Method and device for optimizing distributed photovoltaic access capacity in a virtual power plant

Abstract

The present invention provides a method and device for optimizing distributed photovoltaic access capacity in a virtual power plant, comprising: obtaining a deterministic model of photovoltaic access capacity based on current topological structure information of a distribution network, and obtaining uncertainty random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data of each node in the distribution network and historical load data of each node in the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and operation constraints of the distribution network; determining distributed robustness opportunity constraints based on the operation constraints and the uncertainty random variable information; obtaining an optimization model based on the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints; and obtaining the total access capacity of distributed photovoltaics based on the optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a method and device for optimizing distributed photovoltaic access capacity in a virtual power plant. Background Art

[0002] Distributed photovoltaic power generation is a new and promising method for power generation and comprehensive energy utilization. It promotes the principles of local generation, grid connection, conversion, and utilization. This not only effectively increases the power generation of photovoltaic power plants of similar size, but also effectively addresses power losses during voltage boosting and long-distance transportation. Virtual power plants, leveraging advanced control and communication technologies, can effectively integrate and regulate distributed photovoltaics and flexible loads, effectively reducing the risks associated with photovoltaic output uncertainty.

[0003] Existing distributed photovoltaic access optimization methods directly adopt robust optimization and use the worst-case scenario of various variables in the virtual power plant for planning. The planning results are too conservative, resulting in low access capacity of distributed photovoltaics in the virtual power plant, affecting the overall benefits of the virtual power plant. Summary of the Invention

[0004] The present invention provides a method and device for optimizing the capacity of distributed photovoltaic access in a virtual power plant, which is used to solve the defects in the prior art that distributed photovoltaic access planning is too conservative and the economic efficiency of the virtual power plant is not high.

[0005] The present invention provides a method for optimizing distributed photovoltaic access capacity in a virtual power plant, comprising: obtaining a deterministic model of photovoltaic access capacity based on current topological structure information of a distribution network, and obtaining uncertainty random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data of each node in the distribution network and historical load data of each node in the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and operation constraints of the distribution network; determining distributed robustness opportunity constraints based on the operation constraints and the uncertainty random variable information; obtaining an optimization model based on the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints; and obtaining the total access capacity of distributed photovoltaics based on the optimization model.

[0006] According to a method for optimizing distributed photovoltaic access capacity in a virtual power plant provided by the present invention, the method obtains uncertainty random variable information of the photovoltaic power station output and the distribution network load based on historical photovoltaic power station output data of each node in the distribution network and historical load data of each node in the distribution network, including: obtaining a random variable vector containing all random variables of the node based on the first random variable and the second random variable of each node in the distribution network, wherein the first random variable is a random variable of the uncertainty of the photovoltaic power station output, and the second random variable is a random variable of the uncertainty of the distribution network load; obtaining a support set of the distribution of the random variable vector based on the random variable vector; obtaining moment information of the random variable vector based on the random variable vector, the historical photovoltaic power station output data of each node in the distribution network and the historical load data of each node in the distribution network, the moment information including first-order moment information and second-order moment information; obtaining uncertainty random variable information of the photovoltaic power station output and the distribution network load based on the support set and the moment information.

[0007] According to a method for optimizing distributed photovoltaic access capacity in a virtual power plant provided by the present invention, the random variable vector includes: random variable components corresponding to a target dimension, the random variable components satisfy constraints consisting of preset upper and lower bounds, and the target dimension is a positive integer.

[0008] According to a method for optimizing distributed photovoltaic access capacity in a virtual power plant provided by the present invention, the distributed robustness chance constraint is determined based on the operating constraints and the uncertainty random variable information, including: obtaining a second operating constraint based on the random variable vector and the operating constraints, the second operating constraint being a constraint that takes into account the uncertainty of the photovoltaic power station output and the uncertainty of the distribution network load; and determining the distributed robustness chance constraint based on the second operating constraint and the uncertainty random variable information.

[0009] According to a method for optimizing distributed photovoltaic access capacity in a virtual power plant provided by the present invention, an optimization model is obtained based on the objective function, the photovoltaic power station investment constraints and the distributed robust opportunity constraints, including: determining constraints containing risk conditions based on the distributed robust opportunity constraints; based on the objective function, the photovoltaic power station investment constraints and the constraints containing risk conditions are obtained.

[0010] According to a method for optimizing distributed photovoltaic access capacity in a virtual power plant provided by the present invention, the constraints containing risk conditions include:

[0011]

[0012] H≥0;

[0013]

[0014] Among them, the variable ε represents the tolerable risk level, Tr(·) represents the operator that takes the matrix trace, and the matrix variable described represents the space composed of N-dimensional symmetric matrices, express The covariance matrix of represents the space composed of Z-dimensional symmetric matrices, Represents the vector consisting of the mean of each dimensional component of the random variable vector, γ m is a constant positive scaling factor, y m (x)=( a m) T ·xb m , The M is the total number of constraints contained in the operation constraint, a m and b m is the coefficient of the vector containing the random variables, and are all initial values, x is a vector composed of the access capacity of distributed photovoltaic connected at multiple nodes

[0015] The present invention also provides a distributed photovoltaic access capacity optimization device in a virtual power plant, comprising: a first determination module, for obtaining a deterministic model of photovoltaic access capacity based on the current topological structure information of the distribution network, and based on the historical photovoltaic power station output data of each node of the distribution network and the historical load data of each node of the distribution network, obtaining uncertainty random variable information of photovoltaic power station output and distribution network load, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and operation constraints of the distribution network; a second determination module, for determining distributed robustness opportunity constraints based on the operation constraints and the uncertainty random variable information; a third determination module, for obtaining an optimization model according to the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints; and a fourth determination module, for obtaining the total access capacity of distributed photovoltaics based on the optimization model.

[0016] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for optimizing the capacity of distributed photovoltaic access within a virtual power plant as described above are implemented.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing the capacity of distributed photovoltaic access in a virtual power plant as described in any of the above are implemented.

[0018] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for optimizing distributed photovoltaic access capacity in a virtual power plant.

[0019] The present invention provides a method and device for optimizing the access capacity of distributed photovoltaic power plants in a virtual power plant. The method obtains random variable information of the uncertainty of the output of the photovoltaic power plant and the load of the distribution network based on the historical output data of the photovoltaic power plant and the historical load data of the distribution network, takes into account the uncertainty variables of the output of the photovoltaic power plant and the load of the distribution network. After introducing the uncertainty variables, the distributed robust chance constraints are obtained, and then a complete optimization model is further formed. The model can be efficiently solved by the solver, and the optimization results can be obtained quickly. The optimization results are less conservative, and can give full play to the power generation potential of distributed photovoltaics in the virtual power plant and improve the economy of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 It is a flow chart of the method for optimizing the capacity of distributed photovoltaic access in a virtual power plant provided by the present invention;

[0022] Figure 2 It is a structural diagram of a distributed photovoltaic access capacity optimization device in a virtual power plant provided by the present invention;

[0023] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0025] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "first," "second," and the like generally distinguish objects of a class and do not limit the number of objects. For example, the first object may be one or more.

[0026] The following, in conjunction with the accompanying drawings, describes in detail a log collection method, apparatus, device, and storage medium provided by the embodiments of the present application through specific embodiments and their application scenarios.

[0027] Figure 1 This is a flow chart of the distributed photovoltaic access capacity optimization method in a virtual power plant provided by the present invention. Figure 1 Shown, including:

[0028] Step 110: obtaining a deterministic model of photovoltaic access capacity based on the current topology information of the distribution network, and obtaining uncertain random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints, and distribution network operation constraints;

[0029] Specifically, the current topology information of the distribution network may include the length and cross-sectional area of the distribution lines in the distribution network, which is used to calculate the resistance and reactance of the distribution lines. The current topology information of the distribution network may also directly include the resistance and reactance of the distribution lines. The current topology information of the distribution network may also include basic information such as the total number of all nodes in the distribution network.

[0030] The deterministic model is obtained based on current topological structure information of the distribution network, and includes an objective function, a photovoltaic power station investment constraint, and an operation constraint of the distribution network.

[0031] More specifically, the objective function is:

[0032]

[0033] in, represents the capacity of the distributed photovoltaic power station connected to node i, Φ PV It is the set of all nodes in the distribution network that can be connected to the photovoltaic power station.

[0034] The investment constraints of photovoltaic power stations include the total number of distributed photovoltaics connected and the photovoltaic capacity constraints of a single node.

[0035] More specifically, the total number of connected distributed photovoltaics is constrained as follows:

[0036]

[0037] Among them, the binary variable Indicates whether the node i of the distribution network is connected to a distributed photovoltaic power station. is the minimum number of connected photovoltaic power stations, The maximum number of connected PV power stations.

[0038] The photovoltaic capacity constraint for a single node is:

[0039]

[0040] in, Indicates the maximum capacity of the PV power station connected to the node.

[0041] The operating constraints of the distribution network include the active power balance constraint of each node, the reactive power balance constraint of each node, the power flow equation in the radial distribution network, the capacity constraint of the branch power flow, the first constraint of the node voltage amplitude, the second constraint of the node voltage amplitude, at least one of the node-related first constraint and the node-related second constraint.

[0042] More specifically, the active power balance constraint of each node is:

[0043]

[0044] in, is the active power output of the photovoltaic power station at node i at time k, is the active power output of the distribution network load at node i at time k, p ij，k is the active power flowing in branch ij at time k, Ω i is the set of all branches associated with node i, Φ is the set of all nodes, and T is the set of all time instants.

[0045] The reactive power balance constraint of each node is:

[0046]

[0047] in, is the reactive power output of the photovoltaic power station at node i at time k, is the reactive power output of the distribution network load at node i at time k, q ij，k is the reactive power flowing in branch ij at time k.

[0048] The power flow equation in the radial distribution network is:

[0049]

[0050] Among them, Vi，k is the voltage amplitude of node i at time k, V j，k is the voltage amplitude of node j at time k, r ij is the resistance of branch ij, x ij is the maximum value of the reactance and apparent power of branch ij, and Ω is the set of all branches.

[0051] The capacity constraint of the branch flow is:

[0052]

[0053] Among them, s ij is the maximum value of the apparent power of branch ij.

[0054] The first constraint on the node voltage amplitude is:

[0055]

[0056] Among them, V Ref is the reference voltage amplitude set at the substation node, Φ Sub It is the collection of all substation nodes.

[0057] The second constraint on the node voltage amplitude is:

[0058]

[0059] Among them, V i Upper and V i Lower are the upper and lower bounds of the voltage amplitude respectively.

[0060] The first node-related constraint is:

[0061]

[0062] in, is the ratio of the actual active power output of the photovoltaic power station at node i to its capacity (maximum value) at time k, λ PV It is the power factor angle of the relationship between active and reactive output of the photovoltaic power station.

[0063] The second node-related constraint is:

[0064]

[0065] in, It is the power factor angle of the relationship between the active and reactive output of the distribution network load.

[0066] Furthermore, the secondary network loss term with a relatively small value in the power flow equation (6) in the radial distribution network is discarded, and an auxiliary variable is introduced and The power flow equation in the radial distribution network can be transformed into the following constraints:

[0067]

[0068] The circular feasible region depicting the capacity constraint of the branch flow is approximated by its circumscribed octagon, and the capacity constraint of the branch flow can be linearized into the following constraint:

[0069]

[0070] Furthermore, the historical PV power plant output and load data for each node in the distribution network includes multiple historical data from various scenarios, not just specific cases. Based on these historical data from various scenarios, we can obtain information about the uncertain random variables of PV power plant output and distribution network load.

[0071] Step 120: determining a distributed robustness chance constraint based on the operational constraints and the uncertainty random variable information;

[0072] Specifically, based on the above-mentioned operating constraints and the uncertain random variable information obtained from historical data in various situations, the distributed robustness chance constraint is considered to determine a model that considers the distributed robustness chance constraint.

[0073] Step 130, obtaining an optimization model based on the objective function, the photovoltaic power station investment constraint and the distributed robust opportunity constraint;

[0074] Specifically, according to the objective function (1), the total number of connected distributed photovoltaics (2), the photovoltaic capacity constraint of a single node (3), and the distributed blue chance constraint, the final optimization model is obtained.

[0075] Step 140: Obtain the total access capacity of distributed photovoltaics based on the optimization model.

[0076] Specifically, the optimization model may be solved by a solver, which may be Sedumi, to obtain the maximized total access capacity of the distributed photovoltaic power station in the distribution network.

[0077] This embodiment provides a method for optimizing the access capacity of distributed photovoltaic power plants in a virtual power plant. The method obtains random variable information of the uncertainty of the output of the photovoltaic power plant and the load of the distribution network based on the historical output data of the photovoltaic power plant and the historical load data of the distribution network, takes into account the uncertainty variables of the output of the photovoltaic power plant and the load of the distribution network. After introducing the uncertainty variables, the distributed robust chance constraints are obtained, and then a complete optimization model is further formed. The model can be efficiently solved by the solver, and the optimization results can be obtained quickly. The optimization results are less conservative, and can give full play to the power generation potential of distributed photovoltaics in the virtual power plant and improve the economy of the virtual power plant.

[0078] Optionally, obtaining the uncertainty random variable information of the photovoltaic power station output and the distribution network load based on the historical photovoltaic power station output data and the historical load data of each node of the distribution network includes:

[0079] Obtaining a random variable vector containing all the random variables of the node based on the first random variable and the second random variable of each node in the distribution network, wherein the first random variable is a random variable for the uncertainty of the output of the photovoltaic power station, and the second random variable is a random variable for the uncertainty of the load of the distribution network;

[0080] Obtaining a support set of the random variable vector distribution based on the random variable vector;

[0081] Based on the random variable vector, historical photovoltaic power station output data of each node of the distribution network and historical load data of each node of the distribution network, moment information of the random variable vector is obtained, wherein the moment information includes first-order moment information and second-order moment information;

[0082] Uncertain random variable information of photovoltaic power station output and distribution network load is obtained based on the support set and the moment information.

[0083] Specifically, through the first random variable To characterize the uncertainty of the photovoltaic power station output, the second random variable To characterize the uncertainty of distribution network load.

[0084] Let the random variable vector containing all node random variables be And assume its dimension is Z.

[0085] Considering that the range of random variables is usually limited, it is necessary to set upper and lower bounds for uncertainty changes. Then the random variable vector Any random variable in any dimension must satisfy the upper and lower bounds

[0086] The random variable vector The support of a distribution (the support of a probability distribution is defined as the set of all possible values of the random variable) can be expressed as:

[0087]

[0088] in,

[0089]

[0090] Based on the historical photovoltaic power station output data and historical load data of each node in the distribution network, the random variable vector is obtained The first-order moment information and the second-order moment information.

[0091] Based on the random variable vector, historical photovoltaic power station output data of each node in the distribution network and historical load data of each node in the distribution network, moment information of the random variable vector is obtained, and the moment information includes: first-order moment information and second-order moment information.

[0092] Uncertain random variable information of photovoltaic power station output and distribution network load is obtained based on the support set and the moment information.

[0093] Specifically, represents the vector consisting of the mean of the components of each dimension of the random variable, ( Represents the space composed of Z-dimensional symmetric matrices) The covariance matrix of . At this time, with Ξ as the support set, the probability distribution set with the same moment information as the original random variable vector can be expressed as, that is, the uncertainty random variable information can be expressed as:

[0094]

[0095] in, is the expectation operator, Make sure the probabilities sum to 1, Indicates that the probability distribution is the same as the first-order information of the original random variable vector, Indicates that the probability distribution is the same as the second-order moment information of the original random variable vector. From this we can see that the set Θ Ξ By making full use of all available information related to uncertainty (including moment information and support set information), the uncertainty of random variables can be accurately characterized.

[0096] In this embodiment, random variables are used to form a random variable vector, and based on the moment information of the random variable vector from historical data in various situations, more accurate uncertainty information can be obtained. The obtained uncertainty random variable information can accurately characterize the uncertainty of the random variable and further optimize the results.

[0097] Optionally, determining the distribution robustness chance constraint based on the operation constraint and the uncertainty random variable information includes:

[0098] Obtaining a second operating constraint based on the random variable vector and the operating constraint, wherein the second operating constraint is a constraint that takes into account uncertainty in output of the photovoltaic power station and uncertainty in load of the distribution network;

[0099] Based on the second operation constraint and the uncertainty random variable information, a distributional robustness chance constraint is determined.

[0100] Specifically, all deterministic output variables in the operational constraints (4)-(13) in the deterministic model are All need to be included in the uncertainty of the output variable Replace. The relationship between the two is:

[0101]

[0102] Two points need to be explained here: First, the uncertainty of the output of distributed photovoltaic power stations is actually caused by Secondly, it is assumed that P / Q remains unchanged (constant power factor) during the uncertain fluctuations of the PV power station output and the distribution network load.

[0103] More specifically, a random variable vector is introduced into the aforementioned distribution network operation constraints (4)-(13), and the operation constraints (4)-(13) are all written in the following form:

[0104]

[0105] in, The dimension is the number of nodes to be selected in the photovoltaic power station N, M is the total number of constraints, including the coefficients of random variable constraints and It is described by the following formula:

[0106]

[0107]

[0108] in, and All are initial values.

[0109] For the convenience of expression, the auxiliary function is introduced At this time, formula (18) can be rewritten as:

[0110]

[0111] in,

[0112] In order to avoid being too conservative in dealing with uncertainty, constraint (21) is transformed into a distributed robust joint chance constraint:

[0113]

[0114] The meaning of this formula is that the probability of satisfying the constraint is not less than 1-ε. Among them, 1-ε is the confidence level of the chance constraint, and the size of ε reflects the tolerable risk level. is a probability operator. Risk can be controlled by adjusting ε. Replacing Equation (18) with the distributed robustness chance constraint (22) yields a complete distributed photovoltaic power station access capacity optimization model that considers the distributed robustness chance constraint.

[0115] In this embodiment, by introducing constraints that take into account the uncertainty of the photovoltaic power station output and the uncertainty of the distribution network load, the obtained model can further optimize the results.

[0116] Optionally, obtaining an optimization model based on the objective function, the photovoltaic power station investment constraint, and the distributed robust opportunity constraint includes:

[0117] Determining a constraint including a risk condition based on the distributed robust chance constraint;

[0118] Based on the objective function, an optimization model is obtained for the photovoltaic power station investment constraint and the constraint including the risk condition.

[0119] Specifically, constraint (22) can be further transformed into the following distributed robust monomer chance constraint:

[0120]

[0121] Among them, γ m is a scaling factor that is always positive, and its value does not affect the feasible region described by constraint (23).

[0122] Next, constraint (23) is transformed into a “worst-case conditional risk” constraint (WC-CVaR constraint), where the scaling factor γ is m The value of will affect the accuracy of constraint transformation. The WC-CVaR constraint form obtained after transformation is as follows:

[0123]

[0124] In this embodiment, by considering risk conditions on the basis of the distributed robust chance constraints, the obtained model can further optimize the results.

[0125] Alternatively, for any given γ={γ m}, constraint (24) can be conservatively transformed into the following linear matrix inequality form:

[0126]

[0127] H≥0 (26)

[0128]

[0129] The matrix variables introduced variable Tr(·) represents the operator that takes the matrix trace. H ≥ 0 indicates that the matrix H is a positive semidefinite matrix.

[0130] At this point, by introducing auxiliary variables H and β, the operating constraints (4)-(13) of the original mixed integer linear optimization model have been transformed into semi-positive definite (SDP) constraints, namely, equations (25)-(27). Combined with the photovoltaic power station investment constraints (2) and (3) and the objective function (1) in the original model, the mixed integer semi-positive definite programming (MISDP) model for distributed photovoltaic power station access capacity optimization is finally obtained, which is the final optimization model.

[0131] In this embodiment, the optimization result obtained by the final optimization model is less conservative, which can give full play to the power generation potential of distributed photovoltaics in the virtual power plant and improve the economic efficiency of the virtual power plant.

[0132] Figure 2 This is a schematic diagram of the structure of the distributed photovoltaic access capacity optimization device in the virtual power plant provided by the present invention. Figure 2 As shown, it includes: a first determination module 210, a second determination module 220, a third determination module 230, and a fourth determination module 240; wherein, the first determination module 210 is used to obtain a deterministic model of photovoltaic access capacity based on the current topological structure information of the distribution network, and based on the historical photovoltaic power station output data of each node of the distribution network and the historical load data of each node of the distribution network, obtain the uncertainty random variable information of the photovoltaic power station output and the distribution network load, wherein the deterministic model includes the objective function, the photovoltaic power station investment constraint and the operation constraint of the distribution network; the second determination module 220 is used to determine the distributed robustness opportunity constraint based on the operation constraint and the uncertainty random variable information; the third determination module 230 is used to obtain an optimization model according to the objective function, the photovoltaic power station investment constraint and the distributed robustness opportunity constraint; the fourth determination module 240 is used to obtain the total access capacity of distributed photovoltaics based on the optimization model.

[0133] Optionally, the first determination module is specifically configured to: obtain a random variable vector containing all random variables of the node based on the first random variable and the second random variable of each node in the distribution network, wherein the first random variable is a random variable for the uncertainty of the photovoltaic power station output, and the second random variable is a random variable for the uncertainty of the distribution network load; obtain a support set of the distribution of the random variable vector based on the random variable vector; obtain moment information of the random variable vector based on the random variable vector, the historical photovoltaic power station output data of each node in the distribution network, and the historical load data of each node in the distribution network, wherein the moment information includes first-order moment information and second-order moment information; obtain random variable information of the uncertainty of the photovoltaic power station output and the distribution network load based on the support set and the moment information. The random variable vector includes random variable components corresponding to the target dimension, wherein the random variable components satisfy the constraints consisting of preset upper and lower bounds, and the target dimension is a positive integer.

[0134] Optionally, the second determination module is specifically used to: obtain a second operating constraint based on the random variable vector and the operating constraint, the second operating constraint being a constraint that takes into account the uncertainty of the photovoltaic power station output and the uncertainty of the distribution network load; and determine a distributed robustness chance constraint based on the second operating constraint and the uncertainty random variable information.

[0135] Optionally, the third determination module is specifically configured to: determine a constraint including a risk condition based on the distributed robust opportunity constraint; and obtain an optimization model based on the objective function, the photovoltaic power station investment constraint, and the constraint including the risk condition. The constraint including the risk condition includes:

[0136]

[0137] H≥0;

[0138]

[0139] Among them, the variable ε represents the tolerable risk level, Tr(·) represents the operator that takes the matrix trace, and the matrix variable described represents the space composed of N-dimensional symmetric matrices, express The covariance matrix of represents the space composed of Z-dimensional symmetric matrices, Represents the vector consisting of the mean of each dimensional component of the random variable vector, γ m is a constant positive scaling factor, y m (x)=(a m ) T ·xb m, The M is the total number of constraints contained in the operation constraint, a m and b m is the coefficient of the vector containing the random variables, and are all initial values, x is a vector composed of the access capacity of distributed photovoltaic connected at multiple nodes

[0140] An embodiment of the present invention provides a distributed photovoltaic access capacity optimization device in a virtual power plant. The device obtains random variable information of the uncertainty of the photovoltaic power station output and the distribution network load based on historical photovoltaic power station output data and historical distribution network load data, takes into account the uncertainty variables of the photovoltaic power station output and the distribution network load. After introducing the uncertainty variables, distributed robust chance constraints are obtained, and then a complete optimization model is further formed. The model can be efficiently solved by a solver, and the optimization results can be obtained quickly. The optimization results are less conservative, and can give full play to the power generation potential of distributed photovoltaics in the virtual power plant and improve the economy of the virtual power plant.

[0141] Figure 3 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3 As shown, the electronic device may include: a processor (processor) 310, a communication interface (Communications Interface) 320, a memory (memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call the logic instructions in the memory 330 to execute the distributed photovoltaic access capacity optimization method in the virtual power plant, the method comprising: obtaining a deterministic model of photovoltaic access capacity based on the current topological structure information of the distribution network, and obtaining uncertainty random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data of each node of the distribution network and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and the operation constraints of the distribution network; based on the operation constraints and the uncertainty random variable information, determining the distributed robustness opportunity constraints; obtaining an optimization model based on the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints; and obtaining the total access capacity of distributed photovoltaics based on the optimization model.

[0142] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0143] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the distributed photovoltaic access capacity optimization method within the virtual power plant provided by the above methods. The method includes: obtaining a deterministic model of photovoltaic access capacity based on the current topology information of the distribution network, and obtaining uncertainty random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data of each node of the distribution network and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and operation constraints of the distribution network; based on the operation constraints and the uncertainty random variable information, determining distributed robustness opportunity constraints; according to the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints, obtaining an optimization model; based on the optimization model, obtaining the total access capacity of distributed photovoltaics.

[0144] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the distributed photovoltaic access capacity optimization method within a virtual power plant provided by the above-mentioned methods, the method comprising: obtaining a deterministic model of photovoltaic access capacity based on the current topology information of the distribution network, and obtaining uncertainty random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data of each node of the distribution network and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints and operation constraints of the distribution network; based on the operation constraints and the uncertainty random variable information, determining distributed robustness opportunity constraints; obtaining an optimization model based on the objective function, the photovoltaic power station investment constraints and the distributed robustness opportunity constraints; and obtaining the total access capacity of distributed photovoltaics based on the optimization model.

[0145] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0146] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing distributed photovoltaic access capacity in a virtual power plant, characterized in that: include: A deterministic model of photovoltaic access capacity is obtained based on the current topological structure information of the distribution network, and uncertain random variable information of photovoltaic power station output and distribution network load is obtained based on historical photovoltaic power station output data and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints, and operation constraints of the distribution network; Determining a distributed robustness chance constraint based on the operational constraints and the uncertainty random variable information; According to the objective function, the photovoltaic power station investment constraint and the distributed robust opportunity constraint are optimized. Based on the optimization model, the total access capacity of distributed photovoltaics is obtained; The optimization model obtained based on the objective function, the photovoltaic power station investment constraint and the distributed robust opportunity constraint includes: Determining a constraint including a risk condition based on the distributed robust chance constraint; Based on the objective function, the photovoltaic power station investment constraint and the constraint including the risk condition are optimized; The constraints containing risk conditions include: Among them, the variable ε represents the tolerable risk level, Tr(·) represents the operator that takes the matrix trace, and the matrix variable described represents the space composed of N-dimensional symmetric matrices, represents the covariance matrix of ξ, represents the space composed of Z-dimensional symmetric matrices, Represents the vector consisting of the mean of each dimensional component of the random variable vector, γ m is a scaling factor that is always positive, The M is the total number of constraints contained in the operation constraint, a m and b m is the coefficient of the vector containing the random variables, and are all initial values, and x is a vector composed of the access capacity of distributed photovoltaics connected to multiple nodes.

2. The method for optimizing distributed photovoltaic access capacity in a virtual power plant according to claim 1, characterized in that: The uncertainty random variable information of the photovoltaic power station output and the distribution network load is obtained based on the historical photovoltaic power station output data and the historical load data of each node of the distribution network, including: Obtaining a random variable vector containing all the random variables of the node based on the first random variable and the second random variable of each node in the distribution network, wherein the first random variable is a random variable for the uncertainty of the output of the photovoltaic power station, and the second random variable is a random variable for the uncertainty of the load of the distribution network; Obtaining a support set of the random variable vector distribution based on the random variable vector; Based on the random variable vector, historical photovoltaic power station output data of each node of the distribution network and historical load data of each node of the distribution network, moment information of the random variable vector is obtained, wherein the moment information includes first-order moment information and second-order moment information; Uncertain random variable information of photovoltaic power station output and distribution network load is obtained based on the support set and the moment information.

3. The method for optimizing distributed photovoltaic access capacity in a virtual power plant according to claim 2, characterized in that: The random variable vector includes: The random variable component corresponding to the target dimension satisfies the constraints consisting of preset upper and lower bounds, and the target dimension is a positive integer.

4. The method for optimizing distributed photovoltaic access capacity in a virtual power plant according to claim 2, characterized in that: The determining of the distribution robustness chance constraint based on the operation constraint and the uncertainty random variable information includes: Obtaining a second operating constraint based on the random variable vector and the operating constraint, wherein the second operating constraint is a constraint that takes into account uncertainty in output of the photovoltaic power station and uncertainty in load of the distribution network; Based on the second operation constraint and the uncertainty random variable information, a distributional robustness chance constraint is determined.

5. A distributed photovoltaic access capacity optimization device in a virtual power plant, characterized in that: include: a first determination module, configured to obtain a deterministic model of photovoltaic access capacity based on current topological information of the distribution network, and to obtain uncertain random variable information of photovoltaic power station output and distribution network load based on historical photovoltaic power station output data and historical load data of each node of the distribution network, wherein the deterministic model includes an objective function, photovoltaic power station investment constraints, and distribution network operation constraints; A second determining module is used to determine a distributed robustness chance constraint based on the operation constraint and the uncertainty random variable information; A third determination module is configured to obtain an optimization model based on the objective function, the photovoltaic power station investment constraint, and the distributed robust opportunity constraint; A fourth determination module is configured to obtain a total access capacity of distributed photovoltaics based on the optimization model; The optimization model obtained based on the objective function, the photovoltaic power station investment constraint and the distributed robust opportunity constraint includes: Determining a constraint including a risk condition based on the distributed robust chance constraint; Based on the objective function, the photovoltaic power station investment constraint and the constraint including the risk condition are optimized; The constraints containing risk conditions include: H≥0； Among them, the variable ε represents the tolerable risk level, Tr(·) represents the operator that takes the matrix trace, and the matrix variable described represents the space composed of N-dimensional symmetric matrices, represents the covariance matrix of ξ, represents the space composed of Z-dimensional symmetric matrices, Represents the vector composed of the mean of each dimensional component of the random variable vector, γ m is a scaling factor that is always positive, The M is the total number of constraints contained in the operation constraint, a m and b m is the coefficient of the vector containing the random variables, and are all initial values, and x is a vector composed of the access capacity of distributed photovoltaics connected to multiple nodes.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for optimizing distributed photovoltaic access capacity in a virtual power plant as described in any one of claims 1 to 4 are implemented.

7. A non-transitory 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 for optimizing distributed photovoltaic access capacity in a virtual power plant as claimed in any one of claims 1 to 4 are implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for optimizing distributed photovoltaic access capacity in a virtual power plant as claimed in any one of claims 1 to 4 are implemented.

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