CN112200596B - Method, system, device and medium for determining regional marginal electricity price of power system - Google Patents
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Abstract
The invention discloses a method, a system, a device and a medium for determining regional marginal electricity price of an electric power system, wherein the method comprises the following steps: acquiring node information of each node of the power system, and dividing the power system into a plurality of areas according to the node information; establishing a power generation cost optimization model, and constructing an extended Lagrangian function according to the power generation cost optimization model; and acquiring quotation information of the electric energy spot market, and determining regional marginal electricity prices of all the regions according to the quotation information, the extended Lagrange function and the electricity generation cost optimization model. According to the embodiment of the invention, the node marginal electricity price of each node is not required to be calculated, so that the calculated amount is greatly reduced, the requirement on the system calculation force is reduced, the accuracy of the regional marginal electricity price is improved, the power generation cost is reduced, and the stable operation of the power system is ensured. The invention can be widely applied to the technical field of power systems.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method, a system, a device and a medium for determining regional marginal electricity prices of a power system.
Background
The off-the-shelf electricity price mechanism is a vital ring in off-the-shelf design. In the long-term development of the world's electric power market, three electricity price mechanisms are mainly formed: system marginal electricity price, regional marginal electricity price and node marginal electricity price. The node marginal electricity price is the most accurate, the price is generated by an optimization algorithm, the price is opaque to market members, the understandability and operability are poor, and the node marginal electricity price is not easily accepted by the market members; the system marginal electricity price adopts unified price for nodes in all market places, so that the system marginal electricity price is convenient to understand and operate, but the price difference between different nodes and different areas is stabilized, and the effect of price guiding resource optimization configuration cannot be fully exerted. In an actual power system, when the system load increases to a certain extent, line blockage occurs, so that node marginal prices at two ends of the blocked line are different, but the blockage often occurs in a specific power transmission line, and the blockage does not exist everywhere. The regional marginal electricity price is generated, has the advantages of node marginal electricity price and system marginal electricity price, reserves a part of space signals, reduces data volume, reduces the complexity of settlement, and has good practical value.
In the prior art, area division is generally performed according to node marginal electricity prices of all nodes, nodes with the same or similar node marginal electricity prices are divided into an area, and then weighted average calculation is performed by taking the load of each node in the area as weight, so that the area marginal electricity price of the area is obtained. However, the method needs to obtain the marginal electricity price of each node through an optimization algorithm, so that the calculation amount is huge, and the calculation force requirement on the system is high; in addition, the regional marginal electricity price obtained through weighted average calculation is influenced by the calculation precision of the node marginal electricity price of each node, and the obtained regional marginal electricity price is inaccurate, so that the stable operation of the power system is influenced.
Noun interpretation:
electric energy spot market: the electric energy transaction activities are collectively developed in the day before and in a shorter time through the transaction mechanism from the day before to the real-time dispatching;
day-ahead node electricity price: spot market price of the power system node before the operation day;
regional marginal electricity price: the increase in the total power generation cost per unit load of the region may represent the effect of the increase in the unit load per unit of the region power consumption load on the total power generation cost.
KKT conditions: karush-Kuhn-Tucker Conditions is a necessary and sufficient condition for a nonlinear programming problem to have an optimization solution under some regular conditions, and the KKT condition can extend the Lagrangian multiplier method to constraint optimization problems involving inequality.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art to a certain extent.
Therefore, an object of the embodiment of the invention is to provide a method for determining regional marginal electricity price of an electric power system, which determines regional marginal electricity price of each region through a constraint optimization model and a lagrangian multiplier method without calculating node marginal electricity price of each node, thereby greatly reducing calculated amount, improving accuracy of regional marginal electricity price, reducing power generation cost and guaranteeing stable operation of the electric power system.
It is another object of an embodiment of the present invention to provide a power system regional marginal price determining system.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides a method for determining regional marginal electricity prices of an electric power system, including the steps of:
acquiring node information of each node of the power system, and dividing the power system into a plurality of areas according to the node information;
establishing a power generation cost optimization model, and constructing an extended Lagrangian function according to the power generation cost optimization model;
and acquiring quotation information of the electric energy spot market, and determining regional marginal electricity prices of all the regions according to the quotation information, the extended Lagrange function and the electricity generation cost optimization model.
Further, in an embodiment of the present invention, the node information includes a day-ahead node electricity price of each node of the power system in a preset period and a positional relationship between each node of the power system, and the step of obtaining the node information of each node of the power system, and dividing the power system into a plurality of areas according to the node information specifically includes:
acquiring day-ahead node electricity prices of all nodes of the power system in a preset period, and establishing a node electricity price matrix according to the day-ahead node electricity prices;
acquiring the position relation among all nodes of the power system, and establishing a node adjacency matrix according to the position relation;
establishing a power flow transfer distribution factor matrix according to a preset direct current power flow model, and generating a node similarity matrix according to the node power price matrix, the node adjacent matrix and the power flow transfer distribution factor matrix;
and carrying out clustering calculation on the node similarity matrix to obtain a plurality of areas and determining the area where each node of the power system is located.
Further, in one embodiment of the present invention, the step of performing cluster computation on the node similarity matrix specifically includes:
and obtaining the feature vector of each node of the power system according to the node similarity matrix, and carrying out clustering calculation on the feature vector class through machine learning.
Further, in one embodiment of the present invention, the step of establishing a power generation cost optimization model and constructing an extended lagrangian function according to the power generation cost optimization model specifically includes:
establishing a power generation cost optimization model according to the standard electricity quantity in the generator set, and determining a first constraint condition of the power generation cost optimization model according to the region;
constructing an extended Lagrangian function according to the first constraint condition;
the power generation cost optimization model comprises a first objective function for minimizing power generation cost and a first constraint condition, wherein the first constraint condition comprises an active balance constraint, an area total power load constraint, a unit output constraint and a line power flow constraint.
Further, in one embodiment of the present invention, the first objective function is:
wherein N represents a set of all power system nodes, g i Representing the nominal power quantity f of the generator set at the node i i (g i ) Representing the cost of power generation at node i.
Further, in one embodiment of the invention, the area overall electrical load constraint is:
wherein d j Represents the power load at node j, ζ represents the area, Z represents the set of all areas, N (ζ) represents the set of all power system nodes within area ζ, D ζ Indicating the overall electrical load of the area.
Further, in one embodiment of the present invention, the step of obtaining the quotation information of the electric energy spot market and determining the regional marginal price of each region according to the quotation information, the extended lagrangian function and the power generation cost optimization model specifically includes:
obtaining quotation information of the electric energy spot market according to quotation curves of all the generator sets of the electric power system;
according to quotation information, the extended Lagrangian function and the power generation cost optimization model, calculating to obtain an optimal solution of the power generation cost optimization model and a Lagrangian multiplier of the extended Lagrangian function by using KKT conditions;
and determining regional marginal electricity prices of the regions according to the Lagrangian multipliers.
In a second aspect, an embodiment of the present invention provides a power system regional marginal electricity price determining system, including:
the regional division module is used for obtaining node information of each node of the power system, and carrying out regional division on the power system according to the node information to obtain a plurality of regions;
the optimization model building module is used for building a power generation cost optimization model and constructing an extended Lagrangian function according to the power generation cost optimization model;
and the regional marginal electricity price determining module is used for acquiring quotation information of the electric energy spot market and determining regional marginal electricity prices of all the regions according to the quotation information, the extended Lagrange function and the power generation cost optimizing model.
In a third aspect, an embodiment of the present invention provides an apparatus for determining regional marginal electricity prices of an electric power system, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a power system area marginal electricity price determination method as described above.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium in which a processor-executable program is stored, which when executed by a processor is configured to perform one of the above-described power system area marginal electricity price determining methods.
The advantages and benefits of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
According to the embodiment of the invention, the area division is carried out on the electric power system according to the node information of each node of the electric power system, and then the regional marginal electricity price of each area is determined through the constraint optimization model and the Lagrangian multiplier method, so that the node marginal electricity price of each node is not required to be calculated, the calculated amount is greatly reduced, and the requirement on the system calculation force is reduced; meanwhile, errors caused by node marginal electricity prices and weights of all the nodes are avoided, accuracy of regional marginal electricity prices is improved, power generation cost is reduced, and stable operation of a power system is guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will refer to the drawings that are needed in the embodiments of the present invention, and it should be understood that the drawings in the following description are only for convenience and clarity to describe some embodiments in the technical solutions of the present invention, and other drawings may be obtained according to these drawings without any inventive effort for those skilled in the art.
Fig. 1 is a step flowchart of a method for determining regional marginal electricity price of an electric power system according to an embodiment of the present invention;
fig. 2 is a block diagram of a power system regional marginal electricity price determining system according to an embodiment of the present invention;
fig. 3 is a block diagram of a power system regional marginal electricity price determining device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the description of the present invention, the plurality means two or more, and if the description is made to the first and second for the purpose of distinguishing technical features, it should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a method for determining regional marginal electricity prices of an electric power system, including the steps of:
s101, acquiring node information of each node of a power system, and dividing the power system into a plurality of areas according to the node information;
specifically, the power system can be divided into areas according to the day-ahead node electricity prices of all the nodes of the power system, wherein the day-ahead node electricity prices are spot market prices of the nodes of the power system before the operation day, so that the day-ahead node electricity prices are stored in the system and are acquired without additional calculation.
Further as an optional implementation manner, the node information includes a day-ahead node electricity price of each node of the electric power system in a preset period and a positional relationship between each node of the electric power system. The position relation among the nodes of the power system comprises whether the nodes are adjacent or not, and in order to ensure the node communication in each area after the partition, the power system can be divided into areas according to the current node electricity price of each node of the power system and the position relation among the nodes. The step S101 specifically includes the following steps:
s1011, acquiring day-ahead node electricity prices of all nodes of the power system in a preset period, and establishing a node electricity price matrix according to the day-ahead node electricity prices;
specifically, a day-ahead node electricity price P for a plurality of preset time periods (e.g., 30 days in whole month, 24 time periods in day, and 720 total time periods) is acquired, and a node electricity price matrix is established as follows:
wherein X is 1 Representing a node electricity price matrix, wherein the number of rows of the matrix is the total number T of time periods, and the number of columns is the total number N, P of nodes T-N The node electricity rate before the day of the period T of the node N is represented.
S1012, acquiring the position relation among all nodes of the power system, and establishing a node adjacency matrix according to the position relation;
specifically, to ensure node connectivity in each region, a node adjacency matrix may be constructed according to whether nodes are adjacent, where the matrix is an N-order square matrix, and the number of rows and columns is equal to the total number of nodes N. The matrix element takes 0 or 1, wherein taking 0 indicates that two nodes corresponding to the element are not adjacent, and taking 1 indicates that two nodes corresponding to the element are adjacent. In particular, the value 0 is also taken when the number of rows in which the matrix element is located is equal to the number of columns.
S1013, establishing a power flow transfer distribution factor matrix according to a preset direct current power flow model, and generating a node similarity matrix according to a node power price matrix, a node adjacent matrix and the power flow transfer distribution factor matrix;
specifically, based on a direct current power flow model of the power system, a power flow transfer distribution factor matrix is established as follows:
wherein X is 2 Representing a power flow transfer distribution factor matrix, wherein the number of lines of the matrix is L, and the number of columns is N, G L-N Representing the increment of the power flow of the node N on the line L after the unit power input is increased;
and then constructing a node similarity matrix according to the node electricity price matrix, the power flow transfer distribution factor matrix and the node adjacency matrix.
And S1014, carrying out clustering calculation on the node similarity matrix to obtain a plurality of areas and determining the area where each node of the power system is located.
Further as an optional implementation manner, the step of clustering calculation is performed on the node similarity matrix, which specifically includes:
and obtaining the feature vector of each node of the power system according to the node similarity matrix, and carrying out clustering calculation on the feature vector class through machine learning.
Specifically, the number of areas may be preset, and then the node similarity matrix is clustered according to the number of areas through machine learning, so that each node of the power system is divided into each area. The clustering algorithm which can be adopted is a k-Means clustering algorithm, a DBSCAN clustering algorithm, a spectral clustering algorithm, a depth random walk algorithm and the like. The node similarity matrix obtained by the embodiment of the invention has larger dimension, the clustering is more complex by the k-Means clustering algorithm, and the obtained partition result is inaccurate. Therefore, the embodiment of the invention adopts a spectral clustering algorithm, and the algorithm can vectorize the nodes according to the node similarity matrix and then cluster the nodes, and compared with other clustering algorithms, the partition result obtained by the method is more accurate.
S102, establishing a power generation cost optimization model, and constructing an extended Lagrangian function according to the power generation cost optimization model;
specifically, the power generation cost optimization model includes a first objective function that minimizes the power generation cost and a first constraint condition that includes an active balance constraint, a regional overall power load constraint, a unit output constraint, and a line flow constraint. The step S102 specifically includes the following steps:
s1021, establishing a power generation cost optimization model according to the standard electricity quantity in the generator set, and determining a first constraint condition of the power generation cost optimization model according to the region;
specifically, the first constraint condition includes an equality constraint and an inequality constraint, wherein the equality constraint includes an active balance constraint and a regional overall electrical load constraint, and the inequality constraint includes a unit output constraint and a line power flow constraint.
Further as an alternative embodiment, the first objective function is:
wherein N represents a set of all power system nodes, g i Representing the nominal power quantity f of the generator set at the node i i (g i ) Representing the cost of power generation at node i.
Specifically, in practical use f i (g i ) And the bid amount and the corresponding quotation of the generator set at the node i are calculated.
Further as an alternative embodiment, the area overall electrical load constraint is:
wherein d j Represents the electrical load at node j, ζ representsZone Z represents the set of all zones, N (ζ) represents the set of all power system nodes within zone ζ, D ζ Indicating the overall electrical load of the area,
representing the full scale word.
Optionally, the active balance constraint is that the sum of the power load of each node of the power system is equal to the winning capacity of each node of the generator set. The active balance constraint can be described as:
wherein d j Representing the electrical load at node j.
Alternatively, the unit output constraints are such that the output of each generator unit should be within its maximum/minimum output range. The unit output constraint can be described as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
i Grespectively representing the upper and lower limits of the output of the generator set at the node i.
Optionally, the line power flow is constrained such that the line power flow does not exceed the maximum transmission capacity. Line flow constraints can be described as:
wherein L represents a set of all power transmission lines, GSF represents a transposed matrix of a system power flow transfer distribution factor matrix, and GSF i-l The element of the ith row and the first column in the transposed matrix is in the meaning that the increment of the power flow on the line l is added at the node i when the unit active input is added, U l The limit transport capacity of line l is indicated.
S1022, constructing an extended Lagrangian function according to the first constraint condition.
Specifically, in mathematical optimization problems, the Lagrangian multiplier method is a method of finding extrema of a multiple function whose variables are limited by one or more conditions. This method converts an optimization problem with n variables and k constraints into an extremum problem for a system of equations with n+k variables, which variables are not subject to any constraints. This approach introduces a new scalar unknowns, i.e., lagrangian multipliers. In the embodiment of the invention, the value of the Lagrangian multiplier corresponding to the regional overall power load constraint is the obtained regional marginal electricity price while the extremum of the objective function is obtained.
The extended lagrangian function constructed in the embodiment of the present invention is as follows:
wherein Y represents the total cost of power generation, mu ζ 、
Respectively the lagrangian multipliers of the constraints.
And S103, acquiring quotation information of the electric energy spot market, and determining regional marginal electricity prices of all regions according to the quotation information, the extended Lagrange function and the power generation cost optimization model.
Specifically, according to quotation information of the electric energy spot market, the constructed extended Lagrangian function, the power generation cost optimization model, constraint conditions of the power generation cost optimization model and KKT conditions, solving an optimal solution of the power generation cost optimization model and Lagrangian multipliers corresponding to the constraint conditions, wherein the value of the Lagrangian multiplier corresponding to the regional overall power load constraint is regional marginal electricity price, and particularly, each region corresponds to one Lagrangian multiplier, so that regional marginal electricity price of each region is determined. Step S103 specifically includes the following steps:
s1031, obtaining quotation information of the electric energy spot market according to quotation curves of all the generator sets of the electric power system;
specifically, in the electric energy spot market, a multi-stage quotation curve is declared by the generator set, and is summarized by the electric power transaction system.
S1032, calculating an optimal solution of the power generation cost optimization model and Lagrangian multipliers of the extended Lagrangian function by using KKT conditions according to the quotation information, the extended Lagrangian function and the power generation cost optimization model;
specifically, the KKT conditions of the examples of the present invention are as follows:
(1)
(2)
(3)
/>
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the gradient operator.
Solving by using the KKT condition to obtain an optimal solution g i (i∈N)、D ζ (ζ ε Z) and each Lagrangian multiplier.
S1033, determining regional marginal electricity prices of all the regions according to Lagrange multipliers.
Specifically, the regional marginal electricity price, i.e., the increase in the total cost of electricity generation per unit load of the region, may represent the regional electricity loadThe impact of unit increment of (c) on the total cost of power generation. According to the definition of regional marginal price, the regional marginal price p of the region ζ can be obtained ζ The method comprises the following steps:
from this, it can be seen that the Lagrangian multiplier μ obtained by the solution ζ The regional marginal electricity price of region ζ can be determined.
According to the embodiment of the invention, the area division is firstly carried out on the electric power system according to the node information of each node of the electric power system, then the regional marginal electricity price of each area is determined through the constraint optimization model and the Lagrange multiplier method, and compared with the traditional weighted average calculation method based on the node regional electricity price, the node marginal electricity price of each node is not required to be calculated, so that the calculated amount is greatly reduced, and the requirement on the calculation force of the system is reduced; meanwhile, errors caused by node marginal electricity prices and weights of all the nodes are avoided, accuracy of regional marginal electricity prices is improved, power generation cost is reduced, and stable operation of a power system is guaranteed.
Referring to fig. 2, an embodiment of the present invention provides a power system regional marginal electricity price determining system including:
the regional division module is used for acquiring node information of each node of the power system, and dividing the power system into a plurality of regions according to the node information;
the optimization model building module is used for building a power generation cost optimization model and constructing an extended Lagrangian function according to the power generation cost optimization model;
the regional marginal electricity price determining module is used for acquiring quotation information of the electric energy spot market and determining regional marginal electricity price of each region according to the quotation information, the extended Lagrangian function and the power generation cost optimizing model.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
Referring to fig. 3, an embodiment of the present invention provides a power system regional marginal electricity price determining apparatus, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a power system regional marginal electricity price determination method as described above.
The content in the method embodiment is applicable to the embodiment of the device, and the functions specifically realized by the embodiment of the device are the same as those of the method embodiment, and the obtained beneficial effects are the same as those of the method embodiment.
The embodiment of the present invention also provides a computer-readable storage medium in which a processor-executable program is stored, which when executed by a processor is for performing the above-described one power system area marginal electricity price determination method.
The computer readable storage medium of the embodiment of the invention can execute the method for determining the regional marginal electricity price of the power system, which is provided by the embodiment of the method of the invention, and can execute the implementation steps of any combination of the embodiment of the method, thereby having the corresponding functions and beneficial effects of the method.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the present invention has been described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features described above may be integrated in a single physical device and/or software module or one or more of the functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above-described method of the various embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium upon which the program described above is printed, as the program described above may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (6)
1. The regional marginal electricity price determining method for the electric power system is characterized by comprising the following steps of:
acquiring node information of each node of the power system, and dividing the power system into a plurality of areas according to the node information;
establishing a power generation cost optimization model, and constructing an extended Lagrangian function according to the power generation cost optimization model;
acquiring quotation information of an electric energy spot market, and determining regional marginal electricity prices of all the regions according to the quotation information, the extended Lagrangian function and the power generation cost optimization model;
the node information comprises the day-ahead node electricity price of each node of the power system in a preset period and the position relation among the nodes of the power system, the step of obtaining the node information of each node of the power system and dividing the power system into a plurality of areas according to the node information comprises the following steps:
acquiring day-ahead node electricity prices of all nodes of the power system in a preset period, and establishing a node electricity price matrix according to the day-ahead node electricity prices;
acquiring the position relation among all nodes of the power system, and establishing a node adjacency matrix according to the position relation;
establishing a power flow transfer distribution factor matrix according to a preset direct current power flow model, and generating a node similarity matrix according to the node power price matrix, the node adjacent matrix and the power flow transfer distribution factor matrix;
clustering calculation is carried out on the node similarity matrix to obtain a plurality of areas and the area where each node of the power system is located is determined;
the step of establishing a power generation cost optimization model and constructing an extended Lagrangian function according to the power generation cost optimization model specifically comprises the following steps:
establishing a power generation cost optimization model according to the standard electricity quantity in the generator set, and determining a first constraint condition of the power generation cost optimization model according to the region;
constructing an extended Lagrangian function according to the first constraint condition;
the power generation cost optimization model comprises a first objective function for minimizing power generation cost and a first constraint condition, wherein the first constraint condition comprises an active balance constraint, an area total power load constraint, a unit output constraint and a line power flow constraint;
the first objective function is:
wherein N represents a set of all power system nodes, g i Representing the nominal power quantity f of the generator set at the node i i (g i ) Representing the cost of power generation at node i;
the step of obtaining quotation information of the electric energy spot market and determining regional marginal electricity prices of all the regions according to the quotation information, the extended Lagrangian function and the electricity generation cost optimization model specifically comprises the following steps:
obtaining quotation information of the electric energy spot market according to quotation curves of all the generator sets of the electric power system;
according to quotation information, the extended Lagrangian function and the power generation cost optimization model, calculating to obtain an optimal solution of the power generation cost optimization model and a Lagrangian multiplier of the extended Lagrangian function by using KKT conditions;
and determining regional marginal electricity prices of the regions according to the Lagrangian multipliers.
2. The method for determining regional marginal electricity prices of electric power systems according to claim 1, wherein the step of clustering the node similarity matrix is specifically as follows:
and obtaining the feature vector of each node of the power system according to the node similarity matrix, and carrying out clustering calculation on the feature vector class through machine learning.
3. A method of determining regional marginal electricity prices of an electrical power system in accordance with claim 1 wherein said regional overall electricity load constraints are:
wherein d j Representing the electrical load at node j,
representing the region, Z representing the set of all regions, +.>
Representation area->
A set of all power system nodes in +.>
Indicating the overall electrical load of the area.
4. A power system regional marginal electricity price determining system, comprising:
the regional division module is used for obtaining node information of each node of the power system, and carrying out regional division on the power system according to the node information to obtain a plurality of regions;
the optimization model building module is used for building a power generation cost optimization model and constructing an extended Lagrangian function according to the power generation cost optimization model;
the regional marginal electricity price determining module is used for acquiring quotation information of the electric energy spot market and determining regional marginal electricity price of each region according to the quotation information, the extended Lagrangian function and the power generation cost optimizing model;
the node information comprises the day-ahead node electricity price of each node of the power system in a preset period and the position relation among each node of the power system, and the area dividing module is specifically used for:
acquiring day-ahead node electricity prices of all nodes of the power system in a preset period, and establishing a node electricity price matrix according to the day-ahead node electricity prices;
acquiring the position relation among all nodes of the power system, and establishing a node adjacency matrix according to the position relation;
establishing a power flow transfer distribution factor matrix according to a preset direct current power flow model, and generating a node similarity matrix according to the node power price matrix, the node adjacent matrix and the power flow transfer distribution factor matrix;
clustering calculation is carried out on the node similarity matrix to obtain a plurality of areas and the area where each node of the power system is located is determined;
the optimization model building module is specifically used for:
establishing a power generation cost optimization model according to the standard electricity quantity in the generator set, and determining a first constraint condition of the power generation cost optimization model according to the region;
constructing an extended Lagrangian function according to the first constraint condition;
the power generation cost optimization model comprises a first objective function for minimizing power generation cost and a first constraint condition, wherein the first constraint condition comprises an active balance constraint, an area total power load constraint, a unit output constraint and a line power flow constraint;
the first objective function is:
wherein N represents a set of all power system nodes, g i Representing the nominal power quantity f of the generator set at the node i i (g i ) Representing the cost of power generation at node i;
the regional marginal electricity price determining module is specifically used for:
obtaining quotation information of the electric energy spot market according to quotation curves of all the generator sets of the electric power system;
according to quotation information, the extended Lagrangian function and the power generation cost optimization model, calculating to obtain an optimal solution of the power generation cost optimization model and a Lagrangian multiplier of the extended Lagrangian function by using KKT conditions;
and determining regional marginal electricity prices of the regions according to the Lagrangian multipliers.
5. An electric power system regional marginal electricity price determining apparatus, characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when said at least one program is executed by said at least one processor, said at least one processor is caused to implement a power system area marginal electricity price determination method as claimed in any one of claims 1-3.
6. A computer-readable storage medium in which a processor-executable program is stored, characterized in that the processor-executable program, when executed by a processor, is for performing a power system area marginal electricity price determination method according to any one of claims 1-3.
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