Electric power and electric quantity balance probability confidence evaluation method and system

Abstract

The invention discloses an electric power and electric quantity balance probability confidence evaluation method and system, and relates to the technical field of electric power systems, and the method comprises the steps: defining a distribution function of an adjustment demand; defining a confidence domain and a joint confidence interval of the adjustment demand and mathematical expressions of a balance domain and an unbalance domain; clarifying the physical connotation of the balance probability, the unbalance amount and the unbalance risk index based on the definition of the balance domain and the unbalance domain; and establishing a mathematical model of a probabilistic criterion and a confidence domain criterion for evaluation. According to the electric power and electric quantity balance probability confidence evaluation method provided by the invention, the quantitative judgment whether the system can realize the reliable balance of the electric power and electric quantity can be given by utilizing the balance criterion, and the method is favorably combined with the electric power and electric quantity balance characteristic evaluation and power generation plan optimization problems.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for evaluating the probability confidence of power and electricity balance. Background Art

[0002] For traditional power systems, a certain proportion of reserve capacity reserved by conventional units can effectively guarantee load supply. In fact, the proportion of reserve capacity reserved implies the requirements of system power supply reliability. The traditional method of reserve capacity reservation corresponds to the load loss probability index (LOLP) in the range of 0.1 to 0.4 d / a. Since the power output is highly controllable and the load changes show a clear periodic law, the prediction accuracy is high, and the deterministic balance method can well meet the engineering application requirements. However, for extremely high-ratio systems, the strong uncertainty of wind / solar power generation output leads to the fact that the deterministic balance criterion has no solution or the solution obtained is difficult to achieve economically, and the probabilistic balance analysis method must be introduced. Therefore, the analysis method of power and electricity balance has evolved from the "deterministic balance" method to the "probabilistic balance" method.

[0003] Quantitative evaluation of the simulation results of power and electricity balance is an important basis for decision-making and adjustment. The technical evaluation indicators of power and electricity balance can be divided into probability indicators and time series indicators.

[0004] The most commonly used indicators of probability are system operation reliability indicators, including LOLP, expected value of load power shortage, expected load shortage duration, etc. In addition, there are similar literatures that define indicators such as estimated power abandonment probability, insufficient ramp capacity probability, insufficient ramp resource expectation, etc. These indicators can be estimated through probabilistic power and electricity balance, random unit combination or chance-constrained unit combination results. The definitions of time series indicators vary greatly among the literature. Time series indicators need to be calculated through time series power and electricity balance simulation. In summary, in terms of indicator construction, there is a lack of a unified indicator system that can simultaneously reflect the time series and probability characteristics of power and electricity balance. Except for reliability indicators, the application indicators of different literatures vary greatly. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the results obtained by the current deterministic balancing method under different time series input conditions are very different, and it is difficult to adapt to complex working conditions with high uncertainty. Even if a multi-scenario method is adopted, due to the problem of scenario representativeness and the explosion of the number of simulation scenarios, the balancing results based on limited scenarios are difficult to answer "what is the probability of ensuring power and electricity balance". The present invention proposes a probabilistic confidence evaluation method for power and electricity balance, defines the balance domain and the unbalanced domain from the spatial geometric relationship, and proposes quantitative evaluation indicators such as balance probability, unbalanced amount and unbalanced risk. Based on whether the balance probability meets the threshold requirements, a mathematical criterion for probabilistic power and electricity balance is given.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for evaluating the probability confidence of power and electricity balance, comprising:

[0008] Define the distribution function of the regulation demand;

[0009] Define the confidence region and joint confidence interval of the regulation demand and the mathematical representation of the equilibrium region and imbalance region;

[0010] Based on the definitions of balance domain and imbalance domain, the physical connotations of balance probability, imbalance quantity and imbalance risk index are explained;

[0011] The mathematical models of probability criterion and confidence region criterion are established for evaluation.

[0012] As a preferred solution of the power and electricity balance probability confidence evaluation method of the present invention, wherein: the distribution function defining the regulation demand includes setting is a time section d∈{t1,t2,...t D}, the active power random variable of demand source i is adjusted under As a random process, the D-dimensional random vector The D-dimensional joint distribution function of is defined as the distribution function of the adjustment demand, expressed as,

[0013]

[0014] Among them, ξ represents the value of the random variable, and the distribution function of the D-dimensional regulation demand random vector implies the time correlation of regulation demand between different time sections;

[0015] Assuming that the active power of the demand source i at each time section is a continuous random variable ui (i = 1, 2, 3 ... D), its probability density function can be defined as Satisfy equations (2) to (4);

[0016]

[0017] If Ξ (i)Divide into q-dimensional subvectors With Dq dimension vector So The marginal distribution of is shown in formula (5);

[0018]

[0019] The edge density function of is shown in formula (6);

[0020]

[0021] When q = 1, the marginal distribution degenerates into a one-dimensional distribution function of the random variable;

[0022] Suppose there are m regulation demand sources, and the corresponding regulation demand probability density functions are known, which are: Then the probability density function of the sum of their active powers is It can be calculated by the probability density function of the random vector and, as shown in formula (7); under the independence condition, it can be calculated by the convolution of the probability density function, as shown in formula (8);

[0023]

[0024] in, represents the joint probability density function of m regulation demand random processes;

[0025] From the probability density function Get the corresponding distribution function As shown in formula (9).

[0026]

[0027] When m is expanded to the total number of regulation demand sources in the whole system, m sys When , the obtained distribution function quantitatively describes the regulation demand of the whole system Ξ sys probability characteristics.

[0028] As a preferred solution of the power and electricity balance probability confidence evaluation method described in the present invention, wherein: the confidence domain and joint confidence interval of the definition of the regulation demand and the mathematical representation of the balance domain and the unbalance domain include the confidence domain H of the regulation demand i at the 1-α confidence level α It is defined as the area containing the regulation demand with probability 1-α, as shown in formula (10):

[0029] P(H α (Ξ (i) ))=1-α (10)

[0030] Among them, (i)The random variable of active power of regulating demand source i; P() is the probability calculation representation;

[0031] When the canonical confidence region is a cuboid, the confidence region is equivalent to a joint confidence interval; the joint confidence interval of the adjustment demand i at the confidence level 1-α is of the form A collection of, among which, And satisfy equations (11) to (12):

[0032]

[0033] Where, cl=(cl1,...,cl D ) T and cu=(cu1,...,cu D ) T are the joint lower confidence limit and the joint upper confidence limit respectively; ξ represents the value of the random variable; represents the probability density function of regulation demand source i; represents the feasible region of the jth confidence interval;

[0034] The problem of solving the joint confidence interval can be transformed into the problem of calculating several confidence intervals, as shown in formula (13):

[0035]

[0036] in, represents the marginal density function of the jth time section; when the random variable When they are independent of each other, take α j =1-(1-α) 1 / D , otherwise, we can take α j =α / D, because according to the Bonferroni inequality, equation (14) holds:

[0037]

[0038] in, represents the complement on the R space;

[0039] Assume Ω B represents the balancing domain, which is the region consisting of the D-dimensional timing adjustment requirements that can be balanced, as shown in formula (15); for simplicity, the following formula omits the time scale subscript τ:

[0040]

[0041] Among them, R D It represents the spatial region composed of D-dimensional timing regulation requirements; It means any; is the aggregate feasible domain of all adjustable resources in the system;

[0042] Correspondingly, let Ω U represents the unbalanced domain, which is the complementary space of the balanced domain in the D-dimensional space, as shown in formula (16):

[0043]

[0044] in, Represents R D Complement in space.

[0045] As a preferred solution of the power and electricity balance probability confidence evaluation method described in the present invention, wherein: the balance probability is expressed as,

[0046]

[0047] As a preferred solution of the power and quantity balance probability confidence evaluation method of the present invention, wherein: the imbalance quantity includes a point ξ∈Ω in the D-dimensional imbalance domain U The corresponding unbalance vector Δp ​​is defined as the distance from this point to the closest point p on the equilibrium domain. * The absolute value of the difference is shown in equations (18) and (19):

[0048] Δp:=|ξ-p * | (18)

[0049]

[0050] According to ξ-p * The positive or negative value of each element can determine whether the imbalance is caused by insufficient upward adjustment or insufficient downward adjustment. + and reduce the unbalance vector Δp - The calculation formulas are shown in formulas (20) and (21) respectively:

[0051] Δp + :=(ξ-p * ) + (20)

[0052] Δp - :=-(ξ-p * ) - (twenty one)

[0053] Among them, (a) - =min{a,0}, (a) + =max{a,0}.

[0054] As a preferred solution of the power and quantity balance probability confidence evaluation method described in the present invention, the imbalance risk index is the expectation of the imbalance amount of all unbalanced time series scenarios, as shown in formula (22):

[0055]

[0056] Among them, bold 1 represents a column vector of all 1s;

[0057] Based on the increase of the unbalance value Δp + and reduce the unbalance Δp - We can define upward imbalance risk and downward imbalance risk, denoted as UR + and UR - , calculated as (23) and (24):

[0058]

[0059] As a preferred solution of the power and quantity balance probability confidence evaluation method described in the present invention, wherein: the mathematical model for establishing the probabilistic criterion and the confidence region criterion for evaluation includes establishing a time series-probabilistic power and quantity balance criterion based on the confidence level, and it is considered that the system is reliably balanced when the balance probability is not lower than the 1-α confidence level, as shown in formula (25):

[0060] BP≥1-α (25)

[0061] Combining the above-mentioned definitions of balance domain, confidence domain of regulation demand and joint confidence interval, a probabilistic criterion in the form of joint chance constraints and a confidence domain criterion in the form of algebraic constraints for time-series-probabilistic power balance analysis are proposed.

[0062] Another object of the present invention is to provide a power and electricity balance probability confidence evaluation system, which can solve the balance assessment and uncertainty processing problems in power dispatching by constructing a power and electricity balance probability confidence evaluation system.

[0063] To solve the above technical problems, the present invention provides the following technical solutions: a power and electricity balance probability confidence evaluation system, comprising: a regulation demand definition module, a confidence interval establishment module, an indicator physical connotation module and a mathematical model evaluation module; the regulation demand definition module is used to define the distribution function of the regulation demand; the confidence interval establishment module is used to define the confidence domain and joint confidence interval of the regulation demand and the mathematical representation of the balance domain and the unbalanced domain; the indicator physical connotation module is used to clarify the physical connotation of the balance probability, unbalanced amount and unbalanced risk index based on the definition of the balance domain and the unbalanced domain; the mathematical model evaluation module is used to establish a mathematical model of the probabilistic criterion and the confidence domain criterion for evaluation.

[0064] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned power and electricity balance probability confidence evaluation method when executing the computer program.

[0065] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the power and electricity balance probability confidence evaluation method as described above.

[0066] Beneficial effects of the invention: The power and electricity balance probability confidence evaluation method provided by the invention proposes quantitative evaluation indicators of probabilistic power and electricity balance such as balance probability, imbalance amount and imbalance risk, and explains the meaning of the indicators from the perspective of spatial analysis and time series analysis respectively. These indicators can simultaneously reflect the time series and probability characteristics of power and electricity balance, and are an expansion of traditional probabilistic reliability indicators and deterministic load loss / abandonment indicators.

[0067] The mathematical expressions of the probability criterion and the confidence region criterion of the probabilistic power balance are given. Using the balance criterion, a quantitative judgment can be given on whether a system can achieve reliable power balance, which is conducive to combining it with the evaluation of power balance characteristics and the optimization of power generation plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0069] Figure 1 An overall flow chart of a method for evaluating the probability confidence of power and electricity balance provided by an embodiment of the present invention.

[0070] Figure 2 A schematic diagram of an imbalance quantity of a method for evaluating the probability confidence of electric power balance provided by an embodiment of the present invention.

[0071] Figure 3 A visualization diagram of a probability criterion and a confidence region criterion of a power and electricity balance probability confidence evaluation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0073] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0074] Example 1

[0075] Reference Figure 1-Figure 3 , which is an embodiment of the present invention, provides a method for evaluating the probability confidence of power and electricity balance, comprising:

[0076] Step 1: Define the distribution function of the regulation demand;

[0077] Step 2: define the confidence region and joint confidence interval of the regulation demand and the mathematical representation of the equilibrium region and imbalance region;

[0078] Step 3: Based on the definitions of balance domain and imbalance domain, clarify the physical connotations of balance probability, imbalance quantity and imbalance risk index;

[0079] Step 4: Establish mathematical models of probability criteria and confidence region criteria.

[0080] The distribution function of the regulation demand is described as follows:

[0081] set up is a time section d∈{t1,t2,…t D}, the active power random variable of demand source i is adjusted under As a random process, the D-dimensional random vector The D-dimensional joint distribution function of is defined as the distribution function of the adjustment demand. As shown in formula (1), it is denoted as (i) F (i) .

[0082]

[0083] Where ξ represents the value of the random variable. The distribution function of the D-dimensional regulation demand random vector implies the time correlation of regulation demand between different time sections.

[0084] Assuming that the active power of the demand source i at each time section is a continuous random variable ui (i = 1, 2, 3 ... D), its probability density function can be defined as Satisfies equations (2) to (4).

[0085]

[0086]

[0087] If Ξ (i) Divide into q-dimensional subvectors With Dq dimension vector So The marginal distribution of is shown in formula (5).

[0088]

[0089] The edge density function of is shown in formula (6).

[0090]

[0091] When q=1, the marginal distribution degenerates into a one-dimensional distribution function of the random variable.

[0092] The regulation demand in the system is not unique. In actual engineering, we often care about the statistical characteristics of all regulation demand sources in a certain spatial range or scheduling management level, which involves solving the distribution function of the regulation demand and .

[0093] Suppose there are m regulation demand sources, and the corresponding regulation demand probability density functions are known, which are: Then the probability density function of the sum of their active powers is It can be calculated by the probability density function of the random vector and, as shown in formula (7); under the independence condition, it can be calculated by the convolution of the probability density function, as shown in formula (8).

[0094]

[0095] in, Represents the joint probability density function of m regulation demand random processes.

[0096] Furthermore, the probability density function Get the corresponding distribution function As shown in formula (9).

[0097]

[0098] When m is expanded to the total number of regulation demand sources in the whole system, m sysWhen , the obtained distribution function quantitatively describes the regulation demand of the whole system Ξ sys probability characteristics.

[0099] Based on the distribution function and probability density function of the regulation demand, the confidence region and joint confidence interval of the regulation demand can be obtained, which are the basis for constructing the confidence region criterion of the time-series-probabilistic power and electricity balance.

[0100] The confidence region and joint confidence interval of the adjustment demand as well as the mathematical representation of the equilibrium region and imbalance region are as follows:

[0101] 1) Definition of the confidence region and joint confidence interval of adjustment requirements:

[0102] The confidence region H of the adjustment demand i under the 1-α confidence level α It is defined as the area that contains the regulation demand with probability 1-α, as shown in formula (10).

[0103] P(H α (Ξ (i) ))=1-α (10)

[0104] Among them, (i) The random variable of active power of regulating demand source i; P() is the probability calculation representation.

[0105] When the canonical confidence region is a cuboid, the confidence region is equivalent to a joint confidence interval. The joint confidence interval of the adjustment demand i at the confidence level 1-α is of the form A collection of, among which, And it satisfies equations (11) to (12).

[0106]

[0107] Where, cl=(cl1,...,cl D ) T and cu=(cu1,...,cu D ) T are the joint lower confidence limit and the joint upper confidence limit respectively; ξ represents the value of the random variable; represents the probability density function of regulation demand source i; represents the feasible region of the jth confidence interval.

[0108] The problem of solving the joint confidence interval can be transformed into the problem of calculating several confidence intervals, as shown in formula (13).

[0109]

[0110] in, represents the marginal density function of the jth time section. When they are independent of each other, take α j =1-(1-α) 1 / D , otherwise, we can take α j =α / D, because according to the Bonferroni inequality, equation (14) holds.

[0111]

[0112] in, represents the complement of R space.

[0113] 2) Mathematical representation of balanced domain and unbalanced domain:

[0114] Assume Ω B Denotes the balance domain, which is the region consisting of the D-dimensional timing adjustment requirements that can be balanced, as shown in formula (15). For simplicity, the following formula omits the time scale subscript τ.

[0115]

[0116] R D It represents the spatial region composed of D-dimensional timing regulation requirements; It means any; It is the aggregate feasible domain of all adjustable resources in the system.

[0117] Correspondingly, let Ω U represents the unbalanced domain, which is the complementary space of the balanced domain in the D-dimensional space, as shown in formula (16).

[0118]

[0119] in, Represents R D Complement in space.

[0120] The physical connotations of balance probability, imbalance amount and imbalance risk index are as follows:

[0121] 1) Balance probability:

[0122] The balance probability (BP) is the probability that the system regulation demand is covered by the balance domain, as shown in formula (17).

[0123]

[0124] Since a point in the D-dimensional Euclidean space corresponds to a D-dimensional time series curve, from the perspective of time series, the balance probability describes the proportion of the number of scenarios that can be balanced among all possible adjustment demand time series scenarios consisting of D time sections.

[0125] 2) Unbalance:

[0126] A point ξ∈Ω in the D-dimensional imbalanced domain U The corresponding unbalance vector Δp ​​is defined as the distance from this point to the closest point p on the equilibrium domain. * The absolute value of the difference is shown in equations (18) and (19).

[0127] Δp:=|ξ-p * | (18)

[0128]

[0129] According to ξ-p * The positive or negative value of each element can determine whether the imbalance is caused by insufficient upward adjustment or insufficient downward adjustment. + and reduce the unbalance vector Δp - The calculation formulas are shown in formulas (20) and (21) respectively.

[0130] Δp + :=(ξ-p * ) + (20)

[0131] Δp - :=-(ξ-p * ) - (twenty one)

[0132] Among them, (a) - =min{a,0}, (a) + =max{a,0}.

[0133] The schematic diagram of the spatial representation of the unbalanced quantity is as follows Figure 2 The red area in the figure represents the balanced domain, and the blue area represents the unbalanced domain. Take a point ξ in the unbalanced domain, which is closest to the point p in the balanced domain. * The absolute value of the difference constitutes the unbalance vector: Δp = |ξ-p * |=[a,b] T According to the calculation formula of upward and downward unbalance, Δp can be obtained. + =(ξ-p * ) + =[0,0] T ,as well as

[0134] Δp - =-(ξ-p * ) - =[a,b] T , indicating that the unbalanced consequences of the example are caused by insufficient downward adjustment capacity in the two periods.

[0135] From the perspective of time series analysis, the imbalance corresponds to the increased regulation capacity required at each time section in order to cover the unbalanced regulation demand scenario, or it can be understood as the load loss and power abandonment at each time section of the unbalanced regulation demand scenario, where an upward imbalance corresponds to load loss, and a downward imbalance corresponds to power abandonment, thereby reflecting the severity of the consequences of power imbalance.

[0136] 3) Imbalance Risk:

[0137] Unbalanced Risk (UR) is the expectation of the imbalance amount of all unbalanced time series scenarios, as shown in formula (22).

[0138]

[0139] Among them, the bold 1 represents a column vector of all 1s.

[0140] Furthermore, based on the increase in the unbalance value Δp + and reduce the unbalance Δp - We can define upward imbalance risk and downward imbalance risk, denoted as UR + and UR - , calculated as (23) and (24):

[0141]

[0142] From the perspective of spatial analysis, the imbalance risk describes the expected imbalance amount of all points in the imbalance domain, while from the perspective of temporal analysis, the imbalance risk reflects the expected load loss or power abandonment in all imbalance regulation demand scenarios. The imbalance risk index is an extension of the traditional single-section expectation reliability index.

[0143] The mathematical models of probability criterion and confidence region criterion are as follows:

[0144] Based on the confidence level, the time-series-probability power and quantity balance criterion is established. It is considered that the system is reliably balanced when the balance probability is not lower than the 1-α confidence level, as shown in formula (25).

[0145] BP≥1-α (25)

[0146] Combining the above-mentioned definitions of balance domain, confidence domain of regulation demand and joint confidence interval, a probabilistic criterion in the form of joint chance constraints and a confidence domain criterion in the form of algebraic constraints for time-series-probabilistic power balance analysis are proposed.

[0147] 1) Probabilistic criterion:

[0148] Convert equation (25) into the equivalent probability criterion shown in equation (26). This means that at any time scale τ, the system D-dimensional equilibrium domain ΩB The system regulation demand can be enveloped with a confidence level not less than 1-α, where the system regulation demand Ξ sys Satisfies the D-dimensional distribution function F sys , that is, sys F sys .

[0149]

[0150] Combination Figure 3 The criterion visualization diagram shown in the figure, where the green polygon represents the feasible domain aggregation result of all adjustable resources in the system, that is, the balance domain, the gray point represents the adjustment demand sample, the blue and pink distribution histograms represent the statistical distribution histograms of the adjustment demand samples of section 1 and section 2, respectively, and the corresponding black curves are the marginal probability density functions f1 of the adjustment demand of section 1 and section 2, respectively. sys (ξ1; t1) and The meaning of the probabilistic criterion is that the proportion of regulated demand points covered by the balance domain represented by the green area in the figure is at least 1-α.

[0151] 2) Confidence region criterion:

[0152] Combined with the concept of trust region of regulation demand, we can get the trust region criterion shown in formula (27). Its meaning is that at any time scale τ, the D-dimensional equilibrium domain composed of all adjustable resources of the system should envelop the D-dimensional trust region of the system regulation demand at the 1-α confidence level.

[0153]

[0154] When the joint confidence interval is used as the confidence region, equation (27) can be replaced by equation (28).

[0155]

[0156] Taking 2 as an example, based on f1 sys (ξ1; t1), the 1-α1 confidence interval of the regulation demand of section 1 can be obtained based on The 1-α2 confidence interval of the adjustment demand of section 2 can be obtained Then the 1-α confidence level of the adjustment demand is combined with the confidence interval C α yes and The combined rectangular area is As shown in the gray rectangle in the figure. The confidence region criterion of the time-series-probabilistic power and quantity balance describes that the balance domain must include the 1-α confidence level joint confidence interval of the regulation demand, and the corresponding green polygon in the figure must include the gray rectangle.

[0157] Since the shapes of the confidence region and the equilibrium region are generally inconsistent, the confidence region criterion often constitutes a sufficient condition for the probabilistic criterion. Figure 3 It can be seen intuitively that the green area contains more gray points than the gray area.

[0158] Example 2

[0159] According to an embodiment of the present invention, a power and quantity balance probability confidence evaluation system is provided, comprising:

[0160] Adjustment demand definition module, confidence interval establishment module, indicator physical connotation module and mathematical model evaluation module;

[0161] The regulation demand definition module is used to define the distribution function of the regulation demand;

[0162] The confidence interval establishment module is used to define the confidence domain and joint confidence interval of the regulation demand as well as the mathematical representation of the equilibrium domain and the imbalance domain;

[0163] The indicator physical connotation module is used to clarify the physical connotation of the balance probability, imbalance amount and imbalance risk indicators based on the definition of the balance domain and imbalance domain;

[0164] The mathematical model evaluation module is used to establish mathematical models of probability criteria and confidence region criteria for evaluation.

[0165] Example 3

[0166] An embodiment of the present invention is different from the first two embodiments in that:

[0167] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0168] 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 conjunction with such 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 conjunction with such instruction execution systems, devices or apparatuses.

[0169] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (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 a 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, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0170] 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, a plurality of 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.

[0171] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the probability confidence of power and electricity balance, characterized in that: include: Define the distribution function of the regulation demand; Define the confidence region and joint confidence interval of the regulation demand and the mathematical representation of the equilibrium region and imbalance region; Based on the definitions of balance domain and imbalance domain, the physical connotations of balance probability, imbalance quantity and imbalance risk index are explained; The mathematical models of probability criterion and confidence region criterion are established for evaluation.

2. The method for evaluating the probability confidence of power and quantity balance according to claim 1, characterized in that: The distribution function of defining the regulation demand includes setting is a time section d∈{t1,t2,...t D }, the active power random variable of demand source i is adjusted under As a random process, the D-dimensional random vector The D-dimensional joint distribution function of is defined as the distribution function of the adjustment demand, expressed as, Among them, ξ represents the value of the random variable, and the distribution function of the D-dimensional regulation demand random vector implies the time correlation of regulation demand between different time sections; Assuming that the active power of the demand source i at each time section is a continuous random variable ui (i = 1, 2, 3 ... D), its probability density function f can be defined as τ (i) , satisfying equations (2) to (4); f τ (i) (u1,...,u D ;t1,t2,...t D )≥0 (4) If Ξ (i) Divide into q-dimensional subvectors With Dq dimension vector So The marginal distribution of is shown in formula (5); The edge density function of is shown in formula (6); When q = 1, the marginal distribution degenerates into a one-dimensional distribution function of the random variable; Suppose there are m regulation demand sources, and the corresponding regulation demand probability density functions are known, which are: Then the probability density function of the sum of their active powers is It can be calculated by the probability density function of the random vector and, as shown in formula (7); under the independence condition, it can be calculated by the convolution of the probability density function, as shown in formula (8); Among them, f τ joint represents the joint probability density function of m regulation demand random processes; From the probability density function f τ sum Get the corresponding distribution function F τ sum , as shown in formula (9); When m is expanded to the total number of regulation demand sources in the whole system, m sys When , the obtained distribution function quantitatively describes the regulation demand of the whole system Ξ sys probability characteristics.

3. The power and quantity balance probability confidence evaluation method according to claim 2, characterized in that: The confidence region and joint confidence interval of the regulation demand and the mathematical representation of the balance region and the imbalance region include the confidence region H of the regulation demand i at the 1-α confidence level α It is defined as the area containing the regulation demand with probability 1-α, as shown in formula (10): P(H α (X) (i) ))=1-a (10) Among them, (i) The random variable of active power of regulating demand source i; P() is the probability calculation representation; When the canonical confidence region is a cuboid, the confidence region is equivalent to a joint confidence interval; the joint confidence interval of the adjustment demand i at the confidence level 1-α is of the form A collection of, among which, And satisfy equations (11) to (12): Where, cl=(cl1,...,cl D ) T and cu=(cu1,...,cu D ) T are the joint lower confidence limit and the joint upper confidence limit respectively; ξ represents the value of the random variable; f τ (i) represents the probability density function of regulation demand source i; represents the feasible domain of the jth confidence interval; The problem of solving the joint confidence interval can be transformed into the problem of calculating several confidence intervals, as shown in formula (13): in, represents the marginal density function of the jth time section; when the random variable When they are independent of each other, take α j =1-(1-α) 1 / D , otherwise, we can take α j =α / D, because according to the Bonferroni inequality, equation (14) holds: in, represents the complement on the R space; Assume Ω B represents the balancing domain, which is the region consisting of the D-dimensional timing adjustment requirements that can be balanced, as shown in formula (15); for simplicity, the following formula omits the time scale subscript τ: Among them, R D Represents the spatial region formed by the D-dimensional timing regulation requirements; represents any; is the aggregate feasible domain of all adjustable resources in the system; Correspondingly, Ω U represents the unbalanced domain, which is the complementary space of the balanced domain in the D-dimensional space, as shown in formula (16): in, Represents R D Complement in space.

4. The method for evaluating the probability confidence of power and quantity balance according to claim 3, characterized in that: The equilibrium probability is expressed as, 5. The method for evaluating the probability confidence of power and quantity balance according to claim 4, characterized in that: The imbalance quantity includes a point ξ∈Ω in the D-dimensional imbalance domain U The corresponding unbalance vector Δp ​​is defined as the distance from this point to the closest point p on the equilibrium domain. * The absolute value of the difference is shown in equations (18) and (19): Δp:=|ξ-p * | (18) According to ξ-p * The positive or negative value of each element can determine whether the imbalance is caused by insufficient upward adjustment or insufficient downward adjustment; Increase the unbalance vector Δp + and reduce the unbalance vector Δp - The calculation formulas are shown in formulas (20) and (21) respectively: Δp + :=(ξ-p * ) + (20) Δp - :=-(ξ-p * ) - (21) Among them, (a) - = min{a,0}, (a) + = max{a,0}.

6. The method for evaluating the probability confidence of power and quantity balance according to claim 5, characterized in that: The imbalance risk index is the expectation of the imbalance amount of all unbalanced time series scenarios, as shown in formula (22): Among them, bold 1 represents a column vector of all 1s; Based on the upward adjustment of the unbalance value Δp + and reduce the unbalance Δp - We can define upward imbalance risk and downward imbalance risk, denoted as UR + and UR - , calculated as (23) and (24):

7. The method for evaluating the probability confidence of power and quantity balance according to claim 6, characterized in that: The mathematical model for establishing the probabilistic criterion and the confidence region criterion for evaluation includes establishing a time series-probabilistic power and quantity balance criterion based on the confidence level, and it is considered that the system is reliably balanced when the balance probability is not less than the 1-α confidence level, as shown in formula (25): BP≥1-α (25) Combining the above-mentioned definitions of balance domain, confidence domain of regulation demand and joint confidence interval, a probabilistic criterion in the form of joint chance constraints and a confidence domain criterion in the form of algebraic constraints for time-series-probabilistic power balance analysis are proposed.

8. A system using the power and quantity balance probability confidence evaluation method as claimed in any one of claims 1 to 7, characterized in that: include: Adjustment demand definition module, confidence interval establishment module, indicator physical connotation module and mathematical model evaluation module; The regulation demand definition module is used to define a distribution function of regulation demand; The confidence interval establishment module is used to define the confidence domain and joint confidence interval of the regulation demand and the mathematical representation of the balance domain and the imbalance domain; The indicator physical connotation module is used to explain the physical connotation of the balance probability, imbalance amount and imbalance risk indicators based on the definition of the balance domain and the imbalance domain; The mathematical model evaluation module is used to establish mathematical models of probability criteria and confidence region criteria for evaluation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power flow balance probability confidence assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power flow balance probability confidence assessment method according to any one of claims 1 to 7 are implemented.

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