Optimized scheduling method and device for distributed photovoltaic cluster grid connection

Abstract

The invention discloses an optimal scheduling method and device for distributed photovoltaic cluster grid connection, and the method comprises the steps: constructing an inter-cluster coordinated optimal scheduling model based on cluster data, and a first power flow constraint, a first safe operation constraint and a cluster photovoltaic power constraint corresponding to the model, and solving the model under the constraint conditions, generating the voltage deviation of the dominant node of the cluster, the abandoned light power, and the active power and reactive power of the distributed photovoltaic when the abandoned light power and the voltage deviation of the dominant node are minimum, and carrying out photovoltaic power cooperative scheduling among the clusters according to the solving parameters; constructing an intra-group autonomous optimization scheduling model based on the intra-group data, and a second power flow constraint, an intra-group photovoltaic power station power constraint, a second safe operation constraint and an inter-group and intra-group cooperative control constraint corresponding to the model, solving the model under the constraint conditions, and generating an intra-group autonomous optimization scheduling model when the network loss is minimum. Voltage values of nodes in the group and active power and reactive power of the photovoltaic power stations are calculated, and finally photovoltaic power cooperative scheduling in the group is carried out according to the solving parameters.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation optimization, and in particular to an optimization scheduling method and device for distributed photovoltaic cluster grid connection. Background Art

[0002] As the proportion of distributed photovoltaic grid connection continues to increase, large-scale distributed photovoltaic grid connection will cause problems such as power flow backflow and voltage fluctuation. When using traditional centralized scheduling methods to control the distribution network containing distributed photovoltaics, there are problems such as redundant control objects, huge communication data, and the safety and reliability of power grid operation cannot be guaranteed. In order to solve this problem, the cluster control strategy of distributed photovoltaics has been widely studied.

[0003] However, the current distributed photovoltaic cluster control strategy based on the "cloud-cluster-end" architecture has some problems. An inter-cluster coordination and intra-cluster autonomous optimization model is established with the minimum voltage deviation and network loss as the optimization goals. After the overall active and reactive output of the cluster is obtained, the active and reactive power are distributed within the cluster according to the internal photovoltaic power station capacity or the average value. There is a lack of coordination and cooperation between and within the cluster, and insufficient analysis of the voltage over-limit and power flow fluctuations caused by the access of massive distributed photovoltaics, which affects the safety of power grid operation. Summary of the invention

[0004] The embodiment of the present invention provides a distributed photovoltaic cluster grid-connected optimization scheduling method and device, which realizes power scheduling between clusters and within clusters by introducing inter-cluster and intra-cluster collaborative control constraints, thereby improving the operating efficiency and stability of the power system.

[0005] An embodiment of the present invention provides an optimization scheduling method for grid-connected distributed photovoltaic clusters, including:

[0006] Acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster;

[0007] According to cluster data, with the goal of minimizing abandoned light power and dominant node voltage deviation, an inter-cluster coordinated optimization scheduling model is constructed, and the first power flow constraint, the first safe operation constraint, and the cluster photovoltaic power constraint are constructed;

[0008] Under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint, the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic are generated when the abandoned light power and the voltage deviation of the leading node are minimized;

[0009] The photovoltaic power coordination among clusters is carried out according to the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic;

[0010] Based on the data within the group, with the goal of minimizing network loss, an autonomous optimization scheduling model within the group is constructed, and the second power flow constraint, the power constraint of the photovoltaic power station within the group, the second safe operation constraint, and the inter-group and intra-group collaborative control constraints are constructed;

[0011] Under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station within the group, the second safe operation constraint, and the inter-group and intra-group coordinated control constraint, the voltage value of each node in the group, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station are generated when the network loss is minimized;

[0012] The photovoltaic power in the group is coordinated and dispatched according to the voltage value of each node in the group, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station.

[0013] Furthermore, the inter-cluster coordination optimization scheduling model is as follows:

[0014] minf=ω1f1+ω2f2;

[0015]

[0016] Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.m Indicates the active output of photovoltaic power of each cluster.

[0017] Furthermore, the first power flow constraint is specifically:

[0018]

[0019] (P clu.ij ) 2 +(Q clu.ij ) 2 = l clu.ij u clu.ij ;

[0020] ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -rclu.ij l clu.ij )+g clu.j u clu.j ;

[0021] ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij );

[0022] in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization;

[0023] The first safety operation constraint is:

[0024]

[0025] Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization;

[0026] Cluster PV power constraints are as follows:

[0027]

[0028]

[0029] Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.nrepresents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

[0030] Furthermore, the autonomous optimization scheduling model within the group includes:

[0031]

[0032] Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, l ij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

[0033] Furthermore, the second power flow constraint is specifically:

[0034]

[0035] (P ij ) 2 +(Q ij ) 2 = l ij u ij ;

[0036] -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ;

[0037]

[0038] in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.jrepresents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group;

[0039] The power constraints of the photovoltaic power stations within the cluster are as follows:

[0040]

[0041]

[0042] Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group;

[0043] The second safety operation constraint is as follows:

[0044]

[0045] Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group;

[0046] Inter-group and intra-group collaborative control constraints are as follows:

[0047]

[0048] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a data acquisition module, an inter-group model and constraint condition construction module, an inter-group model solving module, an inter-group collaborative scheduling module, an intra-group model and constraint condition construction module, an intra-group model solving module, and an intra-group collaborative scheduling module;

[0049] A data acquisition module is used to acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster;

[0050] The inter-cluster model and constraint condition construction module is used to build an inter-cluster coordinated optimization scheduling model based on cluster data, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, and to build the first power flow constraint, the first safe operation constraint, and the cluster photovoltaic power constraint;

[0051] The inter-cluster model solving module is used to generate the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaics and the reactive power of the cluster distributed photovoltaics when the abandoned light power and the voltage deviation of the leading node are minimized under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint;

[0052] The inter-cluster collaborative scheduling module is used to perform inter-cluster photovoltaic power collaborative scheduling based on the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic, and the reactive power of the cluster distributed photovoltaic;

[0053] The intra-group model and constraint condition construction module is used to build an intra-group autonomous optimization scheduling model based on intra-group data with the goal of minimizing network loss, and to build the second power flow constraint, intra-group photovoltaic power station power constraint, second safe operation constraint, and inter-group and intra-group collaborative control constraint;

[0054] The intra-cluster model solving module is used to generate the voltage value of each node in the cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station when the network loss is minimized under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station in the cluster, the second safe operation constraint, and the inter-cluster and intra-cluster collaborative control constraint;

[0055] The intra-cluster collaborative scheduling module is used to perform intra-cluster photovoltaic power collaborative scheduling based on the voltage value of each node in the cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0056] Furthermore, the inter-cluster coordination optimization scheduling model is as follows:

[0057] minf=ω1f1+ω2f2;

[0058]

[0059] Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.mIndicates the active output of photovoltaic power of each cluster.

[0060] Furthermore, the first power flow constraint is specifically:

[0061]

[0062] (P clu.ij ) 2 +(Q clu.ij ) 2 = l clu.ij u clu.ij ;

[0063] ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -r clu.ij l clu.ij )+g clu.j u clu.j ;

[0064] ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij );

[0065] in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization;

[0066] The first safety operation constraint is:

[0067]

[0068] Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization;

[0069] Cluster PV power constraints are as follows:

[0070]

[0071] Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.n represents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

[0072] Furthermore, the autonomous optimization scheduling model within the group includes:

[0073]

[0074] Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, l ij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

[0075] Furthermore, the second power flow constraint is specifically:

[0076]

[0077] (P ij ) 2 +(Q ij ) 2 = l ij u ij ;

[0078] -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ;

[0079]

[0080] in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.j represents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group;

[0081] The power constraints of the photovoltaic power stations within the cluster are as follows:

[0082]

[0083] Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group;

[0084] The second safety operation constraint is as follows:

[0085]

[0086] Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group;

[0087] Inter-group and intra-group collaborative control constraints are as follows:

[0088]

[0089] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0090] Based on the cluster data and intra-group data in the power system, the present invention constructs an inter-cluster coordinated optimization scheduling model and an intra-cluster autonomous optimization scheduling model. The model is solved by the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint corresponding to the inter-cluster coordinated optimization scheduling model to generate the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic when the abandoned light power and the voltage deviation of the leading node are minimized. Then, the power system performs photovoltaic power collaborative scheduling between clusters according to the solved data; after the photovoltaic power collaborative scheduling between clusters, the model is solved by the second power flow constraint, the photovoltaic power station power constraint within the group, the second safe operation constraint and the inter-cluster-intra-cluster collaborative control constraint corresponding to the intra-cluster autonomous optimization scheduling model to generate the voltage value of each node within the group, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station when the network loss is minimized. The power system performs photovoltaic power collaborative scheduling within the group according to the data solved within the group. The present invention realizes power scheduling between clusters and within clusters by introducing inter-cluster-intra-cluster collaborative control constraints, so that the operation efficiency and stability of the power system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a flow chart of an optimization scheduling method for grid-connected distributed photovoltaic clusters provided by an embodiment of the present invention;

[0092] Figure 2 It is a structural diagram of an IEEE151 node distribution network provided by an embodiment of the present invention;

[0093] Figure 3 is a voltage amplitude comparison diagram of a cluster leading node provided by an embodiment of the present invention;

[0094] Figure 4 is a voltage amplitude comparison diagram of nodes in a group provided by an embodiment of the present invention;

[0095] Figure 5 It is a structural schematic diagram of an optimized dispatching device for grid-connected distributed photovoltaic clusters provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0096] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0098] like Figure 1 As shown, an embodiment of the present invention provides an optimization scheduling method for grid-connected distributed photovoltaic clusters, the method comprising at least the following steps:

[0099] Step S1: Acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster;

[0100] For step S1, in the present invention, the rated data of the cluster includes: the voltage rated value V of the cluster leading node n , the number of clusters M, the number of PV plants in the cluster N pv , weight coefficients ω1 and ω2 of the objective function;

[0101] The dominant node data of the above cluster includes: the voltage upper limit of the dominant node of the cluster The lower voltage limit of the cluster leader node

[0102] The node data after cluster equalization includes: the node equivalent susceptance g after cluster equalization clu.j , the total active power of each node after cluster equalization ∑P clu.j , the total reactive power of each node after cluster equalization ∑Q clu.j ;

[0103] The branch data after cluster equalization includes: branch resistance value r after cluster equalization clu.ij , branch reactance value x after cluster equalization clu.ij ;

[0104] The photovoltaic predicted power of the above clusters includes: The photovoltaic predicted power of each cluster

[0105] The above-mentioned rated data in the group includes: the number of nodes N in cluster m m ;

[0106] The node data in the above group includes: the equivalent susceptance g of the nodes in the group j , the active power P of the node load in the cluster load.j , reactive power Q of node load in the cluster load.j , the maximum active output of photovoltaic power of each node in the group Voltage upper limit of nodes in the cluster The voltage lower limit of the nodes in the cluster Parallel susceptance value of nodes in the cluster

[0107] The branch data in the above group include: the branch resistance value r ij , reactance value x of the branch within the group ij .

[0108] The objective function f of the inter-cluster coordination optimization scheduling model mentioned in the invention, the voltage deviation f1 of the cluster leading node, the abandoned optical power f2, the voltage value V of the cluster leading node m , the active output P of each cluster's photovoltaic power pv.m , the voltage value of node i after cluster equalization The voltage value of node j after cluster equalization Active power P of branch ij after cluster equalization clu.ij , the PV apparent power S of the nth cluster pv.n (the value is 1.1 times the photovoltaic active power of the cluster), the reactive power Q of the branch ij after the cluster is equalized clu.ij , the dominant node voltage value of the cluster V i.m , the square of the current value of branch ij after cluster equalization l clu.ij , the maximum square value of the current of branch ij after cluster equalization The reactive power output Q of each cluster's photovoltaic power pv.m , the objective function f of the autonomous optimization scheduling model within the group ′ , the square of the current value of the branch ij in the group l ij , the voltage value of node i in the group The voltage value of node j in the cluster Active power P of branch ij in the group ij 、Reactive power Q of branch ij in the group ij , the voltage value u of the branch ij in the group ij , the active output P of distributed photovoltaic power generation of node j in the cluster pv.j , the reactive power output Q of distributed photovoltaic power generation of node j in the cluster pv.j , the voltage value V of the i-th node in the group i , and the maximum square value of the current in the branch ij within the group The apparent power S of each photovoltaic power station in the cluster pv.j (The value is 1.1 times the active power of the photovoltaic power station at each node in the group); these data items are all unknown data items and are calculated in the subsequent process of solving the inter-group coordinated optimization scheduling model and the intra-group autonomous optimization scheduling model.

[0109] like Figure 2 As shown, the IEEE151 node distribution network is taken as an example for optimization scheduling;

[0110] First, photovoltaic power generation units are connected to nodes 6, 12, 14, 17, 22, 26, 31, 35, 37, 41, 44, 48, 52, 56, 59, 61, 71, 77, 80, 82, 88, 91, 96, 98, 100, 102, 107, 111, 114, 116, 120, 127, 128, 135, 138, 140, 147, 149, and 151. The total photovoltaic capacity is 9MW, the total active load of the feeder is 2.95MW, the total reactive load of the feeder is 1.42Mvar, the reference voltage is 10kV, and the voltage fluctuation range is [9.5, 10.5]kV.

[0111] Input the predicted power of each PV power station The distribution network is divided into clusters and the dominant nodes are selected through genetic algorithms to calculate the photovoltaic predicted power of each cluster. Table 1 and Table 2 are obtained:

[0112] Table 1 Cluster division and leading node selection

[0113]

[0114] Table 2 Photovoltaic power of each cluster

[0115]

[0116]

[0117] Step S2: Based on the cluster data, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, an inter-cluster coordinated optimization scheduling model is constructed, and the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint are constructed;

[0118] In a preferred embodiment, the inter-cluster coordination optimization scheduling model is specifically:

[0119] minf=ω1f1+ω2f2;

[0120]

[0121] Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.m Indicates the active output of photovoltaic power of each cluster.

[0122] In a preferred embodiment, the first power flow constraint is specifically:

[0123]

[0124] (P clu.ij ) 2 +(Q clu.ij ) 2 = l clu.ij u clu.ij ;

[0125] ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -r clu.ij l clu.ij )+g clu.j u clu.j ;

[0126] ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij );

[0127] in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization;

[0128] The first safety operation constraint is:

[0129]

[0130] Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization;

[0131] Cluster PV power constraints, specifically:

[0132]

[0133] Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.n represents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

[0134] For step S2, the inter-cluster coordinated optimization scheduling model is a mathematical model for scheduling photovoltaic power between clusters, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, while constructing the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint. Through the inter-cluster coordinated optimization scheduling model to achieve coordinated scheduling of photovoltaic power between clusters, photovoltaic power generation can be fully utilized, reduce the phenomenon of abandoned light, and improve the economy and reliability of power generation; through optimized scheduling, abandoned light power can also be minimized to avoid the situation where photovoltaic power generation cannot be absorbed due to exceeding the load demand; at the same time, the model can also reduce the voltage deviation of the dominant node, ensure the stable operation of the power system, and reduce the impact of voltage fluctuations on equipment and users.

[0135] To ensure safety, the inter-cluster coordinated optimization scheduling model constructs the first flow constraint and the first safe operation constraint. The first flow constraint is used to ensure that the flow calculation results of the power system meet the actual physical characteristics and electrical constraints, avoid problems such as overload and short circuit, and ensure the safe operation of the power system; the first safe operation constraint takes into account factors such as grid stability, voltage control, and equipment power limitation to prevent the power system from becoming unstable or even collapsing.

[0136] In addition, the inter-cluster coordinated optimization scheduling model also constructs cluster photovoltaic power constraints to ensure that the photovoltaic power generation power is within a controllable range, which helps to avoid problems such as power system instability or overload caused by photovoltaic power fluctuations, and at the same time considers the maximum available range of photovoltaic power to ensure the economy and reliability of system operation.

[0137] Step S3: Under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint, the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic are generated when the abandoned light power and the voltage deviation of the leading node are minimized;

[0138] For step S3, specifically, under the above-mentioned constraints, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, the particle swarm intelligent optimization algorithm is used to solve the inter-cluster coordination optimization scheduling model. The algorithm will find the optimal decision variable values ​​according to the set objective function and constraints, thereby obtaining the voltage deviation of the cluster's dominant node, the abandoned light power of the cluster's dominant node, the active power of the cluster's distributed photovoltaics, and the reactive power of the cluster's distributed photovoltaics.

[0139] Step S4: perform photovoltaic power collaborative scheduling among clusters according to the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaics, and the reactive power of the cluster distributed photovoltaics;

[0140] For step S4, after solving the inter-cluster coordinated optimization scheduling model in step S3, the voltage amplitude of each cluster's leading node can be obtained through the voltage deviation of the cluster's leading node. Then, each cluster is subjected to 2.2% abandonment and photovoltaic reactive power regulation. After regulation, the following can be obtained: Figure 3 The voltage amplitude comparison diagram of the cluster leading node is shown in , the voltage deviation and network loss table of the cluster leading node is shown in Table 3, and the output power table of each cluster before and after photovoltaic regulation is shown in Table 4.

[0141] Table 3 Voltage deviation and network loss of cluster leading node

[0142]

[0143] Table 4 Output power of each cluster before and after photovoltaic regulation

[0144]

[0145] Step S5: Based on the data within the group, with the goal of minimizing the network loss, an autonomous optimization scheduling model within the group is constructed, and the second power flow constraint, the power constraint of the photovoltaic power station within the group, the second safe operation constraint, and the inter-group and intra-group collaborative control constraint are constructed;

[0146] In a preferred embodiment, the autonomous optimization scheduling model within the group includes:

[0147]

[0148] Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, lij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

[0149] In a preferred embodiment, the second power flow constraint is specifically:

[0150]

[0151] (P ij ) 2 +(Q ij ) 2 = l ij u ij ;

[0152] -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ;

[0153]

[0154] in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.j represents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group;

[0155] The power constraints of the photovoltaic power stations within the cluster are as follows:

[0156]

[0157] Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group;

[0158] The second safety operation constraint is as follows:

[0159]

[0160] Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group;

[0161] Inter-group and intra-group collaborative control constraints are as follows:

[0162]

[0163] For step S5, the intra-cluster autonomous optimization scheduling model is a mathematical model for optimizing the power scheduling of the photovoltaic power station in the cluster, with the goal of minimizing network loss, and at the same time constructing the second power flow constraint, the photovoltaic power station power constraint in the cluster, the second safe operation constraint, and the inter-cluster and intra-cluster collaborative control constraint. The intra-cluster autonomous optimization scheduling model is used to achieve coordinated scheduling of the photovoltaic power station power in the cluster.

[0164] In addition, the various constraints in the model can ensure the safe and stable operation of the system, avoid problems such as power flow overload and voltage deviation, and take into account the power limit of the photovoltaic power station to avoid negative impact on the power system. Finally, the inter-group-intra-group collaborative control constraints can ensure the collaborative control between the photovoltaic power stations within the group and between groups to achieve efficient operation of the entire power system.

[0165] Step S6: Under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station in the group, the second safe operation constraint, and the inter-group and intra-group collaborative control constraint, generate the voltage value of each node in the group, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station when the network loss is minimized;

[0166] For step S6, specifically, under the above-mentioned constraints, with the goal of minimizing network loss, the particle swarm intelligent optimization algorithm is used to solve the autonomous optimization scheduling model within the group. The algorithm will find the optimal decision variable value according to the set objective function and constraints, thereby obtaining the voltage value of each node in the group, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0167] Step S7: Coordinated scheduling of photovoltaic power within the group is performed according to the voltage value of each node in the group, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0168] For step S7, after solving the autonomous optimization scheduling model in the cluster in step S6, the voltage value of each node in the cluster is obtained. Then, each node in the cluster is subjected to 2.2% abandonment and photovoltaic reactive power regulation. After regulation, the following can be obtained: Figure 4 The voltage amplitude comparison diagram of the nodes in the group is shown in FIG, the voltage deviation and network loss table of the nodes in the group are shown in Table 5, and the output power table of each photovoltaic group before and after regulation is shown in Table 6.

[0169] Table 5. Voltage deviation and network loss of nodes within the group

[0170]

[0171] Table 6 Output power of each photovoltaic group before and after regulation

[0172]

[0173]

[0174] like Figure 5 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0175] An embodiment of the present invention provides an optimization scheduling device for grid-connected distributed photovoltaic clusters, comprising: a data acquisition module, an inter-cluster model and constraint condition construction module, an inter-cluster model solving module, an inter-cluster collaborative scheduling module, an intra-cluster model and constraint condition construction module, an intra-cluster model solving module, and an intra-cluster collaborative scheduling module;

[0176] A data acquisition module is used to acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster;

[0177] The inter-cluster model and constraint condition construction module is used to build an inter-cluster coordinated optimization scheduling model based on cluster data, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, and to build the first power flow constraint, the first safe operation constraint, and the cluster photovoltaic power constraint;

[0178] The inter-cluster model solving module is used to generate the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaics and the reactive power of the cluster distributed photovoltaics when the abandoned light power and the voltage deviation of the leading node are minimized under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint;

[0179] The inter-cluster collaborative scheduling module is used to perform inter-cluster photovoltaic power collaborative scheduling based on the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic, and the reactive power of the cluster distributed photovoltaic;

[0180] The intra-group model and constraint condition construction module is used to build an intra-group autonomous optimization scheduling model based on intra-group data with the goal of minimizing network loss, and to build the second power flow constraint, intra-group photovoltaic power station power constraint, second safe operation constraint, and inter-group and intra-group collaborative control constraint;

[0181] The intra-cluster model solving module is used to generate the voltage value of each node in the cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station when the network loss is minimized under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station in the cluster, the second safe operation constraint, and the inter-cluster and intra-cluster collaborative control constraint;

[0182] The intra-cluster collaborative scheduling module is used to perform intra-cluster photovoltaic power collaborative scheduling based on the voltage value of each node in the cluster, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station.

[0183] In a preferred embodiment, the inter-cluster coordination optimization scheduling model is specifically:

[0184] minf=ω1f1+ω2f2;

[0185]

[0186] Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.m Indicates the active output of photovoltaic power of each cluster.

[0187] In a preferred embodiment, the first power flow constraint is specifically:

[0188]

[0189] (P clu.ij ) 2 +(Q clu.ij ) 2 = l clu.ij u clu.ij ;

[0190] ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -r clu.ij l clu.ij )+g clu.j u clu.j ;

[0191] ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij );

[0192] in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization;

[0193] The first safety operation constraint is:

[0194]

[0195] Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization;

[0196] Cluster PV power constraints, specifically:

[0197]

[0198] Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.n represents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

[0199] In a preferred embodiment, the autonomous optimization scheduling model within the group includes:

[0200]

[0201] Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, l ij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

[0202] In a preferred embodiment, the second power flow constraint is specifically:

[0203]

[0204] (P ij ) 2 +(Q ij ) 2 = l ij u ij ;

[0205] -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ;

[0206]

[0207] in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.j represents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group;

[0208] The power constraints of the photovoltaic power stations within the cluster are as follows:

[0209]

[0210] Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group;

[0211] The second safety operation constraint is as follows:

[0212]

[0213] Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group;

[0214] Inter-group and intra-group collaborative control constraints are as follows:

[0215]

[0216] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention, which can implement the distributed photovoltaic cluster grid-connected optimization scheduling method provided by any one of the above-mentioned method item embodiments of the present invention.

[0217] It should be noted that the device embodiments described above are merely illustrative, and 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 accompanying 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. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0218] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimization scheduling method for distributed photovoltaic cluster grid connection, characterized in that: include: Acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; the intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster; According to the cluster data, with the goal of minimizing abandoned light power and dominant node voltage deviation, an inter-cluster coordinated optimization scheduling model is constructed, and the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint are constructed; Under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint, the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic are generated when the abandoned light power and the voltage deviation of the leading node are minimized; Performing photovoltaic power collaborative scheduling among clusters according to the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaics, and the reactive power of the cluster distributed photovoltaics; Based on the intra-group data, with the goal of minimizing network loss, an intra-group autonomous optimization scheduling model is constructed, and a second power flow constraint, an intra-group photovoltaic power station power constraint, a second safe operation constraint, and an inter-group and intra-group collaborative control constraint are constructed; Under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station within the group, the second safe operation constraint, and the inter-group and intra-group collaborative control constraint, the voltage value of each node in the group, the active power of each photovoltaic power station, and the reactive power of each photovoltaic power station are generated when the network loss is minimized; Photovoltaic power collaborative scheduling within the group is performed according to the voltage value of each node in the group, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station.

2. The distributed photovoltaic cluster grid-connected optimization scheduling method according to claim 1 is characterized in that: The inter-cluster coordination optimization scheduling model is specifically: minf=ω1f1+ω2f2; Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.m Indicates the active output of photovoltaic power of each cluster.

3. The distributed photovoltaic cluster grid-connected optimization scheduling method according to claim 2 is characterized in that: The first power flow constraint is specifically: (P clu.ij ) 2 +(Q clu.ij ) 2 =l clu.ij u clu.ij ; ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -r clu.ij l clu.ij )+g clu.j u clu.j ; ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij ); in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization; The first safety operation constraint is specifically: Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization; The cluster photovoltaic power constraint is specifically: Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.n represents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

4. The distributed photovoltaic cluster grid-connected optimization scheduling method according to claim 3 is characterized in that: The intra-group autonomous optimization scheduling model includes: Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, l ij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

5. The distributed photovoltaic cluster grid-connected optimization scheduling method according to claim 4 is characterized in that: The second power flow constraint is specifically: (P ij ) 2 +(Q ij ) 2 =l ij u ij ; -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ; in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.j represents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group; The power constraints of the photovoltaic power stations within the group are specifically: Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group; The second safety operation constraint is specifically: Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group; The inter-group and intra-group collaborative control constraints are specifically:

6. An optimization dispatching device for distributed photovoltaic cluster grid connection, characterized in that: include: Data acquisition module, inter-cluster model and constraint condition construction module, inter-cluster model solving module, inter-cluster collaborative scheduling module, intra-cluster model and constraint condition construction module, intra-cluster model solving module, and intra-cluster collaborative scheduling module; The data acquisition module is used to acquire cluster data and intra-cluster data; wherein the cluster data includes: rated data of the cluster, dominant node data of the cluster, node data after cluster equalization, branch data after cluster equalization and photovoltaic predicted power of the cluster; the intra-cluster data includes: rated data within the cluster, node data within the cluster and branch data within the cluster; The inter-cluster model and constraint condition construction module is used to construct an inter-cluster coordinated optimization scheduling model based on the cluster data, with the goal of minimizing the abandoned light power and the voltage deviation of the dominant node, and to construct the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint; The inter-cluster model solving module is used to generate the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaics and the reactive power of the cluster distributed photovoltaics when the abandoned light power and the voltage deviation of the leading node are minimized under the constraints of the first power flow constraint, the first safe operation constraint and the cluster photovoltaic power constraint; The inter-cluster collaborative scheduling module is used to perform inter-cluster photovoltaic power collaborative scheduling according to the voltage deviation of the cluster leading node, the abandoned light power of the cluster leading node, the active power of the cluster distributed photovoltaic and the reactive power of the cluster distributed photovoltaic; The intra-group model and constraint condition construction module is used to construct an intra-group autonomous optimization scheduling model based on the intra-group data with the goal of minimizing network loss, and to construct a second power flow constraint, an intra-group photovoltaic power station power constraint, a second safe operation constraint, and an inter-group and intra-group collaborative control constraint; The intra-group model solving module is used to generate the voltage value of each node in the group, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station when the network loss is minimized under the constraints of the second power flow constraint, the power constraint of the photovoltaic power station in the group, the second safe operation constraint and the inter-group and intra-group collaborative control constraint; The intra-group collaborative scheduling module is used to perform intra-group photovoltaic power collaborative scheduling according to the voltage value of each node in the group, the active power of each photovoltaic power station and the reactive power of each photovoltaic power station.

7. The distributed photovoltaic cluster grid-connected optimization scheduling device according to claim 6, characterized in that: The inter-cluster coordination optimization scheduling model is specifically: minf=ω1f1+ω2f2; Where f represents the objective function of the inter-cluster coordination optimization scheduling model, ω1 and ω2 represent the weight coefficients of the objective function, f1 represents the voltage deviation of the cluster leading node, f2 represents the abandoned optical power, M represents the number of clusters, V m Represents the voltage value of the cluster leading node, V n represents the voltage rating of the cluster leader node, Represents the predicted photovoltaic power of each cluster, P pv.m Indicates the active output of photovoltaic power of each cluster.

8. The distributed photovoltaic cluster grid-connected optimization scheduling device according to claim 7, characterized in that: The first power flow constraint is specifically: (P clu.ij ) 2 +(Q clu.ij ) 2 =l clu.ij u clu.ij ; ∑P clu.j =∑ k:j→k P clu.jk -∑ i:i→j (P clu.ij -r clu.ij l clu.ij )+g clu.j u clu.j ; ∑Q clu.j =∑ k:j→k Q clu.jk -∑ i:i→j (Q clu.ij -x clu.ij l clu.ij ); in, represents the voltage value of node i after cluster equalization, represents the voltage value of node j after cluster equalization, r clu.ij represents the resistance value of branch ij after cluster equalization, x clu.ij represents the reactance value of branch ij after cluster equalization, P clu.ij represents the active power of branch ij after cluster equalization, Q clu.ij represents the reactive power of branch ij after cluster equalization, l clu.ij represents the square of the current value of branch ij after cluster equalization, g clu.j represents the equivalent susceptance of node j after cluster equalization, ∑P clu.j Represents the total active power of each node after cluster equalization, ∑Q clu.j It represents the sum of reactive power of each node after cluster equalization; The first safety operation constraint is specifically: Among them, V i.m represents the voltage value of the dominant node of the cluster, represents the lower voltage limit of the cluster's leading node, represents the upper voltage limit of the dominant node of the cluster, l clu.ij represents the square of the current value of branch ij after cluster equalization, It represents the maximum square value of the current of branch ij after cluster equalization; The cluster photovoltaic power constraint is specifically: Among them, Q pv.m Represents the reactive output of photovoltaic power of each cluster, P pv.n represents the active output of the nth PV power station in the cluster, S pv.n Represents the apparent power of the nth PV power station in the cluster, N pv Indicates the number of PV plants in the cluster.

9. The distributed photovoltaic cluster grid-connected optimization scheduling device according to claim 8, characterized in that: The autonomous optimization scheduling model within the group is specifically as follows: Among them, f ′ represents the objective function of the autonomous optimization scheduling model within the group, l ij represents the square of the current value of the branch ij in the group, r ij Represents the resistance value of branch ij in the group, N m Indicates the number of nodes in cluster m.

10. The distributed photovoltaic cluster grid-connected optimization dispatching device according to claim 9, characterized in that: The second power flow constraint is specifically: (P ij ) 2 +(Q ij ) 2 =l ij u ij ; -P load.j +P pv.j =∑ k:j→k P jk -∑ i:i→j (P ij -r ij l ij )+g j u j ; in, represents the voltage value of node i in the group, represents the voltage value of node j in the cluster, r ij represents the resistance value of branch ij within the group, x ij represents the reactance value of the branch ij in the group, P ij represents the active power of branch ij in the group, Q ij represents the reactive power of branch ij in the group, l ij represents the square of the current value of the branch ij in the group, u ij represents the voltage value of branch ij in the group, P load.j represents the active power of the load of node j in the cluster, P pv.j represents the active power output of the jth PV power station in the group, g j represents the equivalent susceptance of node j in the cluster, Q load.j represents the reactive power of the load of node j in the cluster, represents the parallel susceptance value of node j in the cluster, Q pv.j represents the reactive power output of the jth PV power station in the group; The power constraints of the photovoltaic power stations within the group are specifically: Among them, P pv.j represents the active output of the jth PV power station in the group, represents the maximum active output of the jth PV power station in the group, Q pv.j represents the reactive power output of the jth PV power station in the group, S pv.j represents the apparent power of the jth PV plant in the group; The second safety operation constraint is specifically: Among them, V i represents the voltage value of the i-th node in the group, Indicates the voltage lower limit of the nodes in the group, Indicates the voltage upper limit of the nodes in the cluster, It represents the maximum square value of the current of branch ij in the group; The inter-group and intra-group collaborative control constraints are specifically:

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