Evaluation method and device of novel power load management system

Abstract

The invention provides an evaluation method and device for a novel power load management system, and belongs to the field of system evaluation. The method comprises the following steps: determining a plurality of evaluation indexes of a target management system; obtaining the importance of each evaluation index, and determining the subjective weight of each evaluation index according to the importance of each evaluation index; obtaining an index value of each evaluation index, and determining an objective weight of each evaluation index according to the index value of each evaluation index; and obtaining an evaluation result of the target management system according to the subjective weight of each evaluation index and the objective weight of each evaluation index. The target management system is evaluated by integrating a plurality of evaluation indexes, the evaluation is more comprehensive, and the subjective weight and the objective weight of the evaluation indexes are integrated, so that the evaluation result is close to the actual situation as much as possible, and the evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of system evaluation, and particularly to an evaluation method and device for a new type of power load management system. Background Art

[0002] With the continuous growth of energy demand, the imbalance between energy supply and demand has intensified. At the same time, the proportion of renewable energy with volatility in energy supply has gradually increased, and the access of a large number of power electronic devices has posed challenges to the stability and security of the power grid. Therefore, developing flexible resources on the load side and realizing the flexible interaction of the power source, grid, load, and storage has become an important trend in energy development. Load management is an important means of demand-side management, referring to a series of measures and technical means to reduce the peak energy demand and average load by adjusting and optimizing energy consumption behaviors. Load management has become a key strategy to solve the imbalance between energy supply and demand, improve energy utilization efficiency, promote the development of renewable energy, and optimize the operation of the power system.

[0003] As a platform support for the comprehensive implementation of load management, the new type of power load management system has functions such as real-time monitoring, collection, statistics, analysis of data, and scientific formulation of load control strategies. With the wide implementation of load management, the scientificity, economy, etc. of the load management system need to be comprehensively investigated to optimize the functions of the load management system for better implementation of demand-side management.

[0004] In the prior art, the evaluation of the new type of power load management system usually adopts the expert scoring method, and the evaluation is not comprehensive enough, being greatly affected by human subjectivity and unable to comprehensively and accurately evaluate the new type of power load management system. Summary of the Invention

[0005] Embodiments of the present invention provide an evaluation method and device for a new type of power load management system to solve the problem that the evaluation of the new type of power load management system in the prior art is greatly affected by human subjectivity and cannot be comprehensively and accurately evaluated.

[0006] In a first aspect, embodiments of the present invention provide an evaluation method for a new type of power load management system, including:

[0007] Determine multiple evaluation indicators of the target management system;

[0008] Obtain the importance of each evaluation indicator, and determine the subjective weight of each evaluation indicator according to the importance of each evaluation indicator;

[0009] Obtain the indicator value of each evaluation indicator, and determine the objective weight of each evaluation indicator according to the indicator value of each evaluation indicator;

[0010] Based on the subjective weights and objective weights of each evaluation index, the evaluation result of the target management system is obtained.

[0011] In a second aspect, an embodiment of the present invention provides an evaluation device for a new type of power load management system, including:

[0012] An index determination module, configured to determine multiple evaluation indexes of the target management system;

[0013] A first weight determination module, configured to obtain the importance of each evaluation index, and determine the subjective weight of each evaluation index according to the importance of each evaluation index;

[0014] A second weight determination module, configured to obtain the index value of each evaluation index, and determine the objective weight of each evaluation index according to the index value of each evaluation index;

[0015] An evaluation module, configured to obtain the evaluation result of the target management system according to the subjective weight and objective weight of each evaluation index

[0016] An embodiment of the present invention provides an evaluation method and device for a new type of power load management system. The above-mentioned evaluation method for a new type of power load management system includes: determining multiple evaluation indexes of the target management system; obtaining the importance of each evaluation index, and determining the subjective weight of each evaluation index according to the importance of each evaluation index; obtaining the index value of each evaluation index, and determining the objective weight of each evaluation index according to the index value of each evaluation index; obtaining the evaluation result of the target management system according to the subjective weight and objective weight of each evaluation index. In the embodiment of the present invention, the target management system is evaluated by integrating multiple evaluation indexes, and the evaluation is more comprehensive. Moreover, by integrating the subjective weight and objective weight of the comprehensive evaluation index, the evaluation result is made as close as possible to the actual situation, reducing the influence of subjective factors of people and improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 is a flowchart of the implementation of an evaluation method for a new type of power load management system provided by an embodiment of the present invention;

[0019] Figure 2 is an evaluation index system for a new type of power load management system provided by an embodiment of the present invention;

[0020] Figure 3 It is a schematic structural diagram of an evaluation device for a new power load management system provided by an embodiment of the present invention. Specific embodiments

[0021] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented in order to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0023] The new power load management system is a more information-based and intelligent platform compared to the traditional power load management system. The information it needs to process is more complex, with higher refinement requirements, and its economic, social, and environmental benefits are also more diverse compared to the traditional load management system. The evaluation of the new power load management system usually adopts the expert scoring method for evaluation, which is not comprehensive enough and is greatly affected by human subjectivity, and cannot comprehensively and accurately evaluate the new power load management system.

[0024] Based on the above problems, an embodiment of the present invention provides an evaluation method for a new power load management system. Figure 1 The following shows a flowchart for implementing an evaluation method for a new power load management system provided by an embodiment of the present invention, which is described in detail as follows:

[0025] Refer to Figure 1 , the above-mentioned evaluation method for the new power load management system includes:

[0026] S101: Determine multiple evaluation indicators for the target management system;

[0027] There are many evaluation indicators for the new power load management system. How to establish a comprehensive, accurate, and scientific evaluation index system is the premise and key to effectively evaluating the new power load management system. The selected evaluation indicators must comprehensively consider the interests of relevant parties and social interests involved in establishing the new power load management system, and reflect the characteristics of the system. The present invention comprehensively considers the cost-benefits of the user side, power grid side, power generation side, and social side of the new power load management system, follows the principles of comprehensiveness, objectivity, comparability, etc., as well as the considerations for the development of the whole society. Based on this, the embodiments of the present invention select indicators from five aspects: technology, economy, society, environment, and energy efficiency, including: technical benefit indicators, economic benefit indicators

[0028] (1) Technical benefit index A1

[0029] Since the new power load management system applies big data, Internet of Things, 5G communication technology, software technology, etc., and compared with the traditional load management system, the main difference of the new power load management system is the investment in new technologies and the further emphasis on the demand response, a demand-side management method, and there are also high requirements for the load monitoring ability, response speed and response accuracy. Therefore, the embodiments of the present invention include technical benefit indexes within the scope of the index system, and believe that the data mining depth, information security, load monitoring ability, real-time screen call response time, and multi-module application ability can effectively represent the performance of the new power load management system in terms of technical benefits.

[0030] Specifically, the technical benefit indexes include the following:

[0031] 1) System load regulation quality level

[0032] The new power load management system needs to collect and deeply analyze load data from multiple aspects such as power users, load aggregators, and virtual power plant operators to provide a data basis for load analysis and formulating load regulation plans. On the basis of obtaining data, further analysis and prediction are carried out, and load regulation is carried out with reference to the predicted data and display situation. Therefore, the accuracy of data preprocessing, the accuracy of load prediction, and the regulation accuracy of the system load during data mining of the new power load management system fully measure the regulation quality of the new power load management system. The higher the system regulation quality level, the better the data quality, data analysis ability, and combination degree of data business background.

[0033] This patent uses the geometric mean of the mean square error of load data monitoring, regulation accuracy, and load prediction accuracy to comprehensively measure the system load regulation quality level.

[0034]

[0035] Among them, n1 is the number of samples, x obs,i is the observed value, x true,i is the true value; n2 is the number of prediction time points, Load actual is the actual load value, Load forecast is the predicted load value; represents the actually regulated load value, represents the target regulated load value, R target is the target value range.

[0036] 2) Information security A 12

[0037] Building a new power load management system and the key to carrying the business of the new load management system lies in information and communication technology. The new power load management system has functions such as information collection and data monitoring, and information interaction is required among the system terminals of power users, the terminals of the management system, the terminals of regulators, the management terminals of service providers, and the terminals of power suppliers. Therefore, the data involved in the system has characteristics such as wide scope, high level, and strong confidentiality. Therefore, it is necessary to consider the information privacy and security of the built system to protect user privacy.

[0038] 3) Load monitoring ability

[0039] The data monitoring function is crucial for the new load management system, and the load monitoring ability is also one of the main tasks of this system. Compared with the traditional power load management system, the new power load management system needs to achieve real-time monitoring of demand-side flexibility resources. By collecting information such as power quality data and load data at each time period on the user side, generating power quality reports and load curves, and conducting statistical analysis, it provides data support for load forecasting and energy decision-making. Therefore, the load detection ability can be used as an important indicator of the new power load management system.

[0040] A 13 = Load monitorable / Load total

[0041] Among them, Load monitorable is the measurable load, and Load total is the total load in this area. Generally speaking, if the load monitoring ability exceeds 70% of the maximum load in the system location, it indicates that the load detection ability of this system is good.

[0042] 4) Real-time screen call response time A 14

[0043] The real-time screen call response time can be used to measure the latency of the new power load management system. Since there are currently many types of energy access and complex data, to achieve power and electricity balance, especially the balance of electricity, the system requires high real-time performance. A slight time difference will affect the energy situation provided according to the predicted load, resulting in power shortages. Therefore, it is necessary to consider the response speed of the new power load management system to ensure the accuracy of the data analyzed by the new power load management system.

[0044] 5) Multi-module linkage ability A 15

[0045] As an important implementation platform for precise load management, the new power load management system undertakes various functions such as information collection, prediction and analysis, regulation, and interactive services for power users, virtual power plants, and load aggregators, and includes various modules such as load applications, load regulation, and auxiliary functions. The multi-module linkage ability is reflected in: first, the diversity and effectiveness of the new power load management system modules, that is, whether there are sufficient high-quality functional modules to meet the local load management needs. A comprehensive new power load management system should include various functional modules such as load monitoring and prediction, demand response module, orderly power consumption module, user information interaction and feedback module, control effect evaluation module, and government service module; second, the interaction ability between modules, which reflects the information transfer ability, display ability, and aggregation ability between modules. The outputs of the modules can cooperate with each other and provide useful information to each other.

[0046] (2) Economic benefit index A2

[0047] The economic benefit index is an important index to evaluate whether a project is feasible. In the economic benefits of the new power load management system in the embodiments of the present invention, not only the investment cost required for the construction of the new power load management system project and the debt repayment ability are considered, but also its economic benefits and costs that should be considered for the power grid enterprise as a power grid project are considered.

[0048] 1) Initial investment cost

[0049] The initial investment cost measures the economic feasibility of system construction. The initial investment cost of the new power load management system mainly includes two major parts: the cost of system equipment materials and the labor cost of installing the system.

[0050] A 21 = C1 + C2

[0051] In the formula, A 21 is the initial investment cost, C1 is the cost of system equipment materials, and C2 is the labor cost of installing the system.

[0052] 2) Internal rate of return

[0053] Examining the internal rate of return of the new power load management system project can evaluate the financial benefit situation of the project investment. The internal rate of return refers to the discount rate when the total present value of cash inflows is equal to the total present value of cash outflows, that is, when the net present value is equal to zero.

[0054]

[0055] Among them, IRR represents the internal rate of return of the project; n represents the life cycle of the system. Considering that the new power load management system is still in the exploration stage, n is taken as 5 in the embodiments of the present invention.

[0056] 3) Debt service coverage ratio

[0057] The debt service coverage ratio is an important financial indicator to measure an enterprise's debt repayment ability, and its expression is as follows:

[0058]

[0059] Among them, C3 represents the funds available for debt repayment of the project, and C4 represents the amount of debt repayment due in the current period

[0060] 4) Direct system revenue

[0061] The direct revenue from establishing a new power load management system for power grid enterprises mainly includes the operation cost reduction of power grid enterprises by applying this system, mainly including the labor costs saved by the remote automatic meter reading function of the system replacing manual meter reading and the resource waste reduction due to the improved resource utilization efficiency after the system operation.

[0062] A 24 = B1 + B2

[0063] Among them, A 24 is the direct system benefit, B1 is the reduced labor cost, and B2 is the reduced resource loss cost.

[0064] 5) Power grid redundancy optimization potential value

[0065] The power grid redundancy optimization potential value mainly refers to that power grid enterprises, by applying a new power load management system, slow down the upgrade and transformation of power transmission and distribution equipment including power grid lines, improve the utilization degree of power transmission and distribution equipment, optimize and adjust the unnecessary capacity of the power grid, and reduce the power transmission and distribution cost. The power grid redundancy optimization potential value can be expressed by the following formula:

[0066] A 25 =(Load max,former -Load max,current )×i per KW

[0067] Among them, Load max,former is the maximum power grid load before system construction, Load max,current is the maximum power consumption load after system construction, and i per KW is the investment per kilowatt of power supply.

[0068] (3) Energy efficiency indicator A3

[0069] The energy issue is also a key problem to be solved in the construction of a new power load management system. The scientific application of the new power load management system can accurately count and analyze power loads, provide effective references for energy supply, and thus effectively improve energy utilization efficiency. Especially for the utilization of new energy with uncertainty handling, the application of this system can promote the consumption of new energy and increase the proportion of renewable energy in energy utilization.

[0070] 1) New energy penetration rate

[0071] New energy has the characteristics of being renewable, producing few pollutants and greenhouse gas emissions after power generation. The change in the renewable energy penetration rate can be used to reflect the environmental benefits of the operation of the new power load management system. The expression of the renewable energy penetration rate is as follows:

[0072] A 31 = G new energy / G total

[0073] Where G new energy is the new energy power generation in this region, and G total represents the total power generation in this region.

[0074] 2) Power quality A 32

[0075] Power quality can be used to measure the ability of power supply equipment to operate normally without interfering with users' power consumption, improving the power user experience. It represents a physical characteristic. The main indicators to be considered include voltage, frequency, harmonic interference, etc. The new power load management system, through sensitive, high-speed, and accurate sensing and analysis functions, can detect the load level in real time and accurately conduct energy use scheduling, thereby reducing the operating pressure of the power grid and being conducive to improving power quality.

[0076] 3) Energy independence

[0077] Energy independence is used to measure the degree to which energy supply can meet energy demand in a certain region. The new power load management system can flexibly coordinate load resources, upgrade the real-time performance of the system, improve the reliability of the power grid system, reduce resource waste, improve energy utilization efficiency, and reduce the frequency of cross-provincial and cross-regional power purchases.

[0078]

[0079] The energy independence index can be expressed by the ratio of the cross-provincial and cross-regional power purchase volume to the total electricity consumption in the region.

[0080] (4) Social benefits A4

[0081] With the development of the new power system, grid enterprises, as power supply and sales enterprises, not only have the function of providing power services, but also have higher requirements for their "service" nature, demanding that the grid provide more efficient, safe, and high-quality power consumption services. In addition, the construction of the new power load management system also needs to consider the benefits of the whole society, and the industrial benefits and employment benefits brought to society are also important indicators for measuring the comprehensive benefits of the new power load management system.

[0082] 1) Average customer interruption duration

[0083] The average customer interruption duration refers to the average interruption time experienced by customers within a certain time range (usually taken as 1 year). This indicator can reflect the impact of the construction and application of the new power load management system on the reliability of the local power grid.

[0084] A 41 = T length / N

[0085] Where A 41 is the power supply reliability; T length is the total customer interruption duration in X years, in hours, and N is the total number of affected customers.

[0086] 2) System response adequacy

[0087] It measures to what extent the application of the new power load management system can meet the load required for demand response and promote the steady progress of regional demand response.

[0088]

[0089] Where T represents the total number of evaluation time periods; N type represents different customer categories participating in demand response, and this patent divides them into three categories: load aggregators, industrial customers, and commercial buildings; represents the load reduction of customer category i at time period t, that is, the actual response load; Load max represents the maximum load demand at time period t; ω i represents the weight of customer category i participating in demand response.

[0090] Where The calculation formula is as follows:

[0091]

[0092] Where represents the baseline load of customer type i at time period t; represents the actual load of customer type i at time period t.

[0093] Response weights ω for different user types i It is evaluated by analyzing four factors: the historical response time of different user types participating in demand response through the new power load management system, the volume of each participation in demand response, the number of times of participating in demand response, and the response rate.

[0094] 3) User satisfaction

[0095] With the transformation of power grid enterprises, power grid enterprises are no longer just enterprises providing energy sales and supply. Their service nature has become increasingly prominent, and attention also needs to be paid to the user service part. User satisfaction is a manifestation of the customer satisfaction survey system in the service industry. In other words, customers compare the actual impact of a certain product with their expectations to obtain an index. The higher the customer satisfaction, the more the customer base can be increased.

[0096]

[0097] Among them, A 43 is the user satisfaction, S is the user perception, and V is the expected value.

[0098] 4) Industrial benefit A 44

[0099] The new power load management system fully integrates high-tech such as 5G communication technology, computer analysis technology, and automatic control technology, enabling the system to collect, count, analyze, process power load data, and real-time monitor load data, timely discover abnormal data, and reasonably control and dispatch load-side resources. After the system is implemented, it can play a positive role in industrial demonstration, driving the progress of related technologies such as domestic energy-saving and carbon-reduction technologies, communication technologies, and automation technologies, and driving the development of related industries such as equipment manufacturing, network communication, as well as new industries such as integrated energy service providers and load aggregators. In addition, implementing demand response requires participating users to access the system platform and install dispatchable equipment, which also promotes the development of related industries such as smart homes and smart buildings.

[0100] 5) Employment benefit A 45

[0101] The construction and operation of the new power load management system increase the demand for relevant professional and technical personnel and management personnel, and will surely drive the development of related industries such as the communication industry, energy-saving industry, equipment manufacturing, and energy service industry, which will provide more job opportunities locally.

[0102] (5) Environmental benefit A5

[0103] The new power load management system is also of great significance for China's clean, low-carbon and green transformation. For the new power load management system, its positive impact on the environment is mainly reflected in the reduced greenhouse gas emissions due to the increased utilization of new energy and the reduced capacity costs, operating costs, etc. due to the improved overall grid-side operation efficiency, thereby reducing the emissions of greenhouse gases, harmful gases, etc.

[0104] 1) Carbon dioxide emission reduction

[0105] The carbon dioxide emission reduction mainly refers to the reduced carbon dioxide emissions compared to coal through the application of green energy such as wind power and photovoltaic power. Since the generation of green electricity itself hardly produces carbon dioxide, the calculation formula for carbon dioxide emission reduction is as follows:

[0106] A 51 = G green × η

[0107] Among them, G green represents the green electricity generation in the region, KW·h, and η represents the carbon emission factor for coal-fired power generation, taking 0.95 kg CO2 / KW·h.

[0108] 2) Gas pollutant emission reduction

[0109] The gas pollutants referred to in the present invention mainly include two gases, namely sulfur dioxide and nitrogen oxides.

[0110]

[0111] Among them, A 52 is the pollution emission reduction, is the sulfur dioxide emission reduction coefficient, is the nitrogen oxide emission reduction coefficient, and C save is the coal consumption saved by real-time load management.

[0112] 3) Other pollutant emission reductions

[0113] Other pollutant emission reductions mainly include solid waste emissions and wastewater emissions.

[0114] A 53 = (λ S + λ W )·L

[0115] Among them, A 53 is the pollution emission reduction, λ S is the solid waste emission reduction coefficient, λ W is the wastewater emission reduction coefficient, and L is the electricity saved by the whole society.

[0116] In summary, a total of 21 indicators are selected in the embodiments of the present invention to form Figure 2The shown evaluation index system covers multiple benefits such as technical benefits, economic benefits, energy benefits, social benefits and environmental benefits. The evaluation indexes are comprehensive, reasonable and accurate, and can be used to comprehensively evaluate the new power load management system.

[0117] S102: Obtain the importance of each evaluation index, and determine the subjective weight of each evaluation index according to the importance of each evaluation index;

[0118] S103: Obtain the index value of each evaluation index, and determine the objective weight of each evaluation index according to the index value of each evaluation index;

[0119] S104: Obtain the evaluation result of the target management system according to the subjective weight of each evaluation index and the objective weight of each evaluation index.

[0120] Since multiple evaluation indexes are involved, weights are necessarily required. The method for determining subjective weights is simple, but the human factor is too strong: the objective weight completely depends on the sample data. When the sample data changes, the weight will also change. From a statistical law, as the sample size increases, the change in weight should become smaller and smaller and finally tend to a stable value. However, it is impossible to make the sample size large enough in our actual evaluation process. Therefore, both subjective weights and objective weights will cause information loss.

[0121] In the embodiment of the present invention, multiple evaluation indexes are selected to evaluate the target relationship system, and the evaluation is more comprehensive. At the same time, subjective weights and objective weights are combined, and the two complement each other, ensuring the effectiveness, scientificity and accuracy of the evaluation.

[0122] In a possible implementation manner, S102 may include:

[0123] S1021: Take the evaluation index with the highest importance among each evaluation index as the optimal index;

[0124] In the embodiment of the present invention, the importance of each evaluation index is determined based on expert opinions, the optimal index is determined and the evaluation indexes are stratified.

[0125] For example, the index set A = {A 11 , A 12 , …, A mn}, and the optimal index is defined as A αβ .

[0126] S1022: Classify each evaluation index in descending order of importance to obtain multiple index sets. Among them, the importance of each evaluation index in the first index set is less than that of each evaluation index in the previous index set and greater than that of the evaluation index in the next index set. The first index set is any one of the index sets.

[0127] Based on expert opinions, obtain the importance of each evaluation index, sort each evaluation index according to its importance, and stratify them, that is, classify them.

[0128] Exemplarily:

[0129] L1: Classify those evaluation indexes with importance equivalent to the optimal index and those evaluation indexes with importance lower than the optimal index but not exceeding 2 times (excluding 2 times) of it into the same level.

[0130] L2 layer: Among the remaining indexes, those evaluation indexes with importance between 2 times and 3 times (excluding 3 times) of the optimal index will be classified into this specific level.

[0131] And so on

[0132] L K layer: Among the remaining indexes, those evaluation indexes with importance between K times and K + 1 times (excluding K + 1 times) of the optimal index will be classified and divided into the corresponding levels.

[0133] There is no overlap between the evaluation indexes of each level.

[0134] L = L1 ∪ L2 ∪ … ∪ L K

[0135] And for any If Then

[0136] S1023: Determine the elastic coefficient according to the number of evaluation indexes in each index set;

[0137] In the embodiment of the present invention, the elastic coefficient is defined. The elastic coefficient is used to quantify the maximum difference in the judgment of the importance of the indexes within the layer and is used to determine the subjective weight.

[0138] In a possible implementation manner, S1023 may include:

[0139] 1. Determine the elastic coefficient according to the number of evaluation indexes in each index set in combination with the second formula;

[0140] The second formula may include:

[0141] r0 = max{|L1|, |L2|, … Lk ,..., |L K |}+1

[0142] Among them, r0 is the elastic coefficient, and |L k | is the number of evaluation indicators in the k-th index set.

[0143] S1024: For any one evaluation indicator, determine the subjective weight of this evaluation indicator according to the importance of this evaluation indicator, the importance of the optimal indicator, and the elastic coefficient.

[0144] In the embodiment of the present invention, with the importance of the optimal indicator as a reference, the subjective weight is determined according to the importance of each evaluation indicator and the elastic coefficient, and the determined subjective weight is more accurate.

[0145] In a possible implementation manner, S1024 may include:

[0146] 1. Determine the relative importance of this evaluation indicator and the optimal indicator according to the importance of this evaluation indicator and the importance of the optimal indicator; among them, the higher the importance of this evaluation indicator, the smaller the relative importance of this evaluation indicator;

[0147] In the embodiment of the present invention, based on the importance of the optimal indicator, the relative importance of other evaluation indicators and the optimal indicator is determined. Among them, the higher the importance of the evaluation indicator, the smaller its relative importance with the optimal indicator. The value range of the relative importance is limited within the integer interval [0, r], where r = max{|L1|, |L2|,... L k ,..., |L K |}.

[0148] For example, for the optimal indicator, the value of its relative importance is 0.

[0149] 2. Determine the subjective weight of each evaluation indicator in this index set according to the relative importance of this evaluation indicator and the elastic coefficient.

[0150] In a possible implementation manner, determining the subjective weight of each evaluation indicator in this index set according to the relative importance of this evaluation indicator and the elastic coefficient may include:

[0151] 1. Determine the subjective weight of each evaluation indicator in this index set according to the relative importance of this evaluation indicator and the elastic coefficient, in combination with the first formula;

[0152] The first formula may include:

[0153]

[0154] Among them, is the -th evaluation indicator in the k-th index set; is the influence function; r0 is the elastic coefficient; is the relative importance of is ; w αβ is the weight of the optimal index, is the weight of

[0155] Based on the above formula, the subjective weights of each evaluation index can be calculated.

[0156] In a possible implementation, the index values of each evaluation index may include: the index values of each evaluation index determined by the expert scoring method for multiple years; S103 may include:

[0157] S1031: Form an initial index matrix with the index values of each evaluation index for each year;

[0158] In the embodiments of the present invention, the entropy weight method is used to determine the objective weights of each evaluation index.

[0159] First, collect the quantitative index data of a certain new type of power load management system for different years as the data characteristics of the certain new type of power load management system in different states, and measure its qualitative index through expert scoring, scoring with a full score of ten, to obtain the evaluation values of each evaluation index corresponding to each year, and form an initial index matrix X = [x ij [m×n] ; where m represents the year and n is the number of evaluation indexes. For example, n in the embodiments of the present invention may be 21.

[0160] S1032: Preprocess the initial index matrix to obtain a target index matrix;

[0161] For the initial index matrix, perform preprocessing on it to improve the consistency and feasibility of the data.

[0162] In a possible implementation, S1032 may include:

[0163] 1. Standardize the initial index matrix to obtain a standard index matrix;

[0164] The standard index matrix is a dimensionless standardized data matrix Y = [y ij [m×n] ; The standardization calculation formula for positive evaluation indexes is as follows:

[0165]

[0166] The standardization calculation formula for negative evaluation indexes is as follows:

[0167] ​​

[0168] 2. Corresponding to each element in the standard index matrix, the sum of the preset translation amount is used as each element in the target index matrix.

[0169] Considering that the process of data standardization will compress the data within the interval [0, 1], some extremely small values will become 0 after processing, thus affecting subsequent logarithmic operations and resulting in the situation of ln0. Therefore, a small number can be added to the standardized data to obtain the target index matrix. To ensure that the data are all positive numbers and do not affect the final result. Among them, the preset translation amount can be 0.01.

[0170] S1033: Determine the probability of each element in the target index matrix;

[0171] The probability of each element is denoted as P ij , 0 ≤ p j ≤ 1 and there is P ij The calculation formula of is as follows:

[0172]

[0173] S1034: Determine the objective weight of each evaluation index according to the probability of each element in the target index matrix.

[0174] In a possible implementation manner, S1034 may include:

[0175] 1. According to the probability of each element in the target index matrix, combine with the third formula to obtain the objective weight of each evaluation index;

[0176] The third formula includes:

[0177]

[0178] Among them, e j is the information entropy of the jth evaluation index, p ij is the probability of the element in the ith row and jth column in the target matrix, m is the number of rows in the target matrix, and n is the number of columns in the target matrix.

[0179] The information entropy is the expected value of the amount of information of the evaluation index in different states (specifically different years in this application), and the calculation formula is as follows:

[0180]

[0181] In the entropy weight method, the greater the information entropy of the evaluation index, the smaller its weight, and the information entropy and the weight show an inverse proportional relationship. Therefore, in the embodiments of the present invention, 1 - e jIndicates a negative correlation with the weight, obtains the calculation formula of the objective weight, and the calculation result is accurate and close to the actual situation.

[0182] In a possible implementation manner, S104 may include:

[0183] S1041: For any evaluation index, obtain the combined weight of the evaluation index according to the subjective weight and the objective weight of the evaluation index.

[0184] In order to narrow the heterogeneity between the subjective weight and the objective weight, the embodiment of the present invention calculates the combined weight of the evaluation index, and the calculation formula of the combined weight may be as follows:

[0185] ω j = θω 1j +(1 - θ)ω 2j

[0186] Since the objective data of each evaluation index in the current new power load management system is of great significance, and experts' research on the new power load management system is more data-based, in the calculation of the combined weight, a greater weight is given to the objective weight, that is, θ < 0.5. Specifically, θ may be 0.4.

[0187] S1042: Based on the combined weights of each evaluation index, obtain the evaluation result of the target management system based on the matter-element extension model.

[0188] Finally, the matter-element extension model divides the evaluation object into multiple levels (such as: excellent, good, medium, poor, etc.), which has good applicability for evaluating the new power load management system. Therefore, the embodiment of the present invention determines the evaluation result based on the matter-element extension model.

[0189] The matter-element extension method calculates the correlation degree between the matter-element to be evaluated and each evaluation level based on the measured data, and then accurately determines the level attribution of the evaluation object. First, the basic definitions of the model are explained as follows:

[0190] The thing T has several characteristics F, and its corresponding measured value is called V. Therefore, T, F, and V are called the basic elements of the matter-element R, also known as the three elements. Assume that the thing T has n characteristics, then it can also be described by {f1, f2,..., f n} and {v1, v2,..., v n}, and R is also called an n-dimensional matter-element.

[0191]

[0192] The specific implementation steps of the matter-element extension method are as follows.

[0193] (1) Set up the classical domain, the joint domain, and the matter-element to be evaluated.

[0194] Classical domain \(R\) j is

[0195]

[0196] where \(T\) j represents the \(j\)th evaluation level; \(\{f_1,f_2,\cdots,f n \}\) represents the characteristics of \(P\) j ; \(\{v_1,v_2,\cdots,v n \}\) represents the magnitude of \(T\) j , that is, the classical domain; \(\langle a ij ,b ij \rangle\) represent the upper and lower bounds of \(V\) ij respectively. Among them, when setting the classical domain, there are both quantitative indicators and qualitative indicators such as "information security" in the comprehensive benefit evaluation index of the new power load management system. For quantitative indicators, the present invention sets the upper and lower bounds of the indicator magnitude based on relevant literature, national standards, and the expectations for system construction. Qualitative indicators are scored by consulting experts and scholars with a full score of ten points.

[0197] Section domain \(R t is

[0198]

[0199] where \(P\) represents all evaluation levels; \(\{v 1p ,v 2p ,\cdots,v np \}\) are the value ranges corresponding to \(P\) for \(\{c_1,c_2,\cdots,c n \}\) respectively, that is, the section domain.

[0200] The matter element to be evaluated \(R_0\) is

[0201]

[0202] where \(\{v_1,v_2,\cdots,v n \}\) are the measured data of \(P_0\) corresponding to \(\{c_1,c_2,\cdots,c n \}\).

[0203] When the measured data of the evaluation index exceeds the set section domain range, the existing correlation function will not be able to effectively calculate the correlation values of this data with each evaluation level. Thus, the matter element extension method shows its limitations when dealing with such data. To overcome this limitation, we need to make appropriate improvements to the method. The specific improvement steps are as follows, which will help us more accurately evaluate and process data beyond the section domain range.

[0204] (2) Normalization processing.

[0205] Specifically, the normalization process involves converting the original data into dimensionless values, usually by dividing each data point by the maximum value of its corresponding indicator or a certain reference value. In this way, the data of the classical domain and the matter element to be evaluated are unified to a comparable scale, laying a foundation for the subsequent matter-element extension analysis.

[0206]

[0207] (3) Determine the indicator weights. Determine the weight values of each evaluation indicator based on the aforementioned combined weight determination method.

[0208] Traditional matter-element extension methods usually rely on the maximum membership degree principle to solve problems. However, in practical applications, this method may reduce its effectiveness due to excessive information loss. To overcome this limitation, the present invention proposes an improved scheme, that is, using the proximity degree principle to replace the traditional maximum membership degree principle to improve the accuracy and reliability of the method.

[0209] (4) Construct a proximity degree function and calculate the proximity degree function value.

[0210] The distance calculation formula between the matter element to be evaluated and the normalized range is:

[0211]

[0212] where a and b are the left endpoint value and the right endpoint value of the normalized range respectively.

[0213] The asymmetric proximity degree is:

[0214]

[0215] where N represents the proximity degree; D represents the distance; w i is the combined weight.

[0216] Based on the above, the proximity degrees between the matter element to be evaluated and each evaluation level are obtained:

[0217]

[0218] where N j (p0) represents the proximity degree between the matter element to be evaluated and each level; D j (v i ) represents the distance between the matter element to be evaluated and the normalized range; w i (X) represents the weights of each indicator; n is the number of evaluation indicators.

[0219] (5) Determine the level.

[0220] N j′ (t0) = max{N j(t0)}, (j = 1, 2, …, m)

[0221] It means that the object element to be evaluated R0 is closer to the level j′, and thus the evaluation result of the target management system is obtained.

[0222] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0223] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiment above.

[0224] Figure 3 The structure diagram of the evaluation device of the new power load management system provided by the embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0225] As Figure 3 shown, the evaluation device of the new power load management system includes:

[0226] An index determination module 21, configured to determine a plurality of evaluation indexes of the target management system;

[0227] A first weight determination module 22, configured to obtain the importance of each evaluation index, and determine the subjective weight of each evaluation index according to the importance of each evaluation index;

[0228] A second weight determination module 23, configured to obtain the index value of each evaluation index, and determine the objective weight of each evaluation index according to the index value of each evaluation index;

[0229] An evaluation module 24, configured to obtain the evaluation result of the target management system according to the subjective weight of each evaluation index and the objective weight of each evaluation index

[0230] In a possible implementation manner, the first weight determination module 22 may include:

[0231] An optimal index output unit, configured to use the evaluation index with the highest importance among each evaluation index as the optimal index;

[0232] An index stratification unit, configured to classify each evaluation index in descending order of importance to obtain a plurality of index sets; wherein, the importance of each evaluation index in the first index set is less than that of each evaluation index in the previous index set and greater than that of the evaluation index in the next index set; the first index set is any one of the index sets;

[0233] An elastic coefficient determination unit for determining an elastic coefficient according to the number of evaluation indicators in each index set;

[0234] A subjective weight output unit for determining the subjective weight of any evaluation indicator according to the importance of the evaluation indicator, the importance of the optimal indicator, and the elastic coefficient.

[0235] In a possible implementation manner, the subjective weight output unit may specifically be used for:

[0236] 1. Determine the relative importance of the evaluation indicator and the optimal indicator according to the importance of the evaluation indicator and the importance of the optimal indicator; wherein, the higher the importance of the evaluation indicator, the smaller the relative importance of the evaluation indicator;

[0237] 2. Determine the subjective weights of the evaluation indicators in the index set according to the relative importance of the evaluation indicator and the elastic coefficient.

[0238] In a possible implementation manner, determining the subjective weights of the evaluation indicators in the index set according to the relative importance of the evaluation indicator and the elastic coefficient may include:

[0239] Determine the subjective weights of the evaluation indicators in the index set according to the relative importance of the evaluation indicator and the elastic coefficient in combination with the first formula;

[0240] The first formula may include:

[0241]

[0242] Wherein, is the th evaluation indicator in the kth index set; is 's influence function; r0 is the elastic coefficient; is 's relative importance; w αβ is the weight of the optimal indicator, is 's weight.

[0243] In a possible implementation manner, the elastic coefficient determination unit may specifically be used for:

[0244] 1. Determine the elastic coefficient according to the number of evaluation indicators in each index set in combination with the second formula;

[0245] The second formula may include:

[0246] r0 = max{|L1|, |L2|,... L k ,..., |L K |} + 1

[0247] Among them, r0 is the elastic coefficient, and |L k | is the number of evaluation indicators in the kth index set.

[0248] In a possible implementation manner, the index values of each evaluation indicator may include: the index values of each evaluation indicator determined by the expert scoring method for multiple years; the second weight determination module 23 may include:

[0249] A first matrix formation unit, configured to form an initial index matrix with the index values of each evaluation indicator for each year;

[0250] A second matrix formation unit, configured to preprocess the initial index matrix to obtain a target index matrix;

[0251] A probability output unit, configured to determine the probability of each element in the target index matrix;

[0252] An objective weight output unit, configured to determine the objective weight of each evaluation indicator according to the probability of each element in the target index matrix.

[0253] In a possible implementation manner, the second matrix formation unit may specifically be configured to:

[0254] 1. Standardize the initial index matrix to obtain a standard index matrix;

[0255] 2. Sum each element in the standard index matrix with a preset translation amount, and correspondingly use it as each element in the target index matrix.

[0256] In a possible implementation manner, the objective weight output unit may specifically be configured to:

[0257] 1. According to the probability of each element in the target index matrix, combine it with the third formula to obtain the objective weight of each evaluation indicator;

[0258] The third formula may include:

[0259]

[0260] Among them, e j is the information entropy of the jth evaluation indicator, p ij is the probability of the element in the ith row and jth column in the target matrix, m is the number of rows in the target matrix, and n is the number of columns in the target matrix.

[0261] In a possible implementation manner, the evaluation module 24 may include:

[0262] A combined weight determination unit, configured to obtain a combined weight of any evaluation index according to the subjective weight and the objective weight of the evaluation index.

[0263] An evaluation result output unit, configured to obtain an evaluation result of the target management system based on the matter-element extension model according to the combined weights of the respective evaluation indexes.

[0264] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0265] Those of ordinary skill in the art can realize that the templates, units, and algorithm steps of the respective examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0266] If a module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above embodiments of the method of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above embodiments of the evaluation method of the new power load management system can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0267] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the respective embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A novel evaluation method for power load management system, characterized in that: include: Determine multiple evaluation indicators of the target management system; Obtain the importance of each evaluation indicator, and determine the subjective weight of each evaluation indicator according to its importance; Obtaining the index value of each evaluation index, and determining the objective weight of each evaluation index according to the index value of each evaluation index; According to the subjective weight of each evaluation index and the objective weight of each evaluation index, the evaluation result of the target management system is obtained.

2. The evaluation method of the new power load management system according to claim 1 is characterized in that: Determining the subjective weight of each evaluation indicator according to the importance of each evaluation indicator includes: The most important evaluation index among all the evaluation indexes is taken as the optimal index; Classify each evaluation indicator in order from high to low importance to obtain multiple indicator sets; wherein the importance of each evaluation indicator in the first indicator set is less than the importance of each evaluation indicator in the previous indicator set, and is greater than the importance of the evaluation indicator in the next indicator set; the first indicator set is any indicator set in each indicator set; Determine the elasticity coefficient according to the number of evaluation indicators in each indicator set; For any evaluation index, the subjective weight of the evaluation index is determined according to the importance of the evaluation index, the importance of the optimal index and the elasticity coefficient.

3. The evaluation method of the new power load management system according to claim 2 is characterized in that: Determining the subjective weight of the evaluation index according to the importance of the evaluation index, the importance of the optimal index and the elasticity coefficient includes: According to the importance of the evaluation indicator and the importance of the optimal indicator, the relative importance of the evaluation indicator and the optimal indicator is determined; wherein, the higher the importance of the evaluation indicator, the smaller the relative importance of the evaluation indicator; According to the relative importance of the evaluation index and the elasticity coefficient, the subjective weight of each evaluation index in the index set is determined.

4. The evaluation method of the new power load management system according to claim 3 is characterized in that: Determining the subjective weight of each evaluation indicator in the indicator set according to the relative importance of the evaluation indicator and the elasticity coefficient includes: According to the relative importance of the evaluation index and the elasticity coefficient, the subjective weight of each evaluation index in the index set is determined in combination with the first formula; The first formula includes: in, is the kth indicator set evaluation indicators; for The influence function of; r0 is the elastic coefficient; for The relative importance of αβ is the weight of the optimal indicator, for The weight of .

5. The evaluation method of the new power load management system according to claim 2 is characterized in that: Determining the elasticity coefficient according to the number of evaluation indicators in each indicator set includes: Determining the elasticity coefficient according to the number of evaluation indicators in each indicator set in combination with the second formula; The second formula includes: <h2 style=";text-align:left;direction:ltr">r0 = max{L1|,|L2|,…Lk,...,|L<h2 style=";text-align:left;direction:ltr"> K <h2 style=";text-align:left;direction:ltr"> |}+1 Where r0 is the elastic coefficient, |L k | is the number of evaluation indicators in the kth indicator set.

6. The evaluation method of the new power load management system according to any one of claims 1 to 5, characterized in that: The index value of each evaluation index includes: the index value of each evaluation index determined based on the expert scoring method in multiple years; the objective weight of each evaluation index is determined according to the index value of each evaluation index, including: The index values ​​of each evaluation index in each year form an initial index matrix; Preprocessing the initial indicator matrix to obtain a target indicator matrix; Determining the probability of each element in the target indicator matrix; According to the probability of each element in the target indicator matrix, the objective weight of each evaluation indicator is determined.

7. The evaluation method of the new power load management system according to claim 6 is characterized in that: The preprocessing of the initial indicator matrix to obtain a target indicator matrix includes: Standardizing the initial indicator matrix to obtain a standard indicator matrix; The sum of each element in the standard indicator matrix and the preset translation amount is used as the corresponding element in the target indicator matrix.

8. The evaluation method of the new power load management system according to claim 6 is characterized in that: Determining the objective weight of each evaluation indicator according to the probability of each element in the target indicator matrix includes: According to the probability of each element in the target indicator matrix, the objective weight of each evaluation indicator is obtained in combination with the third formula; The third formula includes: Among them, e j is the information entropy of the jth evaluation index, p ij is the probability of the element in the i-th row and j-th column in the target matrix, m is the number of rows in the target matrix, and n is the number of columns in the target matrix.

9. The evaluation method of the new power load management system according to any one of claims 1 to 5, characterized in that: The evaluation result of the target management system is obtained according to the subjective weight of each evaluation indicator and the objective weight of each evaluation indicator, including: For any evaluation index, the combined weight of the evaluation index is obtained according to the subjective weight of the evaluation index and the objective weight of the evaluation index; According to the combined weights of various evaluation indicators, the evaluation result of the target management system is obtained based on the matter-element extension model.

10. A novel evaluation device for a power load management system, characterized in that: include: An indicator determination module is used to determine multiple evaluation indicators of the target management system; The first weight determination module is used to obtain the importance of each evaluation indicator and determine the subjective weight of each evaluation indicator according to the importance of each evaluation indicator; The second weight determination module is used to obtain the index value of each evaluation index and determine the objective weight of each evaluation index according to the index value of each evaluation index; The evaluation module is used to obtain the evaluation result of the target management system according to the subjective weight of each evaluation indicator and the objective weight of each evaluation indicator.

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