User demand elasticity quantification method and system considering multi-user side resource coordination

Abstract

The application belongs to the technical field of power systems, and provides a user demand elasticity quantification method and system considering multi-element user side resource cooperation, comprising: obtaining response models of different user side resources; considering a power grid framework, constructing a multi-element user side resource cooperation and mutual aid optimization model of the obtained response models; solving the constructed cooperation and mutual aid optimization model to obtain cooperation capability quantification results; based on the obtained cooperation capability quantification results, correcting electricity load demand of user participation in demand side response; according to the obtained electricity load demand correction amount, constructing a multi-dimensional elasticity coefficient matrix, quantifying user demand according to the constructed matrix, and completing user demand elasticity quantification considering multi-element user side resource cooperation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a user demand elasticity quantification method and system considering multi-element user side resource coordination. BACKGROUND

[0002] The statements in this section merely provide background information related to the application and do not necessarily constitute prior art.

[0003] With the rise of new power systems, the large-scale access of user side resources to power grids has brought new challenges to power grid operation; multi-element user side resources such as flexible loads, distributed power sources and distributed energy storage devices are growing explosively, and there is a deep coupling relationship between their time and space distribution characteristics and the physical structure of power grids, and their roles in power grid peak shaving and demand response are increasingly prominent. However, the high proportion of new energy access, the complex grid structure and the diversification of user demand have put higher requirements on the coordination ability between resources. Therefore, it is necessary to analyze the coordinated energy between multi-element user side resources under the constraint of the grid structure, depict the multi-dimensional influence of the coordination ability of multi-element user side resources on the load form, and then accurately formulate demand side response strategies.

[0004] At present, the quantification of the coordination ability of user side resources mainly adopts a centralized optimization model, and user side resources are guided to participate in response through economic incentives or dispatch instructions, but the physical limitations of the grid structure (such as line capacity, node voltage and topological connectivity) on the response energy of resources are not considered; for example, the output of distributed power sources may be limited by the carrying capacity of the local power grid, and the charging and discharging behavior of energy storage devices may exceed the limit of reverse power due to weak grid structure; although some related research has proposed a coordinated dispatching model based on grid constraints, it is mainly aimed at specific scenarios (such as fault recovery or new energy consumption), lacks universality, and does not construct an index for quantitatively evaluating the coordination ability.

[0005] At present, the research on user demand elasticity modeling is mainly based on the traditional price elasticity coefficient, which can only reflect the load adjustment capability under a single price signal, and does not fully consider the correction effect of the coordination of multi-element user side resources (such as the complementarity of energy storage and flexible loads) on demand response; for example, the discharge of distributed energy storage at the peak of electricity price may alleviate the load pressure, thereby affecting the load change quantity of demand response, but the existing model does not include such factors in the quantitative modeling of user demand elasticity. In addition, the existing demand elasticity model mainly uses single-dimensional price sensitivity analysis, which cannot represent the response characteristics of multi-dimensional indicators to load changes in peak and valley periods, and lacks a unified multi-dimensional elasticity matrix modeling method.

[0006] In summary, the existing user side resource coordination ability quantification and user demand elasticity analysis have the following defects:

[0007] (1) The coupling mechanism of power grid physical constraints and multi-user side resource coordination has not been fully modeled, and the coordinated response capability of multi-user side resources cannot be accurately quantified;

[0008] (2) The modification of user demand elasticity considering user side resource coordination and the multi-dimensional demand elasticity quantification method need to be improved. SUMMARY

[0009] To solve the above problems, the present application provides a user demand elasticity quantification method and system considering multi-user side resource coordination, which integrates power grid network constraints and multi-user side resource coordination and mutual aid effects, quantifies the coordination capability of multi-user side resources considering the constraints of power grid network structure, constructs a multi-dimensional elasticity coefficient matrix based on the coordination capability quantification results, quantifies the elasticity of user multi-dimensional demand, and realizes the quantification analysis of user demand elasticity considering multi-user side resource coordination.

[0010] According to some embodiments, the first aspect of the present application provides a user demand elasticity quantification method considering multi-user side resource coordination, which adopts the following technical solution:

[0011] A user demand elasticity quantification method considering multi-user side resource coordination, comprising:

[0012] obtaining response models of different user side resources;

[0013] considering the power grid network structure, constructing a multi-user side resource coordination and mutual aid optimization model of the obtained response models;

[0014] solving the constructed coordination and mutual aid optimization model to obtain coordination capability quantification results;

[0015] based on the obtained coordination capability quantification results, modifying the electricity load demand of users participating in demand side response;

[0016] constructing a multi-dimensional elasticity coefficient matrix according to the obtained electricity load demand correction amount, quantifying user demand according to the constructed matrix, and completing the quantification of user demand considering multi-user side resource coordination.

[0017] As a further technical limitation, the objective function of the constructed multi-user side resource coordination and mutual aid optimization model is to minimize the total economic cost, which includes the cost of purchasing electricity from the upper-level power grid, the cost of load interaction, and the cost of energy storage scheduling; the constraint conditions of the coordination and mutual aid optimization model include power balance constraints, line flow constraints, translatable load constraints, transferable load constraints, reducible load constraints, distributed power supply constraints, and energy storage device constraints.

[0018] As a further technical limitation, the synergy capability quantification result is used to characterize the synergy capability between multiple user-side resources, i.e., the ratio of the power value change before and after different user-side resources participate in synergy to the power value when they do not participate in synergy.

[0019] As a further technical limitation, before the power load demand of the user participating in the demand-side response is corrected, a power demand price elasticity coefficient is obtained to characterize the sensitivity of the power demand to the price change, the load change of the user participating in the price-type demand-side response is calculated through the obtained power demand price elasticity coefficient, and the load demand of the user participating in the demand-side response is obtained; the synergy capability quantification result is used to correct the load demand of the user participating in the demand-side response, and the power load demand correction amount is obtained.

[0020] Further, the elements in the constructed multi-dimensional elasticity coefficient matrix are power demand price elasticity coefficients, the corresponding relationship between the power demand change in the peak-valley section and the multi-dimensional price index is obtained in combination with the constructed multi-dimensional elasticity coefficient matrix, the sensitivity of the peak-valley section load to the differentiated price signal is obtained, and the user demand elasticity quantification considering the synergy of multiple user-side resources is completed.

[0021] As a further technical limitation, the obtained response model of different user-side resources at least includes a shiftable load model, a transferable load model, a reducible load model, and a distributed energy storage model.

[0022] According to some embodiments, the second aspect of the present application provides a user demand elasticity quantification system considering the synergy of multiple user-side resources, which adopts the following technical solution:

[0023] A user demand elasticity quantification system considering the synergy of multiple user-side resources, comprising:

[0024] An acquisition module configured to acquire a response model of different user-side resources;

[0025] A modeling module configured to construct a multi-user-side resource synergy optimization model of the acquired response model considering the grid structure;

[0026] A solving module configured to solve the constructed synergy optimization model to obtain a synergy capability quantification result;

[0027] A correction module configured to correct the power load demand of the user participating in the demand-side response based on the obtained synergy capability quantification result;

[0028] A quantification module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained power load demand correction amount, and to quantify the user demand according to the constructed matrix to complete the user demand elasticity quantification considering the synergy of multiple user-side resources.

[0029] According to some embodiments, a third aspect of the present application provides a computer readable storage medium, adopting the technical scheme as follows:

[0030] A computer readable storage medium, having a program stored thereon, which, when executed by a processor, implements the steps in the user demand elasticity quantification method considering multi-element user side resource synergy according to the first aspect of the present application.

[0031] According to some embodiments, a fourth aspect of the present application provides an electronic device, adopting the technical scheme as follows:

[0032] An electronic device, comprising a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor implements the steps in the user demand elasticity quantification method considering multi-element user side resource synergy according to the first aspect of the present application when executing the program.

[0033] According to some embodiments, a fifth aspect of the present application provides a computer program product, adopting the technical scheme as follows:

[0034] A computer program product, comprising software codes, wherein the program in the software codes implements the steps in the user demand elasticity quantification method considering multi-element user side resource synergy according to the first aspect of the present application.

[0035] Compared with the prior art, the present application has the beneficial effects that:

[0036] The present application quantifies the multi-element user side resource synergy capability considering the grid structure restriction, considers the restriction of the grid structure on the user side resource synergy response capability, incorporates the grid topology structure and line capacity constraint into the multi-element user side resource synergy mutual aid optimization model, corrects the user side resource synergy response capability through the grid structure safety boundary, and quantifies the user side resource synergy response capability through the synergy capability quantification result, solving the problem that the existing method ignores the physical network restriction, resulting in inaccurate evaluation of the user side resource synergy response capability.

[0037] The present application quantifies the user multi-dimensional demand elasticity based on the synergy capability quantification result, initiates the elasticity correction mechanism considering the user side resource synergy mutual aid effect, superimposes the synergy effect of flexible load adjustment, distributed energy storage charging and discharging, etc. on the basis of price response, improves the accuracy of user demand elasticity quantification, breaks through the limitation of traditional single-dimensional price elasticity model, quantifies the sensitivity of peak and valley period load to differentiated price signal through the construction of multi-dimensional elasticity coefficient matrix, and realizes the fine evaluation of peak load cutting and valley load filling potential. BRIEF DESCRIPTION OF DRAWINGS

[0038] The drawings constituting a part of the specification illustrate further aspects of the present embodiments, and should be considered in all its illustrative aspects and illustrative embodiments.

[0039] Figure 1 Flow chart of the user demand elasticity quantification method considering multi-user side resource coordination in the first embodiment of the present application;

[0040] Figure 2 Structure block diagram of the user demand elasticity quantification system considering multi-user side resource coordination in the second embodiment of the present application. DETAILED DESCRIPTION

[0041] The present application will be further described below with reference to the drawings and embodiments.

[0042] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0043] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components and / or combinations thereof, but do not preclude the presence or addition of one or more other features, steps, operations, devices, components and / or combinations thereof.

[0044] The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0045] Term explanation:

[0046] User side resource: power generation, power consumption and energy storage devices installed on the power consumption side, owned by users, with the ability to interact with the power grid, flexible resources that can participate in dispatch. The user side resources considered in the present application include distributed power sources, distributed energy storage and flexible loads.

[0047] Demand elasticity, short for demand price elasticity, represents the degree of response of the demand quantity of a commodity to the price change of the commodity within a certain period of time; or, represents the percentage change in demand quantity caused by a percentage change in the price of a commodity within a certain period of time; usually represented by the price elasticity coefficient, i.e. demand price elasticity coefficient = percentage change in demand quantity / percentage change in price.

[0048] Embodiment one

[0049] The embodiment one of the present application introduces a user demand elasticity quantification method considering multi-element user side resource cooperation.

[0050] As shown in a user demand elasticity quantification method considering multi-element user side resource cooperation, comprising: Figure 1

[0051] Obtaining response models of different user side resources;

[0052] Considering the power grid framework structure, constructing a multi-element user side resource cooperation and mutual aid optimization model of the obtained response model;

[0053] Solving the constructed cooperation and mutual aid optimization model to obtain a cooperation capability quantification result;

[0054] Based on the obtained cooperation capability quantification result, correcting the electricity load demand of user participation in demand side response;

[0055] According to the obtained electricity load demand correction amount, constructing a multi-dimensional elasticity coefficient matrix, quantifying the user demand according to the constructed matrix, and completing the user demand elasticity quantification considering multi-element user side resource cooperation.

[0056] The embodiment first quantifies the multi-element user side resource cooperation capability considering the power grid framework structure limitation; first, constructing response models of different user side resources such as flexible load, distributed power supply, and distributed energy storage; second, based on the response models of different user side resources, considering the limitation of the power grid framework structure on the user side resource response capability, constructing a multi-element user side resource cooperation and mutual aid optimization model, and solving to obtain the change of each user side resource participating in cooperation and response; third, using the constructed cooperation capability index to evaluate and analyze the cooperation capability among the multi-element user side resources.

[0057] Different user side resource participation response model

[0058] (1) Translational load model

[0059] Large industrial loads such as production line equipment cannot be stopped during operation and their power is constant. The translational load represented by such loads is characterized by time continuity, and the power size remains unchanged before and after translation at each moment, so the following model can be established:

[0060] ;

[0061] Wherein, and are the power of the load before participating in response at time and time , and and are the power of the load at time and​ Power of the movable load at time t, Power of the movable load at time t, Power of the movable load at time t, Power of the movable load at time t, The user's willingness to move the movable load (i.e., the maximum moving amount of the movable load).

[0062] (2) Movable load model

[0063] Residential loads such as electric vehicle charging loads can be stopped during operation, and the aggregated power can change, as long as the total power is unchanged. Therefore, the movable load represented by such loads is characterized by time discontinuity, change in power size before and after moving, and unchanged total power; that is:

[0064] ;

[0065] wherein, and are the powers of the load before participating in the response at time t and time t+1, respectively, and are the powers of the movable load after participating in the response at time t and time t+1, respectively, is the proportion of the load moved out at time t, is the proportion of the load moved in at time t, is the power of the movable load at time t, is the proportion of the load moved out at time t, is the proportion of the load moved in at time t, is the willingness of the movable load user at time t. (3) Reducible load model Industrial and residential loads such as air conditioners and electric fans can reduce power or even be turned off during operation. Therefore, the reducible load represented by such loads is characterized by power reduction; that is:

[0066]

[0067] ;

[0068] ;

[0069] wherein, and are the powers of the load before participating in the response at time t and time t+1, respectively, is the power of the reducible load after participating in the response at time t, is the proportion of the load reduced by the user at time t, is the willingness of the user to accept load reduction.

[0070] ​​​​​​(4) Distributed energy storage model

[0071] ;

[0072] in, and They are respectively t Time and Time of the first e The total power of each energy storage device; These are the charging efficiency and discharging efficiency of the energy storage device, respectively. For the first e An energy storage device in t The charging power at any given moment; For the first e An energy storage device in t Discharge power at any given moment; For the first e The rated capacity of each energy storage device.

[0073] Multi-user-side resource collaborative and mutual assistance model

[0074] Based on different user-side resource participation response models, and considering the limitations of the power grid structure on the response capability of user-side resources, a collaborative optimization model including flexible loads, distributed power sources, and distributed energy storage is constructed. Solving the constructed model can yield the changes in the collaborative response of each user-side resource. The collaborative capability index is used to evaluate and analyze the collaborative capability among multiple user-side resources.

[0075] (1) Objective function

[0076] ;

[0077] in, The total economic cost; To reduce the cost of purchasing electricity from the higher-level power grid; For load interaction costs; For energy storage dispatch costs; for Always check the electricity purchase price from the higher-level power grid. express Purchase power from the upper-level power grid at all times; and These are the number of loads that can be shifted, loads that can be transferred, loads that can be reduced, and energy storage devices, respectively. For the first a A movable load in t The unit capacity compensation price at any given time; For the first b A transferable load in t The unit capacity compensation price at any given time; For the firstc A load that can be reduced t The unit capacity compensation price at any given time; The first i A load that can be shifted, transferred, or reduced in t The response status at any given time, with a value of 1 indicating participation in regulation and a value of 0 indicating non-participation in regulation; The first a The first movable load, the first b The first transferable load, the first c One load can be reduced The load that changes over time; This is the operation and maintenance cost coefficient for energy storage devices; For the first e An energy storage device in Power at any given moment.

[0078] (2) Constraints

[0079] a. Power balance constraint:

[0080] ;

[0081] in, for t Time node i Rigid loads (i.e., loads that are not subject to regulation, excluding loads that can be shifted, transferred, or reduced). This refers to the number of distributed power sources. For the first d A distributed power source in t Efforts made at all times; The first e An energy storage device in t The charging and discharging states at any given time are represented by a value of 1, indicating participation in regulation, and a value of 0, indicating non-participation in regulation. Indicates a load that can be moved. a Connected to the power grid node i superior; Indicates transferable load b Connected to the power grid node i superior; Indicates that the load can be reduced. c Connected to the power grid node i superior; Distributed power sources d Connected to the power grid node i superior; Indicates energy storage device e Connected to the power grid node i superior.

[0082] b. Line power flow constraints

[0083] ;

[0084] in, For the line ( i , j )exist t The power flowing through the time period; and They are nodes i and nodes j exist t Phase angle of the time period; For the line ( i , j The reactance of ) and They are respectively the lines ( i , j The upper and lower limits of power; and They are nodes i The upper and lower limits of the phase angle; L This is a set of routes.

[0085] c. Transferable load constraints

[0086] ;

[0087] ;

[0088] in, For the first a A movable load in t The translation signal at any given time has a value of 1 indicating a translation command and a value of 0 indicating that power is maintained. Indicates the first a A movable load in t The minimum and maximum translation amounts at each moment; , Indicates the first a The upper and lower limits of the load transfer capacity of a transferable load within a scheduling cycle.

[0089] d. Transferable load constraints

[0090] ;

[0091] ;

[0092] in, For the first b A transferable load in t The transfer signal at a given time has a value of 1 indicating a transfer command and a value of 0 indicating that power should be maintained. Indicates the first b A transferable load in t Minimum and maximum transition amounts at time points; , Indicates the first b The upper and lower limits of the load transfer capacity of a transferable load within a scheduling cycle.

[0093] e. Reduced load constraints

[0094] ;

[0095] ;

[0096] in, For the first c A load that can be reduced The power reduction signal is set at a specific time; a value of 1 indicates a power reduction command, while a value of 0 indicates that power is maintained. Indicates the first c A load that can be reduced t Minimum and maximum reduction amounts at any given time; Indicates the first c The upper and lower limits of the load reduction capacity within a scheduling cycle.

[0097] f. Constraints of distributed power sources:

[0098] ;

[0099] in, For the first d A distributed power source in The output signal at any given time has a value of 1 indicating an output command and a value of 0 indicating no output. Indicates the first d A distributed power source in Maximum output at any given moment.

[0100] g. Constraints of energy storage devices:

[0101] ;

[0102] in, For the first e Rated charging and discharging power of an energy storage device; For the first e The charging status of an energy storage device is 1 for charging and 0 for idle. For the first e The discharge state of an energy storage device is 1, and the idle state is 0. For the first eThe rated capacity of the energy storage device; and respectively the maximum and minimum values of the state of charge of the energy storage device, respectively 0.2 and 0.8. e

[0103] Quantification of synergy capability

[0104] The synergy capability between multiple user-side resources is defined as the ratio of the power value change before and after the participation of different types of user-side resources in synergy to the power value without participation in synergy, and the calculation formula is:

[0105] ;

[0106] wherein, is the power value of the user-side resource u after participating in synergy at time t ; is the power value of the user-side resource u without participating in synergy at time t .

[0107] After obtaining the quantification result of the synergy capability, the embodiment considers the quantification of the multi-dimensional demand elasticity of users in the synergy of multiple user-side resources; first, the power demand price elasticity coefficient is used to calculate the load amount after the user participates in the price-type demand response; second, the influence of the synergy and mutual aid between various user-side resources included by the user on the load change amount is considered, and the load amount after the user participates in the price-type demand response is corrected; third, a multi-dimensional price elasticity coefficient matrix is constructed to quantify the sensitivity of the power demand change of the user in the peak and valley sections to the multi-dimensional price index, so as to represent the peak load cutting and valley load filling change situation of the load curve.

[0108] Calculation of user response amount considering synergy of multiple user-side resources

[0109] As a commodity in the electricity market, electric energy has a certain supply and demand relationship, and the power demand price elasticity coefficient can be expressed as:

[0110] ;

[0111] wherein, denotes the price elasticity of the time period s to the time period t ; and respectively the user electricity load before the time period s and the electricity price of the time period t ; and respectively the user electricity load after the time period s ​User load variation and time period t The amount of price change.

[0112] Using the electricity demand price elasticity coefficient Characterizing the sensitivity of electricity demand to changes in electricity prices, The larger the value, the more sensitive the electricity demand is to changes in electricity prices.

[0113] The load change for user-participated price-based DR is:

[0114] ;

[0115] The load demand after a user participates in DR becomes:

[0116] ;

[0117] in, After users participate in DR t Electricity load during a given time period; Before users participate in DR t Electricity load during a given time period.

[0118] Considering the synergistic effects among various user-side resources, which will influence the user's load variation, the load demand after the user participates in price-based DR becomes:

[0119] ;

[0120] in, To consider the synergistic and mutually supportive effects among user-side resources, after users participate in DR... t Electricity load during a given time period; U The number of resources on the user side; For user-side resources u Collaborative capabilities; For user-side resources u The proportion of all user-side resources included in the user's account.

[0121] User Multidimensional Price Elasticity Quantification Method

[0122] The traditional electricity demand price elasticity coefficient mainly explores the relationship between the overall electricity demand and the change of electricity price. Since the electricity supply guarantee focuses on the change of electricity demand in peak and valley periods, in order to better study the correlation between the change of electricity demand in peak and valley periods and the change of electricity price, the concept of multi-dimensional elasticity coefficient in material mechanics is used for reference. It is considered that each type of electricity load index is similar to the stress of material, which is affected by multi-dimensional electricity price index. On the basis of the traditional electricity demand price elasticity coefficient, a multi-dimensional price elasticity coefficient matrix is proposed. The change of electricity demand in peak and valley periods is refined into multi-dimensional load index, and the change of time-of-use electricity price is refined into multi-dimensional price index. The multi-dimensional price elasticity coefficient matrix is constructed through the influence mapping relationship between the multi-dimensional load index and the multi-dimensional price index, so as to more comprehensively and specifically quantify the sensitivity of the change of user electricity demand in peak and valley periods to the multi-dimensional price index.

[0123] In order to characterize the peak clipping and valley filling change of load curve, based on the basic attributes of peak and valley load, the peak-valley difference rate reduction rate, peak segment electricity transfer rate, valley segment electricity filling rate and peak-valley electricity transfer rate are selected as key load indexes, and the meanings of each variable are as follows:

[0124] 1) Peak-valley difference rate reduction rate Y 1

[0125] The peak-valley difference rate is the difference between the maximum load and the minimum load of the 24-point load curve and the ratio of the maximum load, which can be expressed as:

[0126] ;

[0127] Wherein, L max and L min are the maximum load and the minimum load of the 24-point load curve, respectively.

[0128] Peak-valley difference rate reduction rate Y 1 is:

[0129] ;

[0130] Wherein, is the peak-valley difference rate after the user participates in demand response; is the peak-valley difference rate before the user participates in demand response; Y 1 is negative, indicating that the peak-valley difference decreases, which plays a role in peak clipping and valley filling; Y 1 is positive, indicating that the peak-valley difference increases, and the peak clipping and valley filling effect is not significant.

[0131] 2) Peak segment electricity transfer rate Y 2

[0132] Peak segment (peak) electricity proportion F is:

[0133] ;

[0134] wherein, L f , L a are the load integrals of the 24-point load curve peak (spike) period and the whole day load integral, respectively.

[0135] Peak period electricity transfer rate Y 2 is:

[0136] ;

[0137] wherein, is the peak period (spike) electricity proportion of the user after participating in demand response; is the peak period (spike) electricity proportion of the user before participating in demand response; Y 2 is a negative number, indicating that the peak period (spike) electricity proportion decreases, playing a peak shaving role; Y 2 is a positive number, indicating that the peak period (spike) electricity proportion increases, and the peak shaving effect is not significant.

[0138] 3) Valley period electricity filling rate Y 3

[0139] Valley period electricity proportion G is:

[0140] ;

[0141] wherein, L g is the load integral of the load curve trough period.

[0142] Valley period electricity filling rate Y 3 is:

[0143] ;

[0144] wherein, is the valley period load proportion of the user after participating in demand response; is the valley period electricity proportion of the user before participating in demand response; Y 3 is a positive number, indicating that the valley period electricity proportion increases, playing a valley filling role; Y 3 is a negative number, indicating that the valley period electricity proportion decreases, and the valley filling effect is not significant.

[0145] 4) Peak-valley electricity transfer rate Y 4 is:

[0146] ;

[0147] Y4 is a positive number, indicating that the peak segment power ratio decreases more than the valley segment power ratio or the peak segment power ratio increases less than the valley segment power ratio, which generally plays a role in peak load shifting; Y 4 is a negative number, indicating that the peak segment power ratio decreases less than the valley segment power ratio or the peak segment power ratio increases more than the valley segment power ratio, which generally does not play an obvious role in peak load shifting.

[0148] Since the price change includes the change of peak and valley period electricity price, the high peak electricity price change rate X 1, the low valley electricity price change rate X 2, the peak-valley electricity price ratio change rate X 3 represents the electricity price change situation.

[0149] High peak electricity price change rate X 1 is defined as the change ratio of the peak period electricity price after the price change compared with the change before the price change; low valley electricity price change rate X 2 is defined as the change ratio of the valley period electricity price after the price change compared with the change before the price change; peak-valley electricity price ratio change rate X 3 is defined as the change ratio of the peak-valley electricity price ratio after the price change compared with the change before the price change.

[0150] Since the high peak electricity price change rate X 1, the low valley electricity price change rate X 2, the peak-valley electricity price ratio change rate X 3 will affect the peak-valley difference rate reduction rate Y 1, combined with the definition of the electricity demand elasticity coefficient, it can be known that the peak-valley difference rate reduction rate Y 1 is affected by multiple electricity price indicators and can be represented as:

[0151] ;

[0152] Among them, a 11 、 a 12 、 a 13 is the price elasticity coefficient to be evaluated; b 1 is the constant term to be evaluated.

[0153] Similarly, the regression equations of Y2, Y3, Y4 and X1, X2, X3 can be obtained as:

[0154] ;

[0155] ;

[0156] ;

[0157] The above equation is expressed in matrix form as:

[0158] ;

[0159] ;

[0160] matrix is a multi-dimensional price elasticity matrix, for example: a 12 is the valley electricity price change rate X2 elasticity coefficient of the peak-valley difference rate reduction rate Y1. Through the multi-dimensional price elasticity coefficient matrix A, the influence of each price key indicator on each load key indicator can be comprehensively and systematically represented.

[0161] Based on the historical data of load change and price change, by solving the optimal solution of each price elasticity coefficient, the numerical size of each elasticity coefficient in the matrix A can be obtained, and the numerical absolute value size represents the sensitivity of the corresponding load indicator to the corresponding price indicator.

[0162] The embodiment considers the quantification of the multi-element user side resource collaborative capability considering the grid structure restriction, considers the restriction of the grid structure on the user side resource collaborative response capability, incorporates the grid topology structure and line capacity constraint into the multi-element user side resource collaborative mutual aid optimization model, modifies the collaborative response capability of the user side resource through the grid structure safety boundary, and quantifies the collaborative response capability between the user side resources through the collaborative capability quantification result, solving the problem that the existing method ignores the physical network restriction, resulting in inaccurate evaluation of the collaborative response capability of the user side resource.

[0163] The embodiment quantifies the multi-dimensional demand elasticity of users based on the collaborative capability quantification result, innovatively considers the elasticity correction mechanism of the collaborative mutual aid effect of the user side resource, superimposes the collaborative effect of the flexible load regulation, distributed energy storage charging and discharging and the like on the basis of the price response, improves the accuracy of the quantification of the demand elasticity of users, breaks through the limitation of the traditional single-dimensional price elasticity model, quantifies the sensitivity of the peak and valley period load to the differentiated price signal through the construction of the multi-dimensional elasticity coefficient matrix, and realizes the fine evaluation of the peak load cutting and valley filling potential.

[0164] Embodiment two

[0165] The embodiment two of the present application introduces a user demand elasticity quantification system considering multi-element user side resource collaboration.

[0166] As shown in Figure 2 a user demand elasticity quantification system considering multi-element user side resource collaboration, comprising:

[0167] An acquisition module configured to acquire response models of different user side resources;

[0168] A modeling module configured to consider the grid structure, and construct a multi-element user side resource collaborative mutual aid optimization model of the acquired response model;

[0169] a solving module configured to solve the constructed synergistic optimization model to obtain a synergistic capability quantification result;

[0170] a correcting module configured to correct the electricity load demand of the user participating in the demand side response based on the obtained synergistic capability quantification result;

[0171] a quantifying module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained electricity load demand correction amount, quantify the user demand according to the constructed matrix, and complete the user demand elasticity quantification considering the multi-element user side resource synergy.

[0172] The detailed steps are the same as those of the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1, and will not be repeated here.

[0173] Embodiment 3

[0174] Embodiment 3 of the present application provides a computer readable storage medium.

[0175] A computer readable storage medium has a program stored thereon, and the program is executed by a processor to implement the steps in the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1 of the present application.

[0176] The detailed steps are the same as those of the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1, and will not be repeated here.

[0177] Embodiment 4

[0178] Embodiment 4 of the present application provides an electronic device.

[0179] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, and the processor implements the steps in the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1 of the present application when executing the program.

[0180] The detailed steps are the same as those of the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1, and will not be repeated here.

[0181] Embodiment 5

[0182] Embodiment 5 of the present application provides a computer program product.

[0183] A computer program product includes software code, and the program in the software code performs the steps in the method for quantifying user demand elasticity considering multi-element user side resource synergy provided in Embodiment 1 of the present application.

[0184] The detailed steps are the same as the user demand flexible quantification method considering multi-user side resource coordination provided in Embodiment 1, and will not be described herein again.

[0185] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical memory, etc.) having computer usable program code embodied therein. The routines of the embodiments of the application can be implemented in a variety of computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0186] The application is described in reference to the flowcharts and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flow or flows and / or block or blocks.

[0187] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including instruction means, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flow or flows and / or block or blocks.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flow or flows and / or block or blocks.

[0189] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the preferred embodiments without departing from the spirit and scope of the application. Therefore, it is intended that such additional variations and modifications be included within the scope of the application as defined by the following claims and their equivalents.

[0190] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

[0191] The above description is merely illustrative of the preferred embodiments of the present application and is not intended to limit the scope of the application. Variations and modifications can be made to the preferred embodiments without departing from the spirit and scope of the present application. Therefore, the present application should not be limited to the preferred embodiments described herein but should be given the full scope of the appended claims and their equivalents.

Claims

1. A user demand elastic quantification method considering multi-user side resource coordination, characterized in that, The method comprises the following steps: obtaining response models of different user-side resources; considering the grid structure, constructing a multi-user-side resource collaborative optimization model of the obtained response models; solving the constructed collaborative optimization model to obtain collaborative capability quantification results, wherein the constraint conditions of the collaborative optimization model include power balance constraints, line flow constraints, translatable load constraints, transferable load constraints, reducible load constraints, distributed power supply constraints and energy storage device constraints; based on the obtained collaborative capability quantification results, correcting the electricity load demand of the user participating in the demand side response; based on the obtained electricity load demand correction amount, constructing a multi-dimensional elasticity coefficient matrix, and quantifying the user demand according to the constructed matrix to complete the user demand elasticity quantification considering the collaboration of multi-user-side resources; the collaborative capability quantification results are used to represent the collaborative capability between multi-user-side resources, i.e. the ratio of the power value change amount before and after different user-side resources participate in collaborative interaction to the power value when they do not participate in collaborative interaction; before correcting the electricity load demand of the user participating in the demand side response, obtaining a power demand price elasticity coefficient representing the sensitivity of power demand to price changes, calculating the load change amount of the user participating in the price-type demand side response through the obtained power demand price elasticity coefficient, and obtaining the load demand of the user participating in the demand side response; using the collaborative capability quantification results to correct the load demand of the user participating in the demand side response to obtain the electricity load demand correction amount; the elements in the constructed multi-dimensional elasticity coefficient matrix are power demand price elasticity coefficients, and the corresponding relationship between the peak-valley segment power demand change and the multi-dimensional price index is obtained by combining the constructed multi-dimensional elasticity coefficient matrix, the sensitivity of the peak-valley segment load to the differentiated price signal is obtained, and the user demand elasticity quantification considering the collaboration of multi-user-side resources is completed; considering the user-side resource inter-collaboration interaction after user participation in demand side response t power load of the time period is ; wherein, power load of the time period after user participation in demand side response t ; U is the number of user-side resources; is the collaborative capability of the user-side resource u , that is, , is the power value of the user-side resource u participating in collaboration at the time t ; is the power value of the user-side resource u not participating in collaboration at the time t ; is the proportion of the user-side resource u in all user-side resources contained by the user.

2. The method of claim 1, wherein the user demand elasticity quantization method considers multi-user side resource coordination. the objective function of the constructed multi-user-side resource collaborative optimization model is to minimize the total economic cost, which includes the purchase cost of electricity from the upper-level grid, the load interaction cost and the energy storage scheduling cost.

3. The method of claim 1, wherein the method further comprises: The obtained response models of different user-side resources at least include translatable load models, transferable load models, reducible load models and distributed energy storage models.

4. A user demand elasticity quantization system considering multi-user side resource coordination, adopting the user demand elasticity quantization method considering multi-user side resource coordination according to any one of claims 1-3, characterized in that, The method comprises the following steps: a obtaining module configured to obtain response models of different user-side resources; a modeling module configured to consider the grid structure, and construct a multi-user-side resource collaborative optimization model of the obtained response models; a solving module configured to solve the constructed collaborative optimization model to obtain collaborative capability quantification results; a correction module configured to correct the electricity load demand of the user participating in the demand side response based on the obtained collaborative capability quantification results; a quantification module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained electricity load demand correction amount, and quantify the user demand according to the constructed matrix to complete the user demand elasticity quantification considering the collaboration of multi-user-side resources.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps of the user demand elasticity quantification method considering the collaboration of multi-user-side resources according to any one of claims 1-3.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor implements the steps of the user demand flexible quantization method considering multi-user side resource coordination according to any one of claims 1-3 when executing the program.

7. A computer program product comprising software code, characterized in that, The program in the software code executes the steps of the user demand flexible quantization method considering multi-user side resource coordination according to any one of claims 1-3.

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