CN115776139A - Distributed photovoltaic layered group regulation and group control method and system - Google Patents
Distributed photovoltaic layered group regulation and group control method and system Download PDFInfo
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Abstract
The invention provides a distributed photovoltaic hierarchical group regulation and group control method and a system, wherein a cluster control platform receives distributed photovoltaic information acquired by data acquisition equipment, collects and integrates the distributed photovoltaic information and uploads the collected distributed photovoltaic information to a cloud regulation and control center; the cloud regulation and control center integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the purposes that the node voltage in the whole region is not out of limit and the system network loss is minimum, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and realizes inter-cluster coordination; and after receiving the integral active and reactive power output adjusting instruction of the cluster, the cluster control platform distributes active power output and reactive power output according to the capacity of each photovoltaic power station in the cluster control platform.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a distributed photovoltaic layered group regulation and group control method and system.
Background
With the continuous promotion of the 'double carbon' strategy and the construction process of novel power systems in China, the grid connection proportion of distributed power supplies such as photovoltaic power generation and wind power generation is increased continuously. The output of the distributed power supply has strong randomness and intermittence, the distributed power supply is often connected into a power distribution network in a high-density and dispersed mode, the characteristics of point-multiple-surface, wide-permeability and the like are presented, and the problems of power flow return, voltage fluctuation and the like caused by disordered grid connection of large-scale distributed power supplies require the active power distribution network to effectively regulate and control the distributed power supply. When the traditional centralized scheduling method is applied to the access condition of a large-scale distributed power supply, the problems of complex control object, huge communication data and incapability of ensuring reliability exist, and the traditional centralized scheduling method cannot adapt to coordination optimization of a large number of power supply points in an area. Aiming at the characteristic that distributed power sources of a Henan power grid mainly adopt photovoltaic, research on distributed photovoltaic group regulation and group control strategies is developed, the problems of node voltage out-of-limit, tidal current fluctuation and the like caused by large-scale distributed photovoltaic grid connection can be effectively solved, high-quality, economical and green electric energy is provided for users, distributed photovoltaic controlled by a aggregator is maximally consumed on site and flexibly and friendly grid connection is facilitated, and the reliability and economy of operation of an active power distribution network are improved.
Aiming at the problem of safe and economic operation of a power distribution network caused by large-scale distributed photovoltaic grid connection, scholars at home and abroad adopt various optimization control strategies to regulate and control distributed photovoltaic output so as to guarantee voltage stability, maximum photovoltaic on-site consumption and reduce network loss. Teng Deyun and the like, in a multi-objective reactive power optimization scheduling construction method considering that a plurality of distributed power supplies are connected to a power distribution network, an optimization model taking the minimum active loss and the minimum node voltage deviation as objective functions and taking the reactive power output of capacitors and the distributed power supplies as controlled quantities is solved by utilizing a Whale Optimization Algorithm (WOA), and the effects of the optimization model on the aspects of improving the voltage quality, reducing the network loss and the like are verified through calculation of a typical system. Zhang Jinyuan in the distributed power optimization scheduling strategy based on multi-agent deep reinforcement learning, the distribution network containing distributed power is divided based on a multi-agent deep reinforcement strength learning method, an optimization model of each area is constructed with the lowest daily operation cost as a target, the optimization model of each area is trained by using a near-end strategy optimization (PPO) algorithm, and the capability of the optimization model of each area for dealing with source load randomness and the capability of improving the economy of area collaborative operation are verified. Li Peng, etc. in the distributed photovoltaic cluster hierarchical multi-mode reactive power control strategy, a double-layer control strategy comprising a cluster layer and an inverter layer is proposed, wherein the cluster layer realizes global optimization by adjusting a reactive curve set value in a photovoltaic inverter; and the inverter layer automatically realizes the mode switching of the inverter according to the voltage of the grid-connected point and performs reactive power output according to the set reactive power curves in different modes. Yao Hongmin and the like, in the research of distribution network absorption capacity simulation and voltage control strategies under high photovoltaic permeability, photovoltaic distribution, photovoltaic output randomness and load randomness of each node are fully considered by using a Monte Carlo method, the maximum photovoltaic absorption capacity of a system is calculated, and a voltage control strategy of coordinating photovoltaic reactive power first and then coordinating photovoltaic active power is provided. Wang Yongjie and the like give a photovoltaic robust interval scheduling mode in active and reactive power coordinated active power distribution network robust voltage control, namely, upper and lower limits of a power interval allowed to be generated by a photovoltaic power station are obtained according to load prediction and safety constraint optimization, and a distributed photovoltaic active and reactive power coordinated control strategy taking minimum network loss and minimum light abandoning cost as objective functions is constructed.
However, the above documents mostly focus on a distributed photovoltaic optimization control strategy in a certain area, and research on a group regulation group control architecture and an inter-group coordination control strategy is less for large-scale distributed power grid connection.
In order to solve the above problems, people are always seeking an ideal technical solution.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a distributed photovoltaic layered group regulation and group control method and system, which can effectively solve the problems of node voltage out-of-limit, tide return, large controllable variable and information quantity and the like caused by a large number of distributed photovoltaic accesses.
In order to achieve the purpose, the invention adopts the technical scheme that: a distributed photovoltaic layered group regulation and control method comprises the following steps:
the cluster control platform receives distributed photovoltaic information acquired by the data acquisition equipment, collects and integrates the distributed photovoltaic information and uploads the collected distributed photovoltaic information to the cloud regulation and control center;
the cloud regulation and control center integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the purposes that the node voltage in the whole region is not out of limit and the system network loss is minimum, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and realizes inter-cluster coordination;
after receiving the integral active and reactive power output adjusting instruction of the cluster, the cluster control platform distributes active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster to realize the autonomy in the cluster;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power of the distributed photovoltaic of the cluster m; p is pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s. the pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic plants within the cluster m.
Preferably, the inter-group coordination optimization steps are as follows:
on the basis of cluster division, selecting a leading node of each cluster, wherein the leading node can sensitively sense active and reactive changes of other nodes in the cluster;
establishing an inter-cluster coordination optimization model by taking minimum voltage deviation of cluster leading nodes and minimum system network loss in a master station area as inter-cluster control targets and taking power flow constraint, cluster power constraint and operation safety constraint as inter-cluster constraint conditions;
and solving the inter-cluster coordination optimization model by using a particle swarm algorithm to obtain the whole active and reactive power output scheme of each cluster.
Preferably, the autonomous optimization within the cluster comprises the following steps:
establishing an intra-cluster autonomous optimization model by taking safe operation of voltage of nodes in a cluster, minimum loss of the cluster network and maximum local consumption of distributed photovoltaic as intra-cluster autonomous targets and taking power flow constraint, photovoltaic power station power constraint, safe operation constraint and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the autonomous optimization model in the cluster by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the cluster.
The invention also provides a distributed photovoltaic hierarchical group regulation and control system, which comprises
The data acquisition equipment is used for acquiring distributed photovoltaic information of the terminal equipment in the cluster;
the cluster control platform is connected with the data acquisition equipment and used for receiving the distributed photovoltaic information acquired by the data acquisition equipment, summarizing and integrating the distributed photovoltaic information and uploading the summarized photovoltaic information to the cloud regulation and control center; after receiving the integral active and reactive power output adjusting instruction of the cluster, distributing active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster, and realizing intra-cluster autonomy;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m; p pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic power stations in the cluster m;
the cloud control center is connected with the cluster control platforms, integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the node voltage in the whole area not to exceed the limit and the system network loss as the minimum target, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and achieves inter-cluster coordination.
Based on the above, after the cluster control platform receives the overall active reactive power output regulation instruction of the cluster, the distributed photovoltaic information uploaded by the data acquisition equipment can be analyzed, so that safe operation of the voltage of nodes in the cluster, minimum loss of the cluster network and maximum local consumption of the distributed photovoltaic are realized as intra-cluster autonomous targets, and an intra-cluster autonomous optimization model is constructed by taking power flow constraint, photovoltaic power station power constraint, safe operation constraint and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the intra-group autonomy optimization model by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the group, generating an active and reactive power output regulation instruction in the group and issuing the active and reactive power output regulation instruction to each terminal device to realize intra-group autonomy.
Compared with the prior art, the invention has outstanding substantive characteristics and remarkable progress, particularly,
(1) The distributed photovoltaic group regulation and group control architecture comprises a global optimization layer, a cluster optimization layer and a terminal equipment layer, and can effectively solve the problems of complex regulation and control objects, huge communication data and the like caused by the grid connection of a large number of distributed power grids; the global optimization layer and the cluster optimization layer respectively realize inter-group coordination and intra-group autonomous optimization strategies to realize flexible and safe grid connection of distributed photovoltaic, and realize safe and economic operation of a system and maximum consumption of the distributed photovoltaic.
(2) According to the inter-group coordination control method of the global optimization layer, the minimum voltage deviation and the minimum network loss of the leading node are taken as optimization targets, and the safe and economic operation of the area controlled by the cloud master station is realized by coordinating the overall output of each cluster; the intra-cluster autonomous control method of the cluster optimization layer takes the minimum node voltage deviation and the minimum cluster network loss in the cluster as optimization targets, and realizes safe and economic operation in the cluster and the maximum consumption of distributed photovoltaics by adjusting the output of each photovoltaic power station and reactive power compensation device in the cluster.
(3) The inter-group coordination and intra-group autonomous optimization strategy provided by the invention has good effects on reducing node voltage deviation and reducing network loss in different scenes, and particularly can effectively reduce the system network loss and stabilize the node voltage deviation in a node voltage safe operation scene; under the node voltage out-of-limit scene, the node voltage deviation can be effectively reduced, and the system can safely operate.
Drawings
Fig. 1 is a structural diagram of a distributed photovoltaic hierarchical group regulation and control system according to embodiment 1 of the present invention.
Fig. 2 is a schematic flow chart of the distributed photovoltaic hierarchical group regulation and control method according to embodiment 1 of the present invention.
Fig. 3 is a schematic flow chart of a distributed photovoltaic hierarchical group regulation and control method according to embodiment 2 of the present invention.
Fig. 4 is a schematic diagram of an improved IEEE33 node group coordination simulation.
Fig. 5 is a graph of voltage amplitudes of the nodes before and after the control when the leading node voltage operates within the safety range.
FIG. 6 is a graph of voltage amplitudes of the nodes before and after the dominant node voltage is over.
Fig. 7 is a graph of voltage amplitudes of each node before and after the regulation when the voltage of the dominant node goes lower.
Fig. 8 is a schematic diagram of improved IEEE33 node intra-cluster autonomous simulation.
FIG. 9 is a graph of voltage magnitudes at each node before and after optimization when the node voltage is operating within a safe range.
Fig. 10 is a graph of voltage magnitudes of each node before and after optimization when the node voltage slightly exceeds the upper limit.
FIG. 11 is a graph of voltage amplitudes of nodes before and after optimization when the node voltage severely exceeds the upper limit.
Fig. 12 is a graph of voltage magnitudes of each node before and after optimization when the node voltage slightly goes lower.
Fig. 13 is a graph of voltage amplitudes of respective nodes before and after optimization when the node voltage severely gets lower.
Detailed Description
The technical solution of the present invention is further described in detail by the following embodiments.
Example 1
As shown in fig. 1, the present embodiment provides a distributed photovoltaic hierarchical group regulation and control system, including:
the data acquisition equipment is used for acquiring distributed photovoltaic information of the terminal equipment in the cluster;
the cluster control platform is connected with the data acquisition equipment and used for receiving the distributed photovoltaic information acquired by the data acquisition equipment, summarizing and integrating the distributed photovoltaic information and uploading the summarized photovoltaic information to the cloud regulation and control center; after receiving the integral active and reactive power output adjusting instruction of the cluster, distributing active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster, and realizing intra-cluster autonomy;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m; p pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic power stations in the cluster m;
the cloud control center is connected with the cluster control platforms, integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the node voltage in the whole area not to exceed the limit and the system network loss as the minimum target, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and achieves inter-cluster coordination.
The distributed photovoltaic group regulation and group control architecture comprises a global optimization layer, a cluster optimization layer and a terminal equipment layer, and can effectively solve the problems of complex regulation and control objects, huge communication data and the like caused by the grid connection of a large number of distributed power grids; the global optimization layer and the cluster optimization layer respectively realize inter-cluster coordination and intra-cluster autonomous optimization strategies so as to realize flexible and safe grid connection of distributed photovoltaic, and realize safe and economic operation of a system and maximum consumption of the distributed photovoltaic.
In specific implementation, the distributed photovoltaic hierarchical group regulation and group control system executes the following distributed photovoltaic hierarchical group regulation and group control method, as shown in fig. 2, including the following steps:
the cluster control platform receives distributed photovoltaic information acquired by the data acquisition equipment, gathers and integrates the distributed photovoltaic information and uploads the gathered and integrated photovoltaic information to the cloud regulation and control center;
the cloud regulation and control center integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the purposes that the node voltage in the whole region is not out of limit and the system network loss is minimum, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and realizes inter-cluster coordination;
after receiving the integral active and reactive power output adjusting instruction of the cluster, the cluster control platform distributes active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster to realize the autonomy in the cluster;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m; p pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic plants within the cluster m.
In specific implementation, the inter-group coordination optimization steps are as follows:
(1) On the basis of cluster division, selecting a leading node of each cluster, wherein the leading node can sensitively sense active and reactive changes of other nodes in the cluster;
the selection steps of the leading node are as follows:
computing observability S of node n Gn ；
Calculating controllability S of node n Kn ；
Defining the observable and controllable comprehensive indexes Sn of the nodes n as follows: s n ＝δ 1 S Gn +δ 2 S Kn Wherein, delta 1 And delta 2 Respectively representing observability indexes anda weight coefficient of the controllability index;
selecting a leading node on the basis of eliminating the voltage deviation of the node n to expect the minimum voltage deviation of other nodes in the cluster;
in addition, the voltage level of the nodes in the area cannot be fully represented due to too few dominant nodes, and monitoring objects in the cloud control center are increased due to too much dominant nodes, so that the control is complicated ， Therefore, the number of dominant nodes does not generally exceed the area;
(2) Establishing an inter-cluster coordination optimization model by taking minimum voltage deviation of cluster leading nodes and minimum system network loss in a master station area as inter-cluster control targets and taking power flow constraint, cluster power constraint and operation safety constraint as inter-cluster constraint conditions;
specifically, the inter-group control targets are: minf = ω 1 f 1 +ω 2 f 2 ；
Wherein f is 1 Is the voltage deviation of the dominant node of the cluster,
f 2 in order to reduce the network loss of the system,
P loss,m the loss of the network of cluster m is indicated,
n represents the total number of nodes in the cluster; l represents a node set connected with the node n; i.e. i nl Representing the current, r, of the branch connecting node n to node l nl Representing the branch resistance; j represents the number of dominant nodes; u shape j Representing the voltage amplitude of the leading node j, U for convenient analysis and calculation j Expressed by a per unit value; u shape 0 Representing the reference value of the node voltage, U 0 1p.u.; m represents the number of clusters participating in regulation;
ω 1 and omega 2 Respectively represents f 1 And f 2 When the voltage of the leading node is not out of limit and the regulating system runs safely, the weight coefficient of omega 1 Less than omega 2 (ii) a When in useThe dominant node voltage is out of limit, ω 1 Greater than omega 2 。
Specifically, the power flow constraint condition is as follows:
wherein u is i And u j Respectively representing the node voltages of the node i and the node j; i all right angle ij Representing the current of a branch i-j with a node i as a starting point and a node j as an end point; p ij And Q ij Respectively representing the active and reactive power flowing through the branches i-j; r is ij And x ij Respectively representing the resistance and reactance of the branches i-j; p j And Q j Net active and reactive loads injected into node j are represented respectively; jl represents a branch set which takes the node j as a starting point and the node l as an end point and is connected with the node j; p jl And Q jl Respectively representing the active and reactive power flowing through the branch j-l;
specifically, the cluster power constraint condition is as follows:
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m;
representing the maximum value of the distributed photovoltaic active output of the cluster m;
and
respectively representing the upper limit and the lower limit of the distributed photovoltaic reactive power output of the cluster m;
specifically, the operation safety constraint condition is
Wherein the content of the first and second substances,
and
respectively representing the upper limit and the lower limit of the voltage of the dominant node; in the embodiment, the operating range of the node voltage is 0.95-1.05p.u.;
(3) And solving the inter-cluster coordination optimization model by using a particle swarm algorithm to obtain an integral active and reactive power output scheme of each cluster, and generating an integral active and reactive power output instruction of each cluster according to the integral active and reactive power output scheme of each cluster and sending the instruction to the cluster control platform of each cluster.
The method for coordinating and controlling the groups of the global optimization layer provided by the invention has the advantages that the goal of optimizing the minimum voltage deviation and the minimum network loss of the leading node is taken as an optimization goal, and the safe and economic operation of the area controlled by the cloud master station is realized by coordinating the overall output of each cluster.
Example 2
This example differs from example 1 in that: as shown in fig. 3, after receiving the overall active reactive power output adjustment instruction of the cluster, the cluster control platform may further analyze distributed photovoltaic information uploaded by the data acquisition device to achieve safe operation of node voltage inside the cluster, minimum loss of the cluster network, and maximum local consumption of distributed photovoltaic as an intra-cluster autonomous target, and construct an intra-cluster autonomous optimization model with tidal current constraint, photovoltaic power station power constraint, safe operation constraint, and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the intra-group autonomy optimization model by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the group, generating an active and reactive power output regulation instruction in the group and issuing the active and reactive power output regulation instruction to each terminal device to realize intra-group autonomy.
In specific implementation, the intra-cluster autonomous optimization method comprises the following steps:
establishing an intra-cluster autonomous optimization model by taking safe operation of voltage of nodes in a cluster, minimum loss of the cluster network and maximum local consumption of distributed photovoltaic as intra-cluster autonomous targets and taking power flow constraint, photovoltaic power station power constraint, safe operation constraint and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the autonomous optimization model in the cluster by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the cluster.
Specifically, the intra-group autonomous target is:
wherein f is 3 Is the voltage deviation of the node, f 4 In order to cluster the network loss,
n represents the total number of nodes in the cluster; u shape n Representing the voltage amplitude, U, of node n within the cluster 0 Representing a node voltage reference value; l represents an n-l branch set which takes the node n as a starting point and l as an end point and is connected with the node n; i.e. i nl Representing the current of the n-l branch; r is a radical of hydrogen nl Representing the resistance of the n-l branch;
when the node voltage operates in a safety range, the reactive power output of distributed photovoltaic power stations in the cluster is adjusted, so that the cluster network loss is minimum while the node voltage is in the safety range;
when the node voltage slightly exceeds the limit, the voltage of each node in the cluster can be recovered to operate within a safety range only by adjusting the reactive power output of the distributed photovoltaic power station in the cluster, so that the safe operation in the cluster is realized;
when the node voltage is seriously out of the upper limit, the voltage of each node in the cluster is recovered to be operated in a safety range by adjusting the reactive power output of the distributed photovoltaic power station in the cluster and reducing the active power output of the distributed photovoltaic power station;
when the node voltage is seriously lower than the lower limit, the voltage of each node in the cluster is recovered to be operated in a safety range by adjusting the reactive output of the distributed photovoltaic power station in the cluster and adjusting the reactive output of the reactive compensation device.
The maximum absorption of the distributed photovoltaic is realized mainly by adjusting the reactive output of the distributed photovoltaic firstly and not reducing the active power of the distributed photovoltaic as much as possible when the distributed photovoltaic output is regulated to realize the two aims of safe node voltage operation and minimum cluster network loss; when the node voltage is seriously out of limit and the safe operation of the node cannot be realized by adjusting the photovoltaic reactive power output, the reduction of the photovoltaic active power output is considered to realize the safe operation of the node voltage.
Specifically, the power flow constraint condition is as follows:
wherein u is i And u j Respectively representing the node voltages of the node i and the node j; i.e. i ij Representing the current of a branch i-j with a node i as a starting point and a node j as an end point; p is ij And Q ij Respectively representing the active and reactive power flowing through the branches i-j; r is ij And x ij Respectively representing the resistance and reactance of the branches i-j; p j And Q j Respectively representing net active and reactive loads injected into node j; jl represents a branch set which takes the node j as a starting point and the node l as an end point and is connected with the node j; p jl And Q jl Respectively representing the active and reactive power flowing through the branch j-l;
the power constraint conditions of the photovoltaic power station are as follows:
wherein the content of the first and second substances,
representing the maximum active power output of the nth photovoltaic power station, and representing the minimum power factor of the photovoltaic power station by cos theta; p is pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant;
the safe operation constraint conditions are as follows:
wherein, the first and the second end of the pipe are connected with each other,
and with
Respectively representing the upper and lower limits, U, of the node voltage in the cluster n Representing the magnitude of the voltage at node n, i ij Representing the current of the branch i-j connecting node i to node j,
representing the branch i-j maximum current limit;
the reactive power compensation device has the following operation constraint conditions:
wherein Q is SVC,n The reactive power output of the nth reactive power compensation device in the cluster is represented;
and
respectively representing the upper limit and the lower limit of the reactive power output of the reactive power compensation device.
Different from a traditional point-to-point centralized regulation and control mode, the distributed group regulation and control method provided by the embodiment is based on cluster division, a cloud regulation and control center does not need to master all node information and photovoltaic power station data in an area, only needs to monitor part of leading nodes of each cluster, and realizes the goal of global optimization through coordination among the clusters by regulating and controlling the integral active and reactive power output of the clusters; the cluster control platform monitors each photovoltaic power station, SVC and electricity load information in the cluster, and by controlling the output of the photovoltaic power stations and SVC in the cluster, the voltage out-of-limit is restrained, the maximum photovoltaic consumption is guaranteed, and the goal of autonomy in the cluster is achieved. The safe and economic operation of the power grid and the effective consumption of distributed photovoltaic are realized through inter-group coordination and intra-group autonomous double-layer control, and the electricity consumption cost of residential users is reduced.
Example 3
In this embodiment, the improved IEEE33 node system accessed by the distributed power supply is controlled by applying the distributed group tuning group control method provided in embodiment 2, and by using a multi-objective particle swarm optimization algorithm, an inter-group coordination optimization model of the distributed power supply is solved in a leading node voltage safe operation scene and an out-of-limit scene, so as to analyze and analyze the effects of inter-group coordination optimization strategies in different scenes on node voltage deviation suppression and system network loss reduction.
The network topology, the distributed photovoltaic access condition, the cluster division result and the leading node of the improved IEEE33 node typical system are shown in fig. 4 and table 1. In the system, 33 load nodes and 9 photovoltaic power nodes are arranged.
And comprehensively selecting the leading node according to the observability and the controllability, wherein the result is shown in the table 1.
Table 1 cluster leader node selection results
| Cluster numbering | 1 | 2 | 3 | 4 | 5 | 6 |
| |
21 | 24 | 32 | 11 | 14 | 17 |
Scene one: the leading node voltage operates in a safe range
Aiming at a scene that the node voltage operates in a safety range, the minimum deviation of the leading node voltage and the minimum network loss are taken as control targets, wherein the weight corresponding to the minimum objective function of the network loss is 0.7, and the weight corresponding to the minimum objective function of the leading node voltage deviation is 0.3; the reactive output of each cluster obtained by solving the inter-cluster coordination optimization model is shown in table 2.
TABLE 2 Regulation of front and rear output Power by Each Cluster
TABLE 3 Regulation and control of the front and rear output powers of the photovoltaic power stations
The voltage amplitudes of the nodes before and after the regulation are shown in fig. 5, and the node voltage deviation module values and the system network loss are shown in table 4.
It can be seen that the sum of the voltage deviation moduli of the leading node is reduced by 0.0394p.u, which is reduced by 37.24% compared with that before regulation; after the maximum deviation modulus value of the node voltage is optimized, the node voltage is reduced by 0.0242p.u., and compared with the node voltage before regulation, the node voltage is reduced by 52.27%; the sum of all node voltage deviation modular values is reduced by 0.2552p.u., and is reduced by 42.2% compared with the sum before regulation and control; the system network loss after regulation and control is reduced by 48.83kW, and compared with the system network loss before optimization, the system network loss is reduced by 49.55%.
TABLE 4 node voltage deviation modulus and loss
Scene two: the upper limit of the voltage of the leading node
In the scene, the weight corresponding to the minimum objective function of the network loss is 0.3, and the weight corresponding to the minimum objective function of the voltage deviation of the leading node is 0.7. The reactive power output of each cluster obtained by solving the inter-cluster coordination optimization model is shown in table 5. The intra-cluster autonomy is performed according to the capacity of each photovoltaic power station inside the photovoltaic power station, and the reactive power output of each photovoltaic power station in each cluster is obtained and is shown in table 6.
TABLE 5 Regulation of front and rear output Power by Each Cluster
TABLE 6 output power before and after regulation of each photovoltaic power station
The voltage amplitudes of the nodes before and after regulation are shown in fig. 6. It can be seen that, before regulation, the master node 17 of the cluster 6 goes beyond the upper limit, and emergency regulation is started. After regulation, the voltage of each node is operated in a safety range of 0.95-1.05p.u, and the out-of-limit cluster 6 (comprising three nodes of 16, 17 and 18, the voltage amplitudes of the three nodes before regulation are 1.0450p.u, 1.0503p.u and 1.0544p.u respectively, and the voltage reduction amplitudes of the three nodes before regulation are 1.0034p.u, 1.0033p.u and 1.0055p.u respectively after optimization) are obviously larger than those of other clusters, and the voltage of all the nodes of the cluster 6 is reduced to be below 1.01p.u from above 1.04p.u before regulation.
Table 7 shows the system node voltage deviation modulus and the network loss before and after the regulation, and it can be seen that the sum of the voltage deviation moduli of the main node after the regulation is 0.0214p.u., which is 0.1103 smaller than the sum of the voltage deviation moduli of the main node before the regulation, and the regulation is reduced by 83.75%; the maximum deviation module value of the node voltage after regulation is 0.0135p.u., which is reduced by 75.18% compared with that before regulation; the sum of voltage deviation module values of all nodes coordinated among groups is 0.1467p.u., which is 0.2936p.u. less than that before regulation, and the regulation is reduced by 66.68%; in the process of adjusting the reactive output of each cluster to enable the node voltage to operate in a safe range, the system grid loss is increased by 56.95kW, and is increased by about 26.48%.
TABLE 7 node voltage deviation modulus and loss
Scene three: lower limit of dominant node voltage
In a third scenario, the active power output of each photovoltaic power station is reduced, the inter-cluster control target is the minimum voltage deviation of the leading node of each cluster, and the minimum network loss is achieved. In this scenario, the weight corresponding to the minimum objective function of the network loss is 0.3, the weight corresponding to the minimum objective function of the voltage deviation of the leading node is 0.7, and the active and reactive outputs of each cluster after optimized regulation are shown in table 8.
TABLE 8 regulating front and rear output Power of each Cluster
According to the capacity distribution of each photovoltaic power station of the cluster, the reactive output of each photovoltaic power station is obtained through calculation and is shown in table 9.
TABLE 9 Regulation and control of the front and rear output powers of the photovoltaic power stations
The voltage amplitudes of the nodes before and after the regulation are shown in fig. 6, and it can be seen that the lower limit of the leading node 32 of the cluster 3 before the regulation is lower, the emergency regulation is started. After regulation, the voltage of each node is operated in a safety range of 0.95-1.05p.u.
Table 10 shows the system node voltage deviation mode values and the network loss conditions before and after the regulation, and it can be seen that the sum of the regulated master node voltage deviation mode values is 0.1175p.u., which is 0.086p.u. smaller than the sum of the regulated master node voltage deviation mode values before the regulation, and the regulation is reduced by 42.26%; the maximum deviation modulus of the node voltage before and after regulation is reduced by 0.0162p.u., and is reduced by 25.47% compared with that before regulation; the sum of all node voltage deviation modular values after regulation is 0.6603p.u., and is 0.3767p.u. smaller than that before regulation, and the sum of all node voltage deviation modular values is reduced by 36.33% through regulation; the system network loss is reduced by 45.72kW and 47.58%.
Voltage deviation modulus and loss of meter 10 node
(2) Intra-group autonomic simulation analysis
Due to the fact that the number of the nodes and the photovoltaic power stations in a single cluster is small after the cluster division is carried out, an improved IEEE33 node typical system is selected as one cluster to be analyzed. The network topology, distributed photovoltaic power Plants (PV) and reactive compensation devices (SVC) access information are shown in fig. 8. And solving the intra-group autonomous optimization model in the scenes of safe operation of node voltage, slight threshold crossing, serious threshold crossing and the like by adopting a multi-target particle swarm algorithm, and analyzing the effects of reducing the system grid loss and the node voltage deviation and maximally consuming the photovoltaic in situ in different scenes.
Scene one: the node voltage operates in a safe range
The active power output of each photovoltaic power station in the scene is shown in table 11. Aiming at the scene that the node voltage operates in the safety range, the reactive output of the photovoltaic power station is only adjusted by taking the minimum node voltage deviation and the minimum network loss as control targets, and the optimized reactive output of each photovoltaic is shown in table 11.
TABLE 11 photovoltaic power plant output power before and after optimization
The node voltages before and after the optimization are shown in fig. 9, and the node voltage deviation moduli and the system network loss are shown in table 12.
Table 12 node voltage deviation module value and network loss
It can be seen that the maximum deviation modulus of the node voltage after optimization is reduced to 0.0272p.u., which is reduced by 40.61% compared with that before optimization; the sum of the node voltage deviation modular values is reduced by 0.2096p.u after optimization, and is reduced by 34.66% compared with the sum before optimization; the system network loss after optimization is reduced by 41.76kW, which is reduced by 42.38% compared with that before optimization.
Scene two: the upper limit of the node voltage
(1) Node voltage mild over-limit scenario
For a scene that the node voltage is higher than the upper limit and the voltage can be operated in a safe range only by adjusting the reactive power, the active output of the photovoltaic power station and the reactive output of each optimized photovoltaic power station in the scene are given in table 13 by taking the minimum node voltage deviation and the minimum network loss as control targets.
Table 13 photovoltaic power station access information and output power before and after optimization
The node voltages before and after the optimization are shown in fig. 10, and the system node voltage deviation moduli and the network loss before and after the optimization are shown in table 14.
It can be seen that after optimization, the voltage of each node is below 1.02p.u, and the operation is in the safety range of 0.95-1.05p.u; after optimization, the maximum deviation modulus of the node voltage is reduced to 0.0167p.u., and is reduced by 69.53% compared with that before optimization; the sum of the voltage deviation module values of each node after optimization is 0.2157p.u., which is 0.2246p.u., smaller than that before optimization, and the adjustment of the sum of the voltage deviation module values is reduced by 51.01%; after the node voltage is operated in a safe range by optimizing each photovoltaic reactive power, the system grid loss is increased by 31.2kW, and is increased by 14.51% compared with that before optimization.
Table 14 node voltage deviation module value and network loss
(2) Node voltage severe over-limit scenario
Table 15 shows the active power output of the photovoltaic power plant in this scenario. Aiming at the scene that the node voltage is more serious when the upper limit is higher, the voltage operation is required to be realized in a safe range by reducing the active power, and the active output of 2 percent is reduced to the maximum extent by taking the minimum node voltage deviation and the minimum network loss as control targets. The active and reactive outputs of each photovoltaic after optimization are shown in table 15, the total active reduction amount of nine photovoltaic power stations is 47.51kW, and the reduction percentage is 0.75%.
The node voltages before and after the optimization are shown in fig. 11, and the system node voltage deviation moduli and the network loss before and after the optimization are shown in table 16.
It can be seen that after optimization, the voltage of each node is below 1.03p.u, and the operation is in a safety range of 0.95-1.05p.u; the maximum deviation module value of the node voltage is reduced from 0.0781p.u before optimization to 0.0272p.u after optimization; the sum of the voltage deviation modular values of all the nodes after optimization is 0.3201p.u., is less than 0.3634 before optimization, and the adjustment of the sum of the voltage deviation modular values is reduced by 53.17%. After the node voltage is operated in a safe range by adjusting each photovoltaic reactive power, the system grid loss is increased by 69.8kW, and is increased by about 22.90%.
Table 16 node voltage deviation module value and network loss
Scene three: lower limit of node voltage
(1) Node voltage mild lower-bound scenario
Table 17 shows the active output of the photovoltaic power stations in this scenario, and the active output of each photovoltaic power station decreases. For a scenario that the lower the node voltage is, and the voltage can be operated in a safe range by adjusting only the reactive power, the minimum node voltage deviation and the minimum network loss are also taken as control targets, and the reactive power output of each photovoltaic after optimization is shown in table 17.
TABLE 17 photovoltaic power plant output power before and after optimization
The node voltages before and after the optimization are shown in fig. 12, and the deviation moduli and the network loss of the system node voltages before and after the optimization are shown in table 18.
Table 18 node voltage deviation module value and network loss
It can be seen from the figure that before optimization, the nodes 29-33 cross the lower voltage limit, and after optimization, the voltages of the 5 nodes are all larger than 0.95p.u., and the operation is in a normal range, namely the voltage amplitude of each node is improved and is closer to 1p.u.; the maximum deviation module value of the node voltage is reduced from 0.0631p.u before optimization to 0.0468p.u after optimization; the sum of the node voltage deviation mode values after optimization is 0.3207p.u. less than that before optimization, and the adjustment of the sum of the node voltage deviation mode values is reduced by 30.93%; the system network loss is also reduced by 39.79kW, and compared with the system network loss before optimization, the system network loss is reduced by 41.41%.
(2) Node voltage severe lower bound scenario
Table 19 shows the photovoltaic power plant active power output in this scenario. Aiming at the situation that the lower limit of the node voltage is more serious, the voltage operation in a safety range needs to be realized by adjusting a reactive compensation device, and the control target is that the node voltage deviation is minimum and the network loss is minimum. The reactive power compensation device has access nodes of 13 and 31 and capacity of 500kV & A. The active and reactive outputs of each photovoltaic after the optimization adjustment are shown in table 19. The two reactive power compensation devices before optimization are 0, and the output after optimization is 494.9kV & A and 499.1kV & A respectively.
The node voltages before and after the optimization are shown in fig. 13. The system node voltage deviation moduli and the network loss before and after the optimization are shown in table 20.
It can be seen that after optimization, all node voltages are greater than 0.95p.u., and the operation is in a normal range, namely, the voltage amplitudes of all nodes are improved and are closer to 1p.u.; the maximum deviation module value of the node voltage is reduced from 0.0879p.u before optimization to 0.0482p.u after optimization; the sum of the node voltage deviation module values after optimization is 0.7156 smaller than that before optimization, and the adjustment of the sum of the node voltage deviation module values is reduced by 47.04%; the system network loss is also reduced by 74.92kW, and compared with the system network loss before optimization, the system network loss is reduced by 57.58%.
Table 20 node voltage deviation module value and network loss
Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present invention and not to limit the same; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (10)
1. A distributed photovoltaic layered group regulation and group control method is characterized by comprising the following steps:
the cluster control platform receives distributed photovoltaic information acquired by the data acquisition equipment, collects and integrates the distributed photovoltaic information and uploads the collected distributed photovoltaic information to the cloud regulation and control center;
the cloud regulation and control center integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the purposes that the node voltage in the whole region is not out of limit and the system network loss is minimum, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and realizes inter-cluster coordination;
after receiving the integral active and reactive power output adjusting instruction of the cluster, the cluster control platform distributes active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster to realize the autonomy in the cluster;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m; p pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic plants within the cluster m.
2. The distributed photovoltaic hierarchical group regulation and group control method according to claim 1, characterized in that: after the cluster control platform receives the integral active reactive power output regulation instruction of the cluster, distributed photovoltaic information uploaded by the data acquisition equipment can be analyzed, so that safe operation of voltage of nodes in the cluster, minimum loss of a cluster network and maximum local consumption of distributed photovoltaic are realized as an intra-cluster autonomous target, and an intra-cluster autonomous optimization model is constructed by taking power flow constraint, photovoltaic power station power constraint, safe operation constraint and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the intra-cluster autonomy optimization model by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the cluster, generating an active and reactive power output regulation instruction in the cluster, and issuing the active and reactive power output regulation instruction to each terminal device to realize intra-cluster autonomy.
3. The distributed photovoltaic hierarchical group regulation and group control method according to claim 2, wherein the intra-group autonomous targets are:
wherein, f 3 Is the voltage deviation of the node, f 4 In order to cluster the network loss,
n represents the total number of nodes in the cluster; u shape n Representing the voltage amplitude, U, of node n within the cluster 0 Representing a node voltage reference value; l represents an n-l branch set which takes the node n as a starting point and l as an end point and is connected with the node n; i.e. i nl Representing the current of the n-l branch; r is a radical of hydrogen nl Representing the resistance of the n-l branch;
when the node voltage operates in a safety range, the reactive power output of distributed photovoltaic power stations in the cluster is adjusted, so that the cluster network loss is minimum while the node voltage is in the safety range;
when the node voltage slightly exceeds the limit, the voltage of each node in the cluster can be recovered to operate within a safety range only by adjusting the reactive power output of the distributed photovoltaic power station in the cluster, so that the safe operation in the cluster is realized;
when the node voltage is seriously out of the upper limit, the voltage of each node in the cluster is recovered to be operated in a safety range by adjusting the reactive power output of the distributed photovoltaic power station in the cluster and reducing the active power output of the distributed photovoltaic power station;
when the node voltage is seriously lower than the lower limit, the voltage of each node in the cluster is recovered to be operated in a safety range by adjusting the reactive output of the distributed photovoltaic power station in the cluster and adjusting the reactive output of the reactive compensation device.
4. The distributed photovoltaic layered group regulation and group control method according to claim 2, wherein the power flow constraint condition is:
wherein u is i And u j Respectively representing the node voltages of the node i and the node j; i.e. i ij Representing the current of a branch i-j with a node i as a starting point and a node j as an end point; p ij And Q ij Respectively representing the active and reactive power flowing through the branches i-j; r is ij And x ij Respectively representing the resistance and reactance of the branches i-j; p j And Q j Respectively representing net active and reactive loads injected into node j; jl represents a branch set which takes the node j as a starting point and the node l as an end point and is connected with the node j; p jl And Q jl Respectively representing the active and reactive power flowing through the branch j-l;
the power constraint conditions of the photovoltaic power station are as follows:
wherein the content of the first and second substances,
denotes the nth photovoltaicThe maximum active power output of the power station, cos theta represents the minimum power factor of the photovoltaic power station; p pv,n And Q pv,n Respectively representing active power output and reactive power output of the nth photovoltaic power station in the cluster m; s. the pvn Representing the capacity of the nth photovoltaic power plant;
the safe operation constraint conditions are as follows:
wherein the content of the first and second substances,
and
respectively representing the upper and lower limits, U, of the node voltage in the cluster n Representing the magnitude of the voltage at node n, i ij Representing the current of the branch i-j connecting node i to node j,
representing the branch i-j maximum current limit;
the operation constraint conditions of the reactive power compensation device are as follows:
wherein Q is SVC,n The reactive power output of the nth reactive power compensation device in the cluster is represented;
and
respectively indicating reactive power output of reactive power compensation deviceUpper and lower limits of force.
5. The distributed photovoltaic hierarchical group regulation and group control method according to claim 1 or 2, wherein the inter-group coordination optimization step is as follows:
on the basis of cluster division, selecting a leading node of each cluster, wherein the leading node can sensitively sense active and reactive changes of other nodes in the cluster;
establishing an inter-cluster coordination optimization model by taking minimum voltage deviation of cluster leading nodes and minimum system network loss in a master station area as inter-cluster control targets and taking power flow constraint, cluster power constraint and operation safety constraint as inter-cluster constraint conditions;
and solving the inter-cluster coordination optimization model by using a particle swarm algorithm to obtain the whole active and reactive power output scheme of each cluster.
6. The distributed photovoltaic layered group tuning and group controlling method according to claim 5, wherein the selection steps of the leading node are as follows:
computing the observability S of a node n Gn ；
Calculating controllability S of node n Kn ；
Defining the observable and controllable comprehensive indexes Sn of the nodes n as follows: s n ＝δ 1 S Gn +δ 2 S Kn Wherein, δ 1 And delta 2 Respectively representing the weight coefficients of the observability index and the controllability index;
and selecting the leading nodes on the basis of eliminating the voltage deviation of the node n to expect the minimum voltage deviation of other nodes in the cluster, wherein the number of the leading nodes does not exceed the number of clusters participating in regulation in the region.
7. The distributed photovoltaic layered group regulation and group control method according to claim 5, wherein the inter-group control objective is: minf = ω 1 f 1 +ω 2 f 2 ；
Wherein, f 1 Is the voltage deviation of the dominant node of the cluster,
f 2 in order to reduce the network loss of the system,
P loss,m the loss of the network of cluster m is indicated,
n represents the total number of nodes in the cluster; l represents a set of nodes connected to node n; i.e. i nl Representing the current, r, of the branch connecting node n to node l nl Representing the branch resistance; j represents the number of dominant nodes; u shape j Representing the voltage amplitude of the leading node j, U for convenient analysis and calculation j Expressed by a per unit value; u shape 0 Representing the reference value of the node voltage, U 0 1p.u.; m represents the number of clusters participating in regulation;
ω 1 and omega 2 Respectively represents f 1 And f 2 When the voltage of the leading node is not out of limit and the regulating system runs safely, the weight coefficient of omega 1 Less than omega 2 (ii) a When the dominant node voltage is out of limit, ω 1 Greater than omega 2 。
8. The distributed photovoltaic layered group regulation and control method according to claim 5, wherein the power flow constraint condition is:
wherein u is i And u j Respectively representing the node voltages of the node i and the node j; i all right angle ij Representing the current of a branch i-j with a node i as a starting point and a node j as an end point; p ij And Q ij Respectively representing the active and reactive power flowing through the branches i-j; r is ij And x ij Respectively representing the resistance and reactance of the branches i-j; p j And Q j Respectively representing net active and reactive loads injected into node j; jl represents a branch set which takes the node j as a starting point and the node l as an end point and is connected with the node j; p is jl And Q jl Respectively representing the active and reactive power flowing through the branch j-l;
the cluster power constraint conditions are as follows:
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power output of the distributed photovoltaic of the cluster m;
representing the maximum value of the distributed photovoltaic active output of the cluster m;
and
respectively representing the upper limit and the lower limit of the distributed photovoltaic reactive power output of the cluster m;
the operation safety constraint condition is
Wherein the content of the first and second substances,
and
respectively representing the upper and lower limits of the dominant node voltage.
9. The utility model provides a distributed photovoltaic layering crowd accuse group control system which characterized in that includes:
the data acquisition equipment is used for acquiring distributed photovoltaic information of the terminal equipment in the cluster;
the cluster control platform is connected with the data acquisition equipment and used for receiving the distributed photovoltaic information acquired by the data acquisition equipment, summarizing and integrating the distributed photovoltaic information and uploading the summarized photovoltaic information to the cloud regulation and control center; after receiving the integral active and reactive power output adjusting instruction of the cluster, distributing active and reactive power output to each photovoltaic power station in the cluster according to the capacity of each photovoltaic power station in the cluster, and realizing intra-cluster autonomy;
wherein, P pv,m 、Q pv,m Respectively representing the active and reactive power of the distributed photovoltaic of the cluster m; p is pv,n And Q pv,n Respectively representing active and reactive power output of the nth photovoltaic power station in the cluster m; s pvn Representing the capacity of the nth photovoltaic power plant; n represents the number of photovoltaic power stations in the cluster m;
the cloud control center is connected with the cluster control platforms, integrates and analyzes distributed photovoltaic information uploaded by each cluster, performs inter-cluster coordination optimization by taking the node voltage in the whole area not to exceed the limit and the system network loss as the minimum target, generates an overall active and reactive power output regulation instruction of the cluster and sends the instruction to each cluster control platform, and achieves inter-cluster coordination.
10. The distributed photovoltaic hierarchical group regulation and control system according to claim 9, characterized in that: after the cluster control platform receives the integral active reactive power output regulation instruction of the cluster, distributed photovoltaic information uploaded by the data acquisition equipment can be analyzed, so that safe operation of voltage of nodes in the cluster, minimum loss of a cluster network and maximum local consumption of distributed photovoltaic are realized as an intra-cluster autonomous target, and an intra-cluster autonomous optimization model is constructed by taking power flow constraint, photovoltaic power station power constraint, safe operation constraint and reactive power compensation device operation constraint as intra-cluster constraint conditions;
and solving the intra-cluster autonomy optimization model by using a particle swarm algorithm to obtain an active and reactive power output scheme of each device in the cluster, generating an active and reactive power output regulation instruction in the cluster, and issuing the active and reactive power output regulation instruction to each terminal device to realize intra-cluster autonomy.
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| CN116722561A (en) * | 2023-04-28 | 2023-09-08 | 国网河北省电力有限公司电力科学研究院 | Reactive power compensation system |
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