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

Abstract

The invention belongs to the technical field of power systems, and provides a user demand elastic quantification method and system considering multi-user side resource collaboration, and the method comprises the steps: obtaining response models of different user side resources; considering a grid structure of a power grid, and constructing a multi-user-side resource collaborative mutual aid optimization model of the obtained response model; solving the constructed collaborative mutual aid optimization model to obtain a collaborative capability quantification result; based on the obtained collaborative capability quantification result, correcting the electrical load demand of the user participating in the demand side response; and constructing a multi-dimensional elastic coefficient matrix according to the obtained electrical load demand correction, quantifying the user demand according to the constructed matrix, and completing the user demand elastic quantification considering the multi-user side resource collaboration.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a method and system for quantifying user demand elasticity considering the coordination of multiple user-side resources. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] With the rise of the new power system, the large-scale access of user-side resources poses new challenges to the operation of the power grid; multiple user-side resources such as flexible loads, distributed power sources, and distributed energy storage have shown explosive growth, and there is a deep coupling relationship between their spatio-temporal distribution characteristics and the physical structure of the power grid, and their roles in power grid peak shaving and demand response are becoming increasingly prominent. However, the high proportion of new energy access, the complexity of the power grid network structure, and the diversification of user demands put forward higher requirements for the coordination ability between resources. Therefore, it is necessary to analyze the coordinated and mutual-aid energy between multiple user-side resources under the constraints of the network structure, characterize the multi-dimensional impact of the coordination ability of multiple user-side resources on the load pattern, and then accurately formulate demand-side response strategies.

[0004] At present, the quantification of the coordination ability of user-side resources mainly uses a centralized optimization model to guide user-side resources to participate in the response through economic incentives or dispatching instructions. However, the physical limitations of the power grid network 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 charge and discharge behavior of energy storage may cause reverse power over-limit due to the weak network structure; although relevant research has proposed a coordinated dispatching model based on power grid constraints, it is mostly for specific scenarios (such as fault recovery or new energy consumption), lacks generality, and does not construct an index that can quantitatively evaluate the coordination ability.

[0005] At present, the research on user demand elasticity modeling is mostly based on the traditional price elasticity coefficient, which can only reflect the load adjustment ability under a single electricity price signal, and does not fully consider the correction effect of the coordinated action of multiple user-side resources (such as the complementarity between energy storage and flexible loads) on demand response; for example, the discharge of distributed energy storage during peak electricity price periods may relieve the load pressure, thus affecting the load change amount of demand response, and such factors are not incorporated into the quantitative modeling of user demand elasticity in the existing models. In addition, the existing demand elasticity models mostly use one-dimensional price sensitivity analysis, which cannot characterize the response characteristics of load changes during peak and valley periods to multi-dimensional indicators, and lack a unified multi-dimensional elasticity matrix modeling method.

[0006] In summary, the existing quantification of user-side resource coordination ability and user demand elasticity analysis has the following defects: (1) The coupling mechanism of the physical constraints of the power grid and the coordination of diverse user-side resources has not been fully modeled, making it impossible to accurately quantify the collaborative response capabilities of diverse user-side resources. (2) The correction of user demand elasticity considering the coordination of user-side resources and the method for quantifying multi-dimensional demand elasticity urgently need to be improved. SUMMARY OF THE INVENTION

[0007] To solve the above problems, the present invention proposes a method and system for quantifying user demand elasticity considering the coordination of diverse user-side resources, which integrates the constraints of the power grid framework and the collaborative and complementary effects of diverse user-side resources, quantifies the collaborative capabilities of diverse user-side resources considering the limitations of the power grid framework structure. At the same time, based on the quantification results of the collaborative capabilities, a multi-dimensional elasticity coefficient matrix is constructed to quantify the elasticity of multi-dimensional user demands, realizing the quantitative analysis of the demand elasticity of users considering the coordination of diverse user-side resources.

[0008] According to some embodiments, the first solution of the present invention provides a method for quantifying user demand elasticity considering the coordination of diverse user-side resources, adopting the following technical solutions: A method for quantifying user demand elasticity considering the coordination of diverse user-side resources, comprising: Obtain the response models of different user-side resources; Considering the power grid framework structure, construct a collaborative and complementary optimization model for diverse user-side resources of the obtained response models; Solve the constructed collaborative and complementary optimization model to obtain the quantification result of the collaborative capabilities; Based on the obtained quantification result of the collaborative capabilities, correct the electricity load demand of users participating in demand-side response; According to the obtained correction amount of the electricity load demand, construct a multi-dimensional elasticity coefficient matrix, and quantify user demands according to the constructed matrix, completing the quantification of user demand elasticity considering the coordination of diverse user-side resources.

[0009] As a further technical limitation, the objective function of the constructed collaborative and complementary optimization model for diverse user-side resources is to minimize the total economic cost, and the total economic cost includes the cost of purchasing electricity from the superior power grid, the load interaction cost, and the energy storage scheduling cost; the constraint conditions of the collaborative and complementary optimization model include power balance constraint, line power flow constraint, shiftable load constraint, transferable load constraint, curtailable load constraint, distributed power source constraint, and energy storage device constraint.

[0010] As a further technical limitation, the quantification result of the collaborative capabilities is used to characterize the collaborative capabilities between diverse user-side resources, that is, the ratio of the change in power value before and after different user-side resources participate in collaborative and complementary to their power value when not participating in collaborative and complementary.

[0011] As a further technical limitation, before correcting the electricity load demand of users participating in demand-side response, obtain the price elasticity coefficient of electricity demand used to characterize the sensitivity of electricity demand to electricity price changes, calculate the load change of users participating in price-based demand-side response through the obtained price elasticity coefficient of electricity demand, and obtain the load demand of users participating in demand-side response; use the quantification result of collaborative ability to correct the load demand of users participating in demand-side response to obtain the corrected amount of electricity load demand.

[0012] Further, the elements in the constructed multi-dimensional elasticity coefficient matrix are the price elasticity coefficients of electricity demand. Combining the constructed multi-dimensional elasticity coefficient matrix, obtain the corresponding relationship between the electricity demand changes in the peak-valley periods of users and the multi-dimensional price indicators, and obtain the sensitivity of the peak-valley period load to the differential electricity price signal, so as to complete the quantification of user demand elasticity considering the collaboration of multiple user-side resources.

[0013] As a further technical limitation, the obtained response models of different user-side resources at least include a shiftable load model, a transferable load model, a cuttable load model, and a distributed energy storage model.

[0014] According to some embodiments, the second solution of the present invention provides a user demand elasticity quantification system considering the collaboration of multiple user-side resources, and adopts the following technical solution: A user demand elasticity quantification system considering the collaboration of multiple user-side resources, comprising: An acquisition module configured to acquire response models of different user-side resources; A modeling module configured to construct a collaborative optimization model of multiple user-side resources for the acquired response models considering the power grid network structure; A solving module configured to solve the constructed collaborative optimization model to obtain a quantification result of collaborative ability; A correction module configured to correct the electricity load demand of users participating in demand-side response based on the obtained quantification result of collaborative ability; A quantification module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained corrected amount of electricity load demand, and quantify user demand according to the constructed matrix to complete the quantification of user demand elasticity considering the collaboration of multiple user-side resources.

[0015] According to some embodiments, the third solution of the present invention provides a computer-readable storage medium, and adopts the following technical solution: A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the method for quantifying user demand elasticity considering the collaboration of multiple user-side resources as described in the first solution of the present invention.

[0016] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the user demand elasticity quantification method considering the coordination of multiple user-side resources as described in the first scheme of the present invention.

[0017] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software codes, wherein the program in the software codes executes the steps in the user demand elasticity quantification method considering the coordination of multiple user-side resources as described in the first solution of the present invention.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention quantifies the collaborative capabilities of multiple user-side resources by considering the limitations of the power grid structure, considers the limitations of the power grid structure on the collaborative response capabilities of user-side resources, incorporates the power grid topology and line capacity constraints into the collaborative mutual assistance optimization model of multiple user-side resources, corrects the collaborative response capabilities of user-side resources through the grid safety boundary, and quantifies the collaborative response capabilities between user-side resources through the collaborative capability quantification results, thereby solving the problem of inaccurate collaborative response capability assessment of user-side resources caused by existing methods ignoring physical network limitations.

[0019] The present invention quantifies the multi-dimensional demand elasticity of users based on the results of collaborative capability quantification. It is the first to create an elasticity correction mechanism that takes into account the collaborative and mutual assistance of user-side resources. On the basis of price response, it superimposes synergistic effects such as flexible load regulation and distributed energy storage charging and discharging, thereby improving the accuracy of user demand elasticity quantification. It breaks through the limitations of traditional one-dimensional price elasticity models, and by constructing a multi-dimensional elasticity coefficient matrix, it quantifies the sensitivity of loads in peak and valley periods to differentiated electricity price signals, thereby achieving a refined assessment of the potential for peak shaving and valley filling. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0021] Figure 1 It is a flow chart of a user demand elasticity quantification method considering multi-user side resource collaboration in the first embodiment of the present invention; Figure 2 This is a structural block diagram of a user demand elasticity quantification system considering the coordination of multiple user-side resources in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0026] Term Explanation: User-side resources: Power generation, power consumption, energy storage and other equipment installed on the power consumption side, owned by users, and having the ability to interact with the power grid, which are flexible resources that can participate in scheduling. The user-side resources considered in the present invention include distributed power sources, distributed energy storage, and flexible loads.

[0027] Demand elasticity, which is the abbreviation of demand price elasticity, represents the degree of response of the demand for a commodity to the change in its price within a certain period; or, it represents the percentage change in the demand for a commodity caused by a one-percent change in the price of the commodity within a certain period; usually expressed by the price elasticity coefficient, that is, the demand price elasticity coefficient = percentage change in demand / percentage change in price.

[0028] Embodiment 1 Embodiment 1 of the present invention introduces a method for quantifying user demand elasticity considering the coordination of multiple user-side resources.

[0029] As Figure 1 shown, a method for quantifying user demand elasticity considering the coordination of multiple user-side resources includes: Obtain the response models of different user-side resources; Considering the power grid network structure, construct a multi-user-side resource coordination and mutual assistance optimization model for the obtained response models; Solve the constructed coordination and mutual assistance optimization model to obtain the quantification result of the coordination ability; Based on the obtained quantification result of the coordination ability, correct the electricity load demand of users participating in demand-side response; According to the obtained correction amount of electricity load demand, construct a multi-dimensional elasticity coefficient matrix, and quantify the user demand based on the constructed matrix to complete the elasticity quantification of user demand considering the coordination of multiple user-side resources.

[0030] In this embodiment, the quantification of the collaborative ability of multiple user-side resources considering the limitations of the power grid network structure is first carried out; first, response models of different user-side resources such as flexible loads, distributed power sources, and distributed energy storage are constructed; secondly, based on the participation of different user-side resources in the response model, considering the limitations of the power grid network structure on the response ability of user-side resources, a collaborative and mutual-aid optimization model of multiple user-side resources is constructed, and the change situation of each user-side resource participating in the collaborative response is obtained by solving; thirdly, the constructed collaborative ability index is used to evaluate and analyze the collaborative ability among multiple user-side resources.

[0031] Response models of different user-side resources (1)Shiftable load model Large industrial loads such as production line equipment cannot be shut down during operation and their power is constant. The characteristics of shiftable loads represented by such loads are continuous in time, and the power magnitude remains unchanged at each moment before and after shifting. Therefore, the following model can be established: ; Among them, and are respectively the power of the load before participating in the response at time and and are the power of the shiftable load after participating in the response at time and represents the proportion of the load shifted into at time , is the shifting willingness of the shiftable load user (i.e., the maximum shifting amount of the shiftable load).

[0032] (2)Transferable load model Residential loads such as electric vehicle charging loads can be shut down during operation, and the aggregated power can be changed as long as the total electricity consumption remains unchanged. Therefore, the characteristics of transferable loads represented by such loads are that time can be discontinuous, the power magnitude can be changed before and after transfer, and the total electricity consumption remains unchanged; that is: ; Among them, and are respectively the power of the load before participating in the response at time and and are respectively the power of the transferable load participating in the response at the moment, is the proportion of the load transferred out at the moment of ; is the proportion of the load transferred in at the moment of ; is the transfer willingness of the transferable load users at the moment.

[0033] (3) Curtailable load model Industrial and commercial and residential loads such as air conditioners and electric fans can reduce their power or even turn off during operation. Therefore, the characteristics of the curtailable load represented by such loads are that the power can be curtailed; that is: ; wherein, and are respectively the moment and the power of the load before participating in the response at the moment, is the power of the curtailable load participating in the response at the moment, is the proportion of the load curtailed by the user at the moment of ; is the curtailment willingness acceptable to the user.

[0034] (4) Distributed energy storage model ; wherein, and are respectively t the moment and the total power of the e th energy storage device at the moment; are respectively the charging efficiency and discharging efficiency of the energy storage device; is the charging power of the e th energy storage device at the moment of t ; is the discharging power of the e th energy storage device at the moment of t ; is the rated capacity of the e th energy storage device.

[0035] Multi - user - side resource collaborative and mutual - assistance model Based on different user-side resource participation response models, considering the limitations of the power grid network structure on the response capabilities of user-side resources, a collaborative optimization model including user-side resources such as flexible loads, distributed power sources, and distributed energy storage is constructed. Solving the constructed model can obtain the changes in the participation of each user-side resource in collaborative response, and a collaborative ability index is used to evaluate and analyze the collaborative ability among multiple user-side resources.

[0036] (1) Objective function ; Among them, is the total economic cost; is the cost of purchasing electricity from the superior power grid; is the load interaction cost; is the energy storage scheduling cost; is the electricity price for purchasing electricity from the superior power grid at time represents the power of purchasing electricity from the superior power grid at time and are the numbers of shiftable loads, transferable loads, curtailable loads, and energy storage devices respectively; is the unit capacity compensation price of the a nd shiftable load at time t ; is the unit capacity compensation price of the b th transferable load at time t ; is the unit capacity compensation price of the c th curtailable load at time t ; are the response states of the i th shiftable load, transferable load, and curtailable load at time t respectively. When the value is 1, it means participating in regulation, and when the value is 0, it means not participating in regulation; are the changed load amounts of the a th shiftable load, b th transferable load, c th curtailable load at time respectively; is the operation and maintenance cost coefficient of the energy storage device; is the power of the e th energy storage device at time .

[0037] (2) Constraint conditions a. Power balance constraint: ; Among them, ist The rigid load of the node at the moment (i.e., the load that does not participate in regulation except for the translatable load, transferable load, and reducible load); i where is the number of distributed power sources; is the d th t output of the distributed power source at the moment; are respectively the charging state and discharging state of the e th energy storage device at the moment t , and a value of 1 indicates participation in regulation, while a value of 0 indicates non - participation in regulation; represents the translatable load a connected to the power grid node i ; represents the transferable load b connected to the power grid node i ; represents the reducible load c connected to the power grid node i ; represents the distributed power source d connected to the power grid node i ; represents the energy storage device e connected to the power grid node i ;

[0038] b. Line power flow constraint ; where is the power flowing through the line ([[]] i , j ) during the t period; and are respectively the phase angles of node i and node j during the t period; is the reactance of the line ([[]] i , j ); and are respectively the upper and lower limits of the power of the line ([[]] i , j ); and are respectively the upper and lower limits of the phase angle of node i ; L is the set of lines.

[0039] c. Translatable load constraint ; ; Among them, is the translation signal of the a th translatable load at the t moment. A value of 1 indicates a translation command, and a value of 0 indicates maintaining power consumption; represents the a th translatable load's minimum and maximum translation amounts at the t moment; , represent the upper and lower limits of the load translation capacity of the a th translatable load within a scheduling period.

[0040] d. Transferable load constraint ; ; Among them, is the transfer signal of the b th transferable load at the t moment. A value of 1 indicates a transfer command, and a value of 0 indicates maintaining power consumption; represents the b th transferable load's minimum and maximum transfer amounts at the t moment; , represent the upper and lower limits of the load transfer capacity of the b th transferable load within a scheduling period.

[0041] e. Curtailable load constraint ; ; Among them, is the curtailment signal of the c th curtailable load at the moment. A value of 1 indicates a curtailment command, and a value of 0 indicates maintaining power consumption; represents the c th curtailable load's minimum and maximum curtailment amounts at the t moment; represents the upper and lower limits of the load curtailment capacity of the c th curtailable load within a scheduling period.

[0042] f. Distributed power source constraint: ; Among them, is the d th distributed power source at the The output signal at a moment, where a value of 1 indicates an output command and a value of 0 indicates no output; Indicates the d th distributed power source's maximum output at a moment.

[0043] g. Energy storage device constraint: ; Among them, is the rated charge-discharge power of the e th energy storage device; is the charging state of the e th energy storage device, where the charging state is 1 and the idle state is 0; is the discharging state of the e th energy storage device, where the discharging state is 1 and the idle state is 0; is the rated capacity of the e th energy storage device; and are respectively the maximum and minimum values of the state of charge of the e th energy storage device, taking values of 0.2 and 0.8 respectively.

[0044] Quantification of collaborative ability The collaborative ability among multiple user-side resources is defined as the ratio of the change in power value before and after different types of user-side resources participate in collaborative mutual assistance to their power value without participating in collaboration. The calculation formula is: ; Among them, is the power value of user-side resource u at t moment after participating in collaboration; is the power value of user-side resource u at t moment without participating in collaboration.

[0045] After obtaining the quantification result of the collaborative ability, this embodiment considers the collaboration of multiple user-side resources to quantify the multi-dimensional demand elasticity of users; First, the price elasticity coefficient of electricity demand is used to calculate the load after users participate in price-based demand response; Second, considering the impact of the collaborative mutual assistance among various user-side resources included in users on the change in user load, the load after users participate in price-based demand response is corrected; Third, a multi-dimensional price elasticity coefficient matrix is constructed to quantify the sensitivity of the change in electricity demand during peak and valley periods of users to multi-dimensional price indicators, so as to characterize the peak shaving and valley filling changes of the load curve.

[0046] Calculation of user response volume considering collaboration of multiple user-side resources Electric energy, as a commodity in the power market, has a certain supply-demand relationship. The price elasticity coefficient of electricity demand can be expressed as: ; Among them, represents the time period s the price elasticity of t for the time period and are respectively the user electricity load during the time period s before demand response (Demand Response, abbreviated as DR) and the electricity price during the time period t ; and are respectively the change in user load during the time period s after DR and the change in price during the time period t ;

[0047] Using the price elasticity coefficient of electricity demand to characterize the sensitivity of electricity demand to electricity price changes, the larger it is, the higher the sensitivity of electricity demand to electricity price changes.

[0048] The change in load when users participate in price-based DR is: ; The load demand after users participate in DR becomes: ; Among them, is the electricity load during the time period t after users participate in DR; is the electricity load during the time period t before users participate in DR.

[0049] Considering the synergistic and mutual assistance effects among various user-side resources included in users will affect the change in user load. Therefore, the load demand after users participate in price-based DR becomes: ; Among them, is the electricity load during the time period t after users participate in DR considering the synergistic and mutual assistance effects among user-side resources; U is the number of user-side resources; is the collaborative ability of the user-side resource u ; is the proportion of the user-side resource u in all user-side resources included in the user.

[0050] User multi-dimensional price elasticity quantization method The traditional price elasticity coefficient of electricity demand mainly explores the relationship between the overall electricity demand and the change in electricity price. Since the focus of ensuring electricity supply is on the change in electricity demand during peak and valley periods, in order to better study the correlation between the change in electricity demand and the change in electricity price during peak and valley periods, drawing on the concept of multi-dimensional elasticity coefficient in material mechanics, it is considered that each type of electricity load index, similar to the force on a material from multiple directions, is affected by multi-dimensional electricity price indicators. Based on the traditional price elasticity coefficient of electricity demand, a multi-dimensional price elasticity coefficient matrix is proposed; the change in electricity demand during peak and valley periods is refined into multi-dimensional load indicators, and the time-of-use electricity price change is refined into multi-dimensional price indicators. A multi-dimensional price elasticity coefficient matrix is constructed through the influence mapping relationship between multi-dimensional load indicators and multi-dimensional price indicators, so as to more comprehensively and specifically quantify the sensitivity of the change in electricity demand during peak and valley periods of users to multi-dimensional price indicators.

[0051] In order to characterize the peak shaving and valley filling changes of the load curve, based on the basic attributes of peak and valley loads, the reduction rate of peak-valley difference, the transfer rate of peak-section electricity, the filling rate of valley-section electricity, and the transfer rate of peak-valley electricity are selected as key load indicators. The meanings of each variable are as follows: 1) Reduction rate of peak-valley difference Y 1 The peak-valley difference is the ratio of the difference between the maximum load and the minimum load of the 24-hour load curve to the maximum load, which can be expressed as: ; Among them, L max and L min are the maximum load and the minimum load of the 24-hour load curve respectively.

[0052] The reduction rate of peak-valley difference Y 1 is: ; Among them, is the peak-valley difference after the user participates in demand response; is the peak-valley difference before the user participates in demand response; Y 1 is negative, indicating that the peak-valley difference decreases, playing a role in peak shaving and valley filling; Y 1 is positive, indicating that the peak-valley difference increases and the peak shaving and valley filling effect is not significant.

[0053] 2) Transfer rate of peak-section electricity Y 2 The proportion of peak-section (spike) electricity F is: ; Among them, L f 、 L aThey are the load integral during the peak (spike) period of the 24-hour load curve and the all-day load integral respectively.

[0054] Peak period electricity transfer rate Y 2 is: ; Among them, is the proportion of peak period (spike) electricity after the user participates in demand response; is the proportion of peak period (spike) electricity before the user participates in demand response; Y If 2 is negative, it indicates that the proportion of peak period (spike) electricity has decreased, playing a role in peak shaving; Y If 2 is positive, it indicates that the proportion of peak period (spike) electricity has increased, and the peak shaving effect is not significant.

[0055] 3) Valley period electricity filling rate Y 3 Proportion of valley period electricity G is: ; Among them, L g is the load integral during the low valley period of the load curve.

[0056] Valley period electricity filling rate Y 3 is: ; Among them, is the proportion of valley period load after the user participates in demand response; is the proportion of valley period electricity before the user participates in demand response; Y If 3 is positive, it indicates that the proportion of valley period electricity has increased, playing a role in valley filling; Y If 3 is negative, it indicates that the proportion of valley period electricity has decreased, and the valley filling effect is not significant.

[0057] 4) Peak-valley electricity transfer rate Y 4 is: ; Y If 4 is positive, it indicates that the decrease in the proportion of peak period electricity is greater than the decrease in the proportion of valley period electricity or the increase in the proportion of peak period electricity is less than the increase in the proportion of valley period electricity, generally playing a role in peak shaving and valley filling; Y If 4 is negative, it indicates that the decrease in the proportion of peak period electricity is less than the decrease in the proportion of valley period electricity or the increase in the proportion of peak period electricity is greater than the increase in the proportion of valley period electricity, and the peak shaving and valley filling effect is not obvious generally.

[0058] Since the electricity price change includes the change of peak-valley period electricity prices, select the peak electricity price change rate X 1, valley electricity price change rate X 2, peak-valley electricity price ratio change rateX 3 Characterizes the electricity price changes.

[0059] Peak electricity price change rate X 1 is defined as the proportion of the peak electricity price during the peak period after the electricity price change compared to that before the change; Valley electricity price change rate X 2 is defined as the proportion of the valley electricity price during the valley period after the electricity price change compared to that before the change; Peak-valley electricity price ratio change rate X 3 is defined as the proportion of the peak-valley electricity price ratio after the electricity price change compared to that before the change.

[0060] Due to the peak electricity price change rate X 1, valley electricity price change rate X 2, peak-valley electricity price ratio change rate X 3 will all affect the peak-valley difference rate reduction rate Y 1. Combining with the definition of the electricity demand elasticity coefficient, it can be known that the peak-valley difference rate reduction rate Y 1 affected by multi-dimensional electricity price indicators can be characterized as: ; Among them, a 11 , a 12 , a 13 are the price elasticity coefficients to be evaluated; b 1 is the constant term to be evaluated.

[0061] Similarly, the regression equations of Y2, Y3, Y4 and X1, X2, X3 can be obtained as: ; ; ; Express the above equations in matrix form as: ; ; Matrix is the multi-dimensional price elasticity matrix. For example: a 12 is the elasticity coefficient of the valley electricity price change rate X2 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 characterized.

[0062] Based on the historical data of load changes and price changes, by solving the optimal solutions of each price elasticity coefficient, the numerical values of each elasticity coefficient in matrix A can be obtained, and the absolute value of the numerical value characterizes the sensitivity of the corresponding load indicator to the corresponding price indicator.

[0063] This embodiment quantifies the collaborative capabilities of diverse user-side resources considering the limitations of the power grid network structure. Considering the limitations of the power grid network structure on the collaborative response capabilities of user-side resources, it incorporates the power grid topology and line capacity constraints into the optimization model for the collaborative mutual assistance of diverse user-side resources. It corrects the collaborative response capabilities of user-side resources through the safety boundary of the grid network, and quantifies the collaborative response capabilities among user-side resources through the quantification results of collaborative capabilities, solving the problem that the existing methods ignore physical network limitations and lead to inaccurate evaluation of the collaborative response capabilities of user-side resources.

[0064] This embodiment quantifies the elasticities of multi-dimensional user demands based on the quantification results of collaborative capabilities. It creatively proposes an elastic correction mechanism considering the collaborative mutual assistance of user-side resources, superimposing collaborative effects such as flexible load regulation and distributed energy storage charging and discharging on the basis of price response, improving the accuracy of quantifying user demand elasticities; breaking through the limitations of traditional single-dimensional price elasticity models, and by constructing a multi-dimensional elasticity coefficient matrix, quantifying the sensitivity of peak and valley period loads to differential electricity price signals, and realizing a refined assessment of the peak shaving and valley filling potential.

[0065] Embodiment Two Embodiment Two of the present invention introduces a system for quantifying user demand elasticity considering the collaboration of diverse user-side resources.

[0066] As Figure 2 shown, a system for quantifying user demand elasticity considering the collaboration of diverse user-side resources includes: An acquisition module configured to acquire the response models of different user-side resources; A modeling module configured to construct an optimization model for the collaborative mutual assistance of diverse user-side resources of the acquired response models considering the power grid network structure; A solving module configured to solve the constructed collaborative mutual assistance optimization model to obtain the quantification results of collaborative capabilities; A correction module configured to correct the electricity load demand of users participating in demand-side response based on the obtained quantification results of collaborative capabilities; A quantification module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained electricity load demand correction amount, and quantify user demands according to the constructed matrix, completing the quantification of user demand elasticity considering the collaboration of diverse user-side resources.

[0067] The detailed steps are the same as those of the method for quantifying user demand elasticity considering the collaboration of diverse user-side resources provided in Embodiment One, and will not be elaborated here.

[0068] Embodiment Three Embodiment Three of the present invention provides a computer-readable storage medium.

[0069] A computer-readable storage medium stores a program thereon, and when the program is executed by a processor, it implements the steps in the user demand elastic quantification method considering multi-source user-side resource collaboration as described in Embodiment 1 of the present invention.

[0070] The detailed steps are the same as those in the user demand elastic quantification method considering multi-source user-side resource collaboration provided in Embodiment 1, and will not be elaborated here.

[0071] Embodiment 4 Embodiment 4 of the present invention provides an electronic device.

[0072] An electronic device includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements the steps in the user demand elastic quantification method considering multi-source user-side resource collaboration as described in Embodiment 1 of the present invention.

[0073] The detailed steps are the same as those in the user demand elastic quantification method considering multi-source user-side resource collaboration provided in Embodiment 1, and will not be elaborated here.

[0074] Embodiment 5 Embodiment 5 of the present invention provides a computer program product.

[0075] A computer program product includes software code, and the program in the software code executes the steps in the user demand elastic quantification method considering multi-source user-side resource collaboration as described in Embodiment 1 of the present invention.

[0076] The detailed steps are the same as those in the user demand elastic quantification method considering multi-source user-side resource collaboration provided in Embodiment 1, and will not be elaborated here.

[0077] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0078] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0079] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0082] Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

[0083] The above description is only the preferred embodiments of this example and is not used to limit this example. For those skilled in the art, this example can have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this example shall be included within the protection scope of this example.

Claims

1. A method for quantifying the elasticity of user demand considering the collaboration of multi-source user-side resources, characterized in that, including: acquiring response models of different user-side resources; considering the grid network structure and constructing a collaborative mutual assistance optimization model for the acquired response models of multiple user-side resources; solving the constructed collaborative mutual assistance optimization model to obtain a quantification result of the collaborative ability; based on the obtained quantification result of the collaborative ability, correcting the electricity load demand for users to participate in demand-side response; according to the obtained correction amount of the electricity load demand, constructing a multi-dimensional elasticity coefficient matrix, and quantifying user demands according to the constructed matrix to complete the elasticity quantification of user demands considering the collaboration of multiple user-side resources.

2. The elastic quantification method for user requirements considering multi - user - side resource collaboration as described in claim 1, characterized in that, The objective function of the constructed collaborative mutual assistance optimization model for multiple user-side resources is to minimize the total economic cost, and the total economic cost includes the cost of purchasing electricity from the superior grid, the load interaction cost, and the energy storage scheduling cost; the constraint conditions of the collaborative mutual assistance optimization model include power balance constraints, line power flow constraints, shiftable load constraints, transferable load constraints, curtailable load constraints, distributed power source constraints, and energy storage device constraints.

3. A method for elastic quantification of user requirements considering multi - user - side resource collaboration as described in claim 1, characterized in that, The quantification result of the collaborative ability is used to characterize the collaborative ability between multiple user-side resources, that is, the ratio of the change in power value before and after different user-side resources participate in collaborative mutual assistance to the power value when they do not participate in collaborative mutual assistance.

4. A method for elastic quantification of user requirements considering multi - user - side resource collaboration as described in claim 1, characterized in that, Before correcting the electricity load demand for users to participate in demand-side response, acquiring the price elasticity coefficient of electricity demand characterizing the sensitivity of electricity demand to electricity price changes, calculating the load change amount for users to participate in price-based demand-side response through the acquired price elasticity coefficient of electricity demand, and obtaining the load demand for users to participate in demand-side response; using the quantification result of the collaborative ability to correct the load demand for users to participate in demand-side response to obtain a correction amount of the electricity load demand.

5. A method for elastic quantification of user requirements considering multi - user - side resource collaboration as described in claim 4, characterized in that, The elements in the constructed multi-dimensional elasticity coefficient matrix are the price elasticity coefficients of electricity demand. Combining the constructed multi-dimensional elasticity coefficient matrix to obtain the corresponding relationship between the electricity demand changes in the peak and valley periods of users and the multi-dimensional price indicators, and obtaining the sensitivity of the peak and valley period loads to the differential electricity price signals to complete the elasticity quantification of user demands considering the collaboration of multiple user-side resources.

6. The elastic quantification method for user requirements considering multi - user - side resource collaboration as described in claim 1, characterized in that, The acquired response models of different user-side resources at least include a shiftable load model, a transferable load model, a curtailable load model, and a distributed energy storage model.

7. A user demand elasticity quantification system considering the collaboration of diverse user-side resources, characterized in that, including: an acquisition module configured to acquire response models of different user-side resources; a modeling module configured to consider the grid network structure and construct a collaborative mutual assistance optimization model for the acquired response models of multiple user-side resources; a solving module configured to solve the constructed collaborative mutual assistance optimization model to obtain a quantification result of the collaborative ability; a correction module configured to correct the electricity load demand for users to participate in demand-side response based on the obtained quantification result of the collaborative ability; a quantification module configured to construct a multi-dimensional elasticity coefficient matrix according to the obtained correction amount of the electricity load demand, and quantify user demands according to the constructed matrix to complete the elasticity quantification of user demands considering the collaboration of multiple user-side resources.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method for quantifying the elasticity of user demands considering the collaboration of multiple user-side resources as described in any one of claims 1-6.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, the steps of the user demand elastic quantification method considering the collaboration of multiple user-side resources as described in any one of claims 1-6 are implemented.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the user demand elastic quantification method considering the collaboration of multiple user-side resources as described in any one of claims 1-6.

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