Load-side demand response peak-shaving resource configuration method, device, equipment and medium

Abstract

The present invention relates to the technical field of demand response, and in particular to a load-side demand response peak-shaving resource configuration method, device, equipment and medium, the method comprising: obtaining wind power, photovoltaic, and load power consumption data, and analyzing their multi-dimensional source-load correlation to generate a joint probability density distribution; analyzing the effects of the peak-shaving resources configured by the dispatching center in different scenarios, and establishing utility functions respectively; obtaining typical scenarios according to the joint probability density distribution, and calculating the scenario probabilities to obtain the probability density distribution of the peak-shaving demand; using the probability density distribution of the peak-shaving demand to represent different decision-making behaviors of the dispatching center, and establishing a dispatching center peak-shaving resource configuration model under uncertainty based on the utility function; solving the peak-shaving resource configuration model with the goal of maximizing utility to obtain a peak-shaving resource configuration method. The present invention can improve the effectiveness of configuring resources, reduce configuration costs, and effectively improve the reliability and applicability of planning results.

Description

Technical Field

[0001] The present invention relates to the field of demand response technology, and in particular to a load-side demand response peak-shaving resource configuration method, device, equipment and medium. Background Art

[0002] Under the "dual carbon" goal, the transformation of energy structure and the low-carbonization of power system have become important ways to achieve sustainable development. With the widespread application of renewable energy such as wind power and solar energy, the power system has gradually shifted to a diversified structure dominated by renewable energy. Although renewable energy has made a significant contribution to reducing carbon emissions, it is volatile and uncertain and cannot be output as stably as traditional energy. This has led to severe challenges to the peak-shaving capacity of the power system when facing the grid connection of a large number of renewable energy sources.

[0003] In this context, demand response, as a flexible means of load regulation, has gradually gained attention. Demand response uses incentive mechanisms to guide users to actively adjust their electricity consumption during periods of tight power supply or high electricity prices, thereby balancing supply and demand and alleviating grid pressure. In addition, demand response, as a green peak-shaving method, can effectively regulate power loads and achieve supply and demand balance within the system without increasing carbon emissions.

[0004] In view of the sharp increase in peak-shaving demand in new power systems, using the dispatch center to configure demand-response peak-shaving resources can significantly reduce the demand for spare capacity, thereby reducing the operating cost of the power system. The configuration of its peak-shaving resources is affected by the peak-shaving demand. If the amount of configured resources is too large, some resources will be idle, resulting in cost losses; if the amount configured is too small, the peak-shaving demand cannot be met. However, the uncertainty on both the source and load sides makes it impossible to accurately obtain the peak-shaving demand, which brings challenges to the resource configuration of the dispatch center.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a load-side demand response peak-shaving resource configuration method, device, equipment and medium, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a load-side demand response peak-shaving resource configuration method, comprising the following steps:

[0008] Obtain wind power, photovoltaic, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution;

[0009] Analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions for each scenario;

[0010] Obtaining typical scenarios according to the joint probability density distribution, and calculating the scenario probabilities to obtain the probability density distribution of the peak load demand;

[0011] The probability density distribution of the peak-shaving demand is used to represent different decision-making behaviors of the dispatching center, and a peak-shaving resource allocation model of the dispatching center under uncertainty is established based on the utility function;

[0012] The peak-shaving resource configuration model is solved with the goal of maximizing utility to obtain a peak-shaving resource configuration method.

[0013] Furthermore, the acquisition of wind power, photovoltaic, and load power consumption data and analysis of their multi-dimensional source-load correlations include:

[0014] Construct a data set at time t, P=[P wind , P PV , P load ], where P wind is wind power data, P PV is the photovoltaic data, P load Load power consumption data;

[0015] R-vine Copula is used to analyze the characteristics of multidimensional source loads. If F(x1, x2,…, x n ) are random variables x1, x2, …x with continuous marginal distributions n The joint probability distribution function of , then there exists a unique Copula function C(u1, u2,…, u n )satisfy:

[0016] ;

[0017] In the R-vine Copula structure, x1, x2, ...x n The joint density function is expressed as:

[0018] ;

[0019] In the formula, u i =F i (x i ), F i (x i ) is x i The marginal distribution of E i The edge e=j(e)l(e)|D(e) in C, j(e) and l(e) are the two nodes connected by edge e, and D(e) is the condition set; j(e)l(e)|D(e) represents the Copula density function corresponding to edge e; xD(e) represents the subvector determined by the condition set D(e).

[0020] Furthermore, the use of R Copula to analyze the characteristics of multidimensional source loads includes the following steps:

[0021] Calculate the weight w between all possible variable pairs {j, k} of the original data j,k ;

[0022] Select the variable pairs that maximize the sum of their weights to build a tree:

[0023] ;

[0024] In the selected spanning tree, select a binary copula cluster for each edge {j, k} and estimate the corresponding parameters;

[0025] Iterate the i=2,…, n-1th layer of trees:

[0026] Based on the selected Copula cluster and its i-1 The parameters in the calculation weight w are calculated for all possible conditional variable pairs {j, k|D} in tree i. j,k|D ; All edges need to satisfy the proximity condition;

[0027] Among the above edges, select the variable pairs that maximize the sum of weights to generate the tree:

[0028] ;

[0029] In the selected spanning tree, a binary copula cluster is selected for each edge {j, k|D} and the corresponding parameters are estimated.

[0030] Furthermore, the utility function includes:

[0031] ;

[0032] Where U represents the expected revenue of the dispatch center under the current resource purchase amount; c PS , ω and r are the purchase price of regulation resources, control cost coefficient and peak load compensation coefficient respectively; P PS P is the peak load resource. need To meet peak demand.

[0033] Furthermore, the analysis of the effects of the peak load shaving resources configured by the dispatch center in different scenarios and the establishment of utility functions respectively include:

[0034] Considering the uncertainty of peak-shaving resource application, typical peak-shaving effects are divided into two categories:

[0035] The first type is under-scaling, that is, all configured peak-scaling resources are used for peak-scaling, but the peak-scaling demand cannot be met at this time;

[0036] The other type is over-regulation, that is, only part of the configured peak-shaving resources are used for peak-shaving to meet the peak-shaving demand, and the remaining configured peak-shaving resources are idle;

[0037] Under these two uncertainties, the utility function is decomposed into:

[0038] ;

[0039] ;

[0040] The decision utility function of the dispatch center under different circumstances is obtained.

[0041] Furthermore, the typical scenario is acquired according to the joint probability density distribution, and the scenario probability is calculated to obtain the probability density distribution of the peak load demand, including:

[0042] Generate a typical scenario based on the joint probability density distribution:

[0043] ;

[0044] Where D n is the scenario n generated by the multi-dimensional source-load joint probability density distribution, P wind,n , P PV,n , P load,n These are the specific output values ​​of wind power, photovoltaic power and load generated under n scenarios;

[0045] Using neural networks to make ultra-short-term forecasts of wind power, photovoltaic power and load:

[0046] ;

[0047] P wind,f , P PV,f , P load,f Ultra-short-term forecast values ​​for wind power, photovoltaic power and load;

[0048] Obtain the peak load demand P in typical scenarios need,n :

[0049] ;

[0050] In the formula, It is the daily load valley value;

[0051] Based on the typical scenarios obtained, kernel density estimation is used to obtain the probability density distribution of peak demand:

[0052] ;

[0053] In the formula, represents the estimated probability density function value at point x, M represents the number of sample data; h is the bandwidth, and K(⋅) is the kernel function used to weigh the distance x and each sample point x. i The distance between.

[0054] Furthermore, the use of the probability density distribution of the peak load demand to represent different decision-making behaviors of the dispatching center includes:

[0055] Through the probability density of peak demand, the utility function of the dispatch center under different circumstances is unified:

[0056] ;

[0057] The peak load resource configuration model includes:

[0058] ;

[0059] .

[0060] The present invention also includes a load-side demand response peak-shaving resource configuration device, using the above method, the device includes:

[0061] The acquisition unit is used to acquire wind power, photovoltaic power, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution;

[0062] The utility unit is used to analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions respectively;

[0063] A demand calculation unit, used to obtain typical scenarios according to the joint probability density distribution, and calculate the scenario probability to obtain the probability density distribution of the peak load demand;

[0064] A modeling unit, used to use the probability density distribution of the peak-shaving demand to represent different decision-making behaviors of the dispatching center, and to establish a peak-shaving resource configuration model of the dispatching center under uncertainty based on the utility function;

[0065] The solving unit is used to solve the peak-shaving resource configuration model with the goal of maximizing utility to obtain a peak-shaving resource configuration method.

[0066] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0067] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0068] The beneficial effects of the present invention are as follows: the demand response peak-shaving resource configuration model proposed in the present invention effectively solves the problem of the dispatching center configuring demand response peak-shaving resources, can improve the effectiveness of configuring resources, and reduce configuration costs. The present invention analyzes the uncertain peak-shaving demands faced by the dispatching center based on multi-dimensional source-load correlation, and can effectively improve the reliability and applicability of planning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 is a flow chart of the method of the present invention;

[0071] Figure 2 It is a structural schematic diagram of the device of the present invention;

[0072] Figure 3 It is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0074] Embodiment 1:

[0075] like Figure 1 As shown: A load-side demand response peak-shaving resource configuration method comprises the following steps:

[0076] Obtain wind power, photovoltaic, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution;

[0077] Analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions for each scenario;

[0078] Obtain typical scenarios based on the joint probability density distribution, calculate the scenario probabilities, and obtain the probability density distribution of peak load demand;

[0079] The probability density distribution of peak-shaving demand is used to represent different decision-making behaviors of the dispatch center, and a peak-shaving resource allocation model of the dispatch center under uncertainty is established based on the utility function.

[0080] The peak-shaving resource allocation model is solved with the goal of maximizing utility, and the peak-shaving resource allocation method is obtained.

[0081] The demand response peak-shaving resource configuration model proposed in this embodiment effectively solves the problem of configuring demand response peak-shaving resources in the dispatching center, can improve the effectiveness of configured resources, and reduce configuration costs. Among them, the uncertain peak-shaving demand faced by the dispatching center based on multi-dimensional source-load correlation analysis can effectively improve the reliability and applicability of planning results.

[0082] In this embodiment, wind power, photovoltaic power, and load power consumption data are obtained, and their multi-dimensional source-load correlation is analyzed, including:

[0083] Construct a data set at time t, P=[P wind , P PV , P load ], where P wind is wind power data, P PV is the photovoltaic data, P load Load power consumption data;

[0084] R-vine Copula is used to analyze the characteristics of multidimensional source loads. If F(x1, x2,…, x n ) are random variables x1, x2, …x with continuous marginal distributions n The joint probability distribution function of , then there exists a unique Copula function C(u1, u2,…, u n )satisfy:

[0085] ;

[0086] In the R-vine Copula structure, x1, x2, ...x n The joint density function is expressed as:

[0087] ;

[0088] In the formula, u i =F i (x i ), F i (x i ) is x i The marginal distribution of E i The edge e=j(e)l(e)|D(e) in C, j(e) and l(e) are the two nodes connected by edge e, and D(e) is the condition set; j(e)l(e)|D(e)represents the Copula density function corresponding to edge e; x D(e) represents the subvector determined by the condition set D(e).

[0089] Among them, R Copula is used to analyze the characteristics of multidimensional source loads, including the following steps:

[0090] Calculate the weight w between all possible variable pairs {j, k} of the original data j,k ;

[0091] Select the variable pairs that maximize the sum of their weights to build a tree:

[0092] ;

[0093] In the selected spanning tree, select a binary copula cluster for each edge {j, k} and estimate the corresponding parameters;

[0094] Iterate the i=2,…, n-1th layer of trees:

[0095] Based on the selected Copula cluster and its i-1 The parameters in the calculation weight w are calculated for all possible conditional variable pairs {j, k|D} in tree i. j,k|D ; All edges need to satisfy the proximity condition;

[0096] Among the above edges, select the variable pairs that maximize the sum of weights to generate the tree:

[0097] ;

[0098] In the selected spanning tree, a binary copula cluster is selected for each edge {j, k|D} and the corresponding parameters are estimated.

[0099] As a preference of the above embodiment, the utility function includes:

[0100] ;

[0101] Where U represents the expected revenue of the dispatch center under the current resource purchase amount; c PS , ω and r are the purchase price of regulation resources, control cost coefficient and peak load compensation coefficient respectively; P PS P is the peak load resource. need To meet peak demand.

[0102] In this embodiment, the effects of the peak load resources configured by the dispatch center in different scenarios are analyzed, and utility functions are established respectively, including:

[0103] Considering the uncertainty of peak-shaving resource application, typical peak-shaving effects are divided into two categories:

[0104] The first type is under-scaling, that is, all configured peak-scaling resources are used for peak-scaling, but the peak-scaling demand cannot be met at this time;

[0105] The other type is over-regulation, that is, only part of the configured peak-shaving resources are used for peak-shaving to meet the peak-shaving demand, and the remaining configured peak-shaving resources are idle;

[0106] In these two cases of uncertainty, the utility function is decomposed into:

[0107] ;

[0108] ;

[0109] The decision utility function of the dispatch center under different circumstances is obtained.

[0110] According to the joint probability density distribution, typical scenarios are obtained and the scenario probabilities are calculated to obtain the probability density distribution of the peak load demand, including:

[0111] Generate typical scenarios based on the joint probability density distribution:

[0112] ;

[0113] Where D n is the scenario n generated by the multi-dimensional source-load joint probability density distribution, P wind,n , P PV,n , P load,n These are the specific output values ​​of wind power, photovoltaic power and load generated under n scenarios;

[0114] Using neural networks to make ultra-short-term forecasts of wind power, photovoltaic power and load:

[0115] ;

[0116] P wind,f , P PV,f , P load,f Ultra-short-term forecast values ​​for wind power, photovoltaic power and load;

[0117] Obtain the peak load demand P in typical scenarios need,n :

[0118] ;

[0119] In the formula, It is the daily load valley value;

[0120] Based on the typical scenarios obtained, kernel density estimation is used to obtain the probability density distribution of peak demand:

[0121] ;

[0122] In the formula, represents the estimated probability density function value at point x, M represents the number of sample data; h is the bandwidth, and K(⋅) is the kernel function used to weigh the distance x and each sample point x. i The distance between.

[0123] Among them, the probability density distribution of peak demand is used to represent different decision-making behaviors of the dispatching center, including:

[0124] Through the probability density of peak demand, the utility function of the dispatch center under different circumstances is unified:

[0125] ;

[0126] The peak load resource configuration model includes:

[0127] ;

[0128] .

[0129] like Figure 2 As shown, a load-side demand response peak-shaving resource configuration device uses the above method, and the device includes:

[0130] The acquisition unit is used to acquire wind power, photovoltaic power, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution;

[0131] The utility unit is used to analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions respectively;

[0132] The demand calculation unit is used to obtain typical scenarios according to the joint probability density distribution, calculate the scenario probability, and obtain the probability density distribution of the peak load demand;

[0133] A modeling unit is used to use the probability density distribution of peak-shaving demand to represent different decision-making behaviors of the dispatching center, and to establish a peak-shaving resource allocation model of the dispatching center under uncertainty based on the utility function;

[0134] The solving unit is used to solve the peak-shaving resource configuration model with the goal of maximizing utility to obtain a peak-shaving resource configuration method.

[0135] Embodiment 2:

[0136] In this embodiment, a load-side demand response peak-shaving resource configuration method taking into account the source-load correlation is proposed to improve the effectiveness and economy of peak-shaving resource configuration, thereby maximizing the system peak-shaving effect and reducing the demand response peak-shaving resource procurement cost. Perform the following steps:

[0137] Step 1: Analyze massive wind power, photovoltaic, and load power consumption data, use Rvine Copula to analyze multi-dimensional source-load correlation, and generate a joint probability density distribution;

[0138] Step 2: Analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions respectively;

[0139] Step 3: Obtain typical distribution scenarios based on the multi-dimensional source-load joint probability density distribution and calculate scenario probabilities to obtain the probability density distribution of peak load demand;

[0140] Step 4: Use the probability density distribution of peak-shaving demand to represent different decision-making behaviors of the dispatching center and establish a peak-shaving resource allocation model for the dispatching center under uncertainty.

[0141] In step 1, a long short-term memory neural network is used to make multiple predictions of load and renewable energy generation (wind power generation and photovoltaic power generation). Combined with historical data, the same time period and scale data are used to construct a data set P = [P wind ,P PV , P load ]. After many data points are generated, analysis is required to obtain the characteristics of wind power, PV power, and load in the area.

[0142] R Copula is used to analyze the characteristics of multidimensional sources and decompose the correlation between high-dimensional random variables into the correlation between two variables. n ) are random variables x1, x2, …x with continuous marginal distributions n The joint probability distribution function of , then there exists a unique Copula function C(u1, u2,…, u n ) satisfies the formula . In the R-vine Copula structure, x1, x2, ...x n The joint density function can be expressed as the formula .

[0143]

[0144]

[0145] In the formula, u i =F i (x i ), F i (x i ) is x i The marginal distribution of E iIn the edge e=j(e)l(e)|D(e), j(e) and l(e) are the two nodes connected by edge e, and D(e) is the condition set. j(e)l(e)|D(e) Represents the Copula density function corresponding to edge e. D(e) represents the subvector determined by the condition set D(e).

[0146] The weight coefficients of different binary Copula functions are calculated and the maximum spanning tree method is used to determine the optimal structure of the first layer of trees. After determining the optimal first layer tree structure, the tree structures and corresponding Copula functions of the subsequent layers are determined layer by layer to construct a complete R-vine Copula model.

[0147] 1) Calculate the weight w between all possible variable pairs {j, k} of the original data j,k

[0148] 2) Select the variable pair that maximizes the sum of weights to generate a tree.

[0149]

[0150] 3) In the selected spanning tree, select a binary copula cluster for each edge {j, k} and estimate the corresponding parameters.

[0151] 4) For the i=2,…, n-1th layer tree (the following is the iteration part)

[0152] 5) Based on the selected Copula cluster and its i-1 The parameters in the calculation weight w are calculated for all possible conditional variable pairs {j, k|D} in tree i. j,k|D ; All edges need to satisfy the proximity condition (see the previous definition).

[0153] 6) Among the above edges, select the variable pair that maximizes the sum of weights to generate a tree.

[0154]

[0155] 7) In the selected spanning tree, select a binary copula cluster for each edge {j, k|D} and estimate the corresponding parameters.

[0156] Finally, the joint probability density function of multi-dimensional source-load output can be obtained.

[0157] In step 2, the effects of the peak-shaving resources configured by the dispatch center in different scenarios are analyzed, and utility functions are established respectively. The dispatch center focuses on multiple aspects of peak-shaving, which can be mainly divided into peak-shaving effect and peak-shaving cost, but the two are not subject to the same evaluation perspective. Therefore, the peak-shaving effect is converted into economic effect for evaluation, and the utility function of the dispatch center under deterministic conditions is constructed as follows: , which includes the peak-shaving income of the dispatching center, the peak-shaving resource allocation cost and the peak-shaving expenditure cost.

[0158]

[0159] In the formula, U represents the expected revenue of the dispatch center under the current resource purchase amount. PS , ω and r are the purchase price of regulation resources, control cost coefficient and peak load compensation coefficient respectively. need To meet peak demand.

[0160] The establishment of the above utility function only considers the decision-making behavior of the dispatch center when all information is completely known. Obviously, the optimal amount of configured resources can be directly calculated.

[0161] However, the application scenario of peak-shaving resources is an uncertain scenario, and its typical peak-shaving effects can be refined into two categories: one is under-shaving, that is, all the configured peak-shaving resources are used for peak-shaving, but at this time it still cannot meet the peak-shaving demand; the other is over-shaving, that is, only part of the configured peak-shaving resources are used for peak-shaving to meet the peak-shaving demand, and the remaining configured peak-shaving resources are idle. In these two uncertain situations, the utility function of the dispatch center in the deterministic situation is decomposed into the formula - .

[0162]

[0163]

[0164] At this point, the decision utility function of the dispatch center under different circumstances can be obtained.

[0165] Step 3: Obtain typical distribution scenarios and calculate scenario probabilities based on the multi-dimensional source-load joint probability density distribution to obtain the probability density distribution of peak load demand. Generate massive scenarios using the multi-dimensional source-load joint probability density distribution, as shown in the formula . Use neural networks to make ultra-short-term forecasts of wind power, photovoltaic power and load, as shown in the formula According to the formula The peak load demand P in massive scenarios can be obtained need,n .

[0166]

[0167]

[0168]

[0169] Where D n is the scenario n generated by the multi-dimensional source-load joint probability density distribution, P wind,n , P PV,n , P load,n These are the specific output values ​​of wind power, photovoltaic power and load generated under n scenarios. wind,f , P PV,f , P load,f It is the ultra-short-term forecast value of wind power, photovoltaic power and load.

[0170] According to the obtained D n , use kernel density estimation to obtain the probability density distribution of peak demand, as shown in the formula .

[0171]

[0172] In the formula, represents the estimated probability density function value at point x, M represents the number of sample data. h is the bandwidth, and K(⋅) is the kernel function used to weigh the distance x and each sample point x. i The distance between.

[0173] Step 4: Use the probability density distribution of peak-shaving demand to represent different decision-making behaviors of the dispatch center and establish a dispatch center peak-shaving resource configuration model under uncertainty. In step 2, the utility function of the dispatch center configuring peak-shaving resources was discussed in a classified manner, but it was not unified and could not be compatible with the model for calculation. Therefore, the utility function of the dispatch center under different circumstances is unified through the probability density of peak-shaving demand. The utility function of the dispatch center configuring peak-shaving resources under uncertainty is as follows: shown.

[0174]

[0175] The load-side demand response resource configuration model that ultimately takes into account the source-load correlation is listed below.

[0176]

[0177]

[0178] See also Figure 3 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0179] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0180] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0181] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0182] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0183] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0184] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0185] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0186] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0187] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0188] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A load-side demand response peak-shaving resource configuration method, characterized in that: The steps include: Obtain wind power, photovoltaic, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution; Analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions for each scenario; Obtaining typical scenarios according to the joint probability density distribution, and calculating the scenario probabilities to obtain the probability density distribution of the peak load demand; The probability density distribution of the peak-shaving demand is used to represent different decision-making behaviors of the dispatching center, and a peak-shaving resource allocation model of the dispatching center under uncertainty is established based on the utility function; Solving the peak load resource configuration model with the goal of maximizing utility to obtain a peak load resource configuration method; The utility function includes: U=rP PS P need -c PS P PS -ω(P PS ) 2 ; Where U represents the expected revenue of the dispatch center under the current resource purchase amount; c PS , ω and r are the purchase price of regulation resources, control cost coefficient and peak load compensation coefficient respectively; P PS P is the peak load resource. need To meet peak demand; The analysis of the effects of the peak load resources configured by the dispatch center in different scenarios and the establishment of utility functions respectively include: Considering the uncertainty of peak-shaving resource application, typical peak-shaving effects are divided into two categories: The first type is under-scaling, that is, all configured peak-scaling resources are used for peak-scaling, but the peak-scaling demand cannot be met at this time; The other type is over-regulation, that is, only part of the configured peak-shaving resources are used for peak-shaving to meet the peak-shaving demand, and the remaining configured peak-shaving resources are idle; Under these two uncertainties, the utility function is decomposed into: if P PS <P need ,U=rP PS P need -c PS P PS -ω(P PS ) 2 ; if P Ps >P need ,U=rP need P need -c PS P PS -ω(P need ) 2 ; Obtain the decision utility function of the dispatch center under different circumstances; The obtaining of typical scenarios according to the joint probability density distribution and calculating the scenario probabilities to obtain the probability density distribution of the peak load demand includes: Generate a typical scenario based on the joint probability density distribution: D n =[P wind，n ,P PV，n ,P load，n ]; Where D n is the scenario n generated by the multi-dimensional source-load joint probability density distribution, P wind，n , P PV，n , P load，n These are the specific output values ​​of wind power, photovoltaic power and load generated under n scenarios; Using neural networks to make ultra-short-term forecasts of wind power, photovoltaic power and load: P f =[P wind，f ,P PV，f ,P load,f ]; P wind,f , P PV,f , P load,f Ultra-short-term forecast values ​​for wind power, photovoltaic power and load; Obtain the peak load demand P in typical scenarios need,n : Where P valley It is the daily load valley value; Based on the typical scenarios obtained, kernel density estimation is used to obtain the probability density distribution of peak demand: In the formula, represents the estimated probability density function value at point x, M represents the number of sample data; h is the bandwidth, and K(·) is the kernel function used to weigh the distance x and each sample point x. i The distance between The method of using the probability density distribution of the peak load demand to represent different decision-making behaviors of the dispatching center includes: Through the probability density of peak demand, the utility function of the dispatch center under different circumstances is unified: The peak load resource configuration model includes: max U; s.t.P PS <P DR 。 2. The load-side demand response peak-shaving resource configuration method according to claim 1, characterized in that: The acquisition of wind power, photovoltaic, and load power consumption data and analysis of their multi-dimensional source-load correlation includes: Construct a data set at time t, P = [P wind , P PV , P load ], where P wind is wind power data, P PV is the photovoltaic data, P load Load power consumption data; R-vine Copula is used to analyze the characteristics of multidimensional source loads. If F(x1, x2, ..., x n ) are random variables x1, x2, ...x with continuous marginal distributions n The joint probability distribution function of , then there exists a unique Copula function C(u1, u2, ..., u n )satisfy: F(x1,x2,...,x n )=C(F1(x1),F2(x2),...,F n (x n ))=C(u1,u2,...,u n ); In the R-vine Copula structure, x1, x2, ...x n The joint density function is expressed as: In the formula, u i =F i (x i ), F i (x i ) is x i The marginal distribution of E i The edge e=j(e)l(e)|D(e) in C, j(e) and l(e) are two nodes connected by edge e, and D(e) is the condition set; j(e)l(e)|D(e) represents the Copula density function corresponding to edge e; x D(e) represents the subvector determined by the condition set D(e).

3. The load-side demand response peak-shaving resource configuration method according to claim 2, characterized in that: The method of using R Copula to analyze the characteristics of multidimensional source loads includes the following steps: Calculate the weight w between all possible variable pairs {j, k} of the original data j，k ; Select the variable pairs that maximize the sum of their weights to build a tree: In the selected spanning tree, select a binary copula cluster for each edge {j, k} and estimate the corresponding parameters; Iterate the i=2, ..., n-1th layer of trees: Based on the selected Copula cluster and its i-1 The parameters in the calculation weight w are calculated for all possible conditional variable pairs {j, k|D} in tree i. j,k|D ; All edges need to satisfy the proximity condition; Among the above edges, select the variable pairs that maximize the sum of weights to generate the tree: In the selected spanning tree, a binary copula cluster is selected for each edge {j, k|D} and the corresponding parameters are estimated.

4. A load-side demand response peak-shaving resource configuration device, characterized in that: Using the method according to any one of claims 1 to 3, the device comprises: The acquisition unit is used to acquire wind power, photovoltaic power, and load power consumption data, analyze their multi-dimensional source-load correlation, and generate a joint probability density distribution; The utility unit is used to analyze the effects of the peak-shaving resources configured by the dispatch center in different scenarios and establish utility functions respectively; A demand calculation unit, used to obtain typical scenarios according to the joint probability density distribution, and calculate the scenario probability to obtain the probability density distribution of the peak load demand; A modeling unit, used to use the probability density distribution of the peak-shaving demand to represent different decision-making behaviors of the dispatching center, and to establish a peak-shaving resource configuration model of the dispatching center under uncertainty based on the utility function; The solving unit is used to solve the peak-shaving resource configuration model with the goal of maximizing utility to obtain a peak-shaving resource configuration method.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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