Power distribution network regulation and control method and device considering operation risk and photovoltaic bearing capacity, terminal equipment and storage medium

Abstract

The invention discloses a power distribution network regulation and control method and device considering operation risks and photovoltaic bearing capacity, terminal equipment and a storage medium, and relates to the technical field of power grid operation optimization, and the method comprises the steps: obtaining the parameters of a power distribution network and the predicted power of each photovoltaic power station; performing cluster division on the photovoltaic power station, and calculating the total predicted power of each photovoltaic cluster; constructing an inter-cluster collaborative optimization model by taking the minimum voltage offset rate, the minimum network loss rate and the maximum photovoltaic bearing rate of each photovoltaic cluster dominant node as targets; constructing an intra-group autonomous optimization model by taking the minimum voltage offset rate, the minimum network loss rate and the maximum current offset rate of each photovoltaic power station node as targets; under the constraint condition, solving the model; and regulating and controlling the power distribution network according to the power generated by the model. By implementing the method, the problems that an existing distributed photovoltaic group dispatching and group control method lacks consideration of photovoltaic bearing capacity and voltage and current out-of-limit can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation optimization, and in particular to a distribution network control method, device, terminal equipment and storage medium that take into account operation risks and photovoltaic carrying capacity. Background Art

[0002] After large-scale distributed photovoltaics are connected to the distribution network, the structure and operation mode of the traditional distribution network will change, resulting in increased operational risks such as voltage exceeding the limit and line overload in the distribution network, which brings challenges to the safe and reliable operation of the distribution network. The existing distributed photovoltaic group adjustment and control methods only consider reducing line losses and improving economy, but lack consideration of the carrying capacity of photovoltaic power generation after large-scale distributed photovoltaics are connected to the grid, and also lack consideration of the operational risks of voltage and current exceeding the limit. Summary of the Invention

[0003] The embodiments of the present invention provide a distribution network control method, device, terminal equipment and storage medium that take into account operational risks and photovoltaic carrying capacity. The present invention can solve the problem that existing distributed photovoltaic group control methods lack consideration of photovoltaic carrying capacity and voltage and current limit violations.

[0004] An embodiment of the present invention provides a distribution network control method that takes into account operational risks and photovoltaic load capacity, including:

[0005] Obtain distribution network parameters and predicted power of each photovoltaic power station;

[0006] Cluster the photovoltaic power station to generate several photovoltaic clusters; calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster;

[0007] Based on the distribution network parameters and the total predicted power of each photovoltaic cluster, an inter-cluster collaborative optimization model is constructed with the goal of minimizing the voltage offset rate, network loss rate, and photovoltaic load factor of the leading node of each photovoltaic cluster.

[0008] Based on the distribution network parameters and the predicted power of each PV plant, an intra-clusters autonomous optimization model is constructed with the goal of minimizing the voltage deviation rate, network loss rate, and current deviation rate of each PV plant node.

[0009] Under the comprehensive constraints of grid operation, the inter-cluster collaborative optimization model is solved to generate the total active power and reactive power of each PV cluster when the voltage offset rate of the leading node of each PV cluster is minimized, the network loss rate is minimized, and the PV load factor is maximized. The comprehensive constraints of grid operation include linearized power flow constraints, PV power plant power constraints, and safe operation constraints.

[0010] Under the comprehensive constraints of grid operation and inter-cluster and intra-cluster collaborative control, the intra-cluster autonomous optimization model is solved to generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate, network loss rate, and current deviation rate of each photovoltaic power station node are minimized.

[0011] The distribution network is regulated based on the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0012] Furthermore, the photovoltaic power station is divided into clusters to generate a number of photovoltaic clusters, including:

[0013] Calculate the modularity index based on the weights of the edges between nodes of all photovoltaic power plants;

[0014] According to the modularity index, a genetic algorithm is used to cluster the photovoltaic power station to generate a number of photovoltaic clusters.

[0015] Furthermore, the calculation of the total predicted power of each photovoltaic cluster and determination of the leading node of each photovoltaic cluster include:

[0016] Calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station;

[0017] Based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station, a comprehensive index used to characterize the observability and controllability of the distribution network is calculated;

[0018] The leading node of each photovoltaic cluster is determined according to the comprehensive indicators.

[0019] Furthermore, the inter-group collaborative optimization model includes:

[0020] f1=min(z1 / c F,PV +z2c F,V,m +z3c F,loss );

[0021] in,

[0022]

[0023] Where f1 is the inter-group collaborative optimization model, c F,PV is the photovoltaic load factor, c F,V,m is the voltage deviation rate of all PV cluster leading nodes, c F,loss is the network loss rate, z1 is the weight coefficient of the photovoltaic carrying capacity between groups, z2 is the weight coefficient of the voltage deviation rate between groups, z3 is the weight coefficient of the network loss rate between groups, P pv.i is the active power of the i-th photovoltaic power station, is the predicted power of the i-th photovoltaic power station, N is the total number of photovoltaic power stations, V m is the actual voltage of the leading node m of the photovoltaic cluster, V Nm is the rated voltage of the photovoltaic cluster leading node m, M is the total number of photovoltaic cluster leading nodes in the system, I ij represents the actual current of branch ij, which is the line between the i-th photovoltaic power station and the j-th photovoltaic power station, r ij represents the resistance of branch ij, P S Indicates the active power supplied by the feeder start node.

[0024] Furthermore, the intra-group autonomous optimization model includes:

[0025] f2=min(z4c F,V +z5c F,loss +z6c F,I );

[0026]

[0027] Where f2 is the autonomous optimization model within the group, c F,V is the voltage deviation rate of all PV power station nodes, c F,I is the current deviation rate, z4 is the weight coefficient of the voltage deviation rate within the group, z5 is the weight coefficient of the network loss rate within the group, z6 is the weight coefficient of the current deviation rate within the group, V i is the actual voltage of the photovoltaic power station node i, V N is the rated voltage of the photovoltaic power station node i, I ij.N is the rated current of branch ij.

[0028] Another embodiment of the present invention provides a distribution network control device that takes into account operational risks and photovoltaic load capacity, including: a parameter acquisition module, a cluster division module, an inter-cluster model construction module, an intra-cluster model construction module, an inter-cluster model solving module, an intra-cluster model solving module, and a distribution network control module;

[0029] The parameter acquisition module is used to obtain the distribution network parameters and the predicted power of each photovoltaic power station;

[0030] The cluster division module is used to divide the photovoltaic power station into clusters to generate a number of photovoltaic clusters; calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster;

[0031] The inter-cluster model building module is used to build an inter-cluster collaborative optimization model based on the distribution network parameters and the total predicted power of each photovoltaic cluster, with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the photovoltaic load factor of the leading node of each photovoltaic cluster;

[0032] The intra-cluster model building module is used to build an intra-cluster autonomous optimization model based on the distribution network parameters and the predicted power of each photovoltaic power station, with the goal of minimizing the voltage deviation rate, minimizing the network loss rate, and maximizing the current deviation rate of each photovoltaic power station node;

[0033] The inter-cluster model solving module is used to solve the inter-cluster collaborative optimization model under the comprehensive grid operation constraints to generate the total active power and total reactive power of each photovoltaic cluster when the voltage offset rate of the leading node of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load factor is maximized. The comprehensive grid operation constraints include: linearized power flow constraints, photovoltaic power station power constraints, and safe operation constraints;

[0034] The intra-cluster model solving module is used to solve the intra-cluster autonomous optimization model under the comprehensive constraints of power grid operation and the constraints of inter-cluster and intra-cluster collaborative control, and generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate of each photovoltaic power station node is minimized, the network loss rate is minimized, and the current deviation rate is maximized;

[0035] The distribution network control module is used to control the distribution network according to the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0036] Furthermore, the cluster division module includes a modularity index acquisition unit and a photovoltaic cluster division unit;

[0037] The modularity index obtaining unit is used to calculate the modularity index according to the weights of the edges between nodes of all photovoltaic power stations;

[0038] The photovoltaic cluster division unit is configured to divide the photovoltaic power station into clusters using a genetic algorithm according to the modularity index to generate a plurality of photovoltaic clusters.

[0039] Furthermore, the cluster division module further includes a photovoltaic cluster predicted power calculation unit, a comprehensive index calculation unit and a dominant node determination unit;

[0040] The photovoltaic cluster predicted power calculation unit is used to calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station;

[0041] A comprehensive index calculation unit is used to calculate a comprehensive index used to characterize the observability and controllability of the distribution network based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station;

[0042] The dominant node determination unit is configured to determine the dominant node of each photovoltaic cluster according to the comprehensive indicator.

[0043] Another embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distribution network control method that takes into account operational risks and photovoltaic carrying capacity as described in any one of the embodiments.

[0044] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distribution network control method that takes into account operational risks and photovoltaic carrying capacity as described in any of the above embodiments.

[0045] The following beneficial effects are achieved by implementing the embodiments of the present invention:

[0046] The present invention provides a distribution network control method that takes into account operation risks and photovoltaic carrying capacity, obtains distribution network parameters and predicted power of each photovoltaic power station; divides the photovoltaic power stations into clusters, and calculates the total predicted power of each photovoltaic cluster; constructs an inter-cluster collaborative optimization model with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the photovoltaic carrying rate of the leading nodes of each photovoltaic cluster; constructs an intra-cluster autonomous optimization model with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the current offset rate of each photovoltaic power station node; solves the inter-cluster collaborative optimization model and the intra-cluster autonomous optimization model under constraint conditions to generate the total active power and total reactive power of each photovoltaic cluster and the active power and reactive power of each photovoltaic power station; and controls the distribution network according to the power generated by the model. The present invention constructs an inter-cluster and intra-cluster optimization model to solve the total active power and total reactive power of each photovoltaic cluster when the voltage offset rate of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load rate is maximized, as well as the active power and reactive power of each photovoltaic power station when the voltage offset rate of each photovoltaic power station is minimized, the network loss rate is minimized, and the current offset rate is maximized. The distribution network is regulated by the total active power of each photovoltaic cluster, the total reactive output of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station solved as above, which can reduce the risk of voltage and current exceeding the limit of the distribution network and improve the photovoltaic carrying capacity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention provides a flow chart of a method for controlling a distribution network taking into account operational risks and photovoltaic load capacity, according to an embodiment of the present invention.

[0048] Figure 2 This is a structural diagram of a distribution network control device that takes into account operation risks and photovoltaic load capacity, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0051] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0052] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] like Figure 1 FIG. 1 is a flow chart of a distribution network control method that takes into account operational risks and photovoltaic load capacity, provided by one embodiment of the present invention. The method can address the problem that existing distributed photovoltaic group control methods lack consideration of photovoltaic load capacity and voltage and current limit violations. The method includes the following steps:

[0054] Step S1: Obtain distribution network parameters and predicted power of each photovoltaic power station;

[0055] In the present invention, the distribution network parameters and the predicted power of each photovoltaic power station are used to provide data preparation for the subsequent construction of the inter-group and intra-group optimization model;

[0056] In a preferred embodiment, the distribution network parameters include: branch resistance, branch reactance, branch rated current, photovoltaic power station node rated voltage, load active power of photovoltaic power station node and load reactive power of photovoltaic power station node.

[0057] Step S2: cluster the photovoltaic power station to generate a number of photovoltaic clusters; calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster;

[0058] In the present invention, all photovoltaic power stations are divided into several photovoltaic clusters. Based on the predicted power of the photovoltaic power stations, the total predicted power of each photovoltaic cluster is calculated to provide a data basis for the subsequent construction of the inter-cluster collaborative optimization model and select a dominant node for each photovoltaic cluster.

[0059] In a preferred embodiment, the photovoltaic power station is divided into clusters to generate a number of photovoltaic clusters, including:

[0060] Calculate the modularity index based on the weights of the edges between nodes of all photovoltaic power plants;

[0061] Based on the modularity index, a genetic algorithm is used to cluster the photovoltaic power station to generate a number of photovoltaic clusters;

[0062] Specifically, the expression of the modularity index is:

[0063]

[0064] Where ρ is the modularity index, e ij represents the weight of the edge between photovoltaic power station i and photovoltaic power station j, z=(∑ i ∑ j e ij ) / 2 means 1 / 2 of the sum of the weights of all edges in the region, k i =(∑ i e ij ) represents the sum of the weights of all edges connected to photovoltaic power station i, k j =(∑ j e ij ) represents the sum of the weights of all edges connected to PV station j. When PV station i and PV station j are located in the same area, δ(i, j) = 1, otherwise δ(i, j) = 0;

[0065] In another preferred embodiment, the calculating of the total predicted power of each photovoltaic cluster and determining the dominant node of each photovoltaic cluster includes:

[0066] Calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station;

[0067] Based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station, a comprehensive index used to characterize the observability and controllability of the distribution network is calculated;

[0068] Determine the leading node of each photovoltaic cluster based on the comprehensive indicators;

[0069] Specifically, the expression of the comprehensive index used to characterize the observability and controllability of the distribution network is:

[0070]

[0071] Where S i is a comprehensive indicator, α is the weight factor coefficient, ΔV j / ΔV i is the voltage sensitivity of PV station node j to PV station node i, ΔV j / ΔQ i is the reactive voltage sensitivity of the voltage amplitude of the PV station node j relative to the reactive power injected into the PV station node i, and N is the total number of PV stations;

[0072] Calculate the comprehensive index of each photovoltaic power station node, and select the photovoltaic power station node with the largest comprehensive index as the leading node of the cluster.

[0073] Step S3: Based on the distribution network parameters and the total predicted power of each photovoltaic cluster, an inter-cluster collaborative optimization model is constructed with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the photovoltaic load factor of the leading node of each photovoltaic cluster;

[0074] In the present invention, based on the acquired distribution network parameters and the total predicted power of each photovoltaic cluster, an inter-cluster collaborative optimization model is constructed with the goal of minimizing the voltage offset rate of the dominant node of each photovoltaic cluster, minimizing the network loss rate, and maximizing the photovoltaic load rate. The risk of voltage over-limit between distribution network clusters is reduced by minimizing the voltage offset rate of the dominant node of each photovoltaic cluster, the network loss of the distribution network photovoltaic is reduced by minimizing the network loss rate, and the photovoltaic load rate is maximized to improve the photovoltaic carrying capacity.

[0075] The inter-group collaborative optimization model includes:

[0076] f1=min(z1 / c F,PV +z2c F,V,m +z3c F,loss );

[0077] in,

[0078]

[0079] Where f1 is the inter-group collaborative optimization model, c F,PV is the photovoltaic load factor, c F,V,m is the voltage deviation rate of all PV cluster leading nodes, c F,lossis the grid loss rate, z1 is the weight coefficient of the inter-cluster photovoltaic carrying capacity, z2 is the weight coefficient of the inter-cluster voltage deviation rate, z3 is the weight coefficient of the inter-cluster grid loss rate, P pv.i is the active power of the i-th photovoltaic power station, is the predicted power of the i-th photovoltaic power station, N is the total number of photovoltaic power stations, V m is the actual voltage of the leading node m of the photovoltaic cluster, V Nm is the rated voltage of the photovoltaic cluster leading node m, M is the total number of photovoltaic cluster leading nodes in the system, I ij represents the actual current of branch ij, which is the line between the i-th photovoltaic power station and the j-th photovoltaic power station, r ij Represents the resistance of branch ij, P S Indicates the active power supplied by the feeder start node.

[0080] Step S4: Based on the distribution network parameters and the predicted power of each photovoltaic power station, an intra-cluster autonomous optimization model is constructed with the goal of minimizing the voltage deviation rate, minimizing the network loss rate, and maximizing the current deviation rate of each photovoltaic power station node;

[0081] In the present invention, based on the acquired distribution network parameters and the predicted power of each photovoltaic power station, an intra-cluster autonomous optimization model is constructed with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the current offset rate of each photovoltaic power station node. The voltage offset rate of each photovoltaic power station node is minimized to reduce the risk of voltage over-limit in the distribution network cluster, the network loss of the photovoltaic distribution network is reduced by minimizing the network loss rate, and the current offset rate is maximized to reduce the risk of current over-limit.

[0082] In a preferred embodiment, the intra-group autonomous optimization model includes:

[0083] f2=min(z4c F,V +z5c F,loss +z6c F,I );

[0084]

[0085] Where f2 is the autonomous optimization model within the group, c F,V is the voltage deviation rate of all PV power station nodes, c F,I is the current deviation rate, z4 is the weight coefficient of the voltage deviation rate within the group, z5 is the weight coefficient of the network loss rate within the group, z6 is the weight coefficient of the current deviation rate within the group, V i is the actual voltage of the photovoltaic power station node i, V N is the rated voltage of the photovoltaic power station node i, I ij.N is the rated current of branch ij.

[0086] Step S5: Under the comprehensive grid operation constraints, solve the inter-cluster collaborative optimization model to generate the total active power and total reactive power of each photovoltaic cluster when the voltage offset rate of the leading node of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load factor is maximized; wherein the comprehensive grid operation constraints include: linearized power flow constraints, photovoltaic power plant power constraints, and safe operation constraints;

[0087] In the present invention, the inter-cluster optimization model is constrained by the comprehensive constraints of the grid operation, and the total active power and total reactive power of each photovoltaic cluster are solved when the voltage deviation rate of the leading node of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load rate is maximized;

[0088] In a preferred embodiment, the linearized power flow constraint is specifically:

[0089] V i -V j =2(r ij P ij +x ij Q ij );

[0090] P ij =P pv.j -P L.j -∑ k:j→k P jk ;

[0091] Q ij =Q pv.j -Q L.j -∑ k:j→k Q jk ;

[0092] Where V i is the actual voltage of the photovoltaic power station node i, V j is the actual voltage of the photovoltaic power station node j. ij is the resistance value of branch ij, x ij is the reactance value of branch ij. ij is the active power of line ij, Q ij is the reactive power of line ij. pv.j is the photovoltaic active power of the photovoltaic power station node j, Q pv.j is the reactive power output of photovoltaic power station node j, P L.j is the active power of the load at the photovoltaic power station node j, Q L.j is the reactive power of the load at the photovoltaic power station node j, ∑ k:j→k P jk is the sum of the active power flowing from node j to all downstream nodes k, ∑ k:j→k Q jkis the sum of the reactive power flowing from node j to all downstream nodes k;

[0093] In a preferred embodiment, the photovoltaic power station power constraint is specifically:

[0094]

[0095] Where Q pv.i is the reactive power of the i-th photovoltaic power station, is the minimum reactive power of the i-th photovoltaic power station, is the maximum reactive power of the i-th photovoltaic power station. pv.i is the apparent power of the i-th PV plant in the cluster;

[0096] The reactive power constraint equation is linearized using the dodecagonal approximation method, and the equation can be approximately equivalent to:

[0097] χ c0 P pv,i +χ c1 Q pv,i +χ c2 S pv,i ≤0,c=1,2……12;

[0098] Where, X c0 , X c1 , X c2 is the coefficient of the cth side;

[0099] In a preferred embodiment, the safe operation constraints are specifically:

[0100]

[0101] 0≤I ij ≤I ij.N ;

[0102] Where, is the upper limit of the node voltage of the photovoltaic power station in the cluster, It is the lower limit of the node voltage of the PV power station in the cluster.

[0103] Step S6: Under the comprehensive grid operation constraints and the inter-cluster and intra-cluster collaborative control constraints, solve the intra-cluster autonomous optimization model to generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate of each photovoltaic power station node is minimized, the network loss rate is minimized, and the current deviation rate is maximized;

[0104] In the present invention, on the basis of the comprehensive constraints of power grid operation, the inter-group and intra-group collaborative control constraints are added to constrain the intra-group autonomous optimization model, and the active power and reactive power of each photovoltaic power station are solved when the voltage deviation rate of each photovoltaic power station node is minimized, the network loss rate is minimized, and the current deviation rate is maximized;

[0105] In a preferred embodiment, the inter-group and intra-group collaborative control constraints are specifically:

[0106]

[0107] Where A is the total number of photovoltaic clusters, P pv.a is the active power of the a-th PV cluster, Q pv.m is the reactive power output of the a-th PV cluster.

[0108] Step S7: regulating the distribution network according to the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0109] In the present invention, the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station output by the inter-cluster collaborative optimization model and the intra-cluster autonomous optimization model are used to regulate the distribution network, so as to achieve the purpose of minimizing the voltage offset rate, minimizing the network loss rate and maximizing the photovoltaic load rate of the leading nodes of each photovoltaic cluster between clusters, and minimizing the voltage offset rate, minimizing the network loss rate and maximizing the current offset rate of each photovoltaic power station node within the cluster, thereby reducing the risk of voltage and current exceeding the limit of the distribution network and improving the photovoltaic carrying capacity.

[0110] like Figure 2 FIG. 1 is a schematic diagram of a structure of a distribution network control device taking into account operation risks and photovoltaic load capacity according to an embodiment of the present invention, including:

[0111] Parameter acquisition module, cluster division module, inter-cluster model construction module, intra-cluster model construction module, inter-cluster model solution module, intra-cluster model solution module, distribution network control module;

[0112] The parameter acquisition module is used to obtain the distribution network parameters and the predicted power of each photovoltaic power station;

[0113] The cluster division module is used to divide the photovoltaic power station into clusters to generate a number of photovoltaic clusters; calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster;

[0114] The inter-cluster model building module is used to build an inter-cluster collaborative optimization model based on the distribution network parameters and the total predicted power of each photovoltaic cluster, with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the photovoltaic load factor of the leading node of each photovoltaic cluster;

[0115] The intra-cluster model building module is used to build an intra-cluster autonomous optimization model based on the distribution network parameters and the predicted power of each photovoltaic power station, with the goal of minimizing the voltage deviation rate, minimizing the network loss rate, and maximizing the current deviation rate of each photovoltaic power station node;

[0116] The inter-cluster model solving module is used to solve the inter-cluster collaborative optimization model under the comprehensive grid operation constraints to generate the total active power and total reactive power of each photovoltaic cluster when the voltage offset rate of the leading node of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load factor is maximized. The comprehensive grid operation constraints include: linearized power flow constraints, photovoltaic power station power constraints, and safe operation constraints;

[0117] The intra-cluster model solving module is used to solve the intra-cluster autonomous optimization model under the comprehensive constraints of power grid operation and the constraints of inter-cluster and intra-cluster collaborative control, and generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate of each photovoltaic power station node is minimized, the network loss rate is minimized, and the current deviation rate is maximized;

[0118] The distribution network control module is used to control the distribution network according to the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0119] Furthermore, the cluster division module includes a modularity index acquisition unit and a photovoltaic cluster division unit;

[0120] The modularity index obtaining unit is used to calculate the modularity index according to the weights of the edges between nodes of all photovoltaic power stations;

[0121] The photovoltaic cluster division unit is configured to divide the photovoltaic power station into clusters using a genetic algorithm according to the modularity index to generate a plurality of photovoltaic clusters.

[0122] Furthermore, the cluster division module also includes a photovoltaic cluster predicted power calculation unit, a comprehensive index calculation unit and a dominant node determination unit;

[0123] The photovoltaic cluster predicted power calculation unit is used to calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station;

[0124] A comprehensive index calculation unit is used to calculate a comprehensive index used to characterize the observability and controllability of the distribution network based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station;

[0125] The dominant node determination unit is configured to determine the dominant node of each photovoltaic cluster according to the comprehensive indicator.

[0126] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement a distribution network control method taking into account operational risks and photovoltaic carrying capacity provided by any of the above-mentioned method embodiments of the present invention.

[0127] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0128] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0129] Another preferred embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distribution network control method taking into account operational risks and photovoltaic carrying capacity as described in any one of the above embodiments is implemented.

[0130] It should be noted that the terminal devices mentioned herein may include computing devices such as desktop computers, laptops, PDAs, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that, for example, terminal devices may also include input / output devices, network access devices, and buses.

[0131] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0132] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0133] Another preferred embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distribution network control method taking into account operation risks and photovoltaic carrying capacity as described in any one of the present inventions.

[0134] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0135] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distribution network control method taking into account operational risks and photovoltaic load capacity, characterized in that: include: Obtain distribution network parameters and predicted power of each photovoltaic power station; Divide the photovoltaic power station into clusters to generate several photovoltaic clusters; Calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster; Based on the distribution network parameters and the total predicted power of each photovoltaic cluster, an inter-cluster collaborative optimization model is constructed with the goal of minimizing the voltage offset rate, network loss rate, and photovoltaic load factor of the leading node of each photovoltaic cluster. Based on the distribution network parameters and the predicted power of each PV plant, an intra-clusters autonomous optimization model is constructed with the goal of minimizing the voltage deviation rate, network loss rate, and current deviation rate of each PV plant node. Under the comprehensive constraints of grid operation, the inter-cluster collaborative optimization model is solved to generate the total active power and reactive power of each PV cluster when the voltage offset rate of the leading node of each PV cluster is minimized, the network loss rate is minimized, and the PV load factor is maximized. The comprehensive constraints of grid operation include linearized power flow constraints, PV power plant power constraints, and safe operation constraints. Under the comprehensive constraints of grid operation and inter-cluster and intra-cluster collaborative control, the intra-cluster autonomous optimization model is solved to generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate, network loss rate, and current deviation rate of each photovoltaic power station node are minimized. The distribution network is regulated based on the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

2. A distribution network control method taking into account operational risks and photovoltaic load capacity according to claim 1, characterized in that: The photovoltaic power station is divided into clusters to generate a number of photovoltaic clusters, including: Calculate the modularity index based on the weights of the edges between nodes of all photovoltaic power plants; According to the modularity index, a genetic algorithm is used to cluster the photovoltaic power station to generate a number of photovoltaic clusters.

3. A distribution network control method taking into account operational risks and photovoltaic load capacity according to claim 1, characterized in that: The calculation of the total predicted power of each photovoltaic cluster and determination of the dominant node of each photovoltaic cluster includes: Calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station; Based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station, a comprehensive index used to characterize the observability and controllability of the distribution network is calculated; The leading node of each photovoltaic cluster is determined according to the comprehensive indicators.

4. A distribution network control method taking into account operational risks and photovoltaic load capacity according to claim 1, characterized in that: The inter-group collaborative optimization model includes: f1=min(z1 / c F,PV +z2c F,V,m +z3c F,loss ); in, Where f1 is the inter-group collaborative optimization model, c F,PV is the photovoltaic load factor, c F,V,m is the voltage deviation rate of all PV cluster leading nodes, c F,loss is the network loss rate, z1 is the weight coefficient of the photovoltaic carrying capacity between groups, z2 is the weight coefficient of the voltage deviation rate between groups, z3 is the weight coefficient of the network loss rate between groups, P pv.i is the active power of the i-th photovoltaic power station, is the predicted power of the i-th photovoltaic power station, N is the total number of photovoltaic power stations, V m is the actual voltage of the leading node m of the photovoltaic cluster, V Nm is the rated voltage of the photovoltaic cluster leading node m, M is the total number of photovoltaic cluster leading nodes in the system, I ij represents the actual current of branch ij, which is the line between the i-th photovoltaic power station and the j-th photovoltaic power station, r ij represents the resistance of branch ij, P S Indicates the active power supplied by the feeder start node.

5. A distribution network control method taking into account operation risks and photovoltaic carrying capacity according to claim 4, characterized in that: The intra-group autonomous optimization model includes: <h2 style=";text-align:left;direction:ltr">f2 = min(z4c<h2 style=";text-align:left;direction:ltr"> F,V <h2 style=";text-align:left;direction:ltr"> +z5c<h2 style=";text-align:left;direction:ltr"> F,loss <h2 style=";text-align:left;direction:ltr"> +z6c<h2 style=";text-align:left;direction:ltr"> F,I <h2 style=";text-align:left;direction:ltr"> ); Where f2 is the autonomous optimization model within the group, c F,V is the voltage deviation rate of all PV power station nodes, c F,I is the current deviation rate, z4 is the weight coefficient of the voltage deviation rate within the group, z5 is the weight coefficient of the network loss rate within the group, z6 is the weight coefficient of the current deviation rate within the group, V i is the actual voltage of the photovoltaic power station node i, V N is the rated voltage of the photovoltaic power station node i, I ij.N is the rated current of branch ij.

6. A distribution network control device that takes into account operational risks and photovoltaic load capacity, characterized in that: include: Parameter acquisition module, cluster division module, inter-cluster model construction module, intra-cluster model construction module, inter-cluster model solution module, intra-cluster model solution module, distribution network control module; The parameter acquisition module is used to obtain the distribution network parameters and the predicted power of each photovoltaic power station; The cluster division module is used to divide the photovoltaic power station into clusters to generate a number of photovoltaic clusters; calculate the total predicted power of each photovoltaic cluster and determine the dominant node of each photovoltaic cluster; The inter-cluster model building module is used to build an inter-cluster collaborative optimization model based on the distribution network parameters and the total predicted power of each photovoltaic cluster, with the goal of minimizing the voltage offset rate, minimizing the network loss rate, and maximizing the photovoltaic load factor of the leading node of each photovoltaic cluster; The intra-cluster model building module is used to build an intra-cluster autonomous optimization model based on the distribution network parameters and the predicted power of each photovoltaic power station, with the goal of minimizing the voltage deviation rate, minimizing the network loss rate, and maximizing the current deviation rate of each photovoltaic power station node; The inter-cluster model solving module is used to solve the inter-cluster collaborative optimization model under the comprehensive grid operation constraints to generate the total active power and total reactive power of each photovoltaic cluster when the voltage offset rate of the leading node of each photovoltaic cluster is minimized, the network loss rate is minimized, and the photovoltaic load factor is maximized. The comprehensive grid operation constraints include: linearized power flow constraints, photovoltaic power station power constraints, and safe operation constraints; The intra-cluster model solving module is used to solve the intra-cluster autonomous optimization model under the comprehensive constraints of power grid operation and the constraints of inter-cluster and intra-cluster collaborative control, and generate the active power and reactive power of each photovoltaic power station when the voltage deviation rate of each photovoltaic power station node is minimized, the network loss rate is minimized, and the current deviation rate is maximized; The distribution network control module is used to control the distribution network according to the total active power of each photovoltaic cluster, the total reactive power of each photovoltaic cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

7. A distribution network control device taking into account operational risks and photovoltaic load capacity according to claim 6, characterized in that: The cluster division module includes a modularity index acquisition unit and a photovoltaic cluster division unit; The modularity index obtaining unit is used to calculate the modularity index according to the weights of the edges between nodes of all photovoltaic power stations; The photovoltaic cluster division unit is configured to divide the photovoltaic power station into clusters using a genetic algorithm according to the modularity index to generate a plurality of photovoltaic clusters.

8. A distribution network control device taking into account operational risks and photovoltaic load capacity according to claim 6, characterized in that: The cluster division module also includes a photovoltaic cluster predicted power calculation unit, a comprehensive index calculation unit and a dominant node determination unit; The photovoltaic cluster predicted power calculation unit is used to calculate the total predicted power of each photovoltaic cluster based on the predicted power of each photovoltaic power station; A comprehensive index calculation unit is used to calculate a comprehensive index used to characterize the observability and controllability of the distribution network based on the weight factor coefficient and the voltage sensitivity between nodes of each photovoltaic power station; The dominant node determination unit is configured to determine the dominant node of each photovoltaic cluster according to the comprehensive indicator.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distribution network control method taking into account operation risks and photovoltaic carrying capacity as described in any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distribution network control method taking into account operation risks and photovoltaic carrying capacity as described in any one of claims 1 to 5.

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