CN115169786A - Power market demand side resource aggregation model scheduling method and device - Google Patents

Power market demand side resource aggregation model scheduling method and device Download PDF

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CN115169786A
CN115169786A CN202210542188.7A CN202210542188A CN115169786A CN 115169786 A CN115169786 A CN 115169786A CN 202210542188 A CN202210542188 A CN 202210542188A CN 115169786 A CN115169786 A CN 115169786A
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王蓓蓓
张悦
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Abstract

The invention discloses a demand side resource accurate polymerization model scheduling method, equipment and a storage medium in an electric power market background, wherein the method comprises the following steps: (1) Preparing data, including electricity price data of an electricity market, basic constraints of load equipment on different demand sides, operation parameter related information and the like; (2) Drawing a single equipment operation feasible region based on the Kino polyhedron carving; (3) Aggregating the feasible domains of the single equipment through Minkowski Sum operation to obtain a demand side resource accurate aggregation response model; (4) And delivering the aggregation model to a demand response aggregator agent, directly participating in wholesale market competition, and establishing a bidding strategy optimization decision model with the goal of maximizing the profit of the aggregator. According to the invention, high-dimensionality and large-scale feasible domain aggregation of demand side resources is realized, the complexity of aggregation calculation is reduced based on the expression form of a Cheyno polyhedron, and bidding decision scheduling is carried out based on a demand side resource aggregation response model in the power market background.

Description

Power market demand side resource aggregation model scheduling method and equipment
Technical Field
The invention relates to the power technology, in particular to a method, equipment and a storage medium for scheduling a demand side resource accurate aggregation model in a power market background.
Background
In recent years, a plurality of demand side flexibility resources (flexible loads, energy storage, electric vehicles and the like) are rapidly developed, the demand side resources participate in the market and have the advantages of low carbon, environmental protection, high flexibility, high response speed and the like, and compared with the traditional response resources, the demand side resources can better maintain stable operation of a power grid and can bring certain economic benefits.
However, most of the demand-side resources have the characteristics of dispersion, various types, different characteristics and the like, and are difficult to directly regulate and control. In order to make the demand-side resource fully play its role, improve the consumption of distributed resources, and fully mine the response characteristics of response resources, a resource aggregation technology is developed. Aggregation refers to integrating multiple demand-side resources with single characteristics and low power into a single or a plurality of aggregates with a scheduling mode with certain mobility and better coordinated system scheduling by a certain aggregation principle based on characteristic evaluation.
The demand side resource aggregation response agent may act as an intermediary agent for interaction between the demand side customer and the electricity marketplace. On one hand, the demand side resource aggregation response main body and the supply side power supply operation main body participate in power market transaction together, energy, capacity and auxiliary service are provided in various types of power markets such as the day ahead, standby and real-time power markets, and the overall social benefit is improved while the economic benefit of the demand side resource aggregation response main body and the supply side power supply operation main body are guaranteed.
Disclosure of Invention
The invention aims to provide a demand side resource accurate polymerization model scheduling method in the power market background, aiming at solving the problems in the prior art. Firstly, a single flexible equipment operation feasible region is drawn based on the Kino polyhedron carving and used for expressing single-period flexibility constraint and multi-period coupling constraint of the single-period flexibility constraint, then a demand-side resource accurate polymerization response model is obtained through Minkowski Sum (Minkowski Sum) operation, and finally a demand response aggregator participates in wholesale market competition and carries out decision scheduling with the aggregator profit maximization as a target.
The technical scheme is as follows: the invention relates to a demand side resource accurate polymerization model scheduling method in an electric power market background, which comprises the following steps:
s1, preparing data, including power market electricity price data, basic constraints of load equipment on different demand sides and relevant information of operation parameters;
s2, aiming at single-time-period flexibility constraint and multi-time-period coupling constraint of the distributed flexibility resources, describing a single device operation feasible region based on a Cheyno polyhedron;
s3, aggregating the feasible regions of the single equipment in the S2 through Minkowski Sum operation to obtain a demand side resource accurate aggregation response model;
and S4, delivering the aggregation model in the step S3 to a demand response aggregator agent, directly participating in wholesale market competition, and establishing a bidding strategy optimization scheduling model with the goal of maximizing the profit of the aggregator.
Optionally, the operation constraint of the demand side load device in S2 is specifically analyzed as follows:
considering a finite and discrete operation cycle Nt of demand side equipment, N operation time periods are total, and each time interval is t. p (t) is the operating power of the operating period t e [ (k-1) t, kt, k = 1., N, the flexibility feasible domain of the device i is described by the following single-period flexibility constraint and multi-period coupling constraint:
(1) Power constraint
Assuming that the power is constant for the device over the interval t, the power constraint can be expressed as:
p i，min ≤p i,k ≤p i，max ,k＝1,2,...,N (1)
wherein p is k Constant power for the demand side device i in the kth interval; p is a radical of formula min And p max Respectively the lower limit and the upper limit of the operating power of the equipment at the demand side;
(2) Electric quantity restraint
Figure RE-GDA0003837015260000021
Wherein e is i,min And e i,max Respectively setting a lower limit and an upper limit of the electric quantity constraint of the demand side equipment i;
(3) Climbing restraint
r i,min ≤p i,k -p i,k-1 ≤r i,max ,k＝2,3,...,N (3)
Wherein r is i,min And r i,max Respectively, a lower limit and an upper limit of the power variation of the demand side device i in the adjacent period.
The operating constraints of a series of demand-side devices, such as temperature control loads, storage loads, electric vehicles, and the like, can be described using equations (1) (2) (3). And because all the constraints are linear constraints, the feasible domain range of the equipment can be generalized to a convex polyhedron representation form, namely:
P＝{p∈R N :Ap≤b} (4)
wherein, P is a convex polyhedron representing the range of the feasible region of the equipment; n is the dimension of the convex polyhedron; (A, b) integrating all linear operation constraint coefficients of the plant and expressing the coefficients in a matrix form.
Optionally, the single device feasible domain depiction method based on the knowless polyhedron in S2 is as follows:
the knowns polyhedron is also called a fully symmetric polytope, which is a class of geometric objects made up of flat boundaries. The polytope may exist in any dimension to extend into spaces above three dimensions, such as the polytope. The knowlett-packard polyhedron Z is a special form of a convex polyhedron, has a central symmetry characteristic, and can be defined by a central point, a generator matrix, and corresponding expansion coefficients:
Figure RE-GDA0003837015260000031
wherein Z is a Qinuo polyhedron depicting the feasible region range of the equipment; c is the center of the polyhedron representing the geometric position; beta is a scaling coefficient for representing the corresponding direction of the generator, and determines the extending distance of the Kino polyhedron in the direction;
Figure RE-GDA0003837015260000032
is the upper limit of the scaling factor; g is a generator matrix characterizing the geometry, consisting of a plurality of generators:
G＝{g (1) ,g (2) ,...,g (M) }∈R N×M (6)
wherein M is the number of generators, g (j) ∈R N J =1, 2.., M denotes one of the generators and satisfies: g | | (j) I | =1, i.e. generator g (j) The extension direction of the knoeveness polyhedron is determined as a normal vector.
In the problem of describing a feasible region of a resource on a demand side by approximation of a knowless polyhedron, a generator matrix G is taken as a known quantity, and aiming at power constraint, electric quantity constraint and climbing constraint of the equipment in S1, a corresponding generator matrix is designed as follows:
Figure RE-GDA0003837015260000033
Figure RE-GDA0003837015260000034
Figure RE-GDA0003837015260000035
wherein, the formula (7) is the first n generators, corresponding to the n power constraints of the load devices; formula (8) is the middle n-1 generators, corresponding to n-1 electric quantity constraints of the load equipment; equation (9) is the last n-1 generators, corresponding to the n-1 ramp constraints of the load device. The generator matrix is thus obtained as G ∈ R N×(3N-2) ；
The knoeveness polyhedron has the characteristic of convenience in calculating Minkowski Sum, and the complexity of operation can be greatly simplified in the process of realizing high-dimensionality and large-scale equipment feasible domain aggregation, so that the feasible domain of the equipment can be approximately described by adopting the expression of the knoeveness polyhedron, namely, the expression form of the formula (5) is converted into the expression form of the formula (6), the problem can be modeled into an optimization problem, and the modeling process is as follows:
(1) Objective function
Intuitively, on the premise of ensuring that Z belongs to P, the larger the volume of the knowless polyhedron is, the higher the approximation degree of the knowless polyhedron with the feasible region of the original equipment is, however, the vertex expression is required for calculating the volume of the convex polyhedron, and in the high-dimensional spaceIn the process, a feasible domain is converted into a vertex expression from a half-space expression, the calculation complexity is high, and therefore the n is arbitrarily constructed by adopting a target transformation method f A normal vector f (1) ,f (2) ,...,f (nf) And searching diameters of the feasible regions Z and P in the direction f by solving a linear programming problem, and defining similarity according to the relation between the position and the length ratio:
Figure RE-GDA0003837015260000041
wherein: delta Z,l And Δ P,l Respectively two feasible fields in a normal vector f (l) A diameter in the direction; lambda f Closer to 1 represents a higher degree of similarity between the knoeveness polyhedron and the primitive feasible region. And can be derived by mathematical derivation:
Figure RE-GDA0003837015260000042
wherein F is n f Matrix of normal vectors:
Figure RE-GDA0003837015260000043
substituting equation (10) into equation (11) yields the objective function as:
Figure RE-GDA0003837015260000044
(2) Constraint conditions
The approximation process adopts an internal approximation method, so that the constraint condition is that the obtained knowless polyhedron is located in the feasible domain of the original equipment, namely:
Figure RE-GDA0003837015260000045
formula can be converted into inequality constraint through mathematical derivation
Figure RE-GDA0003837015260000046
Namely:
Figure RE-GDA0003837015260000047
to sum up, the fano polyhedron solution model corresponding to the feasible domain of the equipment is as follows:
Figure RE-GDA0003837015260000048
optionally, the demand-side resource accurate aggregation response model obtained based on Minkowski Sum operation in S3 is as follows:
in order to reduce the decision complexity at the system operator level, the load aggregator needs to aggregate the feasible domains of all users to form an aggregated feasible domain of a user cluster, wherein the aggregated feasible domain represents the flexibility adjustable range of all devices j when controlled simultaneously, and the aggregated feasible domain Z is expressed based on the kiro polyhedron agg Can be implemented by Minkowski summation (Minkowski Sum), which can be expressed as:
Figure RE-GDA0003837015260000049
wherein Z 1 ,Z 2 ,...,Z J For the Sino polyhedron, Z, corresponding to the flexible feasible domain of a single demand side device under a load aggregator agg Is a feasible domain obtained by aggregation.
Minkowski Sum is the Sum of a set of points of the two euclidean spaces a and B, also called the dilated set of these two spaces, a vector is made from the origin to each point inside the graph a, the graph B is moved along each vector, all the final positions are then Minkowski sums (with commutative law). The feasible region Minkowski Sum based on the expression of the Kino polyhedron has high calculation efficiency, large-scale load equipment and the equipment flexibility feasible region with high time dimension can be easily aggregated, and the aggregation model is as follows:
Figure RE-GDA0003837015260000051
Figure RE-GDA0003837015260000052
wherein, c agg And
Figure RE-GDA0003837015260000053
the central point and the expansion coefficient of the singular voronoi polyhedron obtained by aggregation are respectively, namely, the aggregation model can be obtained only by adding the feasible central point corresponding to the feasible domain of the single load equipment and the expansion technology.
Optionally, in S4, the bidding strategy optimization scheduling model based on the demand side aggregation model in the power market is as follows:
obtaining an aggregation response model of demand side resources through aggregation, submitting the aggregation to demand response aggregators to participate in electric power wholesale market competition, and enabling each demand response aggregator to increase upwards in each time period through selection
Figure RE-GDA0003837015260000054
Or cut down
Figure RE-GDA0003837015260000055
The load amount pursues the self profit maximization, and the decision variable is
Figure RE-GDA0003837015260000056
And
Figure RE-GDA0003837015260000057
the demand response aggregator p has a secondary electricity utilization benefit function u at time t p,t (DR p,t ) Can be expressed as:
Figure RE-GDA0003837015260000058
wherein, P =1,2, \8230, P; t =1,2, \ 8230;, T, v p,t And w p,t Are utility function parameters, all are non-negative real numbers; l is p,t Is the base load of the load aggregator p, i.e. the normal electrical load that does not participate in demand response.
The profit of the demand response aggregator in the electric wholesale market is equal to the benefit it gains from using electric energy at all times minus the corresponding electricity purchase cost. The optimization problem of each demand response aggregator in the wholesale market in the T period can be expressed as:
Figure RE-GDA0003837015260000059
wherein r is t Is the price of the wholesale market at time t; l is p,max Represents the maximum load capacity of the demand response aggregator p; lambda i Lambda is a load reduction coefficient of 0 or more i ≤1。
As can be seen from equation (20), the role of the demand side response aggregator varies in different situations:
(1)
Figure RE-GDA0003837015260000061
the method comprises the following steps: at this time, the aggregator is equivalent to selling electric energy in the wholesale market at the electric energy price at the moment, and the role of the aggregator is similar to that of the generator
(2)
Figure RE-GDA0003837015260000062
The method comprises the following steps: at the moment, the role of the system is an electric energy demander, and when one unit of electric energy is consumed, corresponding electricity purchasing cost needs to be paid according to the wholesale market price at the moment.
The invention also provides equipment for a demand side resource accurate polymerization model scheduling strategy in the power market background, which comprises the following steps:
one or more processors;
a memory for storing one or more programs;
the one or more programs are executed by the one or more processors, causing the one or more processors to implement the above-described methods.
Furthermore, the present invention also provides a storage medium containing computer-executable instructions, which stores at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to implement the above method.
Has the beneficial effects that: compared with the prior art, the demand side resource accurate aggregation model scheduling method provided by the invention has the following advantages: the flexible feasible region of the demand side resource is accurately depicted based on the expression form of the Kino polyhedron, high dimensionality and large-scale feasible region aggregation is realized through Minkowski Sum operation, an aggregation response model of the demand side resource is obtained, decision scheduling is carried out under the power market background, and the demand response of flexible users is effectively encouraged while the economic benefit of a load aggregator is improved.
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Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
fig. 2 is a schematic diagram of a scheduling control device of a demand-side resource precision aggregation model in an electric power market background according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus provided in a third embodiment of the present invention;
Detailed Description
The process of the invention is further illustrated below with reference to the examples.
The invention discloses a method for scheduling a demand side resource accurate aggregation model in an electric power market background, which comprises the following steps:
step one, data preparation, including power market electricity price data, basic constraints of load equipment on different demand sides, operation parameter related information and the like;
secondly, aiming at single-time-interval flexibility constraint and multi-time-interval coupling constraint of the distributed flexible resources, a single equipment operation feasible region is carved based on a Knoop polyhedron;
step three, based on a bottom-up aggregation thought, aggregating the feasible regions of the single equipment through Minkowski Sum operation to obtain a demand side resource accurate aggregation response model;
and step four, delivering the aggregation model to a demand response aggregator agent, directly participating in wholesale market competition, and establishing a bidding strategy optimization scheduling model with the goal of maximizing the profit of the aggregator.
Example one
In the step one, the power of a certain air conditioner in two time periods is considered to be x 1 And x 2 The electric quantity constraint and the power constraint are as follows:
Figure RE-GDA0003837015260000071
firstly, the expression mode of a convex polyhedron P (A, b) is used for depicting a feasible region:
Figure RE-GDA0003837015260000072
this gives:
Figure RE-GDA0003837015260000073
namely:
Figure RE-GDA0003837015260000081
solving a kirenol polyhedron corresponding to the feasible region through the model in the step two, wherein a generator matrix obtained by constraint conditions is as follows:
Figure RE-GDA0003837015260000082
constructing a normal vector matrix as follows:
Figure RE-GDA0003837015260000083
substituting the data into the model yields:
Figure RE-GDA0003837015260000084
Figure RE-GDA0003837015260000085
and finally, solving to obtain a Knoop polyhedron as follows:
Figure RE-GDA0003837015260000086
wherein c = (1.5 ), β = [0.25, 0.354]
Example two
Fig. 2 is a schematic diagram of a scheduling control device of a demand-side resource precision aggregation model in an electric power market background according to a second embodiment of the present invention. The present embodiment may be applicable to the case of performing scheduling simulation on the target resource in the day ahead, and the apparatus may be implemented in a software and/or hardware manner, and may be configured in the terminal device. The determination device includes: a measured flexibility resource parameter obtaining module 410 and a flexibility resource scheduling amount output module 420.
The measured flexible resource parameter obtaining module 410 is configured to obtain a measured state parameter and a measured resource parameter of a target resource.
And the measured flexible resource scheduling output module 420 is configured to input the measured parameter of the target flexible resource into the target decision model, so as to obtain scheduling output of the measured flexible resource.
The device for determining the scheduling strategy of the demand side resource precision aggregation model in the power market context provided by the embodiment of the invention can be used for executing the method for determining the scheduling strategy of the demand side resource precision aggregation model in the power market context provided by the embodiment of the invention, and has corresponding functions and beneficial effects of the execution method.
It should be noted that, in the embodiment of the determining apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention, where the embodiment of the present invention provides a service for implementing the method for determining a scheduling policy of a precise aggregation model of resources on a demand side in an electric power market context according to the third embodiment of the present invention, and a device for determining a scheduling policy of a precise aggregation model of resources on a demand side in an electric power market context in the above embodiment may be configured. Fig. 3 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 3 is only an example and should not impose any limitation on the functionality and scope of use of embodiments of the present invention.
As shown in FIG. 3, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 3, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes programs stored in the system memory 28 to execute various functional applications and data processing, for example, to implement the method for determining the scheduling policy of the demand-side resource precision aggregation model in the power market context provided by the embodiment of the present invention.
Through the equipment, under the condition that feasible domains of various demand side flexible resources are fully considered, high-dimensional and large-scale aggregation of the demand side resources is realized, decision scheduling is carried out under the power market background, and the flexible users are effectively encouraged to carry out demand response while the economic benefit of a load aggregator is improved.
Example four
The fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions, when executed by a computer processor, are configured to perform a method for determining a scheduling policy of a demand-side resource precision aggregation model in an electric power market context, where the method includes:
acquiring a measured parameter of a target resource;
and inputting the measured parameters into a preset target demand side resource accurate aggregation main body scheduling model to obtain the scheduling amounts of different flexible resources.
Computer storage media for embodiments of the present invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the above method operations, and may also perform related operations in the method for determining the demand-side resource precision aggregation model scheduling policy in the power market context provided by any embodiment of the present invention.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (7)

1. A demand side resource accurate aggregation model scheduling method in an electric power market background is characterized by comprising the following steps:
s1, preparing data, including power price data of a power market, basic constraints of load equipment on different demand sides and relevant information of operation parameters;
s2, aiming at single-time-interval flexibility constraint and multi-time-interval coupling constraint of the distributed flexible resources, drawing a single equipment operation feasible region based on a Knoop polyhedron;
s3, aggregating the feasible regions of the single equipment in the S2 through Minkowski Sum operation to obtain a demand side resource accurate aggregation response model;
and S4, delivering the aggregation model in the step S3 to a demand response aggregator agent, directly participating in wholesale market competition, and establishing a bidding strategy optimization scheduling model with the goal of maximizing the profit of the aggregator.
2. The method for scheduling the demand side resource precision aggregation model in the power market context according to claim 1, wherein the method for restricting the feasible region of the equipment operation in the step S2 comprises the following steps:
considering a finite and discrete running period Nt of demand side equipment, N running time periods are total, and each time interval is t; p (t) is the operating power of the operating period t e [ (k-1) t, kt, k = 1.
(1) Power constraint
Assuming that the power is constant in the device over the interval t, the power constraint can be expressed as:
p i，min ≤p i,k ≤p i，max ,k＝1,2,...,N (1)
wherein p is k Constant power for the demand side device i in the kth interval; p is a radical of min And p max Respectively, the lower limit and the upper limit of the operating power of the demand side equipment.
(2) Electric quantity restraint
Figure RE-FDA0003837015250000011
Wherein e is i,min And e i,max Respectively, a lower limit and an upper limit of the electric quantity constraint of the demand side device i.
(3) Climbing restraint
r i,min ≤p i,k -p i,k-1 ≤r i,max ,k＝2,3,...,N (3)
Wherein r is i,min And r i,max Respectively setting a lower limit and an upper limit of power variation of the demand side equipment i in adjacent time periods;
the feasible domain range of the device can be generalized to a convex polyhedron representation form, namely:
P＝{p∈R N :Ap≤b} (4)
wherein, P is a convex polyhedron representing the range of the feasible region of the equipment; n is the dimension of the convex polyhedron; (A, b) integrating all linear operation constraint coefficients of the plant and expressing the coefficients in a matrix form.
3. The method for scheduling the demand side resource precision aggregation model in the power market background according to claim 1, wherein the method for drawing the feasible operation domain of the single device based on the knowless polyhedron in the step S2 comprises the following steps:
the kiro polyhedron Z is defined by a central point, generator matrix and corresponding scaling factor:
Figure RE-FDA0003837015250000021
wherein Z is a Qinuo polyhedron depicting the feasible region range of the equipment; c is the center of the polyhedron representing the geometric position; beta is a scaling coefficient for representing the corresponding direction of the generator, and determines the extending distance of the Kino polyhedron in the direction;
Figure RE-FDA0003837015250000022
is the upper limit of the scaling factor; g is a generator matrix characterizing the geometry, consisting of a plurality of generators:
G＝{g (1) ,g (2) ,...,g (M) }∈R N×M (6)
wherein M is the number of generators, g (j) ∈R N J =1, 2., M denotes one of the generators and satisfies: g | | (j) I | =1, i.e. generator g (j) Determining the extending direction of the Qino polyhedron as a normal vector;
aiming at the power constraint, the electric quantity constraint and the climbing constraint of the equipment in the S1, designing a corresponding generator matrix as follows:
Figure RE-FDA0003837015250000023
Figure RE-FDA0003837015250000024
Figure RE-FDA0003837015250000025
wherein, formula (7) is the first n generators, corresponding to the n power constraints of the load device; formula (8) is the middle n-1 generators, corresponding to n-1 electric quantity constraints of the load equipment; the formula (9) is the last n-1 generators, and the matrix of the generators is obtained as G belonged to R corresponding to n-1 climbing constraints of the load equipment N×(3N-2) ；
Constructing an optimization model, wherein the modeling process is as follows:
(1) Objective function
Arbitrary construction of n by target transformation f A normal vector f (1) ,f (2) ,...,f (nf) And searching diameters of the feasible domains Z and P in the f direction by solving a linear programming problem, and defining similarity according to the relation between the position and the length ratio:
Figure RE-FDA0003837015250000031
wherein: delta of Z,l And Δ P,l Respectively two feasible domains in normal vector f (l) A diameter in the direction; lambda f The closer to 1, the higher the similarity between the fano polyhedron and the original feasible region; and can be derived by mathematical derivation:
Figure RE-FDA0003837015250000032
wherein F is n f Matrix of normal vectors:
Figure RE-FDA0003837015250000033
substituting equation (10) into equation (11) yields the objective function as:
Figure RE-FDA0003837015250000034
(2) Constraint conditions
The constraint condition is that the solved knowless polyhedron is positioned in the feasible region of the original equipment, namely:
Figure RE-FDA0003837015250000035
formula can be converted into inequality constraint through mathematical derivation
Figure RE-FDA0003837015250000036
Namely:
Figure RE-FDA0003837015250000037
the kirno polyhedron solving model corresponding to the feasible domain of the equipment is as follows:
Figure RE-FDA0003837015250000038
4. the method for scheduling the demand side resource precision aggregation model in the power market context according to claim 1, wherein the demand side resource precision aggregation response model obtained based on the Minkowski Sum operation in the step S3 includes the following steps:
the load aggregator needs to aggregate the feasible regions of all users to form an aggregate feasible region of a user cluster, and the aggregate feasible region Z expressed based on the Kino polyhedron agg Implementation by Minkowski Sum:
Figure RE-FDA0003837015250000039
wherein Z 1 ,Z 2 ,...,Z J For a single demand side equipment flexibility feasible region under a load aggregator, corresponding Knoop polyhedron, Z agg Feasible domains obtained for aggregation;
the polymerization model is as follows:
Figure RE-FDA0003837015250000041
Figure RE-FDA0003837015250000042
wherein, c agg And
Figure RE-FDA0003837015250000043
the central point and the expansion coefficient of the singular voronoi polyhedron obtained by aggregation are respectively, namely, the aggregation model can be obtained only by adding the feasible central point corresponding to the feasible domain of the single load equipment and the expansion technology.
5. The demand side resource precision aggregation model scheduling method according to claim 1, wherein the method for establishing a bidding strategy optimization scheduling model in step S4 comprises the following steps:
obtaining an aggregate response model of the demand side resources through aggregation, submitting the aggregate response model to demand response aggregators to participate in electric power wholesale market competition, and enabling each demand response aggregator to increase upwards in each time period through selection
Figure RE-FDA0003837015250000044
Or cut down
Figure RE-FDA0003837015250000045
The load amount is used for pursuing the maximization of the profit per se,the decision variable is
Figure RE-FDA0003837015250000046
And
Figure RE-FDA0003837015250000047
the demand response aggregator p has a secondary electricity utilization benefit function u at time t p,t (DR p,t ) Can be expressed as:
Figure RE-FDA0003837015250000048
wherein, P =1,2, \8230, P; t =1,2, \ 8230;, T, v p,t And w p,t Are utility function parameters, all are non-negative real numbers; l is a radical of an alcohol p,t Is the basic load of the load aggregation provider p, i.e. the normal electrical load which does not participate in the demand response;
the optimization problem of each demand response aggregator in the wholesale market in the T period can be expressed as:
Figure RE-FDA0003837015250000049
wherein r is t Is the price of the wholesale market at time t; l is a radical of an alcohol p,max Represents the maximum load capacity of the demand response aggregator p; lambda [ alpha ] i Lambda is a load reduction coefficient and is not less than 0 i ≤1。
6. An apparatus for a demand side accurate aggregation model scheduling policy in an electricity market context, comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs are executed by the one or more processors to cause the one or more processors to implement the demand-side resource precision aggregation model scheduling method in the power market context according to any one of claims 1 to 5.
7. A storage medium containing computer-executable instructions, wherein the storage medium stores at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the method for scheduling demand side accurate aggregation model of resources in power market context according to any one of claims 1 to 5.
CN202210542188.7A 2022-05-17 2022-05-17 Power market demand side resource aggregation model scheduling method and device Pending CN115169786A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611669A (en) * 2023-07-17 2023-08-18 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains
CN118154260A (en) * 2024-05-11 2024-06-07 国网浙江省电力有限公司丽水供电公司 Power distribution network resource aggregation method, system, computer equipment and storage medium

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611669A (en) * 2023-07-17 2023-08-18 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains
CN116611669B (en) * 2023-07-17 2023-09-19 华北电力大学 Method, system and electronic equipment for aggregating demand-side resource feasible domains
CN118154260A (en) * 2024-05-11 2024-06-07 国网浙江省电力有限公司丽水供电公司 Power distribution network resource aggregation method, system, computer equipment and storage medium

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