CN114123187A - Virtual power plant planning method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a virtual power plant planning method and device, electronic equipment and a storage medium, and relates to a power distribution network control partition technology. Firstly, dividing the power distribution network according to the load of each node in the power distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, wherein each partition comprises at least one node; and then, planning a corresponding virtual power plant for each partition according to a preset double-layer planning model so as to respond to the power demand of each node in the corresponding partition. Because every subregion all has the virtual power plant that corresponds, to each node in the subregion, the virtual power plant in the subregion is the nearest virtual power plant, and when the power consumption demand of node changes, the virtual power plant can allocate the resource nearby to satisfy the power consumption demand of node in the subregion, thereby realized that virtual power plant is to the nearby response and the control of node.
Description
Technical Field
The invention relates to the technical field of power distribution network control partitions, in particular to a virtual power plant planning method and device, electronic equipment and a storage medium.
Background
With the large-scale access of loads such as various clean energy systems with uncertain output (output active power), various cooling electrification systems, electric vehicles and the like to the distribution and utilization network, the planning of the regional power distribution network at present has the new characteristics of diversified planning elements, distributed power generation, high-permeability access of demand-side resources and the like, and thus, new challenges and demands are provided for the operation of the regional power distribution network.
The virtual power plant gathers resources such as a distributed new energy power generation power supply, an energy storage system, a controllable power supply and the like in a certain area together to participate in power grid operation as a whole. The virtual power plant stably schedules the new energy with randomness and volatility by coordinating and optimizing the output of the internal controllable power supply, reduces the difficulty of scheduling the power grid, improves the power generation quality of the new energy, and improves the economical efficiency and the environmental protection performance of the power distribution network.
However, most of the current planning for virtual power plants is to plan the entire urban area as a whole, so that the virtual power plants cannot play a good role in the power distribution network.
Disclosure of Invention
The invention provides a virtual power plant planning method, a virtual power plant planning device, an electronic device and a storage medium, which can divide a power distribution network into a plurality of subareas according to loads of nodes in the power distribution network, load time distribution and physical distances among different nodes, and plan a corresponding virtual power plant for each subarea so as to realize the nearby response and control of the virtual power plant to the nodes.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, the present embodiment provides a virtual power plant planning method, where the method includes:
dividing the power distribution network according to the load of each node in the power distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, wherein the partitions comprise at least one node;
and planning a corresponding virtual power plant for each partition according to a preset double-layer planning model, wherein the virtual power plant is used for responding to the power consumption requirement of each node in the corresponding partition.
In a possible implementation manner, the step of dividing the power distribution network according to the load of each node in the power distribution network, the load time distribution, and the physical distance between different nodes to obtain h partitions includes:
at the moment t, dividing the power distribution network according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises h first partitions, and each first partition comprises at least one first node;
selecting a plurality of moments within a set duration according to the load time distribution of each node in the power distribution network, and repeatedly executing the steps to obtain a plurality of partition schemes, wherein each partition scheme comprises h first partitions;
and determining a target partitioning scheme from the plurality of partitioning schemes according to the physical distance between the first node and the first node, wherein the target partitioning scheme comprises h partitions.
In one possible embodiment, said distribution network comprises m of said nodes; at the time t, dividing the power distribution network according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises the following steps:
at the moment t, randomly dividing the power distribution network into h initial partitions, wherein each initial partition comprises at least one initial node;
for each initial partition, taking the load of any one initial node in the initial partition as a first reference load to obtain h first reference loads, wherein the h first reference loads are in one-to-one correspondence with the h initial partitions;
according to the loads of m initial nodes and the h first reference loads, re-dividing m initial nodes in h initial partitions to obtain h second partitions, wherein the second partitions comprise at least one second node;
determining h second reference loads according to the load of each second node in each second partition, wherein the h second reference loads are in one-to-one correspondence with the h second partitions, and the h second reference loads are in one-to-one correspondence with the h first reference loads;
calculating the change rate of each second reference load and each first reference load to obtain h change rates, wherein the h change rates are in one-to-one correspondence with the h second partitions;
comparing the magnitude of each change rate with a preset change rate;
if any one of the change rates is larger than the preset change rate, taking the second reference load as a first reference load and the second partition as an initial partition, and repeatedly executing the step of dividing m nodes into h initial partitions according to the loads of the m nodes and h first reference loads to obtain h second partitions until all the h change rates are smaller than the preset change rate.
And if the h change rates are all smaller than the preset change rate, taking the h second partitions as the h first partitions to obtain the partition scheme.
In a possible implementation manner, the step of repartitioning m initial nodes in h initial partitions according to loads of m initial nodes and h first reference loads to obtain h second partitions includes:
acquiring any one target initial node in m initial nodes;
calculating a load difference value between the load of the target initial node and each first reference load to obtain h load difference values, wherein the h load difference values are in one-to-one correspondence with the h first reference loads;
taking the first reference load corresponding to the minimum load difference value in the h load difference values as a target reference load;
dividing the target initial node into the initial partition corresponding to the target reference load;
and traversing the m initial nodes to complete the repartitioning of the m initial nodes to obtain h second partitions.
In one possible embodiment, the load includes an electrical load and a thermal load;
the step of calculating a load difference between the load of the target initial node and each of the first reference loads to obtain h load differences includes:
according to the formula
Calculating the load difference value of the load of the target initial node and each first reference load to obtain h load difference values;
wherein D isiRepresenting the ith said load difference, Nt(k, f) represents the electrical and thermal load of the target initial node at time t,
represents the electrical load and the thermal load in the ith first reference load at time t, k represents the electrical load, and f represents the thermal load.
In one possible embodiment, the load includes an electrical load and a thermal load;
the step of determining h second reference loads according to the load of each second node in each second partition includes:
according to the formula
Calculating the xth second reference load to obtain h second reference loads;
wherein the content of the first and second substances,
is a second reference load corresponding to the xth second partition at time t, s is the number of the second nodes in the xth second partition,
the electrical load and the thermal load of the y-th second node in the x-th second partition.
In a possible implementation manner, the step of determining a target partition scheme from the plurality of partition schemes according to a physical distance between the first node and the first node includes:
according to the first node and the first nodePhysical distance therebetween, according to the formula
Calculating a sociality index of each division scheme;
wherein R isijIs the physical distance, δ, between the first node i and the first node ji＝∑jRijRepresents the sum of the physical distances, δ, between the first node i and all the first nodes jj＝∑iRijDenotes the sum of the physical distances between the first node j and all the first nodes i, f ═ Σi∑jRij) The/2 is the sum of the physical distances between any two first nodes in the power distribution network, and phi (i, j) is a weight between the first node i and the first node j;
and determining the minimum community index from all the community indexes, and taking the partition scheme corresponding to the minimum community index as a target partition scheme.
In one possible embodiment, the two-tier planning model includes an upper tier model and a lower tier model;
the step of planning the corresponding virtual power plant for each partition according to a preset double-layer planning model comprises the following steps of:
optimizing an upper layer model by taking the annual comprehensive cost of the power distribution network as a target function;
optimizing the lower layer model by taking the annual average network loss and the electricity purchasing quantity of the power distribution network as objective functions to obtain an optimized double-layer planning model;
and planning the corresponding virtual power plant for each subarea according to the optimized double-layer planning model.
In a second aspect, the present embodiment further provides a virtual power plant planning apparatus, the apparatus includes:
the distribution network management system comprises a dividing module, a management module and a management module, wherein the dividing module is used for dividing a distribution network according to the load of each node in the distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, the distribution network comprises at least two nodes, and the partitions comprise at least one node;
and the planning module is used for planning each corresponding virtual power plant for the subarea according to a preset double-layer planning model, wherein the virtual power plants are used for responding and corresponding to the power consumption demand of each node in the subarea.
In a third aspect, this embodiment further provides an electronic device, where the electronic device includes:
one or more processors;
a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the virtual plant planning method described above.
In a fourth aspect, the present embodiment further provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the virtual plant planning method described above.
Compared with the prior art, the virtual power plant planning method, the virtual power plant planning device, the electronic equipment and the storage medium provided by the embodiment of the invention have the advantages that firstly, the power distribution network is divided according to the load of each node in the power distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, wherein each partition comprises at least one node; and then, planning a corresponding virtual power plant for each partition according to a preset double-layer planning model so as to respond to the power demand of each node in the corresponding partition. Because every subregion all has the virtual power plant that corresponds, to each node in the subregion, the virtual power plant in the subregion is the nearest virtual power plant, and when the power consumption demand of node changes, the virtual power plant can allocate the resource nearby to satisfy the power consumption demand of node in the subregion, thereby realized that virtual power plant is to the nearby response and the control of node.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a virtual power plant planning method provided by an embodiment of the present invention.
Fig. 3 is a schematic flow chart of step S110 in the virtual power plant planning method shown in fig. 2.
Fig. 4 is a schematic flowchart of step S1101 in the virtual power plant planning method shown in fig. 3.
Fig. 5 is a schematic diagram of division of an initial partition according to an embodiment of the present invention.
Fig. 6 is a schematic diagram illustrating a partition of a second partition according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of another division of the second partition according to the embodiment of the present invention.
Fig. 8 is a schematic diagram illustrating a division of a second partition according to another embodiment of the present invention.
Fig. 9 is a schematic flowchart of step S130 in the virtual power plant planning method shown in fig. 2.
Fig. 10 is a block diagram of a virtual power plant planning apparatus according to an embodiment of the present invention.
Icon: 100-an electronic device; 101-a memory; 102-a processor; 103-a bus; 200-virtual power plant planning means; 201-a partitioning module; 202-planning module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
The power distribution network refers to a power network in which a power transmission network or a regional power plant receives electric energy and distributes the electric energy to various users on site or step by step according to voltage through a power distribution facility, the power distribution network comprises a plurality of nodes, the load of each node is different, the load of the same node is also different at different moments, the coverage area of the power distribution network is often large, and the distance between different nodes is possibly large.
In the conventional technology, a virtual power plant is often planned by taking the whole urban area as a whole, resources such as a new energy power generation power supply, an energy storage system and a controllable power supply in the urban area are gathered together to participate in the operation of a power grid as a whole for power generation, and the power demand of each node in the power distribution grid is responded. Due to the fact that load differences of all nodes in an urban area are large, and physical distances among different nodes are long, the virtual power plant cannot well achieve nearby response and control, and the effect of the virtual power plant in a power distribution network is weakened.
To solve this problem, this embodiment provides a virtual power plant planning method, according to the load of the node in the power distribution network, the load time distribution, and the physical distance between different nodes, the power distribution network is divided into a plurality of partitions, and a corresponding virtual power plant is planned for each partition, for each node in a partition, the virtual power plant in a partition is the closest virtual power plant, and when the power demand of the node changes, the virtual power plant can allocate resources nearby, so as to implement the nearby response and control of the virtual power plant to the node.
As described in detail below.
Referring to fig. 1, fig. 1 is a block diagram illustrating an electronic device 100 according to the present embodiment, where the electronic device 100 may be, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a server, or other electronic devices with processing capability. Electronic device 100 includes memory 101, processor 102, and bus 103. The memory 101 and the processor 102 are connected by a bus 103.
The memory 101 is used for storing a program, such as the virtual power plant planning apparatus 200, the virtual power plant planning apparatus 200 includes at least one software functional module which can be stored in the memory 101 in a form of software or firmware (firmware), and the processor 102 executes the program after receiving an execution instruction to implement the virtual power plant planning method in this embodiment.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. In the implementation process, the steps of the virtual power plant planning method in this embodiment may be implemented by an integrated logic circuit of hardware in the processor 102 or instructions in the form of software. The processor 102 may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Micro Control Unit (MCU), a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA), and an embedded ARM.
On the basis of the electronic device 100 shown in fig. 1, a virtual power plant planning method provided in this embodiment is described. Referring to fig. 2, fig. 2 shows a schematic flow chart of a virtual power plant planning method provided in this embodiment, where the method includes the following steps:
and S110, dividing the power distribution network according to the load of each node in the power distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, wherein the partitions comprise at least one node.
In this embodiment, the distribution network refers to a power network in which a transmission network or a local power plant receives electric energy and distributes the electric energy to various users on site or step by step according to voltage through a distribution facility, and the power network comprises an overhead line, a cable, a tower, a distribution transformer, an isolating switch, a reactive compensator, some accessory facilities and the like.
The power distribution network comprises at least two nodes, the nodes refer to node facilities such as an opening and closing station and a power distribution room in the power distribution network, each node can be connected with a plurality of user electric equipment, and the total electric quantity of the plurality of user electric equipment is the load of the node.
The load time distribution means that the loads of the nodes are different at different time. Since the power consumption of the consumer electric equipment is changed, the load of the node is also changed, for example, the load of the corresponding node is larger when the power consumption is in the peak period from 19 to 22.
The physical distance between different nodes refers to the shortest spatial connecting line length between two different nodes in the power distribution network at the same time. For example, the physical distance between node i and node j in the power distribution network is 1 km.
And S120, planning a corresponding virtual power plant for each partition according to a preset double-layer planning model, wherein the virtual power plant is used for responding to the power consumption requirement of each node in the corresponding partition.
In this embodiment, the virtual power plant is a power supply coordination management system that disperses distributed power supplies, energy storage, loads and the like in various resources of a power grid through edge intelligence and internet of things technologies, and the power supply coordination management system is used as a special power plant to participate in power market and power grid operation.
The virtual power plant is used for responding to the power demand of each node in the corresponding partition, and means that when the power demand of the nodes in the partition changes, the virtual power plant can allocate resources nearby so as to meet the power demand of the nodes.
Compared with the prior art, the virtual power plant planning method in the embodiment has the advantages that the corresponding virtual power plants are planned for each partition, the virtual power plants in the partitions are the virtual power plants closest to each node in the partition, and when the power demand of the node changes, the virtual power plants can allocate resources nearby so as to meet the power demand of the nodes in the partitions.
Step S110 is described in detail below, and referring to fig. 3 on the basis of fig. 2, step S110 further includes the following detailed steps:
s1101, at the time t, dividing the power distribution network according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises h first partitions, and each first partition comprises at least one first node.
In this embodiment, it should be noted that the node and the first node are only distinguished for easy understanding, and have no special meaning.
And S1102, selecting a plurality of moments within a set duration according to the load time distribution of each node in the power distribution network, and repeatedly executing the step S1101 to obtain a plurality of division schemes, wherein each division scheme comprises h first partitions.
The set time period may be 24 hours, and the selected plurality of times may be integral times selected according to the load time distribution of the nodes, for example, 1 point, 2 points … … 24 points.
Since the load of each node may change over time, it may be possible for the same node to be partitioned into different partitions at different times. For example, for a certain node, the connected user electric equipment is electric equipment of a residential building, and the load of the node is the largest at 20 o' clock within 24 hours, and in the obtained partitioning scheme, the node is partitioned into partitions with large node loads; the load of the node is the smallest at 2 am, and the node is divided into partitions with small loads. Based on this, repeatedly performing step S1101, a plurality of division schemes can be obtained.
S1103, determining a target division scheme from the multiple division schemes according to the physical distance between different nodes, wherein the target division scheme comprises h partitions.
In this embodiment, the target partitioning scheme refers to a partitioning scheme in which the physical locations of nodes in a partition are concentrated.
Step S1101 is described in detail below, and referring to fig. 4 on the basis of fig. 3, step S1101 may further include the following detailed steps:
s11011, at the moment t, the power distribution network is randomly divided into h initial partitions, wherein each initial partition comprises at least one initial node.
In this embodiment, the distribution network includes m nodes, nodes and initial nodes, which are merely for easy understanding and have no special meaning.
Suppose that each node sample of the power distribution network at the time t is
Wherein the content of the first and second substances,
is a two-dimensional vector, k and f represent the electrical and thermal loads at node i, respectively, at time t.
The H initial partitions may be expressed as H ═ H1,H2,....Hh}，HiRepresenting the ith initial partition.
S11012, regarding each initial partition, taking the load of any initial node in the initial partition as a first reference load, and obtaining h first reference loads, wherein the h first reference loads are in one-to-one correspondence with the h initial partitions.
In this embodiment, the first reference load is used to provide a reference for repartitioning each initial node.
The h first reference loads may be expressed as
The first reference load corresponding to the ith initial partition is represented as a two-dimensional vector.
And S11013, re-dividing the m initial nodes in the h initial partitions according to the loads of the m initial nodes and the h first reference loads to obtain h second partitions, wherein the second partitions comprise at least one second node.
In this embodiment, for each initial node, according to each initial node and h first reference loads, the initial node and h first reference loads are re-divided into h initial partitions, so as to obtain h second partitions.
And S11014, determining h second reference loads according to the load of each second node in each second partition, wherein the h second reference loads correspond to the h second partitions one to one, and the h second reference loads correspond to the h first reference loads one to one.
In this embodiment, the second reference load is an average of the loads of all the second nodes in the second partition.
And S11015, calculating the change rate of each second reference load and each corresponding first reference load to obtain h change rates, wherein the h change rates are in one-to-one correspondence with the h second partitions.
In this embodiment, the change rate refers to a change rate of the second reference load relative to the first reference load, for example, the second reference load corresponding to the ith second partition is
The first reference load corresponding to the ith initial partition is
The change rate corresponding to the ith second partition is
Where k% represents the rate of change of the electrical load and f% represents the rate of change of the thermal load.
And S11016, comparing the magnitude of each change rate with the preset change rate.
In the present embodiment, the preset change rate is a preset change rate, and may be 1%. As can be seen from the above, the change rate corresponding to the ith second partition can be expressed as
The comparison of the change rate of each change rate with a preset change rate refers to the comparison of the change rate of the electrical load and the change rate of the thermal load with the preset change rate respectively.
S11017, if any one of the change rates is greater than the preset change rate, taking the second reference load as the first reference load and the second partition as the initial partition, and repeatedly executing steps S11013 to S11016 until all h change rates are less than the preset change rate.
In this embodiment, the existence of any one of the change rates being greater than the preset change rate means that, for any one of the change rates, the change rate of the electrical load is greater than the preset change rate, or the change rate of the thermal load is greater than the preset change rate, or both the change rate of the electrical load and the change rate of the thermal load are greater than the preset change rate.
And S11018, if the h change rates are all smaller than the preset change rate, taking the h second partitions as h first partitions to obtain a partition scheme.
In this embodiment, the h change rates are all smaller than the preset change rate, which means that the electrical load change rate and the thermal load change rate of the h change rates are both smaller than the preset change rate.
The detailed step of dividing the power distribution network according to the load of each node in the power distribution network at the time t to obtain the division scheme is introduced, and through the steps, the load of the first node in each first partition in the obtained division scheme is balanced, so that the allocation and management of resources can be better realized after the virtual power plant is planned subsequently.
In a possible scenario, the step S11013 may further include the following detailed steps:
in the first step, any one target initial node in m initial nodes is obtained.
And secondly, calculating a load difference value between the load of the target initial node and each first reference load to obtain h load difference values, wherein the h load difference values correspond to the h first reference loads one to one.
In this embodiment, the load difference is used to represent the difference between the load of the target initial node and the first reference load.
And thirdly, taking the first reference load corresponding to the minimum load difference value in the h load difference values as a target reference load.
And fourthly, dividing the target initial node into initial partitions corresponding to the target reference load.
And fifthly, traversing the m initial nodes to complete the repartitioning of the m initial nodes to obtain h second partitions.
In this embodiment, traversing m initial nodes means that, for each initial node, the steps from the first step to the fifth step are performed, and the initial node is divided into corresponding initial partitions.
For ease of understanding, the above steps are described below with a specific example.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating division of an initial partition. Assuming that 11 nodes are arranged in the power distribution network, for each node, the electrical load of the node is taken as an abscissa, the thermal load is taken as an ordinate, and the electrical load and the thermal load are drawn on a coordinate graph, wherein the origin of the coordinate axis is a point where the electrical load and the thermal load are both 0, the abscissa axis is the electrical load, and the ordinate axis is the thermal load.
Firstly, randomly dividing a power distribution network into 4 initial partitions, wherein as shown in fig. 5, an initial partition 1 comprises an initial node 1, an initial node 2 and an initial node 3; the initial partition 2 comprises an initial node 4, an initial node 5 and an initial node 6; the initial partition 3 includes an initial node 7 and an initial node 8; the initial partition 4 includes an initial node 9, an initial node 10, and an initial node 11.
Then, for each initial partition, the load of any one initial node in the initial partition is taken as a first reference load. The first reference load corresponding to the initial partition 1 is a load of the initial node 1, the first reference load corresponding to the initial partition 2 is a load of the initial node 6, the first reference load corresponding to the initial partition 3 is a load of the initial node 8, and the first reference load corresponding to the initial partition 4 is a load of the initial node 11.
With the initial node 1 as a target initial node, load difference values between the load of the initial node 1 and all the first reference loads are calculated. In the graph, it can be considered to calculate the distance between the initial node 1 and the point corresponding to the first reference load.
Since the load of the initial node 1 is the first reference load of the initial partition 1, the load difference between them is 0, which is the minimum load difference among the four load differences, the first reference load of the initial partition 1 is the target reference load, and the initial node 1 is re-divided into the initial partition 1.
Similarly, the division of all nodes is completed, resulting in 4 second partitions, as shown in fig. 6. The second partition 1 comprises a second node 1 and a second node 2; the second partition 2 comprises a second node 3, a second node 4, a second node 5, a second node 6, a second node 9 and a second node 10; the second partition 3 comprises a second node 7 and a second node 8; the second partition 4 includes a second node 11 therein.
Referring to fig. 7, for each second partition, a second reference load of each second partition is determined, where the second reference load in the second partition 1 is a load of a point 12, the second reference load in the second partition 2 is a load of a point 13, the second reference load in the second partition 3 is a load of a point 14, and the second reference load in the second partition 4 is a load of a second node 11.
Then, the change rate between each second reference load and each corresponding first reference load is calculated, and each change rate is compared with the magnitude of 1% of a preset change rate.
As can be seen from fig. 7, except for the second reference load corresponding to the second partition 4, the change rate of the remaining second reference loads is greater than 1%, then the second reference load is used as the first reference load, the second partition is used as the initial partition, the load difference between each initial node and each first reference load is continuously calculated, and the initial nodes are re-partitioned into the corresponding initial partitions.
The second partition obtained after the second round of partitioning is shown in fig. 8, and it can be seen that the initial node 2 is partitioned into the second partition 3 because the load of the initial node 2 is closer to the first reference load 14.
And continuously determining the second reference load of each second partition, and calculating the change rate between each second reference load and the corresponding first reference load until each change rate is less than 1% of the preset change rate. Namely, the 4 finally obtained second partitions are used as 4 first partitions to obtain a partitioning scheme.
It should be noted that, each of the above change rates that is less than 1% of the preset change rate is only used as a judgment condition, and in practice, it may be considered that before and after the second partition is obtained by the initial partition division, the nodes in each partition do not change, that is, the division of the initial partition is completed, so as to obtain h second partitions.
In one possible case, the load difference between the load of the target initial node and each first reference load in the third detailed step of step S11013 may be calculated according to the following formula:
wherein D isiRepresenting the ith load difference, Nt(k, f) represents the electrical and thermal loads of the target initial node at time t,
the electric load and the thermal load in the ith first reference load at time t are indicated, k is the electric load, and f is the thermal load.
For example, continuing to refer to fig. 5, for node 2 in the initial partition 1, its coordinates in the load coordinate graph are (8, 11), the first reference load corresponding to the initial partition 1 is the reference load of the initial node 1, its coordinates are (2, 15), and then the load difference value between them is: d1＝||(8，11)-(2，15)||2＝52。
In a possible case, the h second reference loads determined in step S11014 above according to the load of each second node in each second partition may be calculated according to the following formula:
wherein the content of the first and second substances,
is a second reference load corresponding to the xth second partition at the time t, s is the number of second nodes in the xth second partition,
the electrical load and the thermal load of the y second node in the x second partition.
For example, continuing to refer to fig. 6, for a second partition 1, which includes two second nodes, S is taken to be 2, and its corresponding second reference load is:
that is, the second reference load corresponding to the second partition 1 has an electrical load of 5 and a thermal load of 8. Corresponding to point 12 in fig. 6, point 12 is the midpoint of the line connecting the second node 1 and the second node 2, as viewed in position.
In one possible scenario, in step S1103, determining a target partition scheme from a plurality of partition schemes according to physical distances between different nodes may include the following detailed steps:
the first step, according to the physical distance between the first node and the first node, according to the formula
Calculating a sociality index of each division scheme;
wherein R isijIs the physical distance, δ, between the first node i and the first node ji＝∑jRijRepresents the sum of the physical distances, δ, between the first node i and all the first nodes jj＝∑iRijDenotes the sum of the physical distances between the first node j and all the first nodes i, f ═ Σi∑jRij) And phi (i, j) is a weight value between the first node i and the first node j.
And secondly, determining the minimum community index from all the community indexes, and taking the partition scheme corresponding to the minimum community index as a target partition scheme.
In this embodiment, the community index is used to represent the degree of dispersion of physical locations of all the first nodes in the first partition, and the larger the community index is, the more dispersed the physical locations of all the first nodes in the first partition are.
In the above formula, phi (i, j) is a weight between the first node i and the first node j, and when the node i and the node j are in the same first partition, phi (i, j) is 1, and when the node i and the node j are not in the same first partition, phi (i, j) is 0.
In one possible case, the two-layer model in step S120 includes an upper layer model and a lower layer model.
On the basis of fig. 2, please refer to fig. 9, step S120 may include the following detailed steps:
and S1301, optimizing the upper layer model by taking the annual comprehensive cost of the power distribution network as an objective function.
In this embodiment, the annual combined cost of the power distribution network includes: the method comprises the steps of simulating investment cost, operation and maintenance cost, power grid electricity purchasing cost and grid loss cost of a distributed power supply and an energy storage system in a power plant, wherein the distributed power supply and the energy storage system mainly comprise wind power, photovoltaic, energy storage and a gas turbine. The objective function can be expressed as:
min O＝O1+O2+O3
wherein min O is the minimum value of annual comprehensive cost of the power distribution network, O1Represents the investment costs of wind power, photovoltaic, energy storage, gas turbines, O2Representing the operating and maintenance costs, O, of wind, photovoltaic, energy storage, gas turbines3Representing the electricity purchase cost of the load of the nodes in the distribution network and the loss cost of the distribution network lines.
The goal of the upper layer model optimization is to minimize the annual combined cost of the distribution network.
O in the above formula1、O2And O3It can be obtained in the following way:
in practice, wind power, photovoltaic, energy storage, and gas turbine pay rates are considered to be a single-valued function of installation capacity, as is customary in engineering practice. Therefore, the investment cost of wind power, photovoltaic, energy storage and gas turbine in the virtual power plant can be expressed as:
wherein, O1The investment cost of wind power, photovoltaic, energy storage and a gas turbine in a virtual power plant is shown, and N is the number of partitions of a power distribution network; mu is the pasting rate; alpha is the investment age of the equipment;
respectively the photovoltaic capacity, the wind power capacity, the energy storage capacity and the mounting capacity of the gas turbine of the s-th subarea; cPV、CWT、CBA、CGTIs photovoltaic and windElectricity, energy storage, and gas turbine unit capacity installation costs.
The operation and maintenance cost of wind power, photovoltaic, energy storage and gas turbine in the virtual power plant can be expressed as follows:
wherein, O2For the operation and maintenance costs of wind power, photovoltaic, energy storage and gas turbine in the virtual power plant GPV、GWT、GBA、GGTRespectively the unit capacity operating costs of photovoltaic, wind power, energy storage and gas turbines.
The electricity purchase cost of the load of the node in the power distribution network and the network loss cost of the power distribution network line can be expressed as follows:
wherein, O3For the purchase of electricity costs for the loads of the nodes in the distribution network and for the loss of the network lines,
the active power of the ith node of the load, the photovoltaic and the wind power at the typical day t moment respectively; n is a radical ofnodeCounting the number of nodes of the power distribution network;
the power loss refers to the power loss of a power distribution network line;
respectively representing the real-time electricity purchase price and the grid loss price at the typical day t.
The constraint conditions of the objective function mainly consider the installation capacity constraint of each partition, the power balance constraint and the power constraint of branches among the partitions.
The installation capacity constraint of the photovoltaic cells allowed to be installed in each partition can be expressed as:
wherein the content of the first and second substances,
for the installation capacity of the photovoltaic of the s-section,
the maximum photovoltaic installation capacity of each node in the s partition.
The installation capacity constraint of the wind power allowed to be installed in each partition can be expressed as:
wherein the content of the first and second substances,
for the installed capacity of the s-partitioned wind power,
and the maximum wind power installation capacity of each node in the s partition is obtained.
The installation capacity constraint for the energy storage allowed to be installed in each partition can be expressed as:
wherein the content of the first and second substances,
the installed capacity of the stored energy for the s partition,
for each section in s partitionMaximum installed capacity of stored energy of the point.
The installation capacity constraint of the gas turbine allowed to be installed in each section can be expressed as:
wherein the content of the first and second substances,
the installed capacity of the stored energy for the s partition,
the maximum installed capacity of the gas turbine at each node in the s-zone.
The power constraint of the interaction branch between the partitions can be expressed as:
wherein p isl,tFor the interaction constraint of branch i at time t,
the maximum interaction power of branch i.
The magnitude of the photovoltaic output at each moment can be calculated by the working temperature, the illumination condition and the factory calibration parameter at the corresponding moment:
wherein, PPVThe magnitude of the output force at each moment of the photovoltaic,
rated installed capacity for photovoltaic, GcThe solar irradiance is the solar irradiance of the solar panel during actual operation; t iscThe actual working temperature of the solar cell panel; temperature coefficient of powerk is 0.0045; photovoltaic test irradiance GSTC＝1000W/m2。
The output force of each moment of wind power can be calculated by adopting the following formula:
wherein, PWTThe output at each moment of wind power, vcTo cut into the wind speed; v. ofrRated wind speed; v. ofcoCutting out the wind speed; v. oftThe wind speed is the wind speed of the wind power during actual work;
the rated power of the wind power is obtained.
And S1302, optimizing the lower layer model by taking the annual average network loss and the electricity purchasing quantity of the power distribution network as objective functions to obtain an optimized double-layer planning model.
In the embodiment, the optimization goal of the lower layer model is to minimize the annual average power loss and the purchased power capacity of the power distribution network. The expression of the objective function is:
wherein, min PupperIn order to minimize annual average network loss and power purchase of the power distribution network, T is 24,
for the loss of each branch at time t,
the purchased power capacity of the distribution network at the moment t. min PupperMultiplying by a basic electricity price to obtain a formula O in an upper model3Network loss of power distribution network
In the above formula
And
the specific constraint mode is restricted by wind power, photovoltaic, energy storage and the access capacity of the gas turbine, and the following formula can be referred to.
The capacity constraint of the photovoltaic accessed by each node in each partition is as follows:
wherein the content of the first and second substances,
the photovoltaic installation capacity of the node j in the s-number partition is obtained;
total installed capacity of photovoltaic for the s-division.
The capacity constraint of the wind power accessed by each node in each partition is as follows:
wherein the content of the first and second substances,
the installation capacity of the wind power of the node j in the s-number partition is set;
total installed capacity of wind power divided by s.
Because the stored energy is only accessed to 1 node in one partition, the capacity constraint of the stored energy accessed by each partition node is as follows:
wherein the content of the first and second substances,
the installation capacity of the stored energy of the node j in the partition with the number s;
total installed capacity for s number partition energy storage.
Since the gas turbine only accesses 1 node in one partition, the capacity constraint of the gas turbine accessed by each partition node is as follows:
wherein the content of the first and second substances,
the installation capacity of the gas turbine of the node j in the s-number subarea;
is the total installed capacity of the gas turbine divided by s.
The constraint condition of the objective function mainly considers the power flow constraint of the power distribution network, and the power flow constraint of the power distribution network can be expressed as follows:
wherein, Pi、QiRespectively performing active injection and reactive injection on a node i and a node j; u shapeiAnd UjThe voltage amplitudes, G, of nodes i, j, respectivelyijAnd BijAdmittance of the branch for node i and node j; thetaijIs the voltage angle difference between node i and node j.
And S1303, planning a corresponding virtual power plant for each subarea according to the optimized double-layer planning model.
In this embodiment, according to the optimized double-layer planning model, a corresponding virtual power plant is planned for each partition, so that the annual comprehensive cost of the power distribution network can be minimized.
Compared with the prior art, the embodiment has the following beneficial effects:
first, in the virtual power plant planning method provided in this embodiment, because a corresponding virtual power plant is planned for each partition, and each partition is obtained by dividing according to the load of a node, the load time distribution, and the distance between different nodes, for each node in a partition, a virtual power plant in a partition is a virtual power plant closest to the node, and when the power demand of the node changes, the virtual power plant can allocate resources nearby, so as to implement the nearby response and control of the virtual power plant to the node.
And then, the double-layer planning model is optimized by taking the minimum annual comprehensive cost of the power distribution network as a target function, and the corresponding virtual power plants are planned for each partition according to the optimized double-layer planning model, so that the annual comprehensive cost of the power distribution network is reduced.
In order to execute the corresponding steps in the above embodiment of the virtual power plant planning method, an implementation manner applied to the virtual power plant planning apparatus is given below.
Referring to fig. 10, fig. 10 is a block diagram illustrating a virtual power plant planning apparatus 200 according to the present embodiment. The virtual power plant planning apparatus 200 is applied to the electronic device 100, and includes: a dividing module 201 and a planning module 202.
The dividing module 201 is configured to divide the power distribution network according to the load of each node in the power distribution network, the load time distribution, and the physical distance between different nodes, so as to obtain h partitions, where the power distribution network includes at least two nodes, and a partition includes at least one node.
And the planning module 202 is configured to plan a corresponding virtual power plant for each partition according to a preset double-layer planning model, where the virtual power plant is configured to respond to the power demand of each node in the corresponding partition.
Optionally, the dividing module 201 is specifically configured to:
at the moment t, the power distribution network is divided according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises h first partitions, and each first partition comprises at least one first node;
selecting a plurality of moments within a set duration according to the load time distribution of each node in the power distribution network, and repeatedly executing the steps to obtain a plurality of division schemes, wherein each division scheme comprises h first partitions;
and determining a target partitioning scheme from the multiple partitioning schemes according to the physical distance between the first node and the first node, wherein the target partitioning scheme comprises h partitions.
Optionally, the dividing module 201 is specifically configured to:
at the moment t, randomly dividing the power distribution network into h initial partitions, wherein each initial partition comprises at least one initial node;
aiming at each initial partition, taking the load of any initial node in the initial partition as a first reference load to obtain h first reference loads, wherein the h first reference loads are in one-to-one correspondence with the h initial partitions;
dividing m initial nodes into h initial partitions according to the loads of the m initial nodes and h first reference loads to obtain h second partitions, wherein the second partitions comprise at least one second node;
determining h second reference loads according to the load of each second node in each second partition, wherein the h second reference loads are in one-to-one correspondence with the h second partitions, and the h second reference loads are in one-to-one correspondence with the h first reference loads;
calculating the change rate of each second reference load and each corresponding first reference load to obtain h change rates, wherein the h change rates are in one-to-one correspondence with the h second partitions;
comparing the magnitude of each change rate with a preset change rate;
if any change rate is larger than the preset change rate, taking the second reference load as a first reference load and the second partition as an initial partition, and repeatedly executing the step of dividing m nodes into h initial partitions according to the loads of the m nodes and h first reference loads to obtain h second partitions until the h change rates are all smaller than the preset change rate;
and if the h change rates are all smaller than the preset change rate, taking the h second partitions as h first partitions to obtain a partition scheme.
Optionally, the dividing module 201 is specifically configured to:
acquiring any one target initial node in m initial nodes;
calculating a load difference value between the load of the target initial node and each first reference load to obtain h load difference values, wherein the h load difference values correspond to the h first reference loads one to one;
taking a first reference load corresponding to the minimum load difference value in the h load difference values as a target reference load;
dividing the target initial node into initial partitions corresponding to target reference loads;
and traversing the m initial nodes to complete the repartitioning of the m initial nodes to obtain h second partitions.
Optionally, the dividing module 201 is specifically configured to:
according to the formula
Calculating the load difference value of the load of the target initial node and each first reference load to obtain h load difference values;
wherein D isiRepresenting the ith load difference, Nt(k, f) represents the electrical and thermal loads of the target initial node at time t,
the electric load and the thermal load in the ith first reference load at time t are indicated, k is the electric load, and f is the thermal load.
Optionally, the dividing module 201 is specifically configured to:
according to the formula
Calculating the xth second referenceLoading to obtain h second reference loads;
wherein the content of the first and second substances,
is a second reference load corresponding to the xth second partition at the time t, s is the number of second nodes in the xth second partition,
the electrical load and the thermal load of the y second node in the x second partition.
Optionally, the dividing module 201 is specifically configured to:
according to the physical distance between the first node and the first node, according to the formula
Calculating a sociality index of each division scheme;
wherein R isijIs the physical distance, δ, between the first node i and the first node ji＝∑jRijRepresents the sum of the physical distances, δ, between the first node i and all the first nodes jj＝∑iRijDenotes the sum of the physical distances between the first node j and all the first nodes i, f ═ Σi∑jRij) The/2 is the sum of the physical distances between any two first nodes in the power distribution network, and phi (i, j) is the weight between the first node i and the first node j;
and determining the minimum community index from all the community indexes, and taking the partition scheme corresponding to the minimum community index as a target partition scheme.
Optionally, the planning module 202 is specifically configured to:
optimizing the upper layer model by taking the annual comprehensive cost of the power distribution network as a target function;
optimizing the lower layer model by taking the annual average network loss and the electricity purchasing quantity of the power distribution network as objective functions to obtain an optimized double-layer planning model;
and planning a corresponding virtual power plant for each subarea according to the optimized double-layer planning model.
As will be apparent to those skilled in the art, the above description of the specific operation of the virtual plant planning apparatus 200 is provided for the sake of convenience and brevity. Reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
The present embodiment also provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by the processor 102, implements the virtual power plant planning method disclosed in the above embodiments.
In summary, according to the virtual power plant planning method, device, electronic device, and storage medium provided by the embodiments of the present invention, since a corresponding virtual power plant is planned for each partition, and each partition is obtained by dividing according to the load of a node, the load time distribution, and the distance between different nodes, for each node in a partition, a virtual power plant in a partition is a closest virtual power plant, and when the power demand of a node changes, the virtual power plant can allocate resources nearby, so as to implement the nearby response and control of the virtual power plant to the node.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (11)
1. A method of virtual plant planning, the method comprising:
dividing the power distribution network according to the load of each node in the power distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, wherein the partitions comprise at least one node;
and planning a corresponding virtual power plant for each partition according to a preset double-layer planning model, wherein the virtual power plant is used for responding to the power consumption requirement of each node in the corresponding partition.
2. The method according to claim 1, wherein the step of dividing the distribution network into h partitions according to the load of each node in the distribution network, the load time distribution and the physical distance between different nodes comprises:
at the moment t, dividing the power distribution network according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises h first partitions, and each first partition comprises at least one first node;
selecting a plurality of moments within a set duration according to the load time distribution of each node in the power distribution network, and repeatedly executing the steps to obtain a plurality of partition schemes, wherein each partition scheme comprises h first partitions;
and determining a target partitioning scheme from the plurality of partitioning schemes according to the physical distance between the first node and the first node, wherein the target partitioning scheme comprises h partitions.
3. The method of claim 2, wherein said power distribution network comprises m of said nodes; at the time t, dividing the power distribution network according to the load of each node in the power distribution network to obtain a division scheme, wherein the division scheme comprises the following steps:
at the moment t, randomly dividing the power distribution network into h initial partitions, wherein each initial partition comprises at least one initial node;
for each initial partition, taking the load of any one initial node in the initial partition as a first reference load to obtain h first reference loads, wherein the h first reference loads are in one-to-one correspondence with the h initial partitions;
according to the loads of m initial nodes and the h first reference loads, re-dividing m initial nodes in h initial partitions to obtain h second partitions, wherein the second partitions comprise at least one second node;
determining h second reference loads according to the load of each second node in each second partition, wherein the h second reference loads are in one-to-one correspondence with the h second partitions, and the h second reference loads are in one-to-one correspondence with the h first reference loads;
calculating the change rate of each second reference load and each corresponding first reference load to obtain h change rates, wherein the h change rates are in one-to-one correspondence with the h second partitions;
comparing the magnitude of each change rate with a preset change rate;
if any one of the change rates is larger than the preset change rate, taking the second reference load as a first reference load and the second partition as an initial partition, and repeatedly executing the step of dividing m nodes into h initial partitions according to the loads of the m nodes and h first reference loads to obtain h second partitions until all the h change rates are smaller than the preset change rate;
and if the h change rates are all smaller than the preset change rate, taking the h second partitions as the h first partitions to obtain the partition scheme.
4. The method as claimed in claim 3, wherein the step of repartitioning m of the initial nodes of the h initial partitions according to the loads of the m initial nodes and the h first reference loads to obtain h second partitions comprises:
acquiring any one target initial node in m initial nodes;
calculating a load difference value between the load of the target initial node and each first reference load to obtain h load difference values, wherein the h load difference values are in one-to-one correspondence with the h first reference loads;
taking the first reference load corresponding to the minimum load difference value in the h load difference values as a target reference load;
dividing the target initial node into the initial partition corresponding to the target reference load;
and traversing the m initial nodes to complete the repartitioning of the m initial nodes to obtain h second partitions.
5. The method of claim 4, wherein the load comprises an electrical load and a thermal load;
the step of calculating a load difference between the load of the target initial node and each of the first reference loads to obtain h load differences includes:
according to the formula
Calculating the load difference value of the load of the target initial node and each first reference load to obtain h load difference values;
wherein D isiRepresenting the ith said load difference, Nt(k, f) represents the electrical and thermal load of the target initial node at time t,
represents the electrical load and the thermal load in the ith first reference load at time t, k represents the electrical load, and f represents the thermal load.
6. The method of claim 3, wherein the load comprises an electrical load and a thermal load;
the step of determining h second reference loads according to the load of each second node in each second partition includes:
according to the formula
Computing the xth of theObtaining h second reference loads by the two reference loads;
wherein the content of the first and second substances,
is a second reference load corresponding to the xth second partition at time t, s is the number of the second nodes in the xth second partition,
the electrical load and the thermal load of the y-th second node in the x-th second partition.
7. The method of claim 2, wherein said step of determining a target partitioning scheme from a plurality of said partitioning schemes based on a physical distance between the first node and the first node comprises:
according to the physical distance between the first node and the first node, according to the formula
Calculating a sociality index of each division scheme;
wherein R isijIs the physical distance, δ, between the first node i and the first node ji＝∑jRijRepresents the sum of the physical distances, δ, between the first node i and all the first nodes jj＝∑iRijDenotes the sum of the physical distances between the first node j and all the first nodes i, f ═ Σi∑jRij) The/2 is the sum of the physical distances between any two first nodes in the power distribution network, and phi (i, j) is a weight between the first node i and the first node j;
and determining the minimum community index from all the community indexes, and taking the partition scheme corresponding to the minimum community index as a target partition scheme.
8. The method of claim 1, wherein the two-tier planning model comprises an upper tier model and a lower tier model;
the step of planning the corresponding virtual power plant for each partition according to a preset double-layer planning model comprises the following steps of:
optimizing an upper layer model by taking the annual comprehensive cost of the power distribution network as a target function;
optimizing the lower layer model by taking the annual average network loss and the electricity purchasing quantity of the power distribution network as objective functions to obtain an optimized double-layer planning model;
and planning the corresponding virtual power plant for each subarea according to the optimized double-layer planning model.
9. A virtual plant planning apparatus, the apparatus comprising:
the distribution network management system comprises a dividing module, a management module and a management module, wherein the dividing module is used for dividing a distribution network according to the load of each node in the distribution network, the load time distribution and the physical distance between different nodes to obtain h partitions, and the partitions comprise at least one node;
and the planning module is used for planning each corresponding virtual power plant for the subarea according to a preset double-layer planning model, wherein the virtual power plants are used for responding and corresponding to the power consumption demand of each node in the subarea.
10. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the virtual plant planning method of any of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the virtual plant planning method according to any one of claims 1-8.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117036100A (en) * | 2023-08-18 | 2023-11-10 | 北京知达客信息技术有限公司 | Dynamic scheduling system for virtual power plant resource aggregation |
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