connected equipment, and determines the state variables and control variables of the grid-connected equipment;

[0033] Based on the matrix corresponding to the state variables and the control variables, a discrete linear dynamic model for the power flow corresponding to the grid-connected equipment is constructed to predict the power output of the grid-connected equipment to the distribution network at a preset time;

[0034] Based on the matrix form corresponding to the preset control domain evolution state variable, and according to the matrix form, the state quantity in the control domain is determined to implement rolling optimization processing on the single-objective quadratic programming function;

[0035] The optimized single-objective quadratic programming function and the auxiliary variables in the error fuzzy set generation process are used to define the distribution network planning form after determining the specific form of the sub-problem predicted by the model control strategy.

[0036] According to a second aspect of an embodiment of the present invention, a distribution network collaborative target planning system is provided.

[0037] In one embodiment, a distribution network collaborative target planning system includes:

[0038] An error fuzzy set building unit is used to build an error fuzzy set based on historical collaborative planning data of distribution networks using probability distribution measurement technology and uniform empirical distribution strategy;

[0039] A planning problem restatement unit is used to construct a multi-objective coordination function based on the distribution network operation information, and restate the multi-objective coordination function into a single-objective quadratic programming function using the risk condition value;

[0040] The planning form determination unit is used to implement rolling optimization processing on the single-objective quadratic programming function based on the pre-configured model control strategy prediction and error fuzzy set, and determine the distribution network planning form based on the processing result.

[0041] In one embodiment, the planning problem restatement unit includes:

[0042] An information acquisition definition module is used to define a distribution network operation cost function based on distribution network operation information, and to obtain a device constraint function set and a network constraint function set according to the charge and discharge power information corresponding to the distribution network equipment;

[0043] A coordination function definition module is used to calculate a constraint violation risk function based on a set of device constraint functions and a set of network constraint functions, and to define a multi-objective coordination function based on the constraint violation risk function and a distribution network operation cost function;

[0044] The constraint expression module is used to perform linearization processing based on linearized power flow technology to determine the distribution network equipment constraints and voltage constraints at different times and nodes, and to restate the voltage constraints using risk condition values;

[0045] The equivalent restatement module is used to define an affine constraint set based on the expression results, determine the risk condition value under the confidence level, and equivalently restate the multi-objective collaborative function into a single-objective quadratic programming function according to the risk condition value.

[0046] In one embodiment, defining a distribution network operation cost function based on distribution network operation information includes:

[0047] Based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus are obtained, and the purchase and sale electricity prices corresponding to the active and reactive power are obtained, and the economic value of electricity is defined according to the active and reactive power loads and the purchase and sale electricity prices;

[0048] Obtain the maintenance cost and charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network, and obtain the available active power of the photovoltaic device based on the distribution network operation information;

[0049] The distribution network operation and maintenance cost, abandoned solar power cost and subsidy cost are calculated based on the maintenance cost and available active power, and the distribution network operation cost function is determined according to the economic value of electricity, the distribution network operation and maintenance cost, abandoned solar power cost and subsidy cost.

[0050] In one embodiment, the calculation formula of the power economic value K is:

[0051]

[0052] In the formula, K represents the economic value of electricity, C buy With C sell They represent the electricity purchase cost and electricity sales income respectively, N represents the busbar set, n represents the number of busbar sets, Indicates the active power purchase price, represents the active power load of bus n at time t, Indicates the charging and discharging power of the energy storage device, represents the power reduction factor, Indicates the available active power generated by photovoltaics. Display reactive power purchase price, represents the reactive power load of the bus at time t, Indicates the available reactive power generated by photovoltaics, Indicates the active power selling price.

[0053] In one embodiment, the constraint violation risk function is expressed as:

[0054]

[0055] In the formula, represents the constraint violation risk value corresponding to time t, N l represents the constraint set, i represents the busbar node of the distribution network, CvaR represents the conditional value at risk, and β represents the random variable ξ t exist The confidence level of the conditional VaR under the distribution, represents the error fuzzy set, x t represents the state variable of the distribution network equipment at time t, u t Represents the control variable of the distribution network equipment at time t.

[0056] In one embodiment, defining an affine constraint set based on the expression result, determining a risk condition value at a confidence level, and restating the multi-objective coordination function as a single-objective quadratic programming function equivalently according to the risk condition value includes:

[0057] Based on the expression results, several groups of affine constraints including photovoltaic curtailment variables and controllable device setting points are defined, and the several groups of affine constraints are integrated into an affine constraint set;

[0058] Determine the risk condition value of the affine constraint set at the confidence level, restate the expectation operation according to the risk condition value, and perform distributed robustness optimization operations on the constraint violation risk function based on the restatement result;

[0059] The multi-objective coordination function is updated based on the optimized constraint violation risk function, and the multi-objective coordination function is equivalently restated as a single-objective quadratic programming function.

[0060] In one embodiment, the planning form determination unit includes:

[0061] The variable judgment module is used to define the grid-connected devices including energy storage devices and distributed power sources based on the framework of the model control strategy prediction, and to judge the state variables and control variables of the grid-connected devices;

[0062] A prediction and analysis module is used to construct a discrete linear power model for power flow corresponding to the grid-connected equipment based on the matrix corresponding to the state variables and the control variables, and predict the power output of the grid-connected equipment to the distribution network at a preset time;

[0063] A rolling optimization module is used to implement rolling optimization processing on a single-objective quadratic programming function based on a matrix form corresponding to a preset control domain evolution state variable and determining a state quantity in the control domain according to the matrix form;

[0064] The planning form definition module is used to define the distribution network planning form after utilizing the optimized single-objective quadratic programming function and the auxiliary variables in the error fuzzy set generation process and determining the specific form of the sub-problem predicted by the model control strategy.

[0065] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0066] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0067] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0068] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0069] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0070] 1. The present invention discloses a multi-objective planning strategy for active and reactive power coordination of distribution networks based on model predictive control. It uses historical data to establish error fuzzy sets on a Wasserstein sphere with controllable radius, and simultaneously constructs a multi-objective optimization strategy that emphasizes both benefits and risks, and uses the risk condition value to convert it into a single-objective solution to achieve multi-step rolling optimization. It can effectively improve the economy and robustness of distribution network planning for renewable energy uncertainty, and can effectively suppress the impact of large-scale distributed photovoltaic output uncertainty on distribution network planning.

[0071] 2. While ensuring robustness and improving the economy of the system, the present invention can transform complex multi-objective calculations into single-objective solutions, overcoming the difficulties of traditional methods in multi-objective calculations. In addition, by adjusting the radius of the Wasserstein sphere, the planning scheme can be made more flexible and applicable to different scenarios.

[0072] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0074] Figure 1 is a flow chart of a distribution network collaborative target planning method according to an exemplary embodiment;

[0075] Figure 2 is a structural schematic diagram of a distribution network collaborative target planning system according to an exemplary embodiment;

[0076] Figure 3 is a modified single-phase equivalent network shown according to an exemplary embodiment;

[0077] Figure 4 is the total available photovoltaic power and total load demand in a day according to an exemplary embodiment;

[0078] Figure 5 is the system operation cost from 10:00 to 15:00 under three different methods according to an exemplary embodiment;

[0079] Figure 6 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0080] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0081] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0082] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0083] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0084] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0085] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0086] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0087] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0088] Figure 1An embodiment of the distribution network collaborative target planning method of the present invention is shown.

[0089] In this optional embodiment, the distribution network collaborative target planning method includes:

[0090] Step S101, based on the historical collaborative planning data of the distribution network, an error fuzzy set is established using a probability distribution measurement technology and a uniform empirical distribution strategy;

[0091] Step S103, constructing a multi-objective coordination function based on the distribution network operation information, and restating the multi-objective coordination function into a single-objective quadratic programming function using risk condition value;

[0092] Step S105, based on the pre-configured model control strategy prediction and error fuzzy set, a rolling optimization process is performed on the single-objective quadratic programming function, and a distribution network planning form is determined based on the processing result.

[0093] In this optional embodiment, when constructing a multi-objective collaborative function based on the distribution network operation information and restating the multi-objective collaborative function as a single-objective quadratic programming function using the risk condition value, the distribution network operation cost function can be defined based on the distribution network operation information, and the equipment constraint function set and the network constraint function set can be obtained according to the charging and discharging power information corresponding to the distribution network equipment; the constraint violation risk function is calculated based on the equipment constraint function set and the network constraint function set, and the multi-objective collaborative function is defined based on the constraint violation risk function and the distribution network operation cost function; linearization processing is performed based on the linearized power flow technology to determine the distribution network equipment constraints and voltage constraints at different times and nodes, and the voltage constraints are restated using the risk condition value; an affine constraint set is defined based on the representation result, the risk condition value at the confidence level is determined, and the multi-objective collaborative function is equivalently restated as a single-objective quadratic programming function according to the risk condition value.

[0094] In this optional embodiment, when defining the distribution network operation cost function based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus can be obtained based on the distribution network operation information, and the purchase and sale electricity prices corresponding to the active and reactive power can be obtained, and the power economic value is defined according to the active and reactive power loads and the purchase and sale electricity prices; the maintenance cost and the charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network are obtained, and the available active power of the photovoltaic device is obtained according to the distribution network operation information; the distribution network operation and maintenance cost, abandoned light cost and subsidy cost are calculated based on the maintenance cost and the available active power, and the distribution network operation cost function is determined according to the power economic value, the distribution network operation and maintenance cost, abandoned light cost and subsidy cost.

[0095] In this optional embodiment, when defining an affine constraint set based on the expression result, determining the risk condition value at the confidence level, and restating the multi-objective collaborative function as a single-objective quadratic programming function according to the risk condition value, several groups of affine constraints including photovoltaic reduction variables and controllable device setting points can be defined based on the expression result, and the several groups of affine constraints can be integrated into an affine constraint set; the risk condition value of the affine constraint set at the confidence level is determined, and the expectation operation is restated according to the risk condition value, and a distributionally robust optimization operation is performed on the constraint violation risk function based on the restated result; the multi-objective collaborative function is updated based on the optimized constraint violation risk function, and the multi-objective collaborative function is restated as a single-objective quadratic programming function.

[0096] In this optional embodiment, when a rolling optimization process is performed on a single-objective quadratic programming function based on a pre-configured model control strategy prediction and an error fuzzy set, and the distribution network planning form is determined based on the processing result, the grid-connected devices including energy storage devices and distributed power sources can be defined based on the framework of the model control strategy prediction, and the state variables and control variables of the grid-connected devices can be determined; a discrete linear dynamic model for the power flow corresponding to the grid-connected devices is constructed based on the matrix corresponding to the state variables and the control variables, and the power output of the grid-connected devices to the distribution network at a preset time is predicted; a rolling optimization process is performed on the single-objective quadratic programming function based on the matrix form corresponding to the state variables of the preset control domain evolution, and the state quantity in the control domain is determined according to the matrix form; the distribution network planning form is defined by using the auxiliary variables in the optimized single-objective quadratic programming function and the error fuzzy set generation process, and determining the specific form of the sub-problem predicted by the model control strategy.

[0097] Figure 2 An embodiment of a distribution network collaborative target planning system of the present invention is shown.

[0098] In this optional embodiment, the distribution network collaborative target planning system includes:

[0099] The error fuzzy set establishing unit 201 is used to establish the error fuzzy set based on the historical collaborative planning data of the distribution network by using the probability distribution measurement technology and the uniform empirical distribution strategy;

[0100] The planning problem restatement unit 203 is used to construct a multi-objective coordination function based on the distribution network operation information, and restate the multi-objective coordination function into a single-objective quadratic programming function using the risk condition value;

[0101] The planning form determination unit 205 is used to perform rolling optimization processing on the single-objective quadratic programming function based on the pre-configured model control strategy prediction and error fuzzy set, and determine the distribution network planning form based on the processing result.

[0102] In this optional embodiment, the planning problem restatement unit 201 includes: an information acquisition definition module (not shown in the figure), a collaborative function definition module (not shown in the figure), a constraint expression module (not shown in the figure) and an equivalent restatement module (not shown in the figure), wherein:

[0103] An information acquisition definition module is used to define a distribution network operation cost function based on distribution network operation information, and to obtain a set of equipment constraint functions and a set of network constraint functions according to the charging and discharging power information corresponding to the distribution network equipment; a collaborative function definition module is used to calculate a constraint violation risk function based on a set of equipment constraint functions and a set of network constraint functions, and to define a multi-objective collaborative function based on the constraint violation risk function and the distribution network operation cost function; a constraint expression module is used to perform linearization processing based on linearized power flow technology to determine distribution network equipment constraints and voltage constraints at different times and nodes, and to restate the voltage constraints using risk condition values; an equivalent restatement module is used to define an affine constraint set based on the expression results, determine the risk condition value at the confidence level, and restate the multi-objective collaborative function as a single-objective quadratic programming function based on the risk condition value.

[0104] In this optional embodiment, when defining the distribution network operation cost function based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus can be obtained based on the distribution network operation information, and the purchase and sale electricity prices corresponding to the active and reactive power can be obtained, and the power economic value is defined according to the active and reactive power loads and the purchase and sale electricity prices; the maintenance cost and the charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network are obtained, and the available active power of the photovoltaic device is obtained according to the distribution network operation information; the distribution network operation and maintenance cost, abandoned light cost and subsidy cost are calculated based on the maintenance cost and the available active power, and the distribution network operation cost function is determined according to the power economic value, the distribution network operation and maintenance cost, abandoned light cost and subsidy cost.

[0105] In this optional embodiment, when defining an affine constraint set based on the expression result, determining the risk condition value at the confidence level, and restating the multi-objective collaborative function as a single-objective quadratic programming function according to the risk condition value, several groups of affine constraints including photovoltaic reduction variables and controllable device setting points can be defined based on the expression result, and the several groups of affine constraints can be integrated into an affine constraint set; the risk condition value of the affine constraint set at the confidence level is determined, and the expectation operation is restated according to the risk condition value, and a distributionally robust optimization operation is performed on the constraint violation risk function based on the restated result; the multi-objective collaborative function is updated based on the optimized constraint violation risk function, and the multi-objective collaborative function is restated as a single-objective quadratic programming function.

[0106] In one embodiment, the planning form determination unit 205 includes: a variable judgment module (not shown in the figure), a prediction analysis module (not shown in the figure), a rolling optimization module (not shown in the figure) and a planning form definition module (not shown in the figure), wherein:

[0107] The variable judgment module is used to define the grid-connected devices including energy storage devices and distributed power sources based on the framework of the model control strategy prediction, and to judge the state variables and control variables of the grid-connected devices; the prediction and analysis module is used to construct a discrete linear dynamic model for the power flow corresponding to the grid-connected devices based on the matrix corresponding to the state variables and the control variables, and predict the power output of the grid-connected devices to the distribution network at the preset time; the rolling optimization module is used to evolve the matrix form corresponding to the state variables based on the preset control domain, and determine the state quantity in the control domain according to the matrix form to perform rolling optimization processing on the single-objective quadratic programming function; the planning form definition module is used to use the auxiliary variables in the optimized single-objective quadratic programming function and the error fuzzy set generation process, and define the distribution network planning form after determining the specific form of the sub-problem predicted by the model control strategy.

[0108] In specific applications, the coordinated target planning of distribution networks is mainly based on historical data to establish error fuzzy sets on the Wasserstein sphere with controllable radius. At the same time, it constructs a multi-objective optimization of active and reactive power coordination that emphasizes both benefits and risks, and uses the risk condition value to transform it into a single-objective solution. It also implements multi-step rolling optimization under the framework of model predictive control and then performs linearization processing, which can effectively improve the economy and robustness of distribution network planning to the uncertainty of renewable energy.

[0109] The process of establishing error fuzzy sets on a Wasserstein sphere with controllable radius based on historical data is as follows:

[0110] Assuming that the uncertainty set is a polyhedron, the data set Π of the prediction error ξ is:

[0111]

[0112] Where N ξ represents the number of prediction error samples, d ξ represents the Wasserstein distance, and R represents the set of real numbers.

[0113] E Q [||ξ||]=∫ Π ||ξ||Q(dξ)<∞;

[0114] In the formula, ||·|| represents the norm, E Q [·] represents the expected operation under Q layout, and Q represents the probability distribution function.

[0115] Define Wasserstein distance dw Represents the distance of all probability distributions Q of the data set ∏ in the space m(∏), let F represent the set of all Lipschitz continuous functions f, and the constant is less than or equal to 1, then The distance d between Q2∈m(Π) w The calculation formula is as follows:

[0116]

[0117] Furthermore, the Wasserstein metric represents the shortest "distance" to transform data from one distribution to another, and uses all distributions of the Wasserstein ball with a controllable radius of γ to determine the fuzzy set:

[0118]

[0119] In the formula, Contains all distributions within the Wasserstein sphere of radius γ centered on the uniform empirical distribution The radius γ is adjusted so that the sphere contains the true distribution P with a specified confidence level and with good performance guarantees.

[0120] Specifically, the process of constructing a multi-objective coordination function of active and reactive power that emphasizes both benefits and risks and converting it into a single-objective solution using the risk condition value is as follows:

[0121] The system optimization goal is to find a balance between the distribution network operation cost and risk. Therefore, the objective function contains the operating cost function J cost and the constraint violation risk function J risk The sum of J = J cost +J risk ;

[0122] And the operating cost function J cost The calculation formula is: cost =C buy +C sell +C pre +C cur +C car Where, C buy , C sell , C pre , C cur , C car They are electricity purchase cost, electricity sales income, operation and maintenance cost, abandoned solar cost, and carbon subsidy cost. buy With C sell The sum is the economic value of electricity.

[0123] The calculation formula of the power economic value is:

[0124]

[0125] C pre The calculation formula is:

[0126]

[0127] C cur The calculation formula is:

[0128]

[0129] C car The calculation formula is

[0130]

[0131] In the formula, K represents the economic value of electricity, C buy With C sell They represent the electricity purchase cost and electricity sales revenue respectively, N represents the busbar set (N = 1, 2, 3, ..., n), n represents the number of busbar sets, Indicates the active power purchase price, represents the active power load of bus n at time t, Indicates the charging and discharging power of the energy storage device, represents the power reduction factor, Prevent overvoltage hazards caused by high photovoltaic penetration, Indicates the available active power generated by photovoltaics. Display reactive power purchase price, represents the reactive power load of the bus at time t, Indicates the available reactive power generated by photovoltaics, represents the active power selling price, n∈N av , n∈N B Respectively represent the busbars where the photovoltaic and energy storage devices are located; a av 、a B Represent the maintenance cost of photovoltaic and energy storage per unit power, represents the cost of abandoned light, Represents the unit price of the government’s carbon subsidy for photovoltaic power generation.

[0132] The risk function J associated with constraint violation risk The sum of the CVaRs of the set of network and device constraint functions is defined as:

[0133]

[0134] In the formula, represents the constraint violation risk value corresponding to time t, N lrepresents the constraint set, i represents the bus node of the distribution network, CvaR represents the conditional value at risk, and β represents the random variable ξ t exist The confidence level of the conditional VaR under the distribution, represents the error fuzzy set, x t represents the state variable of the distribution network equipment at time t, u t Represents the control variable of the distribution network equipment at time t.

[0135] Among them, the adjustable reactive power of the photovoltaic inverter on the bus n∈N and the inverter capacity F av,n The relationship can be expressed as:

[0136]

[0137] Reactive output is affected by power factor angle θ n The restrictions are:

[0138]

[0139] And the photovoltaic power factor angle θ n Subject to 0<cos(θ n )≤1 limit.

[0140]

[0141] Where, T represents the duration time interval of (t, t+1]; Indicates the charge state of the energy storage device on bus n; η c , η d Represent the charge and discharge efficiency respectively, and They are the charging and discharging power of the energy storage at time t, and the charging / discharging power satisfies

[0142] At the same time, the inequality constraints for the capacity and power limits of the energy storage device are:

[0143]

[0144] In the formula, and Respectively represent the minimum and maximum capacity of the energy storage device; and Respectively represent the charge and discharge power limits.

[0145] And suppose there is a distribution network with N buses, N = 1, 2, 3, ..., n, represents the line connection matrix, Γ represents the line connection matrix. Then let They represent the voltage and current at node i at time t, i∈N, V t with I t They represent the voltage and current sets at time t respectively, T represents transposition, and C represents the voltage and current sets.

[0146] Let z ij ∈Z represents the impedance between nodes i and j, Z represents the impedance set, then the line admittance is y ij =1 / z ij =g ij +jb ij ; where g ij ∈G, b ij ∈B represents the conductance and susceptance between nodes, respectively, G and B represent the conductance set and susceptance set, respectively.

[0147] The busbar is modeled using the power-current model, and the admittance y ij The matrix Y ij ∈C N*N It can be expressed as:

[0148]

[0149] In the formula, l~i means that nodes i and j are connected. According to Kirchhoff's law and Ohm's law, T t =Y ij V t , the network complex power injection can be expressed as:

[0150] S t =V t (I t ) * =diag(V t )(YV t ) * ;

[0151] In the formula, * represents the conjugate operation, and the complex power It can be decomposed into rectangular coordinates in and They represent the active power and reactive power injected into the node respectively.

[0152] Specifically, the process of implementing multi-step rolling optimization under the framework of model predictive control is as follows:

[0153] Consider N d The grid-connected devices include traditional generators and inverter-based distributed power sources, time-varying loads, energy storage devices such as batteries and plug-in electric vehicles, which can act as both generators and loads. The power flow of each controllable device is modeled as a discrete linear dynamic system:

[0154]

[0155] Where, the state variable of device d at time t is Dynamic Matrix Control variables The coefficient input matrix The first element of corresponds to the power output by device d to the distribution network at time t.

[0156] Let the control domain be H, and the matrix form of system evolution is expressed as:

[0157]

[0158] In the formula, represents the state vector, Contains all state quantities in the control domain, represents the control quantity matrix, and T stands for transpose;

[0159] The calculation formula can be expressed as:

[0160]

[0161] The calculation formula can be expressed as:

[0162]

[0163] Specifically, the linearization process based on the linearized power flow method is as follows:

[0164] The linearization method is used to linearize the power flow model shown in the network complex power injection. In a balanced and symmetrical distribution network, the common coupling point connected to the grid is recorded as node 0, which is the bus set connecting the load and the distributed generator.

[0165] The complex form of the node voltage is The node injection current is in, and The corresponding RMS value is and are the relative phase angles of voltage and current respectively. Node 0 is used as a relaxation node, and other nodes are PQ nodes for injecting complex power. The admittance matrix can be divided into:

[0166]

[0167] Where, V0 represents the relaxed bus voltage; represents the current injected into the relaxed busbar at time t; y 00represents the relaxed node self-admittance; y is the transfer admittance, then the injection power of the admittance matrix can be expressed as:

[0168]

[0169] Also assume is the preset nominal voltage value, and ΔV t Indicates the difference between the actual voltage and the nominal voltage, then the voltage is expressed as The injected power of the admittance matrix is:

[0170]

[0171] And ignore the higher-order term diag(ΔV t )Y * (ΔV t ) * The power constraint equation is transformed into:

[0172] ΛΔV t +Φ(ΔV t ) * =S t +Ψ;

[0173] In the formula, Among them, Λ, Φ, and Ψ represent auxiliary variables and have no specific meanings. Y * , and denote the nominal voltage, admittance matrix, transfer admittance and the conjugate of relaxation voltage respectively.

[0174] Given Λ=0 N×N and ψ=0 N , the nominal voltage is The linearized power is then expressed as:

[0175]

[0176] Then the voltage deviation becomes:

[0177]

[0178] Order Z R is the real part of impedance, Z I is the imaginary part of impedance, then Y -1 =(G+jB) -1 =Z R +jZ I , M and N are active and reactive component coefficients respectively, and △V t Expanded into rectangular coordinate form:

[0179]

[0180] The voltage amplitude is approximately equal to R(·) represents the real part operation, Representing the unit matrix, the linear relationship between voltage and power can be referred to, and the voltage amplitude is finally expressed as:

[0181]

[0182] The voltage constraint is then:

[0183] V t [p t ,q t ]-V max ≤0,V min -V t [p t ,q t ]≤0;

[0184] Where V min With V max are the matrix forms of the lower and upper limits of the line voltage respectively. t With q t Respectively represent the active and reactive power at time t.

[0185] The device constraints and voltage constraints at different times and nodes can be summarized as follows:

[0186]

[0187] In the formula, A general transformation that represents an inequality constraint into a random form;

[0188] Contains various local constraints on grid-connected equipment; and Respectively represent the coefficient matrices of equipment state variables, control variables and uncertain errors; ω d Represents the local constraint parameters.

[0189] At the same time, this embodiment uses CVaR to restate the voltage affine constraint, and the remaining constraints are evaluated using sample average.

[0190] Define a containing N l The affine constraint set v of the device constraints and voltage constraints i , where each affine constraint can be expressed as:

[0191]

[0192] Where: Yes iThe oth affine constraint in , o = 1, ..., N l , the decision variable y t Including PV curtailment variables and controllable device set point, v i The CVaR of the constraint under the confidence level β is calculated as follows:

[0193]

[0194] In the formula, As auxiliary variables, the expectation operation can be restated as:

[0195]

[0196] Since the result is the maximum of two affine functions, for each fixed ξ t , the expression in y t is convex, then the risk objective function is expressed by the distributed robust optimization form of CVaR as:

[0197]

[0198] The multi-objective distributed robust optimization is equivalently restated as a single-objective quadratic programming function, where the goal is to minimize the function f and the total worst-case CvaR of the affine constraint. The specific form of the MPC subproblem is as follows:

[0199]

[0200] In the formula, and All are CVaR auxiliary variables, and All are auxiliary variables for the reconstruction of the distributed blue-rod Wasserstein sphere. The power factor constraint and the apparent power constraint are processed by sample average.

[0201] The following is an example to specifically illustrate the distribution network multi-objective planning strategy based on the distributed robust (DRO) model predictive control (MPC) of the present invention. The modified network is a single-phase equivalent network such as Figure 3 As shown in Figure 1, it contains 21 PV inverters. Table 1 lists their locations and capacities. The total available PV power and total load demand in a day are shown in Table 1. Figure 4 As shown, the energy storage charging and discharging efficiency is set to 90%.

[0202] Table 1 Location and capacity of photovoltaic and energy storage devices

[0203]

[0204] In this verification case, the voltage optimization upper and lower limits are set to V min =0.95pu, Vmax =1.05pu, the power factor is set to 0.9, the optimization decision interval is set to 5min, and the other parameters are shown in Table 2. Since the solution of the distributed robust optimization problem is difficult and complicated, the cvx convex optimization solution toolbox is called in MATLB for calculation.

[0205] Table 2 System parameter settings

[0206]

[0207] The energy storage configuration results of the DRO-based MPC method proposed in this embodiment are compared with those of the traditional RO and SO methods at nodes 29 and 35. The photovoltaic prediction error of the SO method conforms to the normal distribution. The number of scenarios is 100. The energy storage configuration results of different optimization methods are shown in Table 3. The results show that the RO method aims to seek the result under the worst distribution. The conservative strategy requires a larger energy storage configuration to cope with the impact of uncertainty. The SO method configures and plans the energy storage capacity under the preset probability distribution, but is not flexible in actual scheduling. When the DRO method is used, the real data not only provides more flexible and reliable support for the planning, but also avoids being too conservative and sacrificing economy.

[0208] Table 3 Energy storage configuration under different strategic planning

[0209] method Node 29 Node 35 Cost / 10,000 yuan RO 300kWh 150kWh 48 SO 150kWh 80kWh 26 DRO 200kWh 120kWh 34

[0210] Under the same planning scheme, SO, RO and DRO methods are used for optimization. Figure 5 The system operating costs from 10:00 to 15:00 under three different methods are given. Table 3 gives the maximum, minimum, average and standard deviation of the system operating costs under different methods. The results show that the method proposed in this embodiment is superior to the traditional RO method in terms of operating cost and has better economy. Compared with the SO method, the proposed method is slightly less economical, but has a smaller skewness, and the system runs more smoothly under uncertainty shocks.

[0211] Table 4 Comparison of operating costs under different strategic planning

[0212]

[0213] Based on the operating voltages under the three planning strategies, it can be seen that when facing high uncertainty shocks, the SO method places excessive emphasis on economy, resulting in voltage over-limit phenomenon, which greatly reduces the robustness of the system. The worst-case planning result obtained by the method proposed in this embodiment considering the distribution cluster containing empirical distribution is more in line with the modeling of uncertain output of different renewable energy sources, so its robustness is stronger than traditional random optimization. Therefore, the DROMPC method proposed in this embodiment can better balance the relationship between economy and robustness. The proposed MVSbMPC control strategy comprehensively considers the probability of prediction error, and the average photovoltaic power reduction reaches 645.510kW, which can provide better control strategies for larger photovoltaic prediction deviations. While ensuring voltage safety and stability, the average power reduction of MVSbMPC increases by 36.851%, which improves the robustness of the distribution network to cope with uncertain shocks of renewable energy prediction.

[0214] In order to demonstrate the influence of the Wasserstein ball radius on the planning results, different radius values ​​are used to compare the total cost of planning operation. For the convenience of analysis, it can be seen from Table 5 that with the increase of the Wasserstein ball radius γ, the fuzzy set will cover more uncertain factors, which makes the decision more conservative, resulting in an increase in operating costs and a smoother operation mode. Therefore, the proposed method can more flexibly regulate the robustness and economy by controlling the size of the Wasserstein ball radius.

[0215] Table 5 Effect of different Wasserstein sphere radii on planning results

[0216]

[0217] In summary, the present invention proposes a distribution network multi-objective planning strategy based on distributed robustness (DRO) model predictive control (MPC), which can flexibly adjust the balance between economy and robustness. DROMPC can effectively suppress the impact of uncertainty in large-scale distributed photovoltaic output on distribution network planning, while ensuring robustness and improving system economy. The proposed method can transform complex multi-objective calculations into single-objective solutions, overcoming the difficulties of traditional methods in multi-objective calculations. By adjusting the radius of the Wasserstein sphere, the planning scheme can be made more flexible and applicable to different scenarios.

[0218] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0219] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0220] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0221] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0222] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0223] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distribution network collaborative target planning method, characterized in that: The goal planning method includes: Based on the historical collaborative planning data of distribution networks, the error fuzzy set is established by using the probability distribution measurement technology and the uniform empirical distribution strategy. A multi-objective coordination function is constructed based on the distribution network operation information, and the multi-objective coordination function is restated as a single-objective quadratic programming function using the risk conditional value. Based on the pre-configured model control strategy prediction and error fuzzy set, the single-objective quadratic programming function is subjected to rolling optimization processing, and the distribution network planning form is determined based on the processing results.

2. The distribution network collaborative target planning method according to claim 1, characterized in that: The method of constructing a multi-objective coordination function based on the distribution network operation information and restating the multi-objective coordination function into a single-objective quadratic programming function using the risk condition value includes: Based on the distribution network operation information, the distribution network operation cost function is defined, and the device constraint function set and the network constraint function set are obtained according to the charging and discharging power information corresponding to the distribution network equipment; The constraint violation risk function is calculated based on the device constraint function set and the network constraint function set, and a multi-objective coordination function is defined based on the constraint violation risk function and the distribution network operation cost function; Based on the linearized power flow technology, linearization processing is performed to determine the distribution network equipment constraints and voltage constraints at different times and nodes, and the voltage constraints are restated using the risk condition value. Based on the expression results, an affine constraint set is defined, the risk condition value under the confidence level is determined, and the multi-objective collaborative function is equivalently restated as a single-objective quadratic programming function according to the risk condition value.

3. The distribution network collaborative target planning method according to claim 2, characterized in that: Defining the distribution network operation cost function based on the distribution network operation information includes: Based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus are obtained, and the purchase and sale electricity prices corresponding to the active and reactive power are obtained, and the economic value of electricity is defined according to the active and reactive power loads and the purchase and sale electricity prices; Obtain the maintenance cost and charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network, and obtain the available active power of the photovoltaic device based on the distribution network operation information; The distribution network operation and maintenance cost, abandoned light cost and subsidy cost are calculated based on the maintenance cost and available active power, and the distribution network operation cost function is determined according to the economic value of electricity, the distribution network operation and maintenance cost, abandoned light cost and subsidy cost.

4. The distribution network collaborative target planning method according to claim 3, characterized in that: The calculation formula of the power economic value is: In the formula, K represents the economic value of electricity, C buy With C sell They represent the electricity purchase cost and electricity sales income respectively, N represents the busbar set, n represents the number of busbar sets, Indicates the active power purchase price, represents the active power load of bus n at time t, Indicates the charging and discharging power of the energy storage device, represents the power reduction factor, Indicates the available active power generated by photovoltaics. Display reactive power purchase price, represents the reactive power load of the bus at time t, Indicates the available reactive power generated by photovoltaics, Indicates the active power selling price.

5. The distribution network collaborative target planning method according to claim 4, characterized in that: The expression of the constraint violation risk function is: In the formula, represents the constraint violation risk value corresponding to time t, N l represents the constraint set, i represents the bus node of the distribution network, CvaR represents the conditional value at risk, and β represents the random variable ξ t exist The confidence level of the conditional VaR under the distribution, represents the error fuzzy set, x t represents the state variable of the distribution network equipment at time t, u t Represents the control variable of the distribution network equipment at time t.

6. The distribution network collaborative target planning method according to claim 5, characterized in that: The method of defining an affine constraint set based on the expression result, determining a risk condition value at a confidence level, and restating the multi-objective collaborative function as a single-objective quadratic programming function according to the risk condition value comprises: Based on the expression results, several groups of affine constraints including photovoltaic curtailment variables and controllable device setting points are defined, and the several groups of affine constraints are integrated into an affine constraint set; Determine the risk condition value of the affine constraint set at the confidence level, restate the expectation operation according to the risk condition value, and perform distributed robustness optimization operations on the constraint violation risk function based on the restatement result; The multi-objective coordination function is updated based on the optimized constraint violation risk function, and the multi-objective coordination function is equivalently restated as a single-objective quadratic programming function.

7. The distribution network collaborative target planning method according to claim 6, characterized in that: The method of implementing rolling optimization processing on the single-objective quadratic programming function based on the pre-configured model control strategy prediction and error fuzzy set, and determining the distribution network planning form based on the processing result includes: The framework definition of the model-based control strategy prediction includes energy storage equipment and distributed power grid-connected equipment, and determines the state variables and control variables of the grid-connected equipment; Based on the matrix corresponding to the state variables and the control variables, a discrete linear dynamic model for the power flow corresponding to the grid-connected equipment is constructed to predict the power output of the grid-connected equipment to the distribution network at a preset time; Based on the matrix form corresponding to the preset control domain evolution state variable, and according to the matrix form, the state quantity in the control domain is determined to implement rolling optimization processing on the single-objective quadratic programming function; The optimized single-objective quadratic programming function and the auxiliary variables in the error fuzzy set generation process are used to define the distribution network planning form after determining the specific form of the sub-problem predicted by the model control strategy.

8. A distribution network collaborative target planning system, characterized in that: The goal planning system includes: An error fuzzy set building unit is used to build an error fuzzy set based on historical collaborative planning data of distribution networks using probability distribution measurement technology and uniform empirical distribution strategy; A planning problem restatement unit is used to construct a multi-objective coordination function based on the distribution network operation information, and restate the multi-objective coordination function into a single-objective quadratic programming function using the risk condition value; The planning form determination unit is used to implement rolling optimization processing on the single-objective quadratic programming function based on the pre-configured model control strategy prediction and error fuzzy set, and determine the distribution network planning form based on the processing result.

9. The distribution network collaborative target planning system according to claim 8, characterized in that: The planning problem restatement unit includes: An information acquisition definition module is used to define a distribution network operation cost function based on distribution network operation information, and to obtain a device constraint function set and a network constraint function set according to the charge and discharge power information corresponding to the distribution network equipment; A coordination function definition module is used to calculate a constraint violation risk function based on a set of device constraint functions and a set of network constraint functions, and to define a multi-objective coordination function based on the constraint violation risk function and a distribution network operation cost function; The constraint expression module is used to perform linearization processing based on linearized power flow technology to determine the distribution network equipment constraints and voltage constraints at different times and nodes, and to restate the voltage constraints using risk condition values; The equivalent restatement module is used to define an affine constraint set based on the expression results, determine the risk condition value under the confidence level, and equivalently restate the multi-objective collaborative function into a single-objective quadratic programming function according to the risk condition value.

10. The distribution network collaborative target planning system according to claim 9, characterized in that: Defining the distribution network operation cost function based on the distribution network operation information includes: Based on the distribution network operation information, the active and reactive power loads corresponding to the distribution network bus are obtained, and the purchase and sale electricity prices corresponding to the active and reactive power are obtained, and the economic value of electricity is defined according to the active and reactive power loads and the purchase and sale electricity prices; Obtain the maintenance cost and charging and discharging power corresponding to the photovoltaic and energy storage devices used in the distribution network, and obtain the available active power of the photovoltaic device based on the distribution network operation information; The distribution network operation and maintenance cost, abandoned light cost and subsidy cost are calculated based on the maintenance cost and available active power, and the distribution network operation cost function is determined according to the economic value of electricity, the distribution network operation and maintenance cost, abandoned light cost and subsidy cost.

11. The distribution network collaborative target planning system according to claim 10, characterized in that: The calculation formula of the power economic value is: In the formula, K represents the economic value of electricity, C buy With C sell They represent the electricity purchase cost and electricity sales income respectively, N represents the busbar set, n represents the number of busbar sets, Indicates the active power purchase price, represents the active power load of bus n at time t, Indicates the charging and discharging power of the energy storage device, represents the power reduction factor, Indicates the available active power generated by photovoltaics. Display reactive power purchase price, represents the reactive power load of the bus at time t, Indicates the available reactive power generated by photovoltaics, Indicates the active power selling price.

12. The distribution network collaborative target planning system according to claim 11, characterized in that: The expression of the constraint violation risk function is: In the formula, represents the constraint violation risk value corresponding to time t, N l represents the constraint set, i represents the busbar node of the distribution network, CvaR represents the conditional value at risk, and β represents the random variable ξ t exist The confidence level of the conditional VaR under the distribution, represents the error fuzzy set, x t represents the state variable of the distribution network equipment at time t, u t Represents the control variable of the distribution network equipment at time t.

13. The distribution network collaborative target planning system according to claim 12, characterized in that: The method of defining an affine constraint set based on the expression result, determining a risk condition value at a confidence level, and restating the multi-objective collaborative function as a single-objective quadratic programming function according to the risk condition value comprises: Based on the expression results, several groups of affine constraints including photovoltaic curtailment variables and controllable device setting points are defined, and the several groups of affine constraints are integrated into an affine constraint set; Determine the risk condition value of the affine constraint set at the confidence level, restate the expectation operation according to the risk condition value, and perform distributed robustness optimization operations on the constraint violation risk function based on the restatement result; The multi-objective coordination function is updated based on the optimized constraint violation risk function, and the multi-objective coordination function is equivalently restated as a single-objective quadratic programming function.

14. The distribution network collaborative target planning system according to claim 13, characterized in that: The planning form determination unit comprises: The variable judgment module is used to define the grid-connected devices including energy storage devices and distributed power sources based on the framework of the model control strategy prediction, and to judge the state variables and control variables of the grid-connected devices; A prediction and analysis module is used to construct a discrete linear power model for power flow corresponding to the grid-connected equipment based on the matrix corresponding to the state variables and the control variables, and predict the power output of the grid-connected equipment to the distribution network at a preset time; A rolling optimization module is used to implement rolling optimization processing on a single-objective quadratic programming function based on a matrix form corresponding to a preset control domain evolution state variable and determining a state quantity in the control domain according to the matrix form; The planning form definition module is used to define the distribution network planning form after utilizing the optimized single-objective quadratic programming function and the auxiliary variables in the error fuzzy set generation process and determining the specific form of the sub-problem predicted by the model control strategy.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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