Novel power system regulation capability quantitative evaluation method and device

Abstract

The invention discloses a novel power system regulation capability quantitative evaluation method. The method comprises the following steps: constructing an evaluation index system for measuring the regulation capability quantification of a novel power system; performing index weighting on the evaluation index system by using an index weight weighting method based on an entropy weight method; performing novel power system regulation capability quantitative evaluation on the index weight and the index value by using an improved cloud model method to obtain a novel power system regulation capability condition; when the improved cloud model method is used for quantitative evaluation of the regulation capability of the novel power system, firstly, the membership degree of each index to each grade is obtained through evaluation, then the obtained membership degree is compared with each grade of a standard cloud model, and finally, the current risk degree of each index is visually displayed in a cloud picture mode. The qualitative and quantitative conversion is realized; safe and stable operation and scientific planning of the novel power system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system evaluation, and more specifically, to a novel method and device for quantitatively evaluating the regulation capability of a power system. Background Art

[0002] Energy structure transformation is the key to achieving sustainable development in the power industry. Driven by the goal of clean and low-carbon transformation, my country's installed capacity of new energy continues to grow rapidly. However, with the increase in the proportion of new energy access such as wind power and photovoltaics and the gradual withdrawal of traditional fossil energy units, the volatility and uncertainty faced by the power system have increased significantly, which poses a huge challenge to the safe and stable operation of the system. Therefore, building a reasonable and effective new power system regulation capacity evaluation system and model method is of great significance to improving the overall regulation capacity of the new power system, promoting energy transformation and the economic and sustainable development of the power system.

[0003] However, the above-disclosed technical solution has at least the following technical problems: previous new power system regulation capacity assessment technologies often only consider technical aspects without considering economic aspects, and the assessment model method rarely considers the comprehensive impact of multi-dimensional indicators on the regulation capacity of the new power system. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a novel method, device and medium for evaluating the regulation capability of a power system, and solves the problem of evaluating the regulation capability of a novel power system from multiple directions.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A novel quantitative evaluation method for regulating capacity of a power system includes the following steps:

[0007] Construct an evaluation index system to quantify the regulation capability of new power systems;

[0008] The evaluation index system is weighted by using an index weighting method based on the entropy weight method;

[0009] The index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method to obtain the regulation capability of the new power system;

[0010] Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

[0011] Furthermore, the evaluation index system includes first-level indicators: economic indicators and technical indicators. The economic indicators include three second-level indicators: new construction and reconstruction investment cost, variable cost, and service life; the technical indicators include four second-level indicators: climbing rate, start and stop time, adjustment range, and adjustment time scale.

[0012] Furthermore, the indicator weighting method based on the entropy weight method is used to weight the indicators, including:

[0013] (1) Constructing a standardized judgment matrix

[0014] Assume that the number of provincial new power systems for comprehensive evaluation is z, the number of indicators is n, and the standardized judgment matrix X constructed from the standardized data is * for:

[0015]

[0016] in, is the standardized value of the jth indicator of the i-th provincial new power system;

[0017] (2) Calculate the information entropy of each indicator:

[0018]

[0019]

[0020]

[0021] Among them, H j is the information entropy of the jth index, f ij is the proportion of the standardized value of the jth indicator of the i-th provincial new power system in the overall value, and q is the weight factor of the evaluation object;

[0022] (3) Indicator empowerment:

[0023]

[0024] Among them, w j is the weight of the jth indicator,

[0025] Furthermore, the index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method. The method for obtaining the regulation capability of the new power system is as follows:

[0026] Construct a cloud model based on Bayesian feedback correction;

[0027] The modified cloud model is used to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system.

[0028] Furthermore, the method for constructing a cloud model based on Bayesian feedback correction is:

[0029] 1) Digital feature calculation

[0030] Ex=(l+m) / 2

[0031]

[0032] He=k

[0033] Where: l and m are the upper and lower limits of the indicator evaluation value range; k is a constant, which is adjusted according to the fuzziness and randomness of the indicator;

[0034] 2) Initial cloud model cloud droplet acquisition

[0035] The forward cloud generator is used to convert the three digital eigenvalues ​​Ex, Em, and He into a cloud composed of p cloud droplet samples:

[0036]

[0037] Where: It obeys the normal distribution with Em as expectation and He as variance, x i Obey Ex as expectation, is a normal distribution with variance;

[0038] 3) Cloud droplet screening and inspection

[0039] According to the probability density function, X~N(Ex,Em 2 +He 2 ), according to the central limit theorem, select Cloud droplet samples within the range, where z α / 2 It is the bilateral percentile of the standard normal distribution to reduce sample dispersion and improve the accuracy of evaluation and prediction;

[0040] To verify the aggregation degree of cloud droplets after screening, XL is set to The cloud droplet sample mean, XH is The cloud droplet sample mean is When, d = XH-XL, The sample is considered to be corrected. If the condition is not met, the concentration of the screened sample is increased by modifying the value α, and the sample is rescreened and tested until the standard is met;

[0041] 4) Bayesian feedback correction of cloud model expectations

[0042] In Bayesian feedback correction, the probability distribution in the cloud model expectation is determined by subjective judgment as the prior distribution, and then the probability distribution guess is corrected according to the cloud droplet samples, and the new distribution obtained is the posterior distribution;

[0043] Let x1,x2,...,x i ,...x n is the cloud droplet sample selected, satisfying x i ~N(Ex,Em 2 +He 2 ), and Em and He are unknown parameters, and are expected to satisfy subjective judgment The prior distribution of

[0044] Considering the independence between different variables, the joint probability distribution follows the Bayesian rule, and P(Ex|X) can be expressed as follows:

[0045]

[0046] in, That is, Ex|X satisfies N(B / A,1 / A); the posterior distribution of Ex is inferred as:

[0047]

[0048] in, is the moment estimate of Em,He;

[0049] 5) Moment estimation based on expected posterior distribution

[0050] The sample mean is an unbiased estimate of the expectation. The digital features of the cloud model can be estimated by moments based on the expected posterior probability distribution, and the corrected digital features of the cloud model can be further obtained. The posterior moment estimates of the entropy and super entropy of the digital features of the cloud model are:

[0051]

[0052]

[0053] In summary, the digital features of the cloud model (Ex c , ).

[0054] Furthermore, the method of using the modified cloud model to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system is as follows:

[0055] 1) Construction of secondary indicator floating cloud

[0056] Since the underlying indicators are relatively independent, floating clouds can be used to calculate the secondary eigenvalues:

[0057]

[0058]

[0059]

[0060] Where: Ex i is the expectation of the i-th secondary index, En i is the entropy of the i-th secondary index, He i is the super entropy of the i-th secondary indicator;

[0061] 2) First-level indicator virtual cloud construction

[0062] There is correlation between the secondary indicators in the new power system regulation capacity quantitative evaluation system. The comprehensive cloud method can be used to calculate the characteristic values ​​to obtain the characteristic values ​​of the primary indicators:

[0063]

[0064]

[0065]

[0066] A novel device for quantitatively evaluating the regulation capability of a power system, comprising:

[0067] An evaluation index system construction module is used to construct an evaluation index system to quantify the regulation capability of the new power system;

[0068] An indicator weighting module is used to weight the indicators of the evaluation indicator system using an indicator weighting method based on an entropy weight method;

[0069] The power system regulation capacity quantitative evaluation module is used to use the improved cloud model method to perform quantitative evaluation on the regulation capacity of the new power system by using the indicator weights and indicator values ​​to obtain the regulation capacity of the new power system;

[0070] Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

[0071] A computing device comprising:

[0072] one or more processing units;

[0073] A storage unit for storing one or more programs,

[0074] Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the novel method for quantitatively evaluating the regulation capability of a power system as described above.

[0075] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the novel method for quantitatively evaluating the regulation capability of a power system as described above are implemented.

[0076] The present invention provides a novel method and device for quantitatively evaluating the regulation capability of a power system, which has the following beneficial effects:

[0077] 1. The present invention considers the regulation capability requirements of the new power system and constructs a quantitative evaluation index system for the regulation capability of the new power system, which can comprehensively evaluate the regulation capability of the new power system, thereby accurately identifying the regulation capability of the new power system, and then proposing improvement suggestions for improving the regulation capability of the new power system, thereby ensuring the safe and stable operation and scientific planning of the new power system;

[0078] 2. In evaluating the regulation capability of the new power system, the present invention takes into account the economic and technical characteristics of the regulation capability of the new power system, and evaluates the regulation capability of the provincial new power system through a comprehensive evaluation model based on the entropy weight-improved cloud model, so that the evaluation results can better provide support for the development plan of the power company. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a novel method for quantitatively evaluating regulation capability of a power system provided by an embodiment of the present invention;

[0080] Figure 2 A cloud diagram of a new type of power system regulation capability quantitative evaluation standard provided by an embodiment of the present invention;

[0081] Figure 3 A cloud diagram for quantitatively evaluating the regulation capability of a novel power system provided by one embodiment of the present invention;

[0082] FIG4( a ) is a schematic diagram of a reverse cloud generator process provided by an embodiment of the present invention;

[0083] FIG4( b ) is a schematic diagram of a forward cloud generator process provided by an embodiment of the present invention;

[0084] Figure 5 A cloud model calculation flow chart based on Bayesian feedback correction is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] Example 1

[0087] A novel quantitative evaluation method for regulating capacity of a power system includes the following steps:

[0088] Construct an evaluation index system to quantify the regulation capability of new power systems;

[0089] The evaluation index system is weighted by using an index weighting method based on the entropy weight method;

[0090] The index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method to obtain the regulation capability of the new power system;

[0091] Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

[0092] The new power system regulation capability evaluation system consists of 2 first-level indicators and 7 second-level indicators, as shown in Table 1.

[0093] Economic indicators: As an objective basis for decision-making analysis, economic cost can fully reflect the true cost of the regulation capacity of the new power system, help relevant stakeholders and policy makers to more comprehensively evaluate the system regulation capacity, and promote the sustainable and healthy development of the power industry. The economic cost of the quantitative evaluation of the regulation capacity of the new power system is mainly reflected through three economic indicators: new construction and reconstruction investment cost, variable cost, and service life.

[0094] Technical indicators: Technical indicators can fully understand the regulation potential and limitations of regulation resources in the power system by evaluating the specific parameters of the units, and provide an important basis for system scheduling and planning. In the new power system, regulation resources can respond to rapid load fluctuations and maintain the smooth operation of the power system by quickly adjusting the output range, time, and rate. Therefore, the technical characteristics of the regulation capacity of flexible resources can be reflected by four technical indicators: ramp rate, start-stop time, regulation range, and regulation time scale.

[0095] Table 1 Quantitative evaluation index system for regulation capability of new power system

[0096]

[0097] The indicator weighting method based on the entropy weight method is used to weight indicators, including:

[0098] (1) Constructing a standardized judgment matrix

[0099] Assume that the number of provincial new power systems for comprehensive evaluation is z, the number of indicators is n, and the standardized judgment matrix X constructed from the standardized data is * for:

[0100]

[0101] in, is the standardized value of the jth indicator of the i-th provincial new power system.

[0102] (2) Calculate the information entropy of each indicator:

[0103]

[0104]

[0105]

[0106] Among them, H j is the information entropy of the jth index, f ij is the proportion of the standardized value of the jth indicator of the i-th provincial new power system in the overall value, and q is the weight factor of the evaluation object;

[0107] (3) Indicator empowerment:

[0108]

[0109] Among them, w j is the weight of the jth indicator,

[0110] It should be noted that the indicator data needs to be standardized as follows:

[0111] Due to the different measurement units, economic significance and impact of evaluation indicators, it is necessary to standardize the evaluation indicators to ensure the scientificity, comprehensiveness and reliability of the comprehensive evaluation results. Three types of quantitative indicators, namely, very large, very small and interval, are used. First, the indicators are uniformly converted into very large ones, and on this basis, the data are standardized.

[0112] Interval indicator processing method:

[0113]

[0114] Among them, d1 is the minimum value of the indicator, d u is the maximum value of the indicator, and [q1,q2] is the optimal interval range.

[0115] Very small indicator processing method:

[0116]

[0117] After the qualitative indicators are quantified and the process is consistent, the standardization of each evaluation indicator is as follows:

[0118]

[0119] The index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method. The method for obtaining the regulation capability of the new power system is as follows:

[0120] Construct a cloud model based on Bayesian feedback correction;

[0121] The modified cloud model is used to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system.

[0122] The cloud model uses three numerical features: expectation, entropy, and super entropy to characterize the randomness and fuzziness of concepts. Among them, expectation Ex is the center of the concept and the most representative value. Entropy Em reflects the degree of fuzziness of the concept. The larger the Em, the more vague the concept. Super entropy He reflects the degree of discreteness of cloud droplets. Cloud droplets are data groups with randomness and volatility. The larger He, the greater the randomness of concept membership.

[0123] The cloud generator is a mapping of the conversion between qualitative concepts and quantitative concepts. The forward cloud generator can realize the conversion between qualitative and quantitative concepts. The n moving cloud droplets are generated with the help of data eigenvalues. In this process, the randomness of the concept conversion process can be expressed. The reverse cloud generator realizes the conversion from quantitative to qualitative concepts, that is, the conversion from concept extension to connotation. In this process, the fuzziness of the concept conversion process can be expressed. The forward and reverse cloud generator process is shown in the figure below. Figure 4(a) , 4(b) shown.

[0124] In the new cloud model for quantitative assessment of the regulation capacity of the power system, the degree of membership of each indicator to each level is first obtained through expert evaluation, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map, realizing the conversion of qualitative and quantitative concepts.

[0125] In order to take into account the good expression of the normal cloud uncertainty information and the stability and rationality after the fusion of multivariate information, this embodiment adopts the Bayesian feedback correction result to reduce the randomness and uncertainty of the cloud model; the method for constructing the cloud model based on Bayesian feedback correction is:

[0126] 1) Digital feature calculation

[0127] Ex=(l+m) / 2

[0128]

[0129] He=k

[0130] Where: l and m are the upper and lower limits of the indicator evaluation value range; k is a constant, which is adjusted according to the fuzziness and randomness of the indicator;

[0131] 2) Initial cloud model cloud droplet acquisition

[0132] The forward cloud generator is used to convert the three digital eigenvalues ​​Ex, Em, and He into a cloud composed of p cloud droplet samples:

[0133]

[0134] Where: It obeys the normal distribution with Em as expectation and He as variance, x i Obey Ex as expectation, is a normal distribution with variance;

[0135] 3) Cloud droplet screening and inspection

[0136] According to the probability density function, X~N(Ex,Em 2 +He 2 ), according to the central limit theorem, select Cloud droplet samples within the range, where z α / 2 It is the bilateral percentile of the standard normal distribution to reduce sample dispersion and improve the accuracy of evaluation and prediction;

[0137] To verify the aggregation degree of cloud droplets after screening, XL is set to The cloud droplet sample mean, XH is The cloud droplet sample mean is When, d = XH-XL, The sample is considered to be corrected. If the condition is not met, the concentration of the screened sample is increased by modifying the value α, and the sample is rescreened and tested until the standard is met;

[0138] 4) Bayesian feedback correction of cloud model expectations

[0139] In Bayesian feedback correction, the probability distribution in the cloud model expectation is determined by subjective judgment as the prior distribution, and then the probability distribution guess is corrected according to the cloud droplet samples, and the new distribution obtained is the posterior distribution;

[0140] Let x1,x2,...,x i ,...x n is the cloud droplet sample selected, satisfying x i ~N(Ex,Em 2 +He 2 ), and Em and He are unknown parameters, and are expected to satisfy subjective judgment The prior distribution of

[0141] Considering the independence between different variables, the joint probability distribution follows the Bayesian rule, and P(Ex|X) can be expressed as follows:

[0142]

[0143] in, That is, Ex|X satisfies N(B / A,1 / A); the posterior distribution of Ex is inferred as:

[0144]

[0145] in, is the moment estimate of Em,He;

[0146] 5) Moment estimation based on expected posterior distribution

[0147] The sample mean is an unbiased estimate of the expectation. The digital features of the cloud model can be estimated by moments based on the expected posterior probability distribution, and the corrected digital features of the cloud model can be further obtained. The posterior moment estimates of the entropy and super entropy of the digital features of the cloud model are:

[0148]

[0149]

[0150] In summary, the digital features of the cloud model (Ex c , ).

[0151] The method of using the modified cloud model to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system is as follows:

[0152] 1) Construction of secondary indicator floating cloud

[0153] Since the underlying indicators are relatively independent, floating clouds can be used to calculate the secondary eigenvalues:

[0154]

[0155]

[0156]

[0157] Where: Ex i is the expectation of the i-th secondary index, En i is the entropy of the i-th secondary index, He i is the super entropy of the i-th secondary indicator;

[0158] 2) First-level indicator virtual cloud construction

[0159] There is correlation between the secondary indicators in the new power system regulation capacity quantitative evaluation system. The comprehensive cloud method can be used to calculate the characteristic values ​​to obtain the characteristic values ​​of the primary indicators:

[0160]

[0161]

[0162]

[0163] Example 2

[0164] Figure 1 A schematic diagram of a novel method for quantitatively evaluating the regulation capability of a power system according to this embodiment is provided. The method comprises the following steps:

[0165] S1, used to construct a new quantitative evaluation index system for the regulation capability of power systems;

[0166] The quantitative evaluation index system for the regulation capability of the new power system is constructed, including 2 primary indicators and 7 secondary indicators, as shown in Table 1.

[0167] Economic indicators: As an objective basis for decision-making analysis, economic cost can fully reflect the true cost of the regulation capacity of the new power system, help relevant stakeholders and policy makers to more comprehensively evaluate the system regulation capacity, and promote the sustainable and healthy development of the power industry. The economic cost of the quantitative evaluation of the regulation capacity of the new power system is mainly reflected through three economic indicators: new construction and reconstruction investment cost, variable cost, and service life.

[0168] Technical indicators: Technical indicators can fully understand the regulation potential and limitations of regulation resources in the power system by evaluating the specific parameters of the units, and provide an important basis for system scheduling and planning. In the new power system, regulation resources can respond to rapid load fluctuations and maintain the smooth operation of the power system by quickly adjusting the output range, time, and rate. Therefore, the technical characteristics of the regulation capacity of flexible resources can be reflected by four technical indicators: ramp rate, start-stop time, regulation range, and regulation time scale.

[0169] Table 1 Quantitative evaluation index system for regulation capability of new power system

[0170]

[0171] S2, used to assign weights to the evaluation index system;

[0172] Three provincial new power systems were selected for evaluation. Based on the actual data, they were standardized and the entropy weight method was used to obtain the weights of each indicator, as shown in Table 2.

[0173] Table 2 Index weights based on entropy weight method

[0174] index Weight C1 0.143 C2 0.047 C3 0.189 C4 0.426 C5 0.114 C6 0.081

[0175] S3 is used to evaluate the regulation capacity of the three provincial new power systems and to find out the regulation capacity of the new power systems;

[0176] A comprehensive evaluation based on the improved cloud model was conducted, including:

[0177] (1) Conduct quantitative evaluation of the regulation capability of new power systems based on improved cloud models

[0178] 1) Standard classification

[0179] Experts were invited to score based on the basic data of the three provincial new power system regulation capacity quantitative evaluation indicators, and the scoring was based on a percentage system. Evaluation grade standards were set for each secondary indicator, as shown in the following table.

[0180] Table 3 Evaluation level classification

[0181] Level range grade (100,90] excellent (90,80] good (80,70] generally (70,60] Poor <60 Difference

[0182] The characteristic values ​​of the cloud model of the quantitative evaluation standard for the regulation capability of the new power system calculated from the various level ranges divided in the above table are: excellent (100, 10 / 3, 0.5), good (85, 5 / 3, 0.5), average (75, 5 / 3, 0.5), poor (65, 5 / 3, 0.5), and bad (0, 20, 0.5). Based on this, the cloud map of the quantitative evaluation standard for the regulation capability of the new power system can be drawn as follows Figure 2As shown, it is divided into five grades: excellent, good, average, poor and relatively poor. The excellent and poor grades are drawn using the half cloud method.

[0183] 2) Characteristic values ​​of each indicator

[0184] According to the indicator weights and scores, the characteristic values ​​of the regulation capabilities of each new power system can be calculated using a comprehensive cloud computing method:

[0185] Table 4 Characteristic values ​​of primary and secondary indicators

[0186]

[0187] 3) Cloud chart analysis

[0188] According to the characteristic values ​​of each indicator, the quantitative evaluation cloud diagrams of each primary indicator and the regulation capacity of the new power system are drawn, such as Figure 3 Show.

[0189] It can be seen that the regulation capability of the new power system in Province A is the best, with an evaluation level between "good" and "excellent", belonging to the "good" to "excellent" level. The regulation capability of the new power system in Province S is second best, with an evaluation level between "good" and "excellent", belonging to the "good" to "excellent" level; the regulation capability of the new power system in Province H is the worst, with an evaluation level of "good", indicating that relevant work on improving the regulation capability of the new power system should be strengthened.

[0190] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0191] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0192] Example 3

[0193] This embodiment provides a novel power system regulation capability quantitative evaluation device, including:

[0194] An evaluation index system construction module is used to construct an evaluation index system to quantify the regulation capability of the new power system;

[0195] An indicator weighting module is used to weight the indicators of the evaluation indicator system using an indicator weighting method based on an entropy weight method;

[0196] The power system regulation capacity quantitative evaluation module is used to use the improved cloud model method to perform quantitative evaluation on the regulation capacity of the new power system by using the indicator weights and indicator values ​​to obtain the regulation capacity of the new power system;

[0197] Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

[0198] A computing device is also provided, comprising:

[0199] one or more processing units;

[0200] A storage unit for storing one or more programs,

[0201] Among them, when the one or more programs are executed by the one or more processing units, the one or more processing units execute a new method for quantitatively evaluating the regulation capability of a power system in this embodiment; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device including a processing unit and a storage unit does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components, for example, the computing device may also include input and output devices, network access devices, buses, etc.

[0202] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of a novel method for quantitatively evaluating the regulation capacity of a power system in this embodiment are implemented; it should be noted that the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof; the program contained on the readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wired, optical cable, RF, etc., or any suitable combination thereof. For example, the program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0203] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0204] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A new quantitative evaluation method for regulating capacity of power system, characterized in that: The steps include: Construct an evaluation index system to quantify the regulation capability of new power systems; The evaluation index system is weighted by using an index weighting method based on the entropy weight method; The index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method to obtain the regulation capability of the new power system; Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

2. A novel method for quantitatively evaluating the regulation capability of a power system according to claim 1, characterized in that: The evaluation index system includes first-level indicators: economic indicators and technical indicators. The economic indicators include three second-level indicators: new construction and reconstruction investment cost, variable cost, and service life; the technical indicators include four second-level indicators: climbing rate, start-stop time, adjustment range, and adjustment time scale.

3. A novel method for quantitatively evaluating the regulation capability of a power system according to claim 1, characterized in that: The indicator weighting method based on the entropy weight method is used to weight indicators, including: (1) Constructing a standardized judgment matrix Assume that the number of provincial new power systems for comprehensive evaluation is z, the number of indicators is n, and the standardized judgment matrix X constructed from the standardized data is * for: in, is the standardized value of the jth indicator of the i-th provincial new power system; (2) Calculate the information entropy of each indicator: Among them, H j is the information entropy of the jth index, f ij is the proportion of the standardized value of the jth indicator of the i-th provincial new power system in the overall value, and q is the weight factor of the evaluation object; (3) Indicator empowerment: Among them, w j is the weight of the jth indicator, 4. A novel method for quantitatively evaluating the regulation capability of a power system according to claim 1, characterized in that: The index weights and index values ​​are used to quantitatively evaluate the regulation capability of the new power system using the improved cloud model method. The method for obtaining the regulation capability of the new power system is as follows: Construct a cloud model based on Bayesian feedback correction; The modified cloud model is used to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system.

5. A novel method for quantitatively evaluating the regulation capability of a power system according to claim 4, characterized in that: The method for constructing a cloud model based on Bayesian feedback correction is: 1) Digital feature calculation Ex=(l+m) / 2 He=k Where: l and m are the upper and lower limits of the indicator evaluation value range; k is a constant, which is adjusted according to the fuzziness and randomness of the indicator; 2) Initial cloud model cloud droplet acquisition The forward cloud generator is used to convert the three digital eigenvalues ​​Ex, Em, and He into a cloud composed of p cloud droplet samples: Where: It obeys the normal distribution with Em as expectation and He as variance, x i Obey Ex as expectation, is a normal distribution with variance; 3) Cloud droplet screening and inspection According to the probability density function, X~N(Ex,Em 2 +He 2 ), according to the central limit theorem, select Cloud droplet samples within the range, where z α / 2 It is the bilateral percentile point of the standard normal distribution to reduce sample dispersion and improve the accuracy of evaluation and prediction; To verify the aggregation degree of cloud droplets after screening, XL is set to The cloud droplet sample mean, XH is The cloud droplet sample mean is When, d = XH-XL, The sample is considered to be corrected. If the condition is not met, the concentration of the screened sample is increased by modifying the value α, and the sample is rescreened and tested until the standard is met; 4) Bayesian feedback correction of cloud model expectations In Bayesian feedback correction, the probability distribution in the cloud model expectation is determined by subjective judgment as the prior distribution, and then the probability distribution guess is corrected according to the cloud droplet samples, and the new distribution obtained is the posterior distribution; Let x1,x2,...,x i ,...x n is the cloud droplet sample selected, satisfying x i ~N(Ex,Em 2 +He 2 ), and Em and He are unknown parameters, and are expected to satisfy subjective judgment The prior distribution of Considering the independence between different variables, the joint probability distribution follows the Bayesian rule, and P(Ex|X) can be expressed as follows: in, That is, Ex|X satisfies N(B / A,1 / A); the posterior distribution of Ex is inferred as: in, is the moment estimate of Em,He; 5) Moment estimation based on expected posterior distribution The sample mean is an unbiased estimate of the expectation. The digital features of the cloud model can be estimated by moments based on the expected posterior probability distribution, and the corrected digital features of the cloud model can be further obtained. The posterior moment estimates of the entropy and super entropy of the digital features of the cloud model are: In summary, the digital features of the cloud model after Bayesian feedback correction can be obtained:

6. A novel method for quantitatively evaluating the regulation capability of a power system according to claim 5, characterized in that: The method of using the modified cloud model to perform comprehensive cloud computing on the indicators in the quantitative evaluation system of the regulation capacity of the new power system is as follows: 1) Construction of secondary indicator floating cloud Since the underlying indicators are relatively independent, floating clouds can be used to calculate the secondary eigenvalues: Where: Ex i is the expectation of the i-th secondary index, En i is the entropy of the i-th secondary index, He i is the super entropy of the i-th secondary indicator; 2) First-level indicator virtual cloud construction There is correlation between the secondary indicators in the new power system regulation capacity quantitative evaluation system. The comprehensive cloud method can be used to calculate the characteristic values ​​to obtain the characteristic values ​​of the primary indicators:

7. A novel device for quantitatively evaluating the regulation capability of a power system, characterized in that: include: An evaluation index system construction module is used to construct an evaluation index system to quantify the regulation capability of the new power system; An indicator weighting module is used to weight the indicators of the evaluation indicator system using an indicator weighting method based on an entropy weight method; The power system regulation capacity quantitative evaluation module is used to use the improved cloud model method to perform quantitative evaluation on the regulation capacity of the new power system by using the indicator weights and indicator values ​​to obtain the regulation capacity of the new power system; Among them, when using the improved cloud model method to quantitatively evaluate the regulation capacity of the new power system, the membership of each indicator to each level is first evaluated, and then the obtained membership is compared with the levels of the standard cloud model. Finally, the current risk level of each indicator is intuitively displayed through a cloud map to achieve qualitative and quantitative conversion.

8. A computing device comprising: one or more processing units; A storage unit for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the method as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium having a non-volatile program code executable by a processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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