CN114565248A - Method and device for evaluating security risk of high-proportion new energy provincial power grid - Google Patents
Method and device for evaluating security risk of high-proportion new energy provincial power grid Download PDFInfo
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- 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
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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- 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]
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- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The method includes the steps of simulating a provincial power grid new energy field station space-time output scene according to output models of all wind power plants, output models of all photovoltaic power stations and historical data correlation matrixes, then carrying out random power flow calculation based on Monte Carlo simulation, and further determining probability density curves and statistical rules of a key channel power flow and a key bus voltage. And finally, establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability, and performing comprehensive safety evaluation by using a fuzzy mathematical theory. According to the method, the output difference and the correlation between different stations of the high-proportion new energy provincial power grid are considered to perform random load flow calculation, and a safety risk assessment system is established from three layers by combining the random load flow calculation results, so that the safety risk of the operation mode can be effectively and comprehensively evaluated.
Description
Technical Field
The application relates to the technical field of electric power system safety and stability analysis, in particular to a method and a device for evaluating safety risks of a high-proportion new energy provincial power grid.
Background
In recent years, new energy is developed rapidly, new energy is consumed in a grid-connected mode to provide a large amount of clean energy, energy conservation and emission reduction are facilitated, environmental protection is facilitated, and due to the fact that the new energy has strong randomness and weak controllability, structural risks of a power supply of a grid-connected system are accumulated continuously, safe and stable operation of a power grid is threatened, and the method mainly comprises the following steps: firstly, the new energy output strong fluctuation causes uncertainty of tidal current distribution, and large-scale new energy grid connection easily causes main transformers in local areas and critical section tidal current out-of-limit; and secondly, the new energy adopts a large number of power electronic devices, the rotational inertia of the system is reduced after a conventional power supply is replaced, the primary frequency modulation capability and the supporting capability are weakened, and the operation risk of the system is aggravated after the system is subjected to large-scale fault impact. Therefore, quantitative evaluation needs to be performed on the security risk of the high-proportion new energy provincial power grid, and weak links and potential risks endangering the security and stability of the system are excavated and controlled.
At present, a safety evaluation method aiming at the lack of integrity of a high-proportion new energy provincial power grid is difficult to adapt to the strong volatility and uncertainty of large-scale new energy by a traditional deterministic power flow calculation method in steady-state safety check. Although research has been carried out to consider the random trend of the new energy power grid, the spatial differences and the correlations of a plurality of new energy stations in the provincial power grid are not considered.
Disclosure of Invention
The application discloses a method and a device for evaluating safety risks of a high-proportion new energy provincial power grid, and aims to solve the technical problems that in the prior art, the space differences and the correlations of a plurality of new energy stations in the provincial power grid are not considered in a safety evaluation method aiming at the lack of integrity of the high-proportion new energy provincial power grid.
The application discloses in a first aspect a method for evaluating security risks of a high-proportion new energy provincial power grid, which comprises the following steps:
acquiring pre-constructed output models of each wind power plant, output models of each photovoltaic power station and historical data correlation matrixes, wherein the output models of each wind power plant are used for simulating respective output scenes of the wind power plant, the output models of each photovoltaic power station are used for simulating respective output scenes of the photovoltaic power station, and the historical data correlation matrixes are used for representing spatial correlation of the stations;
simulating a provincial power grid new energy station space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix;
according to the provincial power grid new energy station space-time output scene, ensuring system power balance according to a conventional unit output regulation rule under new energy random fluctuation, performing Monte Carlo simulation-based random power flow calculation, and according to the sampling result of the random power flow calculation, determining probability density curves and statistical rules of the power flow of a key channel and the voltage of a key bus;
according to the probability density curve and the statistical law of the key channel tide and the key bus voltage, a safety risk assessment system suitable for the high-proportion new energy provincial power grid is established from three aspects of overall performance, steady-state safety and transient safety and stability, safety comprehensive evaluation is carried out by utilizing a fuzzy mathematical theory, and the safety risk of the high-proportion new energy provincial power grid under different operation modes is assessed.
Optionally, the step of constructing the output model of each wind farm includes:
acquiring historical output data of each wind power plant in a provincial power grid;
and simulating the output condition of a single wind power plant by using a two-parameter Weibull distribution model, and performing parameter fitting according to the historical output data of each wind power plant in the provincial power grid to construct the output model of each wind power plant.
Optionally, the step of constructing the output model of each photovoltaic power station includes:
acquiring historical output data of each photovoltaic power station in a provincial power grid;
and simulating the output condition of a single photovoltaic power station by using a two-parameter Beta distribution model, and performing parameter fitting according to the historical output data of each photovoltaic power station in the provincial power grid to construct the output model of each photovoltaic power station.
Optionally, the step of constructing the historical data correlation matrix includes:
obtaining a Spearman rank correlation coefficient according to the historical output data of each wind power plant in the provincial power grid and the historical output data of each photovoltaic power station in the provincial power grid, and determining a historical data correlation matrix.
Optionally, the simulating a provincial power grid new energy station space-time output scene by using a latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix includes:
generating a random variable matrix by using a Latin hypercube sampling method according to the output models of the wind power plants, the output models of the photovoltaic power stations and the historical data correlation matrix, and determining a rank correlation coefficient matrix;
performing Cholesky decomposition on the rank correlation coefficient matrix, determining a first lower triangular matrix, eliminating original correlation between random variables according to the first lower triangular matrix and the random variable matrix, and determining an independent sample matrix;
performing Cholesky decomposition on the historical data correlation matrix, determining a second lower triangular matrix, and determining a random variable correlation sample matrix according to the second lower triangular matrix and the independent sample matrix, thereby realizing the simulation of the space-time output scene of the new energy station of the provincial power grid.
Optionally, the overall performance evaluation index includes a new energy admission saturation, a system power flow entropy expected value under new energy fluctuation, a system average load rate expected value under new energy fluctuation, a system multi-feed short-circuit ratio minimum value and an average short-circuit current margin value.
Optionally, the evaluation index of the steady-state safety includes the number of lines with overload risk under new energy fluctuation, a line out-of-limit probability, a line maximum out-of-limit rate, a line out-of-limit risk value, the number of buses with voltage out-of-limit risk, a bus voltage out-of-limit probability, a bus voltage maximum out-of-limit rate, and a bus voltage out-of-limit risk value.
Optionally, the evaluation index of the transient safety stability includes a regional new energy offline transient frequency safety margin, a system maximum dc blocking transient frequency safety margin, an expected fault set minimum transient voltage safety margin, an expected fault set average transient voltage safety margin, an expected fault set minimum transient stability margin, and an expected fault set average transient stability margin.
Optionally, the performing of the comprehensive safety evaluation by using the fuzzy mathematical theory includes:
mapping the index numerical values of all levels to an evaluation set by utilizing a triangular and semi-trapezoidal membership function, and constructing a fuzzy relation matrix, wherein the evaluation set comprises five grades of comments, and each grade of comment corresponds to a score;
performing weighted synthesis operation on the fuzzy relation matrix according to preset index weight to determine a fuzzy comprehensive evaluation set;
determining a final comprehensive evaluation score according to the fuzzy comprehensive evaluation set and the score;
and performing comprehensive safety evaluation according to the final comprehensive evaluation score.
The second aspect of the present application discloses an evaluation apparatus for security risk of a high-proportion new energy provincial power grid, which is applied to the evaluation method for security risk of a high-proportion new energy provincial power grid disclosed in the first aspect of the present application, and the evaluation apparatus for security risk of a high-proportion new energy provincial power grid includes:
the system comprises a pre-construction module, a correlation matrix and a correlation matrix, wherein the pre-construction module is used for acquiring pre-constructed output models of each wind power plant, output models of each photovoltaic power station and the correlation matrix of historical data, the output models of each wind power plant are used for simulating respective output scenes of the wind power plant, the output models of each photovoltaic power station are used for simulating respective output scenes of the photovoltaic power station, and the correlation matrix of the historical data is used for representing spatial correlation of the stations;
the scene simulation module is used for simulating a provincial power grid new energy station space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix;
the curve and rule determining module is used for ensuring the power balance of the system according to the provincial power grid new energy station space-time output scene and the conventional unit output adjusting rule under the random fluctuation of new energy, performing Monte Carlo simulation-based random load flow calculation, and determining the probability density curve and the statistical rule of the key channel load flow and the key bus voltage according to the sampling result of the random load flow calculation;
and the evaluation module is used for establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability according to the probability density curve and the statistical rule of the key channel tide and the key bus voltage, performing comprehensive safety evaluation by using a fuzzy mathematical theory and evaluating the safety risk of the high-proportion new energy provincial power grid in different operation modes.
Optionally, the pre-construction module is configured to construct the wind farm output models, and includes:
the wind power plant historical data acquisition unit is used for acquiring historical output data of each wind power plant in the provincial power grid;
and the wind power plant output model building unit is used for simulating the output condition of a single wind power plant by using a two-parameter Weibull distribution model, performing parameter fitting according to the historical output data of each wind power plant in the provincial power grid, and building each wind power plant output model.
Optionally, the pre-construction module is configured to construct the photovoltaic power station output model, and includes:
the photovoltaic power station historical data acquisition unit is used for acquiring historical output data of each photovoltaic power station in the provincial power grid;
and the photovoltaic power station output model building unit is used for simulating the output condition of a single photovoltaic power station by using the two-parameter Beta distribution model, performing parameter fitting according to the historical output data of each photovoltaic power station in the provincial power grid and building the output model of each photovoltaic power station.
Optionally, the pre-construction module is configured to construct the historical data correlation matrix, and includes:
and the correlation matrix determining unit is used for acquiring a Spearman rank correlation coefficient according to the historical output data of each wind power plant in the provincial power grid and the historical output data of each photovoltaic power station in the provincial power grid, and determining a historical data correlation matrix.
Optionally, the scene simulation module includes:
the rank correlation coefficient matrix determining unit is used for generating a random variable matrix by using a Latin hypercube sampling method according to the wind power plant output models, the photovoltaic power plant output models and the historical data correlation matrix, and determining a rank correlation coefficient matrix;
an independent sample matrix determining unit, configured to perform Cholesky decomposition on the rank correlation coefficient matrix, determine a first lower triangular matrix, eliminate an original correlation between random variables according to the first lower triangular matrix and the random variable matrix, and determine an independent sample matrix;
and the simulation unit is used for performing Cholesky decomposition on the historical data correlation matrix, determining a second lower triangular matrix, and determining a random variable correlation sample matrix according to the second lower triangular matrix and the independent sample matrix to realize the simulation of the space-time output scene of the provincial power grid new energy station.
Optionally, the overall performance evaluation index includes a new energy admission saturation, a system power flow entropy expected value under new energy fluctuation, a system average load rate expected value under new energy fluctuation, a system multi-feed short-circuit ratio minimum value and an average short-circuit current margin value.
Optionally, the evaluation index of the steady-state safety includes the number of lines with overload risk under new energy fluctuation, a line out-of-limit probability, a line maximum out-of-limit rate, a line out-of-limit risk value, the number of buses with voltage out-of-limit risk, a bus voltage out-of-limit probability, a bus voltage maximum out-of-limit rate, and a bus voltage out-of-limit risk value.
Optionally, the evaluation index of the transient safety stability includes a regional new energy offline transient frequency safety margin, a system maximum dc blocking transient frequency safety margin, an expected fault set minimum transient voltage safety margin, an expected fault set average transient voltage safety margin, an expected fault set minimum transient stability margin, and an expected fault set average transient stability margin.
Optionally, the evaluation module includes:
the fuzzy relation matrix building unit is used for mapping the index numerical values of all layers to an evaluation set by utilizing a triangular and semi-trapezoidal membership function to build a fuzzy relation matrix, wherein the evaluation set comprises five grades of comments, and each grade of comment corresponds to a score;
the fuzzy comprehensive evaluation set determining unit is used for performing weighted synthesis operation on the fuzzy relation matrix according to preset index weight to determine a fuzzy comprehensive evaluation set;
a final comprehensive evaluation score determining unit, configured to determine a final comprehensive evaluation score according to the fuzzy comprehensive evaluation set and the score;
and the safety comprehensive evaluation unit is used for carrying out safety comprehensive evaluation according to the final comprehensive evaluation score.
The application relates to the technical field of power system safety and stability analysis, and discloses a method and a device for evaluating safety risks of a high-proportion new energy provincial power grid. And finally, establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability by combining random load flow calculation results, and evaluating the safety risk of the high-proportion new energy provincial power grid by comprehensively evaluating the safety by utilizing a fuzzy mathematical theory.
According to the method, the output difference and the correlation between different stations of the high-proportion new energy provincial power grid are considered to perform random power flow calculation, and a safety risk assessment system suitable for the high-proportion new energy provincial power grid is established from three levels of overall performance, steady-state safety and transient safety and stability by combining the random power flow calculation results, so that the safety risk of the operation mode can be effectively and comprehensively evaluated.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic work flow diagram of a method for evaluating security risk of a high-proportion new energy provincial power grid disclosed in an embodiment of the present application;
fig. 2 is a schematic diagram of a security risk assessment system of a lower-provincial power grid to which large-scale new energy is connected in the security risk assessment method of the high-proportion new-energy provincial power grid disclosed in the embodiment of the present application;
fig. 3 is a schematic diagram of forward index triangle and semi-trapezoid membership function in the method for evaluating the security risk of the high-proportion new energy provincial power grid disclosed in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an evaluation apparatus for security risk of a high-proportion new energy provincial power grid disclosed in an embodiment of the present application.
Detailed Description
In order to solve the technical problems that the safety evaluation method for the lack of integrity of a high-proportion new energy provincial power grid does not consider the spatial difference and correlation of a plurality of new energy stations in the provincial power grid, the application discloses a method and a device for evaluating the safety risk of the high-proportion new energy provincial power grid through the following embodiments.
The application discloses a method for evaluating the security risk of a high-proportion new energy provincial power grid in a first aspect, which is shown in a working flow diagram of fig. 1, and comprises the following steps:
step S1, pre-constructed wind power plant output models, photovoltaic power station output models and historical data correlation matrixes are obtained, wherein the wind power plant output models are used for simulating respective output scenes of the wind power plants, the photovoltaic power station output models are used for simulating respective output scenes of the photovoltaic power stations, and the historical data correlation matrixes are used for representing spatial correlation of the stations.
In some embodiments of the present application, the step of constructing the output model of each wind farm includes:
and acquiring historical output data of each wind power plant in the provincial power grid.
And simulating the output condition of a single wind power plant by using a two-parameter Weibull distribution model, and performing parameter fitting according to the historical output data of each wind power plant in the provincial power grid to construct the output model of each wind power plant.
Specifically, the wind farm output model is as follows:
wherein V is the wind speed at the hub of the fan impeller; (v) represents a wind speed probability density function; k and c are shape parameters and scale parameters, respectively; p isWTG、Pr WTGRespectively representing the actual output and rated capacity of the fan; vci、VrAnd VcoRespectively cut-in wind speed, rated wind speed and cut-out wind speed.
In some embodiments of the present application, the step of constructing the photovoltaic power plant output model includes:
and acquiring historical output data of each photovoltaic power station in the provincial power grid.
And simulating the output condition of a single photovoltaic power station by using a two-parameter Beta distribution model, and performing parameter fitting according to the historical output data of each photovoltaic power station in the provincial power grid to construct the output model of each photovoltaic power station.
Specifically, the photovoltaic power plant output model is as follows:
wherein I is the illumination intensity, IrIs the upper limit value; f (I) represents the illumination intensity probability density function; r (·) is a gamma function; pPVGRepresenting the actual output of the photovoltaic power supply; pr PVGThe rated capacity of the photovoltaic power supply is the rated capacity of the single machine.
In some embodiments of the present application, the step of constructing the historical data correlation matrix comprises:
obtaining a Spearman rank correlation coefficient according to the historical output data of each wind power plant in the provincial power grid and the historical output data of each photovoltaic power station in the provincial power grid, and determining a historical data correlation matrix.
Specifically, the output of the new energy is not an independent random variable, and certain correlation generally exists among wind speeds of different wind power plants with similar spatial positions, among illumination intensities of different photovoltaic power stations, and even between the wind speeds and the illumination intensities, so that potential coupling correlation exists among the output of the new energy station. Because wind power and photovoltaic output belong to non-normally distributed random variables, the correlation between the new energy source field stations can be quantitatively represented by a Spearman rank correlation coefficient based on historical operation data. The correlation is calculated on the basis of first arranging samples of the random variables from small to large and then calculating their respective rank (i.e., the ordered sequence number). M random variables X1,X2,…,XMThe rank correlation coefficient matrix p of (a) may be expressed as:
where ρ isijDenotes a random variable XiAnd XjSpearman rank correlation coefficient of (d); riAnd RjAre respectively random variables XiAnd XjA corresponding rank; cov (R)i,Rj) Represents rank RiAnd RjThe covariance between; sigma (R)i) And σ (R)j) Are respectively rank RiAnd RjStandard deviation of (2). RhoijThe value range is [ -1,1 [)]In between, | ρijThe larger the | is, the more the random variable X is indicatediAnd XjThe stronger the correlation between them.
And step S2, simulating the provincial power grid new energy station space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix.
In some embodiments of the present application, simulating a provincial power grid new energy plant station space-time output scene by using a latin hypercube sampling method and a Cholesky decomposition method according to the wind farm output models, the photovoltaic power plant output models, and the historical data correlation matrix includes:
and generating a random variable matrix by using a Latin hypercube sampling method according to the output models of the wind power plants, the output models of the photovoltaic power stations and the historical data correlation matrix, and determining a rank correlation coefficient matrix.
Performing Cholesky decomposition on the rank correlation coefficient matrix, determining a first lower triangular matrix, eliminating the original correlation between random variables according to the first lower triangular matrix and the random variable matrix, and determining an independent sample matrix.
Performing Cholesky decomposition on the historical data correlation matrix, determining a second lower triangular matrix, and determining a random variable correlation sample matrix according to the second lower triangular matrix and the independent sample matrix, thereby realizing the simulation of the space-time output scene of the new energy station of the provincial power grid.
Specifically, for the provincial power grid, because the system scale is large, a simulation method is preferably adopted for random power flow calculation, that is, sampling statistics is performed based on monte carlo simulation, and therefore a new energy output scene in the province needs to be simulated. Establishing a corresponding output probability model according to the historical output condition of the typical station of each station, representing the correlation between areas by using a Spearman rank correlation coefficient of historical data, and randomly simulating the provincial power grid new energy output scene by applying a Latin hypercube sampling method and Cholesky decomposition.
And step S3, according to the provincial power grid new energy station space-time output scene, according to the conventional unit output regulation rule under the new energy random fluctuation, ensuring the power balance of the system, performing Monte Carlo simulation-based random power flow calculation, and according to the sampling result of the random power flow calculation, determining the probability density curve and the statistical rule of the key channel power flow and the key bus voltage.
Specifically, according to a large amount of new energy output scenes, in combination with actual conditions such as system load, conventional unit output, grid structure and the like, system power balance is guaranteed according to a conventional unit output regulation rule under new energy random fluctuation, random power flow calculation based on Monte Carlo simulation is carried out, and a probability density curve and a statistical rule of a key channel power flow and key bus voltage are obtained according to power flow calculation sampling results.
And step S4, according to the probability density curves and statistical rules of the key channel tide and the key bus voltage, establishing a safety risk assessment system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability, and performing comprehensive safety evaluation by using a fuzzy mathematical theory to assess the safety risk of the high-proportion new energy provincial power grid in different operation modes.
Specifically, a safety risk evaluation system suitable for a high-proportion new energy provincial power grid is established from three aspects of overall performance, steady-state safety and transient safety and stability by combining random load flow calculation results, safety comprehensive evaluation is carried out by utilizing a fuzzy mathematical theory, and the safety level of the power grid under different operation modes is evaluated. The uncertainty and weak controllability influence of large-scale new energy are highlighted, and a high new energy permeability safety assessment system is constructed from three angles of overall performance assessment, steady-state safety assessment and transient safety and stability assessment, as shown in fig. 2. The overall performance evaluation is to evaluate the overall influence brought by the new energy from the system level, the steady-state safety evaluation highlights the uncertainty influence of the large-scale new energy and carries out random load flow calculation to carry out steady-state operation risk evaluation, and the transient-state safety and stability evaluation carries out safety check by constructing an expected fault set to reflect the system stability level after the new energy is accessed.
Further, the overall performance evaluation indexes comprise new energy admission saturation, system power flow entropy expected value under new energy fluctuation, system average load rate expected value under new energy fluctuation, system multi-feed short-circuit ratio minimum value, average short-circuit current margin value and the like.
The new energy admission saturation represents the ratio of the total network new energy consumption capacity to the total installed capacity, and reflects the provincial power grid new energy consumption capacity, and the calculation method comprises the following steps:
wherein, PnIndicating new energy capacity, P, which can be taken up in operationG,nAnd the total installed capacity of the new energy power supply is represented.
The load flow entropy can measure the distribution balance condition of the load flow of the alternating current system, and the load flow entropy expected value H is defined by considering that the load flow distribution has uncertainty under the condition of new energy accessEThe power transmission network flow distribution balance condition under the condition of high-proportion new energy access is reflected:
wherein, muiIs the power flow distribution rate, P, of the AC line iiAnd Pi0Respectively representing the active power of the AC line i and its limit, NlFor the number of lines, E (-) represents the expected value of the random variable.
Further, the evaluation indexes of the steady-state safety include the number of lines with overload risks under the fluctuation of new energy, line out-of-limit probability, line maximum out-of-limit rate, line out-of-limit risk value, the number of buses with voltage out-of-limit risks, bus voltage out-of-limit probability, bus voltage maximum out-of-limit rate and bus voltage out-of-limit risk value. The indexes are obtained by calculating through a random power flow method.
The line tide out-of-limit probability is the area surrounded by the out-of-limit part of the probability density curve, and the maximum out-of-limit probability P of the lineolThe calculation formula is as follows:
wherein, Pr (P)i>Pi0) Representing line i flow PiExceeds a limit value Pi0Can be determined by a probability density function fPi(Pi) And (6) obtaining an integral.
The product of the line tide out-of-limit severity and the out-of-limit probability is used for representing a line out-of-limit risk value, and the line out-of-limit severity and the risk value under the random fluctuation of new energy are defined as follows:
wherein S isol,iShowing the out-of-limit severity of the line i, and reflecting the relation between the severity and the out-of-limit degree by using a utility theory; rolFor a total out-of-limit risk value for all lines, NlThe integral value of the product of the out-of-limit severity and the probability density of each line represents the out-of-limit risk.
Further, the evaluation index of the transient safety stability comprises a regional new energy offline transient frequency safety margin, a system maximum direct current blocking transient frequency safety margin, an expected fault set minimum transient voltage safety margin, an expected fault set average transient voltage safety margin, an expected fault set minimum transient stability margin and an expected fault set average transient stability margin.
And checking large-scale power impact faults such as regional new energy offline, direct current blocking and the like, and evaluating the system frequency safety under new energy access. The standard for transient frequency safety is usually represented in binary formcr,tcr,f]Is given in the form of a transient frequency response crossing a frequency limit fcrMust not exceed tcr,f. Transient frequency safety margin index KfThe calculation formula is as follows:
wherein f is0Represents a system rated frequency; f. ofcrRepresenting a frequency safety limit; f'crThe time for which the frequency response curve crosses a certain value is just the time limit tcr,fThe frequency value corresponding to the time. A negative margin value indicates that the transient frequency is unsafe.
After the large-scale new energy power supply is connected, the voltage regulating performance of the system is weakened, and the transient voltage safety under the expected fault needs to be checked. The standard for transient voltage safety is usually represented by a binary table Ucr,tcr,v]Is given by the form that the transient voltage crosses the voltage limit U after the expected faultcrMust not exceed the time limit tcr,v. Defining a bus i transient voltage safety margin index K after the occurrence of the expected fault cv,i,cComprises the following steps:
wherein, Ui,0Represents the rated voltage of the system; u shapecrRepresenting a voltage safety limit; u shapei,c,minThe minimum value of the transient voltage of the bus i after the expected fault c occurs; t is tcr,vRepresents a voltage limit; t is ti,cRepresenting a time limit. A negative margin value indicates that the transient voltage is unsafe.
The transient power angle stability level reflects the capability of the system for resisting the large disturbance risk, transient stability evaluation is carried out by utilizing time domain simulation, and the transient power angle stability margin K after the expected fault c occurs is definedt,cComprises the following steps:
wherein: t is tcct,cFor the limit cut-off time after the occurrence of a fault c, tcl,cIs the actual ablation time. And when the margin is negative, the transient power angle instability is shown.
In some embodiments of the present application, the comprehensive safety evaluation using fuzzy mathematics theory includes:
and mapping the index numerical values of all levels to an evaluation set by utilizing a triangular and semi-trapezoidal membership function, and constructing a fuzzy relation matrix, wherein the evaluation set comprises five grades of comments, and each grade of comment corresponds to a score.
And performing weighted synthesis operation on the fuzzy relation matrix according to a preset index weight to determine a fuzzy comprehensive evaluation set.
And determining a final comprehensive evaluation score according to the fuzzy comprehensive evaluation set and the score.
And performing comprehensive safety evaluation according to the final comprehensive evaluation score.
Specifically, based on the safety assessment indexes, corresponding quantitative assessment is carried out on the specific operation mode of the power grid. Because the meaning and the dimension of each index are different, the weighted integration can not be directly carried out, and the multi-attribute comprehensive evaluation is carried out by adopting a fuzzy evaluation method. In the present embodiment, when the large grid security risk assessment is performed, five grades of "poor", "general", "good", and "good" are set for evaluation, and the score value corresponding to each grade is {1,2,3,4,5}, where it is considered that the higher the score is, the better the score is. The membership function is a function for mapping the index value to the evaluation set, and the fuzzy relation matrix R from the factor set B to the comment set V is (mu)jk)n×pIn the patent, triangular and semi-trapezoidal membership functions are used to describe various factors, and an example of a forward index membership function is shown in fig. 3.
And (3) performing weighted synthesis operation on the fuzzy relation matrix R according to each index weight in the factor set B to obtain a fuzzy comprehensive evaluation set S of the factor set B:
S＝W·R＝[S1,S2,L,Sp]；
wherein S isiAnd describing the weighted sum of the comments belonging to the comment set by the indexes in the B, and weighting the score set E according to the weighted sum to obtain a final comprehensive evaluation score.
Quantitative evaluation is carried out on all indexes of the high-proportion new energy power grid to obtain evaluation results of all safety attributes; and comprehensively evaluating each index by using a fuzzy comprehensive evaluation method to obtain a comprehensive evaluation result of the power grid safety.
According to the method for evaluating the safety risk of the high-proportion new energy provincial power grid, firstly, a Latin hypercube sampling method and a Cholesky decomposition method are used for simulating a provincial power grid new energy field station space-time output scene according to output models of all wind power plants, output models of all photovoltaic power stations and historical data correlation matrixes, then, the power balance of the system is guaranteed according to the output regulation rule of a conventional unit under the random fluctuation of new energy, the random power flow calculation based on Monte Carlo simulation is carried out, and the probability density curve and the statistical rule of the key channel power flow and the key bus voltage are determined according to the sampling result of the random power flow calculation. And finally, establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability by combining random load flow calculation results, and evaluating the safety risk of the high-proportion new energy provincial power grid by comprehensively evaluating the safety by utilizing a fuzzy mathematical theory.
According to the method, the output difference and the correlation between different stations of the high-proportion new energy provincial power grid are considered to perform random power flow calculation, and a safety risk assessment system suitable for the high-proportion new energy provincial power grid is established from three levels of overall performance, steady-state safety and transient safety and stability by combining the random power flow calculation results, so that the safety risk of the operation mode can be effectively and comprehensively evaluated.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
The second embodiment of the present application discloses an evaluation apparatus for security risk of a high-proportion new energy provincial power grid, which is applied to the evaluation method for security risk of a high-proportion new energy provincial power grid disclosed in the first embodiment of the present application, and referring to a schematic structural diagram shown in fig. 4, the evaluation apparatus for security risk of a high-proportion new energy provincial power grid includes:
the pre-construction module 10 is configured to obtain pre-constructed wind farm output models, photovoltaic power station output models and historical data correlation matrices, where the wind farm output models are used to simulate respective output scenes of a wind farm, the photovoltaic power station output models are used to simulate respective output scenes of a photovoltaic power station, and the historical data correlation matrices are used to represent spatial correlations of a station.
And the scene simulation module 20 is configured to simulate a provincial power grid new energy station space-time output scene by using a latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix.
The curve and rule determining module 30 is configured to ensure system power balance according to the provincial power grid new energy station space-time output scene and the conventional unit output regulation rule under the new energy random fluctuation, perform monte carlo simulation-based random power flow calculation, and determine probability density curves and statistical rules of the key channel power flow and the key bus voltage according to the sampling result of the random power flow calculation.
And the evaluation module 40 is used for establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability according to the probability density curve and the statistical rule of the key channel tide and the key bus voltage, performing comprehensive safety evaluation by using a fuzzy mathematical theory, and evaluating the safety risk of the high-proportion new energy provincial power grid in different operation modes.
Further, the pre-construction module is configured to construct the wind farm output models, and includes:
and the wind power plant historical data acquisition unit is used for acquiring historical output data of each wind power plant in the provincial power grid.
And the wind power plant output model building unit is used for simulating the output condition of a single wind power plant by using a two-parameter Weibull distribution model, performing parameter fitting according to the historical output data of each wind power plant in the provincial power grid, and building each wind power plant output model.
Further, the pre-construction module is configured to construct the photovoltaic power plant output models, and includes:
and the photovoltaic power station historical data acquisition unit is used for acquiring historical output data of each photovoltaic power station in the provincial power grid.
And the photovoltaic power station output model building unit is used for simulating the output condition of a single photovoltaic power station by using the two-parameter Beta distribution model, performing parameter fitting according to the historical output data of each photovoltaic power station in the provincial power grid and building the output model of each photovoltaic power station.
Further, the pre-construction module is configured to construct the historical data correlation matrix, and includes:
and the correlation matrix determining unit is used for acquiring a Spearman rank correlation coefficient according to the historical output data of each wind power plant in the provincial power grid and the historical output data of each photovoltaic power station in the provincial power grid, and determining a historical data correlation matrix.
Further, the scene simulation module includes:
and the rank correlation coefficient matrix determining unit is used for generating a random variable matrix by using a Latin hypercube sampling method according to the wind power plant output models, the photovoltaic power plant output models and the historical data correlation matrix, and determining a rank correlation coefficient matrix.
And the independent sample matrix determining unit is used for performing Cholesky decomposition on the rank correlation coefficient matrix, determining a first lower triangular matrix, eliminating the original correlation between random variables according to the first lower triangular matrix and the random variable matrix, and determining an independent sample matrix.
And the simulation unit is used for performing Cholesky decomposition on the historical data correlation matrix, determining a second lower triangular matrix, and determining a random variable correlation sample matrix according to the second lower triangular matrix and the independent sample matrix to realize the simulation of the space-time output scene of the provincial power grid new energy station.
Further, the evaluation index of the overall performance comprises new energy admission saturation, a system power flow entropy expected value under new energy fluctuation, a system average load rate expected value under new energy fluctuation, a system multi-feed short-circuit ratio minimum value and an average short-circuit current margin value.
Further, the evaluation indexes of the steady-state safety include the number of lines with overload risks under the fluctuation of new energy, line out-of-limit probability, line maximum out-of-limit rate, line out-of-limit risk value, the number of buses with voltage out-of-limit risks, bus voltage out-of-limit probability, bus voltage maximum out-of-limit rate and bus voltage out-of-limit risk value.
Further, the evaluation index of the transient safety stability comprises a regional new energy offline transient frequency safety margin, a system maximum direct current blocking transient frequency safety margin, an expected fault set minimum transient voltage safety margin, an expected fault set average transient voltage safety margin, an expected fault set minimum transient stability margin and an expected fault set average transient stability margin.
Further, the evaluation module comprises:
and the fuzzy relation matrix construction unit is used for mapping the index numerical values of all levels to an evaluation set by utilizing the triangular and semi-trapezoidal membership functions to construct a fuzzy relation matrix, wherein the evaluation set comprises five grades of comments, and each grade of comment corresponds to one score.
And the fuzzy comprehensive evaluation set determining unit is used for performing weighted synthesis operation on the fuzzy relation matrix according to preset index weight to determine a fuzzy comprehensive evaluation set.
And the final comprehensive evaluation score determining unit is used for determining a final comprehensive evaluation score according to the fuzzy comprehensive evaluation set and the score.
And the safety comprehensive evaluation unit is used for carrying out safety comprehensive evaluation according to the final comprehensive evaluation score.
The present application has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to limit the application. Those skilled in the art will appreciate that various equivalent substitutions, modifications or improvements may be made to the presently disclosed embodiments and implementations thereof without departing from the spirit and scope of the present disclosure, and these fall within the scope of the present disclosure. The protection scope of this application is subject to the appended claims.
Claims (10)
1. A method for evaluating security risks of a high-proportion new energy provincial power grid is characterized by comprising the following steps:
acquiring pre-constructed output models of each wind power plant, output models of each photovoltaic power station and historical data correlation matrixes, wherein the output models of each wind power plant are used for simulating respective output scenes of the wind power plant, the output models of each photovoltaic power station are used for simulating respective output scenes of the photovoltaic power station, and the historical data correlation matrixes are used for representing spatial correlation of the stations;
simulating a provincial power grid new energy station space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix;
according to the provincial power grid new energy station space-time output scene, ensuring system power balance according to a conventional unit output regulation rule under new energy random fluctuation, performing Monte Carlo simulation-based random power flow calculation, and according to the sampling result of the random power flow calculation, determining probability density curves and statistical rules of the power flow of a key channel and the voltage of a key bus;
according to the probability density curve and the statistical law of the key channel tide and the key bus voltage, a safety risk assessment system suitable for the high-proportion new energy provincial power grid is established from three aspects of overall performance, steady-state safety and transient safety and stability, safety comprehensive evaluation is carried out by utilizing a fuzzy mathematical theory, and the safety risk of the high-proportion new energy provincial power grid under different operation modes is assessed.
2. The method for assessing the safety risk of the high-proportion new energy-provincial power grid according to claim 1, wherein the step of constructing each wind farm output model comprises the steps of:
acquiring historical output data of each wind power plant in a provincial power grid;
and simulating the output condition of a single wind power plant by using a two-parameter Weibull distribution model, and performing parameter fitting according to the historical output data of each wind power plant in the provincial power grid to construct the output model of each wind power plant.
3. The method for assessing the safety risk of the high-proportion new energy provincial power grid according to claim 1, wherein the step of constructing the output model of each photovoltaic power station comprises:
acquiring historical output data of each photovoltaic power station in a provincial power grid;
and simulating the output condition of a single photovoltaic power station by using the two parameter Beta distribution models, and performing parameter fitting according to the historical output data of each photovoltaic power station in the provincial power grid to construct the output model of each photovoltaic power station.
4. The method for assessing the security risk of the high-proportion new energy provincial power grid according to claim 1, wherein the step of constructing the historical data correlation matrix comprises:
obtaining a Spearman rank correlation coefficient according to the historical output data of each wind power plant in the provincial power grid and the historical output data of each photovoltaic power station in the provincial power grid, and determining a historical data correlation matrix.
5. The method for assessing the security risk of the high-proportion new energy provincial power grid according to claim 1, wherein the simulation of the provincial power grid new energy field space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind farm output models, the photovoltaic power plant output models and the historical data correlation matrix comprises:
generating a random variable matrix by using a Latin hypercube sampling method according to the output models of the wind power plants, the output models of the photovoltaic power stations and the historical data correlation matrix, and determining a rank correlation coefficient matrix;
performing Cholesky decomposition on the rank correlation coefficient matrix, determining a first lower triangular matrix, eliminating original correlation between random variables according to the first lower triangular matrix and the random variable matrix, and determining an independent sample matrix;
performing Cholesky decomposition on the historical data correlation matrix, determining a second lower triangular matrix, and determining a random variable correlation sample matrix according to the second lower triangular matrix and the independent sample matrix, thereby realizing the simulation of the space-time output scene of the new energy station of the provincial power grid.
6. The method for assessing the safety risk of the high-proportion new energy provincial power grid according to claim 1, wherein the overall performance assessment indexes comprise new energy admission saturation, system power flow entropy expected value under new energy fluctuation, system average load rate expected value under new energy fluctuation, system multi-feed short-circuit ratio minimum value and average short-circuit current margin value.
7. The method for evaluating the safety risk of the high-proportion new energy provincial power grid according to claim 1, wherein the evaluation indexes of the steady-state safety comprise the number of lines with overload risks under the fluctuation of new energy, the line out-of-limit probability, the maximum line out-of-limit rate, the line out-of-limit risk value, the number of buses with voltage out-of-limit risks, the bus voltage out-of-limit probability, the maximum bus voltage out-of-limit rate and the bus voltage out-of-limit risk value.
8. The method for assessing the safety risk of the high-proportion new energy provincial power grid according to claim 1, wherein the assessment indicators of the transient safety stability comprise a regional new energy off-grid transient frequency safety margin, a system maximum DC blocking transient frequency safety margin, an expected fault set minimum transient voltage safety margin, an expected fault set average transient voltage safety margin, an expected fault set minimum transient stability margin and an expected fault set average transient stability margin.
9. The method for evaluating the safety risk of the high-proportion new energy provincial power grid according to claim 1, wherein the comprehensive safety evaluation by using the fuzzy mathematical theory comprises the following steps:
mapping the index numerical values of all levels to an evaluation set by utilizing a triangular and semi-trapezoidal membership function, and constructing a fuzzy relation matrix, wherein the evaluation set comprises five grades of comments, and each grade of comment corresponds to a score;
performing weighted synthesis operation on the fuzzy relation matrix according to preset index weight to determine a fuzzy comprehensive evaluation set;
determining a final comprehensive evaluation score according to the fuzzy comprehensive evaluation set and the score;
and performing comprehensive safety evaluation according to the final comprehensive evaluation score.
10. An assessment device for the safety risk of a high-proportion new energy provincial power grid, which is applied to the assessment method for the safety risk of a high-proportion new energy provincial power grid according to any one of claims 1 to 9, and comprises:
the system comprises a pre-construction module, a correlation matrix and a correlation matrix, wherein the pre-construction module is used for acquiring pre-constructed output models of each wind power plant, output models of each photovoltaic power station and the correlation matrix of historical data, the output models of each wind power plant are used for simulating respective output scenes of the wind power plant, the output models of each photovoltaic power station are used for simulating respective output scenes of the photovoltaic power station, and the correlation matrix of the historical data is used for representing spatial correlation of the stations;
the scene simulation module is used for simulating a provincial power grid new energy station space-time output scene by using a Latin hypercube sampling method and a Cholesky decomposition method according to the wind power plant output models, the photovoltaic power station output models and the historical data correlation matrix;
the curve and rule determining module is used for ensuring the power balance of the system according to the provincial power grid new energy station space-time output scene and the conventional unit output adjusting rule under the random fluctuation of new energy, performing Monte Carlo simulation-based random load flow calculation, and determining the probability density curve and the statistical rule of the key channel load flow and the key bus voltage according to the sampling result of the random load flow calculation;
and the evaluation module is used for establishing a safety risk evaluation system suitable for the high-proportion new energy provincial power grid from three aspects of overall performance, steady-state safety and transient safety and stability according to the probability density curve and the statistical rule of the key channel tide and the key bus voltage, performing comprehensive safety evaluation by using a fuzzy mathematical theory and evaluating the safety risk of the high-proportion new energy provincial power grid in different operation modes.
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