CN117371662A - Evaluation system and method for adjustment capability of virtual power plant - Google Patents
Evaluation system and method for adjustment capability of virtual power plant Download PDFInfo
- Publication number
- CN117371662A CN117371662A CN202311380904.7A CN202311380904A CN117371662A CN 117371662 A CN117371662 A CN 117371662A CN 202311380904 A CN202311380904 A CN 202311380904A CN 117371662 A CN117371662 A CN 117371662A
- Authority
- CN
- China
- Prior art keywords
- data
- evaluation
- power plant
- virtual power
- adjustment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 329
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000004044 response Effects 0.000 claims abstract description 59
- 230000007613 environmental effect Effects 0.000 claims abstract description 26
- 238000013441 quality evaluation Methods 0.000 claims abstract description 16
- 230000008901 benefit Effects 0.000 claims description 33
- 238000004364 calculation method Methods 0.000 claims description 27
- 238000013210 evaluation model Methods 0.000 claims description 23
- 238000004422 calculation algorithm Methods 0.000 claims description 22
- 238000012937 correction Methods 0.000 claims description 18
- 238000004140 cleaning Methods 0.000 claims description 11
- 239000003344 environmental pollutant Substances 0.000 claims description 10
- 231100000719 pollutant Toxicity 0.000 claims description 10
- 238000002360 preparation method Methods 0.000 claims description 9
- 230000002860 competitive effect Effects 0.000 claims description 7
- 238000010801 machine learning Methods 0.000 claims description 7
- 238000013075 data extraction Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 5
- 238000000611 regression analysis Methods 0.000 claims description 5
- 238000012300 Sequence Analysis Methods 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 27
- 238000007405 data analysis Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 230000002776 aggregation Effects 0.000 description 4
- 238000004220 aggregation Methods 0.000 description 4
- 238000004146 energy storage Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000002411 adverse Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000001303 quality assessment method Methods 0.000 description 2
- 230000004043 responsiveness Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 238000000528 statistical test Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000035899 viability Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/002—Flicker reduction, e.g. compensation of flicker introduced by non-linear load
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Nonlinear Science (AREA)
- Power Engineering (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an evaluation system and method for the adjustment capability of a virtual power plant. The evaluation system for the adjustment capability of the virtual power plant comprises a historical data acquisition module, an evaluation data acquisition module and an adjustment capability comprehensive evaluation module. According to the method, the comprehensive evaluation coefficient of the regulating capacity is obtained by constructing the comprehensive evaluation coefficient model of the regulating capacity and carrying out the quality evaluation, the frequency response evaluation, the stable and reliable adjustment evaluation, the economic energy efficiency evaluation and the environmental protection adjustment evaluation on the evaluation data of the regulating capacity of the virtual power plant, so that the regulating capacity of the virtual power plant is evaluated in the comprehensive evaluation module of the regulating capacity according to the comprehensive evaluation coefficient of the regulating capacity, the comprehensive evaluation authenticity of the regulating capacity of the virtual power plant is improved, and the problem that the comprehensive evaluation authenticity of the regulating capacity of the virtual power plant is insufficient in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of virtual power plants, in particular to an evaluation system for the adjustment capability of a virtual power plant.
Background
The virtual power plant is a power coordination management system which is used as a special power plant to participate in the operation of a power market and a power grid and realizes the aggregation and coordination optimization of distributed energy resources DER such as a distributed power source DG, an energy storage system, a controllable load, an electric automobile and the like through an advanced information communication technology and a software system, the evaluation of the adjustment capability of the virtual power plant has importance in the current energy field, and renewable energy integration is realized: as the specific gravity of renewable energy sources (such as solar and wind) in energy production increases, the uncertainty and volatility of the grid also increases. Grid stability: regulatory capability is a key element in maintaining grid stability. The energy market participates in: virtual power plants typically participate in energy markets, including energy trading and electricity price fluctuations. Sustainable development: assessing the regulatory capabilities of virtual power plants helps to drive a sustainable development goal. The quality of electric energy: the ability of a virtual power plant to cope with transient fluctuations or voltage instabilities may improve power quality. Smart grid development: the virtual power plant is a part of the intelligent power grid, and helps to improve the intelligent degree of the power grid.
The existing evaluation system for the adjustment capability of the virtual power plant is realized by the following technology, which comprises the following steps: data acquisition and monitoring technology: virtual power plants need to monitor the output of various energy resources and the state of the power grid in real time. Data analysis and prediction: data analysis and machine learning techniques may be used to analyze historical data, predict future energy production, and grid demand. Energy storage technology: virtual power plants typically include energy storage devices such as batteries, supercapacitors, and the like. An intelligent control system: the intelligent control system of the virtual power plant can respond to the power grid demand in real time, and makes decisions according to the data and the prediction. Communication technology: virtual power plants require real-time communication with grid operators, other energy facilities, and market participants. Market access system: virtual power plants typically participate in the energy market, sell surplus energy, or provide regulated services. Simulation and emulation tool: the turndown capability of the virtual power plant can also be assessed by simulation and emulation tools. Network security technology: since virtual power plants involve critical infrastructure, network security is critical. Regulatory and standard compliance: virtual power plants need to comply with various regulatory standards and regulations.
For example, publication No.: the method and system for evaluating the adjustment capability of the virtual power plant based on the aggregation of various resources disclosed in CN114429274A comprise the following steps: establishing a physical analysis model, selecting an evaluation index, and evaluating the adjustment capability and the response capability of various resources; meanwhile, a real-time system scheduling model is built, an uncertainty model which is caused by considering energy storage stabilizing new energy sources is built through calculating fluctuation parameters and reliability parameters of the virtual power plant, and the running risk condition of the virtual power plant in each scene is estimated.
For example, publication No.: CN115986722a discloses a dynamic aggregate response capability assessment method for flexible resources of a virtual power plant, which comprises the following steps: constructing an EVs resource state model to obtain EVs resource state model parameters; constructing a TCLs resource state model to obtain TCLs resource related key state model parameters; constructing an EVs resource priority state queue; and constructing a TCLs resource priority state queue, and evaluating the VPP cluster dynamic aggregation response capability.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the inventor of the application finds that at least the following technical problems exist in the above technology:
in the prior art, the influence of multivariate mutual interference in the adjusting process is easily ignored in the evaluation of the adjusting capability of the virtual power plant, so that the problem of insufficient comprehensive authenticity in the evaluation of the adjusting capability of the virtual power plant exists.
Disclosure of Invention
According to the evaluation system and the evaluation method for the adjustment capability of the virtual power plant, the problem that the evaluation comprehensive authenticity of the adjustment capability of the virtual power plant is insufficient in the prior art is solved, and the improvement of the evaluation comprehensive authenticity of the adjustment capability of the virtual power plant is realized.
The embodiment of the application provides an evaluation system for the adjustment capability of a virtual power plant, which comprises the following steps: a historical data acquisition module: the method comprises the steps of obtaining virtual power plant historical data, wherein the virtual power plant historical data comprise virtual power plant historical operation data and virtual power plant adjustment data; an evaluation data acquisition module: the virtual power plant adjustment capability evaluation module is used for evaluating the virtual power plant adjustment capability according to the virtual power plant historical data to obtain virtual power plant adjustment capability evaluation data; and the comprehensive adjustment capability evaluation module is used for: and the method is used for constructing an adjustment capacity comprehensive evaluation coefficient model according to the virtual power plant adjustment capacity evaluation data and calculating to obtain an adjustment capacity comprehensive evaluation coefficient.
The embodiment of the application provides a method for evaluating the adjustment capability of a virtual power plant, which comprises the following steps: obtaining virtual power plant historical data, wherein the virtual power plant historical data comprises virtual power plant historical operation data and virtual power plant adjustment data; evaluating the virtual power plant adjustment capacity according to the virtual power plant historical data to obtain virtual power plant adjustment capacity evaluation data; and constructing an adjusting capacity comprehensive evaluation coefficient model according to the virtual power plant adjusting capacity evaluation data, and calculating to obtain an adjusting capacity comprehensive evaluation coefficient.
Further, the specific method for obtaining the virtual power plant adjustment capability evaluation data comprises the following steps: data cleaning: performing data cleaning on the virtual power plant regulation data to obtain effective virtual power plant historical data; characteristic engineering: selecting characteristic data related to the adjustment capacity of the virtual power plant from the effective virtual power plant historical data through a characteristic engineering selection algorithm to obtain effective virtual power plant historical characteristic data; establishing an evaluation model: according to the effective virtual power plant historical characteristic data, a characteristic evaluation model is established through a machine learning algorithm; acquiring evaluation data: and respectively comparing the results of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant to obtain the adjustment capability evaluation data of the virtual power plant.
Further, the specific calculation formula of the comprehensive evaluation coefficient of the adjusting capability is as follows:wherein, xi represents the comprehensive evaluation coefficient of the adjustment capability; alpha represents an adjustment data quality evaluation coefficient, mu represents an adjustment frequency response evaluation coefficient, phi represents an adjustment stability reliability evaluation coefficient, +.>Representing an adjustment economic energy efficiency evaluation coefficient, and θ represents an adjustment environmental protection evaluation coefficient; u represents the weight factor of the adjusting frequency response evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, v represents the weight factor of the adjusting stable and reliable evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, and w represents the weight factor of the adjusting economic efficiency evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient.
Further, the specific calculation formula of the adjustment data quality evaluation coefficient is as follows:wherein a is 0 A represents the number of data categories in the adjustment capability raw data set, a 0 =1, 2,..a, a represents the total number of adjustment capability raw data set data categories, c 0 A corresponding to the original data set representing the adjustment capability 0 Numbering of individual data in individual data categories, c 0 =1, 2,..c, c represents a corresponding a-th of the adjustment capability raw data set 0 Total number of data in each data category, +.>Representing the a-th in the adjustment capability raw data set 0 Class IIIc 0 Availability evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined availability criterion value of the individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Quality evaluation value of individual data,/->Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined quality standard value of the individual data, < >>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Accuracy evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined accuracy standard values of the individual data, beta representing the data noise value reading error factor, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 The standard value of the data noise difference value, b represents the a-th data in the original data set of the regulation capacity 0 Class c 0 And the median noise filter correction value of the data.
Further, the specific calculation formula of the adjusting frequency response evaluation coefficient is as follows:wherein d 0 Number d representing class of data in the frequency response data set 0 =1, 2,..d, d represents the frequency response numberBased on total number of data classes, f 0 Represents the d-th corresponding to the frequency response data set 0 Numbering of individual data in individual data types, f 0 =1, 2,..f, f is the d-th corresponding to the frequency response data set 0 Total number of data in data type +.>Representing the d-th in the frequency response data set 0 Class f 0 Frequency adjustment performance matching evaluation value of individual data extraction, +.>Representing the d-th in the frequency response data set 0 Class f 0 Response time performance evaluation value of individual data extraction, +.>The maximum response time standard value is represented, epsilon represents a predefined power response matching factor corresponding to the frequency response data set data, delta represents a predefined start-stop frequency influence correction factor, and χ represents a predefined frequency response delay droop control coefficient.
Further, the specific calculation formula of the stable and reliable adjustment evaluation coefficient is as follows:wherein C represents an energy stability rate evaluation value, D represents an excessive frequency evaluation value, E represents an adjustment deviation control evaluation value, F represents an adjustment flexibility correction evaluation value, gamma represents a matching harmonic factor of the energy stability rate evaluation value and the excessive frequency evaluation value, g represents a weight factor of the adjustment energy stability rate evaluation value to the adjustment stable and reliable evaluation coefficient, h represents a weight factor of the excessive frequency evaluation value to the adjustment stable and reliable evaluation coefficient, D Pre-preparation Represents a predefined overregulation frequency assessment criterion value, E Pre-preparation Representing a predefined adjustment deviation control evaluation criterion value, +.>Matching harmonic factor representing the adjustment deviation control evaluation value for adjusting the stable and reliable evaluation coefficient, +.>And the matching harmonic factors corresponding to the predefined adjustment flexibility correction evaluation value to the adjustment requirement type are represented, eta represents the adjustment energy stability rate evaluation value, the adjustment overfrequency evaluation value and the adjustment deviation control evaluation value, the mutual superposition negative influence coefficients are represented, and lambda represents the predefined virtual power plant equipment maintenance period related correction coefficients.
Further, the specific calculation formula of the economic energy efficiency evaluation coefficient is as follows:wherein G represents an energy utilization benefit matching evaluation value, H represents an energy return on investment, I represents a market economic benefit competitive matching evaluation value, J represents an energy loss rate, K represents a predefined virtual power plant operation cost impact matching coefficient, m represents an impact matching factor of the energy utilization benefit matching evaluation value on an adjustment economic energy efficiency evaluation coefficient, o represents an impact matching factor of a data tag adaptation evaluation value on the adjustment economic energy efficiency evaluation coefficient, I represents a weight factor of the adjustment economic energy efficiency evaluation coefficient corresponding to the energy utilization benefit matching evaluation value, J represents a weight factor of the adjustment economic energy efficiency evaluation coefficient corresponding to the energy return on investment, H Pre-preparation The minimum return rate of the predefined energy investment is represented, n represents the influence superposition factor of the competitive matching evaluation value of the market economic benefit on the adjustment economic energy efficiency evaluation coefficient, and k represents the influence superposition factor of the energy loss rate on the adjustment economic energy efficiency evaluation coefficient.
Further, the specific calculation formula of the environmental protection evaluation coefficient is as follows:wherein M represents an evaluation value of running resource consumption, N represents a sustainability matching coefficient, P represents a pollutant discharge amount matching influence coefficient, M Efficacy of Indicating the adjustment of the resource benefit evaluation value, p tableThe environmental protection weight factor showing the operation resource consumption evaluation value, q represents the environmental protection factor of the sustainability matching coefficient, r represents the influence matching factor of the pollutant emission amount matching influence coefficient on the environmental protection evaluation coefficient, θ represents the operation resource consumption evaluation value, the sustainability matching coefficient and the superimposed negative influence factor of the pollutant emission amount matching influence coefficient, S represents the clean energy correction matching factor, and σ represents the influence matching factor of the resource consumption type proportion on the environmental protection evaluation coefficient.
Further, the specific method for obtaining the virtual power plant adjustment capability evaluation data further includes: defining performance indexes: defining a plurality of performance indexes according to the characteristic evaluation model for evaluating the adjustment capability of the virtual power plant; and (3) comparison calculation: comparing and calculating the output of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant through a regression analysis algorithm and a time sequence analysis algorithm, and then carrying out correlation calculation of a Chebyshev correlation coefficient algorithm on the difference data to obtain a comparison data correlation result; generating evaluation data: and generating the adjustment capability evaluation data of the virtual power plant according to the compared data correlation result.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the adjusting capacity comprehensive evaluation module is used for obtaining an adjusting data quality evaluation coefficient, an adjusting frequency response evaluation coefficient, an adjusting stability and reliability evaluation, an adjusting economic energy efficiency evaluation and an adjusting environment protection evaluation through an adjusting data quality evaluation, an adjusting frequency response evaluation coefficient, an adjusting stability and reliability evaluation coefficient, an adjusting economic energy efficiency evaluation coefficient and an adjusting environment protection evaluation coefficient, and then the adjusting capacity comprehensive evaluation coefficient is obtained by integrating the above evaluation coefficients through an adjusting capacity comprehensive evaluation coefficient model, so that the adjusting capacity of the virtual power plant is evaluated according to the adjusting capacity comprehensive evaluation coefficient and displayed comprehensively, the evaluation comprehensive reality of the adjusting capacity of the virtual power plant is improved, and the problem that the evaluating comprehensive reality of the adjusting capacity of the virtual power plant is insufficient in the prior art is effectively solved.
2. The evaluation data acquisition module is used for obtaining the evaluation data of the adjustment capability of the virtual power plant through data cleaning, feature engineering, establishment of an evaluation model and acquisition of the evaluation data, so that objective and verifiable evaluation data are provided, the influence of subjective errors is reduced, and further the evaluation precision of the adjustment capability of the virtual power plant is improved.
3. The virtual power plant adjustment capability evaluation data is obtained through defining performance indexes, comparing, calculating and generating evaluation data by the evaluation data obtaining module, so that the accuracy and reliability of the feature evaluation model are improved, and the authenticity of the obtained virtual power plant adjustment capability evaluation data is further realized.
Drawings
FIG. 1 is a schematic structural diagram of an evaluation system for the adjustment capability of a virtual power plant according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for evaluating the capacity of a virtual power plant according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a specific method for obtaining virtual power plant adjustment capability assessment data according to an embodiment of the present application.
Detailed Description
According to the evaluation system and the evaluation method for the adjustment capability of the virtual power plant, the problem that the evaluation of the adjustment capability of the virtual power plant is insufficient in comprehensive authenticity in the prior art is solved, the comprehensive evaluation coefficient of the adjustment capability is obtained by constructing the comprehensive evaluation coefficient model of the adjustment capability and comprehensively evaluating the evaluation data of the adjustment capability of the virtual power plant, and the evaluation comprehensive authenticity of the adjustment capability of the virtual power plant is improved.
The technical scheme in the embodiment of the application aims to solve the problem that the comprehensive authenticity of the virtual power plant adjustment capability is insufficient in evaluation, and the overall thought is as follows:
The comprehensive adjustment capacity evaluation coefficient is obtained by constructing an adjustment capacity comprehensive evaluation coefficient model and carrying out adjustment data quality evaluation, adjustment frequency response evaluation, adjustment stability and reliability evaluation, adjustment economic energy efficiency evaluation and adjustment environmental protection evaluation on the virtual power plant adjustment capacity evaluation data, so that the virtual power plant adjustment capacity is evaluated in the adjustment capacity comprehensive evaluation module according to the comprehensive adjustment capacity evaluation coefficient, and the effect of improving the evaluation comprehensive reality of the virtual power plant adjustment capacity is achieved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a schematic structural diagram of an evaluation system for adjusting capacity of a virtual power plant according to an embodiment of the present application is provided, where the evaluation system for adjusting capacity of a virtual power plant according to an embodiment of the present application includes: a historical data acquisition module: the method comprises the steps of obtaining virtual power plant historical data, wherein the virtual power plant historical data comprise virtual power plant historical operation data and virtual power plant adjustment data; an evaluation data acquisition module: the virtual power plant adjustment capability evaluation module is used for evaluating the virtual power plant adjustment capability according to the virtual power plant historical data to obtain virtual power plant adjustment capability evaluation data; and the comprehensive adjustment capability evaluation module is used for: and the method is used for constructing an adjustment capacity comprehensive evaluation coefficient model according to the virtual power plant adjustment capacity evaluation data and calculating to obtain an adjustment capacity comprehensive evaluation coefficient.
In this embodiment, the evaluation system for the adjustment capability of the virtual power plant generally includes a comprehensive display module for integrating each evaluation result into a comprehensive evaluation coefficient and displaying the comprehensive evaluation coefficient. The specific display method of the comprehensive display module comprises the following steps: and displaying the calculated comprehensive evaluation coefficient of the regulating capacity on an evaluation comprehensive display interface, displaying the related factors contained in the comprehensive evaluation coefficient model of the regulating capacity to obtain a method, giving out a corresponding association relation according to the influence of the related factors contained in the comprehensive evaluation coefficient model of the regulating capacity on the regulating capacity of the virtual power plant by a linear regression algorithm, and displaying the association relation, and updating the comprehensive evaluation coefficient of the regulating capacity according to the collected historical operation data of the virtual power plant and the regulating data of the virtual power plant periodically. The following is one possible way to implement this integrated display module: and (3) comprehensive evaluation coefficient display: and displaying the calculated comprehensive evaluation coefficient on a user interface of an evaluation system. The composite evaluation coefficient may be presented as a numerical value or may be presented in a visual manner, such as a histogram, pie chart, or radar chart, to help the user understand the evaluation results more easily. Interpretation and advice: an explanation is provided regarding the comprehensive assessment coefficients to assist the user in understanding the meaning of such coefficients. Based on the evaluation results, advice or improvement measures may also be provided to improve the capacity of the virtual power plant and reduce its adverse effects on the environment. Updating and monitoring: the comprehensive evaluation coefficients are updated periodically to reflect the performance changes of the virtual power plant. Meanwhile, a monitoring and feedback mechanism is implemented to ensure that the system maintains accuracy and timeliness. The related factor obtaining method comprises the following steps: linear regression analysis: and (3) establishing a model: and (3) using a linear regression model to correlate the selected related factors with the comprehensive evaluation coefficients of the adjustment capability. The linear regression model may describe a linear relationship between these factors and the composite evaluation coefficients. Coefficient estimation: and estimating coefficients of each relevant factor through regression analysis, wherein the coefficients represent the influence degree of the factors on the comprehensive evaluation coefficient of the adjustment capacity. Statistical significance: and carrying out statistical test to determine whether the related factors have significant influence on the comprehensive evaluation coefficient of the regulatory capability. Model interpretation: the results of the regression model are interpreted so that the user can understand which factors have an important influence on the capacity of the virtual power plant. Benefits of periodically updating the comprehensive evaluation coefficients: real-time performance: by collecting historical operation data and regulation data of the virtual power plant, the real-time performance of the comprehensive evaluation coefficients can be maintained, and the latest conditions can be reflected. And (3) performance monitoring: periodic updating of the assessment coefficients allows the manager to monitor the performance of the virtual power plant's capacity to detect potential trends and problems over time. Decision support: based on the updated data, the manager can better make decisions, take measures to improve regulatory capabilities, reduce costs, increase efficiency, and reduce adverse effects on the environment. Optimizing operation: through the change of demodulation capacity related factors, the virtual power plant can better optimize an operation strategy to adapt to the continuously changing conditions.
As shown in fig. 2, a flow chart of a method for evaluating the adjustment capability of a virtual power plant according to an embodiment of the present application is shown, where the method for evaluating the adjustment capability of the virtual power plant according to the embodiment of the present application includes: obtaining virtual power plant historical data, wherein the virtual power plant historical data comprises virtual power plant historical operation data and virtual power plant adjustment data; evaluating the virtual power plant adjustment capacity according to the virtual power plant historical data to obtain virtual power plant adjustment capacity evaluation data; and constructing an adjusting capacity comprehensive evaluation coefficient model according to the virtual power plant adjusting capacity evaluation data, and calculating to obtain an adjusting capacity comprehensive evaluation coefficient.
As shown in fig. 3, a flowchart of a specific method for obtaining virtual power plant adjustment capability assessment data provided in an embodiment of the present application is shown, and further, the specific method for obtaining virtual power plant adjustment capability assessment data includes: data cleaning: performing data cleaning on the virtual power plant regulation data to obtain effective virtual power plant historical data; characteristic engineering: selecting characteristic data related to the adjustment capacity of the virtual power plant from the effective virtual power plant historical data through a characteristic engineering selection algorithm to obtain effective virtual power plant historical characteristic data; establishing an evaluation model: according to the effective virtual power plant historical characteristic data, a characteristic evaluation model is established through a machine learning algorithm; acquiring evaluation data: and respectively comparing the results of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant to obtain the adjustment capability evaluation data of the virtual power plant.
In this embodiment, the described virtual power plant adjustment capability assessment data acquisition method has the following benefits: data cleaning: data cleansing is a critical step in ensuring the quality of the historical data of the virtual power plant. By data cleaning, errors, duplicates or missing information in the data can be removed, and the data used in the evaluation process is ensured to be accurate. This helps to improve the reliability and accuracy of the evaluation result. Characteristic engineering: feature engineering is the extraction of features from raw data that are related to the capacity of the virtual power plant. Selecting appropriate features can reduce the complexity of the model, improve the training and evaluation efficiency of the model, and also help to improve the performance of the evaluation model. By specifically selecting features related to the capacity of the regulation, the operation of the virtual power plant may be better understood. Establishing an evaluation model: the feature evaluation model is established through a machine learning algorithm, so that the adjustment capability of the virtual power plant can be quantified. This allows quantitative assessment of the regulatory capability, not just subjective judgment. The assessment model may predict the turndown performance of the virtual power plant based on features in the historical data. Acquiring evaluation data: and comparing the result of the evaluation model with the historical operation data and the adjustment data of the virtual power plant, so that specific evaluation data of the adjustment capability of the virtual power plant can be obtained. These data may be used to monitor the performance of the virtual power plant, identify potential problems, and provide decision support. This data driven approach may help virtual power plant operators make more informed decisions, improving operation. In a word, the data acquisition method combines the steps of data cleaning, feature engineering, machine learning modeling and data comparison, and is beneficial to improving the accuracy and reliability of the assessment of the adjustment capability of the virtual power plant. The method not only enables the virtual power plant to better understand the performance of the virtual power plant, but also helps operators to take targeted improvement measures to improve the regulation capacity and the overall efficiency of the virtual power plant.
Further, the specific calculation formula of the comprehensive evaluation coefficient of the adjusting capability is as follows:wherein, xi represents the comprehensive evaluation coefficient of the adjustment capability; alpha represents an adjustment data quality evaluation coefficient, mu represents an adjustment frequency response evaluation coefficient, phi represents an adjustment stability reliability evaluation coefficient, +.>Representing an adjustment economic energy efficiency evaluation coefficient, and θ represents an adjustment environmental protection evaluation coefficient; u represents the weight factor of the adjusting frequency response evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, v represents the weight factor of the adjusting stable and reliable evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, and w represents the weight factor of the adjusting economic efficiency evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient.
In this embodiment, this formula describes a model for calculating the adjustment capability comprehensive assessment coefficient, which includes different sub-terms and weighting factors. The specific steps and the advantages are as follows: adjusting a data quality evaluation coefficient: for evaluating the quality of the regulation data of the virtual power plant. Including accuracy, integrity, and timeliness of the regulatory data. The method has the advantages of ensuring that the used data are reliable and accurate, thereby improving the credibility and accuracy of the model. Adjusting a frequency response evaluation coefficient: for evaluating the responsiveness of the virtual power plant to grid frequency fluctuations. The fast responsiveness and frequency control capability of the power plant may be considered. The method has the advantages that the virtual power plant can provide stable support when the frequency of the power grid fluctuates, and stable operation of the power grid is facilitated. Adjusting a stable and reliable evaluation coefficient: for assessing the stability and reliability of a virtual power plant under various operating conditions. This may include availability of virtual power plant equipment, maintenance levels, and the ability to handle emergency events. The method has the advantages that reliable adjustment capability can be provided for the virtual power plant under various conditions, and the risk of power system interruption is reduced. Adjusting an economic energy efficiency evaluation coefficient: for assessing economic benefits and energy efficiency of virtual power plants. This includes cost effectiveness, fuel utilization efficiency, and the like. The benefits are to ensure that the virtual power plant provides regulation in the most economical and efficient way, to reduce costs and to reduce resource waste. Adjusting an environmental protection evaluation coefficient: for assessing the environmental impact of virtual power plants, including carbon emissions, pollution, and renewable energy utilization. The method has the advantages of ensuring that the influence of the adjustment activities of the virtual power plant on the environment is minimal, and being beneficial to sustainability and environmental protection. Comprehensive evaluation weight factor of regulation capacity: these weighting factors determine the relative importance of the individual sub-items in the overall evaluation. They allow the weights of the different sub-items to be adjusted according to the specific needs and priorities of the virtual power plants. Benefits include: comprehensively: by taking into account the adjustment capabilities of the different aspects, the comprehensive assessment coefficients provide a comprehensive assessment of the overall adjustment capabilities of the virtual power plant. Quantitative evaluation: the model provides a quantitative method to evaluate the turndown capability of the virtual power plant, enabling a decision maker to make decisions based on the data. And (5) weight adjustment: by means of the weighting factors, the importance of each sub-item can be adjusted according to specific requirements and priorities, and the model is more customizable. Decision support: based on the results of the model, virtual power plant managers can formulate strategies and improvement plans to improve regulatory capacity, reduce costs, improve reliability, and reduce environmental impact.
Advancing oneThe specific calculation formula of the data quality evaluation coefficient is:wherein a is 0 A represents the number of data categories in the adjustment capability raw data set, a 0 =1, 2,..a, a represents the total number of adjustment capability raw data set data categories, c 0 A corresponding to the original data set representing the adjustment capability 0 Numbering of individual data in individual data categories, c 0 =1, 2,..c, c represents a corresponding a-th of the adjustment capability raw data set 0 Total number of data in each data category, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Availability evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined availability criterion value of the individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Quality assessment values of the individual data are obtained,representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined quality standard value of the individual data, < >>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Accuracy evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefining of individual dataAccuracy standard value, beta represents data noise value reading error factor, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 The standard value of the data noise difference value, b represents the a-th data in the original data set of the regulation capacity 0 Class c 0 And the median noise filter correction value of the data.
In this embodiment, an adjustment capability raw data set is obtained from the virtual power plant adjustment capability evaluation data, a basis for evaluating the adjustment capability of the virtual power plant is constructed by constructing an adjustment data quality evaluation coefficient model, that is, the quality of operation data used for adjusting the basis is obtained, and the data quality evaluation data can be obtained in various manners, for example, the actual equipment simulated by the virtual power plant is subjected to comparison and detection, and the actual line loss is considered, so that deviation is certain; the availability index is an index for measuring the availability or reliability of a system, device or service. It is typically a value between 0 and 1, representing the ratio of the system uptime to the total time, data quality assessment: data integrity: and checking whether the data has missing values or not, and ensuring the integrity of the data. Accuracy of data: and verifying the accuracy of the data, including the matching degree with the actual situation. Data consistency: it is checked whether the data from different sources are consistent and contradictory. Data precision: the units of measure and decimal places of the data are evaluated to ensure data accuracy. Data availability assessment: data acquisition frequency: the frequency of acquisition of the data is determined to ensure that the data has sufficient time resolution. Reliability of data acquisition: reliability of the data acquisition system is assessed, including reliability of data transmission and storage. Data timeliness: ensuring that the data is timely and not outdated to reflect the current situation. The method for evaluating the accuracy of data analysis comprises the following steps: verifying source data: before any type of data analysis can be performed, the reliability and accuracy of the source data must first be verified. Repeated test: for critical data analysis, multiple independent analyses are preferably performed to ensure consistency of results. Checking results: verification refers to a second check of the results of the data analysis. Benchmark test: prior to data analysis, a benchmark test may be constructed using known datasets. Evaluation model: when data analysis involves machine learning or other complex statistical models, the model used must be evaluated. Thereby obtaining a quality evaluation value, an accuracy evaluation value and an availability evaluation value of the data.
Further, a specific calculation formula of the adjusting frequency response evaluation coefficient is as follows:wherein d 0 Number d representing class of data in the frequency response data set 0 =1, 2,..d, d represents the total number of frequency response data set data categories, f 0 Represents the d-th corresponding to the frequency response data set 0 Numbering of individual data in individual data types, f 0 =1, 2,..f, f is the d-th corresponding to the frequency response data set 0 Total number of data in data type +.>Representing the d-th in the frequency response data set 0 Class f 0 Frequency adjustment performance matching evaluation value of individual data extraction, +.>Representing the d-th in the frequency response data set 0 Class f 0 Response time performance assessment values for the individual data extractions,the maximum response time standard value is represented, epsilon represents a predefined power response matching factor corresponding to the frequency response data set data, delta represents a predefined start-stop frequency influence correction factor, and χ represents a predefined frequency response delay droop control coefficient.
In this embodiment, the frequency response raw data set is obtained from the virtual power plant adjustment capability assessment data, the frequency response capability: response time: the response time is the time from receipt of the control signal by the virtual power plant to actual execution and completion of the regulation. A shorter response time generally indicates a faster turndown capability. And (3) frequency adjustment: the frequency adjustment is that the virtual power plant maintains the frequency stability of the power system by adjusting the output power. The efficiency and accuracy of frequency adjustment is a key indicator. Power response: this is the response capability of the virtual power plant to power demand changes, including fast start-stop capability and steady state power regulation.
Further, a specific calculation formula for adjusting the stable and reliable evaluation coefficient is as follows:wherein C represents an energy stability rate evaluation value, D represents an excessive frequency evaluation value, E represents an adjustment deviation control evaluation value, F represents an adjustment flexibility correction evaluation value, gamma represents a matching harmonic factor of the energy stability rate evaluation value and the excessive frequency evaluation value, g represents a weight factor of the adjustment energy stability rate evaluation value to the adjustment stable and reliable evaluation coefficient, h represents a weight factor of the excessive frequency evaluation value to the adjustment stable and reliable evaluation coefficient, D Pre-preparation Represents a predefined overregulation frequency assessment criterion value, E Pre-preparation Representing a predefined adjustment deviation control evaluation criterion value, +.>Matching harmonic factor representing the adjustment deviation control evaluation value for adjusting the stable and reliable evaluation coefficient, +.>And the matching harmonic factors corresponding to the predefined adjustment flexibility correction evaluation value to the adjustment requirement type are represented, eta represents the adjustment energy stability rate evaluation value, the adjustment overfrequency evaluation value and the adjustment deviation control evaluation value, the mutual superposition negative influence coefficients are represented, and lambda represents the predefined virtual power plant equipment maintenance period related correction coefficients.
In this embodiment, a stable and reliable original data set is obtained from data affecting the stable and reliable aspect of the regulation capability in the virtual power plant regulation capability evaluation data, and the factors used in the formula are obtained from the stable and reliable original data set, and all the following factors can be used to obtain an evaluation value through the historical operation data of the virtual power plant and the actual real situation through a comparison analysis algorithm. Stability: the stability index considers the stability of the virtual power plant under various operating conditions, ensuring that system oscillations or instability are not induced. Overshoot frequency: overshoot refers to whether the virtual power plant is out of the desired range during the regulation and then readjusted back. The reduction of overshoot may improve efficiency and stability. Deviation control: the deviation control takes into account the error between the actual output of the virtual power plant and the required output to ensure the accuracy of the regulation control. Flexibility: consider whether a virtual power plant is able to accommodate different operating conditions and regulatory requirements, including load variations and instability of renewable energy sources. Reliability: reliability metrics take into account the reliability of virtual power plants in long-term operation, including equipment failure rates and maintenance requirements.
Further, a specific calculation formula for adjusting the economic energy efficiency evaluation coefficient is as follows:wherein G represents an energy utilization benefit matching evaluation value, H represents an energy return on investment, I represents a market economic benefit competitive matching evaluation value, J represents an energy loss rate, K represents a predefined virtual power plant operation cost impact matching coefficient, m represents an impact matching factor of the energy utilization benefit matching evaluation value on an adjustment economic energy efficiency evaluation coefficient, o represents an impact matching factor of a data tag adaptation evaluation value on the adjustment economic energy efficiency evaluation coefficient, I represents a weight factor of the adjustment economic energy efficiency evaluation coefficient corresponding to the energy utilization benefit matching evaluation value, J represents a weight factor of the adjustment economic energy efficiency evaluation coefficient corresponding to the energy return on investment, H Pre-preparation The minimum return rate of the predefined energy investment is represented, n represents the influence superposition factor of the competitive matching evaluation value of the market economic benefit on the adjustment economic energy efficiency evaluation coefficient, and k represents the influence superposition factor of the energy loss rate on the adjustment economic energy efficiency evaluation coefficient.
In this embodiment, the raw economic energy efficiency data set is obtained from the virtual power plant adjustment capability evaluation data, and the factors and energy efficiency used in the formula are obtained from the raw economic energy efficiency data set: the energy efficiency index of the virtual power plant takes into account its energy utilization efficiency at the capacity of regulation, i.e. the energy consumption at the time of providing the required regulation, such as: energy loss rate, energy utilization benefit; economy: this index relates to the operating cost of the virtual power plant, the energy return on investment, and the market economic competitiveness to ensure economic viability. The energy loss rate and the energy return on investment rate can be obtained from the historical operation data of the virtual power plant through a comparison analysis method algorithm, the market economic benefit competitive matching evaluation value, the energy utilization benefit matching evaluation value and the predefined virtual power plant operation cost influence matching coefficient can be obtained from the historical operation data of the virtual power plant and the operation data of other virtual power plants through a fuzzy comprehensive evaluation algorithm,
Further, a specific calculation formula for adjusting the environmental protection evaluation coefficient is as follows:wherein M represents an evaluation value of running resource consumption, N represents a sustainability matching coefficient, P represents a pollutant discharge amount matching influence coefficient, M Efficacy of The method comprises the steps of representing an adjustment resource benefit evaluation value, p representing an environmental protection weight factor of an operation resource consumption evaluation value, q representing an environmental protection factor of a sustainability matching coefficient, r representing an influence matching factor of a pollutant emission amount matching influence coefficient on the environmental protection evaluation coefficient, θ representing a superimposed negative influence factor of the operation resource consumption evaluation value, the sustainability matching coefficient and the pollutant emission amount matching influence coefficient, S representing a clean energy correction matching factor, and sigma representing an influence matching factor of a specific gravity of a resource consumption type on the environmental protection evaluation coefficient.
In this embodiment, the environment protection raw data set is obtained from the virtual power plant adjustment capability evaluation data, and the factors used in the formula, the environmental impact, are obtained from the environment protection raw data set: reflecting the environmental protection level of the virtual power plant in terms of energy utilization, the impact of the virtual power plant on the environment is considered, including pollutant emissions, operating resource consumption, and sustainability. And evaluating and obtaining the sustainability matching coefficient through a comprehensive index algorithm.
Further, the specific method for obtaining the virtual power plant adjustment capability assessment data further comprises the following steps: defining performance indexes: defining a plurality of performance indexes according to the characteristic evaluation model for evaluating the adjustment capability of the virtual power plant; and (3) comparison calculation: comparing and calculating the output of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant through a regression analysis algorithm and a time sequence analysis algorithm, and then carrying out correlation calculation of a Chebyshev correlation coefficient algorithm on the difference data to obtain a comparison data correlation result; generating evaluation data: and generating the adjustment capability evaluation data of the virtual power plant according to the compared data correlation result.
In this embodiment, a performance index is defined: by defining performance indicators, a definitive quantitative measure can be used to evaluate the turndown capability of the virtual power plant. These metrics may cover critical regulatory aspects such as response time, frequency stability, efficiency, load tracking accuracy, etc. These indices provide the basis for evaluation to draw meaningful conclusions from the data. And (3) comparison calculation: by performing regression analysis and time series analysis on the output of the feature evaluation model and the virtual power plant historical operation data and the adjustment data, the relationship and the difference between them can be obtained. This helps to identify the deviation between the model output and the actual operating conditions, as well as the differences between the actual performance and the expectations in the performance metrics. Data correlation results: after the correlation calculation of the chebyshev correlation coefficient algorithm is carried out, the degree of correlation between the data can be obtained. This helps to confirm the degree of correlation between the model output and the historical operating and regulatory data, helping to determine the accuracy and reliability of the model. Generating evaluation data: by comprehensively considering the data correlation results and the defined performance indexes, evaluation data of the adjustment capability of the virtual power plant can be generated. These data may indicate the level of turndown capability of the virtual power plant, as well as the gap from the expected performance. Such assessment data may provide visual information to the decision maker to help them understand the performance of the virtual power plant.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages: relative to publication No.: according to the virtual power plant adjustment capability assessment method and system based on multiple resource aggregation disclosed by CN114429274A, the assessment data acquisition module is used for acquiring the virtual power plant adjustment capability assessment data through data cleaning, feature engineering, establishing an assessment model and acquiring the assessment data, so that objective and verifiable assessment data are provided, the influence of subjective errors is reduced, and further the accuracy of virtual power plant adjustment capability assessment is improved; relative to publication No.: according to the dynamic aggregate response capability assessment method for the flexible resources of the virtual power plant disclosed by the CN115986722A, the assessment data acquisition module is used for acquiring the assessment data of the adjustment capability of the virtual power plant by defining the performance index, comparing calculation and generating the assessment data, so that the accuracy and the reliability of the feature assessment model are improved, and the authenticity of the acquired assessment data of the adjustment capability of the virtual power plant is further realized.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of systems, apparatuses (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A virtual power plant regulation assessment system, comprising:
A historical data acquisition module: the method comprises the steps of obtaining virtual power plant historical data, wherein the virtual power plant historical data comprise virtual power plant historical operation data and virtual power plant adjustment data;
an evaluation data acquisition module: the virtual power plant adjustment capability evaluation module is used for evaluating the virtual power plant adjustment capability according to the virtual power plant historical data to obtain virtual power plant adjustment capability evaluation data;
and the comprehensive adjustment capability evaluation module is used for: and the method is used for constructing an adjustment capacity comprehensive evaluation coefficient model according to the virtual power plant adjustment capacity evaluation data and calculating to obtain an adjustment capacity comprehensive evaluation coefficient.
2. A method for evaluating the regulation capability of a virtual power plant, comprising:
obtaining virtual power plant historical data, wherein the virtual power plant historical data comprises virtual power plant historical operation data and virtual power plant adjustment data;
evaluating the virtual power plant adjustment capacity according to the virtual power plant historical data to obtain virtual power plant adjustment capacity evaluation data;
and constructing an adjusting capacity comprehensive evaluation coefficient model according to the virtual power plant adjusting capacity evaluation data, and calculating to obtain an adjusting capacity comprehensive evaluation coefficient.
3. The method for evaluating the capacity of a virtual power plant according to claim 2, wherein the specific method for acquiring the capacity evaluation data of the virtual power plant is as follows:
Data cleaning: performing data cleaning on the virtual power plant regulation data to obtain effective virtual power plant historical data;
characteristic engineering: selecting characteristic data related to the adjustment capacity of the virtual power plant from the effective virtual power plant historical data through a characteristic engineering selection algorithm to obtain effective virtual power plant historical characteristic data;
establishing an evaluation model: according to the effective virtual power plant historical characteristic data, a characteristic evaluation model is established through a machine learning algorithm;
acquiring evaluation data: and respectively comparing the results of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant to obtain the adjustment capability evaluation data of the virtual power plant.
4. The method for evaluating the capacity of a virtual power plant according to claim 2, wherein the specific calculation formula of the comprehensive evaluation coefficient of the capacity is:
wherein, xi represents the comprehensive evaluation coefficient of the adjustment capability; alpha represents an adjustment data quality evaluation coefficient, mu represents an adjustment frequency response evaluation coefficient, phi represents an adjustment stability reliability evaluation coefficient,representing an adjustment economic energy efficiency evaluation coefficient, and θ represents an adjustment environmental protection evaluation coefficient; u represents the weight factor of the adjusting frequency response evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, v represents the weight factor of the adjusting stable and reliable evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient, and w represents the weight factor of the adjusting economic efficiency evaluation coefficient to the adjusting capacity comprehensive evaluation coefficient.
5. The method for evaluating the capacity of a virtual power plant according to claim 4, wherein the specific calculation formula of the quality evaluation coefficient of the regulation data is:
wherein a is 0 A represents the number of data categories in the adjustment capability raw data set, a 0 =1, 2,..a, a represents the total number of adjustment capability raw data set data categories, c 0 A corresponding to the original data set representing the adjustment capability 0 Numbering of individual data in individual data categories, c 0 =1, 2,..c, c represents a corresponding a-th of the adjustment capability raw data set 0 The total number of data in the data category,representing the a-th in the adjustment capability raw data set 0 Class c 0 Availability evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined availability criterion value of the individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Quality evaluation value of individual data,/->Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined quality standard value of the individual data, < >>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Accuracy evaluation value of individual data, +.>Representing the a-th in the adjustment capability raw data set 0 Class c 0 Predefined accuracy standard values of the individual data, beta representing the data noise value reading error factor, +. >Representing the a-th in the adjustment capability raw data set 0 Class c 0 The standard value of the data noise difference value, b represents the a-th data in the original data set of the regulation capacity 0 Class c 0 And the median noise filter correction value of the data.
6. The method for evaluating the capacity of a virtual power plant according to claim 4, wherein the specific calculation formula of the frequency response evaluation coefficient is:
wherein d 0 Number d representing class of data in the frequency response data set 0 =1, 2,..d, d represents the total number of frequency response data set data categories, f 0 Represents the d-th corresponding to the frequency response data set 0 Numbering of individual data in individual data types, f 0 =1, 2,..f, f is the d-th corresponding to the frequency response data set 0 The total number of data in the data type,representing the d-th in the frequency response data set 0 Class f 0 Frequency adjustment performance matching evaluation value of individual data extraction, +.>Representing the d-th in the frequency response data set 0 Class f 0 Response time performance evaluation value of individual data extraction, +.>The maximum response time standard value is represented, epsilon represents a predefined power response matching factor corresponding to the frequency response data set data, delta represents a predefined start-stop frequency influence correction factor, and χ represents a predefined frequency response delay droop control coefficient.
7. The method for evaluating the capacity of a virtual power plant according to claim 4, wherein the specific calculation formula of the stable and reliable evaluation coefficient of the regulation is:
wherein C represents an energy stability rate evaluation value, D represents an excessive regulation frequency evaluation value, E represents a regulation deviation control evaluation value, F represents a regulation flexibility correction evaluation value, and gamma representsThe energy stability rate evaluation value and the matching harmonic factor of the excessive frequency evaluation value are adjusted, g represents the weight factor of the energy stability rate evaluation value for adjusting the stable and reliable evaluation coefficient, h represents the weight factor of the excessive frequency evaluation value for adjusting the stable and reliable evaluation coefficient, and D Pre-preparation Represents a predefined overregulation frequency assessment criterion value, E Pre-preparation Represents a predefined adjustment deviation control evaluation criterion value,matching harmonic factor representing the adjustment deviation control evaluation value for adjusting the stable and reliable evaluation coefficient, +.>And the matching harmonic factors corresponding to the predefined adjustment flexibility correction evaluation value to the adjustment requirement type are represented, eta represents the adjustment energy stability rate evaluation value, the adjustment overfrequency evaluation value and the adjustment deviation control evaluation value, the mutual superposition negative influence coefficients are represented, and lambda represents the predefined virtual power plant equipment maintenance period related correction coefficients.
8. The method for evaluating the capacity of a virtual power plant according to claim 4, wherein the specific calculation formula of the energy efficiency evaluation coefficient of the regulation is:
wherein G represents an energy utilization benefit matching evaluation value, H represents an energy return on investment, I represents a market economic benefit competitive matching evaluation value, J represents an energy loss rate, K represents a predefined virtual power plant operation cost influence matching coefficient, m represents an influence matching factor of the energy utilization benefit matching evaluation value on an adjustment economic energy efficiency evaluation coefficient, o represents an influence matching factor of a data tag adaptation evaluation value on the adjustment economic energy efficiency evaluation coefficient, I represents a weight factor of the adjustment economic energy efficiency evaluation coefficient corresponding to the energy utilization benefit matching evaluation valueThe sub, j represents the weight factor of the energy resource investment return rate corresponding to the economic energy efficiency evaluation coefficient, H Pre-preparation The minimum return rate of the predefined energy investment is represented, n represents the influence superposition factor of the competitive matching evaluation value of the market economic benefit on the adjustment economic energy efficiency evaluation coefficient, and k represents the influence superposition factor of the energy loss rate on the adjustment economic energy efficiency evaluation coefficient.
9. The method for evaluating the capacity of a virtual power plant according to claim 4, wherein the specific calculation formula of the environmental protection evaluation coefficient is:
Wherein M represents an evaluation value of running resource consumption, N represents a sustainability matching coefficient, P represents a pollutant discharge amount matching influence coefficient, M Efficacy of The method comprises the steps of representing an adjustment resource benefit evaluation value, p representing an environmental protection weight factor of an operation resource consumption evaluation value, q representing an environmental protection factor of a sustainability matching coefficient, r representing an influence matching factor of a pollutant emission amount matching influence coefficient on the environmental protection evaluation coefficient, θ representing a superimposed negative influence factor of the operation resource consumption evaluation value, the sustainability matching coefficient and the pollutant emission amount matching influence coefficient, S representing a clean energy correction matching factor, and sigma representing an influence matching factor of a specific gravity of a resource consumption type on the environmental protection evaluation coefficient.
10. The virtual power plant capacity assessment system according to claim 3, wherein the specific method for obtaining virtual power plant capacity assessment data further comprises:
defining performance indexes: defining a plurality of performance indexes according to the characteristic evaluation model for evaluating the adjustment capability of the virtual power plant;
and (3) comparison calculation: comparing and calculating the output of the characteristic evaluation model with the historical operating data of the virtual power plant and the adjustment data of the virtual power plant through a regression analysis algorithm and a time sequence analysis algorithm, and then carrying out correlation calculation of a Chebyshev correlation coefficient algorithm on the difference data to obtain a comparison data correlation result;
Generating evaluation data: and generating the adjustment capability evaluation data of the virtual power plant according to the compared data correlation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311380904.7A CN117371662A (en) | 2023-10-24 | 2023-10-24 | Evaluation system and method for adjustment capability of virtual power plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311380904.7A CN117371662A (en) | 2023-10-24 | 2023-10-24 | Evaluation system and method for adjustment capability of virtual power plant |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117371662A true CN117371662A (en) | 2024-01-09 |
Family
ID=89399992
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311380904.7A Pending CN117371662A (en) | 2023-10-24 | 2023-10-24 | Evaluation system and method for adjustment capability of virtual power plant |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117371662A (en) |
-
2023
- 2023-10-24 CN CN202311380904.7A patent/CN117371662A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108375715B (en) | Power distribution network line fault risk day prediction method and system | |
| Xu et al. | Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project | |
| AU2001255994B2 (en) | Method of business analysis | |
| US20120166249A1 (en) | Asset management system | |
| Hargreaves et al. | REFLEX: An adapted production simulation methodology for flexible capacity planning | |
| CN106529704A (en) | Monthly maximum power load forecasting method and apparatus | |
| JP7053152B2 (en) | Systems and methods for optimizing recommended inspection intervals | |
| CN115882456B (en) | Power control method and system based on large-scale power grid tide | |
| JP2017151980A5 (en) | ||
| CN116402528A (en) | Power data processing system | |
| CN115860562A (en) | Software workload rationality evaluation method, device and equipment | |
| CN117371662A (en) | Evaluation system and method for adjustment capability of virtual power plant | |
| CN112380641B (en) | Emergency diesel engine health state evaluation method and computer terminal | |
| CN114049103A (en) | Power grid infrastructure project management and control method, system, equipment and medium | |
| CN105260789A (en) | Wind power data time scale optimization method for short-term forecast of wind power | |
| Tao et al. | An opportunistic joint maintenance strategy for two offshore wind farms | |
| CN117949886B (en) | Intelligent regulation and control method and system for transformer calibrator, electronic equipment and storage medium | |
| CN117498348B (en) | Operation optimization scheduling method for comprehensive energy system | |
| CN115292150B (en) | Method for monitoring health state of IPTV EPG service based on AI algorithm | |
| CN118195410A (en) | New energy power generation post-evaluation method, system, equipment and readable storage medium based on project life cycle | |
| Wakiru et al. | Influence of maintenance and operations strategies on the availability of critical power plant equipment: A simulation approach. | |
| CN114936683A (en) | Power grid bus load analysis and prediction assessment management method, device and system | |
| Damavandi et al. | Project cost forecasting based on earned value management and Markov chain | |
| Pathirana | Asset management of aging devices in energy systems based on probabilistic modelling: a case study on diesel engines | |
| Guo et al. | The Study of a Quantification Prediction Method of Production Development Schedule Risk |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |