CN115034832A - Natural gas and electric power coordinated scheduling method, device and system - Google Patents
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Abstract
The invention discloses a method, a device and a system for coordinately scheduling natural gas and electric power. The method, the device and the system for coordinating and scheduling are characterized in that a BEKK-GARCH-based gas price and electricity price fluctuation overflow model is built, fluctuation correlation of an electric power market and a natural gas market is measured through a multi-element GARCH model, price conduction coefficients between the two markets can be specifically calculated, BEKK-GARCH fitting modeling is continuously carried out through historical data through a preset rolling window prediction method, the gas-electricity price fluctuation overflow degree in different periods can be analyzed through multiple groups of parameter estimation results, gas-electricity price conduction risk early warning lines under different confidence levels are formulated based on VaR, and the method, the device and the system for coordinating and scheduling are provided.
Description
Technical Field
The invention relates to the technical field of natural gas and electric power coordinated scheduling, in particular to a natural gas and electric power coordinated scheduling method, a natural gas and electric power coordinated scheduling device, a computer readable storage medium and a computer readable storage system.
Background
With the continuous and deep energy transformation worldwide, the gas power generation plays an important role in the power system. The behavior of the market subject can strengthen the connection between the electric power market and the natural gas market, influence the transmission of price information, and with frequent natural gas price and electric power price fluctuation events in the world, it is very important to calculate the transmission of energy price risks and carry out coordinated dispatching on the natural gas and the electric power according to the calculation result so as to ensure the stability of the natural gas and the electric power. In order to provide accurate data support for coordinated scheduling, calculation of the price conduction coefficients of natural gas and electricity is crucial.
In the prior art, the fluctuation overflow effect is generally used to measure the conduction of fluctuation between financial markets, and when the asset price or income of a market fluctuates, the fluctuation performance and risk information of the market is transmitted to other markets through information. The research progress on the wave overflow effect is mainly reflected on the study subjects and the analysis method. In the prior art, a VAR-GARCH-BEKK model is adopted to research the information overflow effect between a natural gas market and a power market, and the transmission of price and fluctuation is analyzed: the method comprises the steps of establishing a VAR model through stability inspection and hysteresis order determination by adopting price data of electric power and natural gas futures, obtaining a mean value overflow result of an electric power market and a natural gas market, analyzing the conduction of fluctuation information between the electric power market and the natural gas market through establishing a BEKK-GARCH model, performing WALD inspection on coefficients, judging the information overflow direction between the two markets, and analyzing the dynamic correlation relationship between the markets by using DCC-GARCH. And finally, performing coordinated scheduling on the natural gas and the electric power according to the analysis result to maintain stability.
However, the prior art still has the following defects: the analysis is not sufficient and accurate enough, and accurate prediction of the conduction risk level of the natural gas market and the electric power market cannot be realized, reference of an energy supply chain risk source is provided for a natural gas supplier, a gas unit and a dispatching mechanism, and the stability of electric power supply is improved.
Therefore, there is a need for a method, apparatus, computer readable storage medium, and system for coordinated scheduling of natural gas and electricity to overcome the above-mentioned deficiencies in the prior art.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a computer-readable storage medium, and a system for coordinated scheduling of natural gas and electric power, so as to improve accurate prediction of a natural gas market and an electric power market conduction risk level, and further improve efficiency and effect of coordinated scheduling of natural gas and electric power.
An embodiment of the present invention provides a method for coordinated scheduling of natural gas and electric power, where the method includes: acquiring a natural gas historical price time sequence and an electric power historical price time sequence from a preset gas-electricity historical data set, performing stationarity test and ARCH effect check on the natural gas historical price time sequence and the electric power historical price time sequence, and constructing a gas price and electricity price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect test is passed; calculating a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model; and acquiring a gas-electricity real-time price data set, calculating a real-time price conduction coefficient between natural gas and electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and carrying out price risk early warning and gas-electricity coordinated dispatching according to the risk early warning line and the real-time price conduction coefficient.
As an improvement of the above scheme, the coordinated scheduling method further includes: and when the ARCH effect is not detected, fitting the autocorrelation relation in the residual square sequence of the power price change rate and the natural gas price change rate according to a preset regression model, the natural gas historical price time sequence and the power historical price time sequence.
As an improvement of the above scheme, a gas-electricity historical price conduction level data set is calculated according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time series and an electric power historical price time series, and specifically includes: estimating a model parameter group of the gas price and electricity price fluctuation overflow model according to a preset log likelihood function, and substituting the model parameter group into the gas price and electricity price fluctuation overflow model to obtain a first gas price and electricity price fluctuation overflow model; and setting a first length of a rolling time window, and rolling and predicting the historical gas and electricity price conduction level according to the first length, a first gas price and electricity price fluctuation overflow model, a historical natural gas price time sequence and a historical electricity price time sequence to calculate a historical gas and electricity price conduction level data set.
As an improvement of the above scheme, determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model specifically includes: calculating the risk value and the corresponding confidence level of the gas-electricity historical price conduction level data corresponding to each rolling time window period according to a historical data method, the gas-electricity historical price conduction level data set and a preset risk value model; and determining and setting a plurality of risk early warning lines with different levels according to the confidence level, the corresponding risk value and a preset early warning requirement.
As an improvement of the above scheme, performing stationarity check and ARCH effect check on the natural gas historical price time series and the electric power historical price time series specifically includes: carrying out logarithm taking and difference processing on the natural gas historical price time sequence and the electric power historical price time sequence to obtain a first natural gas historical price change rate sequence and a first electric power historical price change rate sequence; according to an ADF (automatic document delivery) inspection method, performing stationarity inspection on the first natural gas historical price change rate sequence and the first power historical price change rate sequence, and judging whether the stationarity inspection is passed; and if the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power pass stationarity check, calculating a fitting coefficient of a preset check auxiliary equation according to the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power by a Lagrange multiplier check method so as to carry out ARCH check, and when the fitting coefficient is calculated to be remarkably zero, considering that the ARCH effect check is passed.
As an improvement of the above solution, the risk value model: p u (U t >V aR |Φ t-1 )＝1-α；U t ＝[a 12,t a 21,t b 12,t b 21,t ] T (ii) a In the formula, P u Representing a probability statistic, U t The price fluctuation coefficient is obtained by a BEKK-GARCH model in the t stage; v aR The risk value at a confidence level of α; phi t-1 Is the collection of all previous information. The formula indicates that the market price risk is greater than V within delta t aR Has a probability of 1-alpha, or a market price risk of not more than V within delta t aR The probability of (a) is alpha.
As an improvement of the above scheme, the test assistance equation is:
in the formula, epsilon t Residual errors of the power price change rate and the natural gas price change rate at the time t are alpha, assuming that the variance of the residual errors is the same and changes along with the time i As fitting coefficient, ω t Is an error term.
The invention correspondingly provides a natural gas and power coordinated dispatching device, which comprises a model establishing unit, a risk early warning unit and a coordinated dispatching unit, wherein the model establishing unit is used for acquiring a natural gas historical price time sequence and a power historical price time sequence from a preset gas-power historical data set, carrying out stability inspection and ARCH effect check on the natural gas historical price time sequence and the power historical price time sequence, and constructing a gas price and power price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect check passes; the risk early warning unit is used for calculating a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model; the coordination scheduling unit is used for acquiring a gas-electricity real-time price data set, calculating a real-time price conduction coefficient between natural gas and electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and performing price risk early warning and gas-electricity coordination scheduling according to the risk early warning line and the real-time price conduction coefficient.
As an improvement of the above solution, the coordinated scheduling apparatus further includes an autocorrelation analysis unit, and the autocorrelation analysis unit is configured to: and when the ARCH effect is not detected, fitting the autocorrelation relation in the residual square sequence of the power price change rate and the natural gas price change rate according to a preset regression model, the natural gas historical price time sequence and the power historical price time sequence.
As an improvement of the above scheme, the risk early warning unit is further configured to: estimating a model parameter group of the gas price and electricity price fluctuation overflow model according to a preset log likelihood function, and substituting the model parameter group into the gas price and electricity price fluctuation overflow model to obtain a first gas price and electricity price fluctuation overflow model; and setting a first length of a rolling time window, and rolling and predicting the historical gas and electricity price conduction level according to the first length, a first gas price and electricity price fluctuation overflow model, the historical natural gas price time series and the historical electricity price time series to calculate a historical gas and electricity price conduction level data set.
As an improvement of the above solution, the risk early warning unit is further configured to: calculating the risk value and the corresponding confidence level of the gas-electricity historical price conduction level data corresponding to each rolling time window period according to a historical data method, the gas-electricity historical price conduction level data set and a preset risk value model; and determining and setting a plurality of risk early warning lines with different levels according to the confidence level, the corresponding risk value and a preset early warning requirement.
As an improvement of the above solution, the model building unit is further configured to: carrying out logarithm taking and difference processing on the natural gas historical price time sequence and the electric power historical price time sequence to obtain a first natural gas historical price change rate sequence and a first electric power historical price change rate sequence; according to an ADF (automatic document feeder) inspection method, carrying out stationarity inspection on the first natural gas historical price change rate sequence and the first power historical price change rate sequence, and judging whether the stationarity inspection is passed or not; and if the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power pass stationarity check, calculating a fitting coefficient of a preset check auxiliary equation according to the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power by a Lagrange multiplier check method so as to carry out ARCH check, and when the fitting coefficient is calculated to be remarkably zero, considering that the ARCH effect check is passed.
Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for coordinated scheduling of natural gas and electric power as described above.
Another embodiment of the present invention provides a coordinated scheduling system for natural gas and electric power, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the coordinated scheduling method for natural gas and electric power is implemented as described above.
Compared with the prior art, the technical scheme has the following beneficial effects:
the invention provides a natural gas and electric power coordinated scheduling method, a natural gas and electric power coordinated scheduling device, a computer readable storage medium and a natural gas and electric power coordinated scheduling system.
Drawings
Fig. 1 is a schematic flow chart of a coordinated scheduling method for natural gas and electric power according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a coordinated scheduling apparatus for natural gas and electric power according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Detailed description of the preferred embodiment
The embodiment of the invention firstly describes a natural gas and electric power coordinated scheduling method. Fig. 1 is a schematic flow chart of a coordinated scheduling method for natural gas and electric power according to an embodiment of the present invention.
As shown in fig. 1, the coordinated scheduling method includes:
and S1, acquiring a natural gas historical price time sequence and an electric power historical price time sequence from a preset gas-electricity historical data set, performing stationarity test and ARCH effect check on the natural gas historical price time sequence and the electric power historical price time sequence, and constructing a gas price and electricity price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect test is passed.
The linkage mechanism obtained based on historical data can objectively describe the correlation between gas and electricity prices. The fluctuation overflow effect measures the conduction of fluctuation between the power market and the natural gas market by using the variance of historical data, and a Generalized Autoregressive Conditional variance model (GARCH) can more comprehensively analyze the fluctuation overflow effect between the natural gas market and the power market. The GARCH model first needs to ensure that the time sequence is stable and the square term of the residual of the mean equation from the sequence regression has auto-correlation, i.e., there is an auto-regressive Conditional differential (ARCH) effect. Before the model is established, stability test needs to be carried out on the price time series of the electric power and the natural gas, and ARCH effect test needs to be carried out additionally.
For electric power price time series P e And natural gas price time series P gas Logarithm processing is carried out on the data, the purpose is to avoid the occurrence of variance in the time sequence, so that the time sequence is processed in a stabilizing way, and various prices are respectively subjected to logarithm taking and then are differentiated to obtain a price change rate sequence, namely:
in the formula (I), the compound is shown in the specification,
and
respectively, the rates of change of electricity prices and gas prices during the t period.
Time series with rate of change of electricity price
Taking the stationarity test of (1) as an example, consider the electrovalence regression equation:
wherein k is a regression coefficient, ε t Are error terms and are subject to independent co-distribution.
The electricity price at the time t is represented by the acquired data of the previous n days:
if k is 1, that is, the unit root exists in the electricity price time series, the variance of the electricity price time series is increased, the influence of the residual error cannot be eliminated, and the series is unstable. ADF (ADF) test method judges whether the time series is stable according to whether a unit root exists in the system. First using least squares estimation
Then, establishing a test statistic t to judge whether the equation at least contains one unit root:
time series of gas price change rate
The same stationarity check should also be performed.
In one embodiment, the performing stationarity test and ARCH effect check on the natural gas historical price time series and the electric power historical price time series specifically includes: carrying out logarithm taking and difference processing on the natural gas historical price time sequence and the electric power historical price time sequence to obtain a first natural gas historical price change rate sequence and a first electric power historical price change rate sequence; according to an ADF (automatic document feeder) inspection method, carrying out stationarity inspection on the first natural gas historical price change rate sequence and the first power historical price change rate sequence, and judging whether the stationarity inspection is passed or not; and if the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power pass stationarity check, calculating a fitting coefficient of a preset check auxiliary equation according to the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power by a Lagrange multiplier check method so as to carry out ARCH check, and when the fitting coefficient is calculated to be remarkably zero, considering that the ARCH effect check is passed.
In one embodiment, the inspection assistance equation is:
in the formula, epsilon t Residual errors of the power price change rate and the natural gas price change rate at the time t are alpha, assuming that the variance of the residual errors is the same and changes along with the time i As fitting coefficient, ω t Is an error term.
The coefficients in the above equation are examined, and if all are significantly zero, the initial hypothesis is accepted, the sequence has no ARCH effect, otherwise, the original hypothesis is rejected, the sequence residual squared sequence is considered to be autocorrelation, and an auto-regression model of order q can be used to fit the autocorrelation relationship in the residual squared sequence.
In one embodiment, the coordinated scheduling method further includes: and when the ARCH effect is not detected, fitting the autocorrelation relation in the residual square sequence of the power price change rate and the natural gas price change rate according to a preset regression model, the natural gas historical price time sequence and the power historical price time sequence.
After the ARCH effect is checked, a gas price and electricity price fluctuation overflow model can be constructed.
For the natural gas market and the electric power market, when the market price is changed under the influence of the long-term historical information or the short-term information of the market, the fluctuation is increased, and if the market price is influenced by continuous conduction, the price is continuously fluctuated, so that the risk accumulation effect is formed. The risk accumulation is the persistent enhancement of self risk, and is influenced by long-term historical fluctuation to show an ARCH effect, and is influenced by short-term fluctuation to show a GARCH effect. The risk accumulation situation of each market can be analyzed by observing the conditional variance of the BEKK-GARCH model. The GARCH (1,1) model is used as a high-order ARCH cluster model, and can better identify and estimate a market model in application. Analyzing price fluctuations between the natural gas market and the electricity market, the mean equation and variance equation of the binary GARCH (1,1) are as follows:
R t ＝R 0 +γR t-1 +ε t ,ε t ～N(0,H t )；
H t ＝C′C+A′ε t-1 ε′ t-1 A+B′H t-1 B；
in the formula, R t Is a matrix of gas and electricity rates of change, R 0 Is constant, H t Is a random perturbation term epsilon t C is a constant matrix, a is a covariance autoregressive coefficient matrix for measuring long-term fluctuation (ARCH effect), B is a moving average coefficient matrix for measuring short-term fluctuation (GARCH effect), epsilon t-1 ε′ t-1 The disturbance term lagging by a time period is the ARCH term, H, used to measure the prior information t-1 To measure the GARCH term.
Writing the variance equation into a matrix form can obtain:
in the formula, h ii,t Is the conditional variance of the variable i at time t, h ij,t Is the conditional covariance of variable i and variable j at time t; a is ii 、b ii Respectively representing the ARCH effect and the GARCH effect of the variable i fluctuation; a is ij 、b ij Respectively, i and j are 1 and 2.
And S2, calculating a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model.
In one embodiment, the calculating the gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time series and an electric power historical price time series specifically comprises: estimating a model parameter group of the gas price and electricity price fluctuation overflow model according to a preset log likelihood function, and substituting the model parameter group into the gas price and electricity price fluctuation overflow model to obtain a first gas price and electricity price fluctuation overflow model; and setting a first length of a rolling time window, and rolling and predicting the historical gas and electricity price conduction level according to the first length, a first gas price and electricity price fluctuation overflow model, the historical natural gas price time series and the historical electricity price time series to calculate a historical gas and electricity price conduction level data set.
In one embodiment, assuming that the conditional residual vector follows a binary conditional normal distribution, and the estimation of the BEKK-GARCH model is determined by the maximum likelihood method, the preset log-likelihood function is as follows:
in the formula, T is a time period, theta is a parameter vector to be estimated, and N is the market number.
The rolling window method can perform local calculation in the current rolling window, and continuously acquire new environment information along with the advancing of the rolling window to form dynamic analysis. The BEKK-GARCH model fitting modeling is continuously carried out on the period data with fixed length by using a rolling window method, and multiple groups of A, B matrix coefficients [ a ] can be used ij,1 ,a ij,2 ,…,a ij,m ]And [ b) ij,1 ,b ij,2 ,…,b ij,m ]And analyzing the gas-electricity price fluctuation overflow degree in different periods, calculating the dynamic market potential price fluctuation conduction level, and then performing statistical analysis.
In one embodiment, the risk value model:
P u (U t >V aR |Φ t-1 )＝1-α；
U t ＝[a 12,t a 21,t b 12,t b 21,t ] T ；
in the formula, P u Representing a probability statistic, U t The price fluctuation coefficient is obtained by a BEKK-GARCH model in the t stage; v aR The risk value at a confidence level of α; phi t-1 Is the collection of all previous information. The formula indicates that the market price risk is greater than V within delta t aR Has a probability of 1-alpha, or a market price within delta tLattice risk no greater than V aR The probability of (a) is alpha.
In one embodiment, determining a risk early warning alert line according to the gas-electricity historical price conduction level data set and a preset risk value model specifically includes: calculating the risk value and the corresponding confidence level of the gas-electricity historical price conduction level data corresponding to each rolling time window period according to a historical data method, the gas-electricity historical price conduction level data set and a preset risk value model; and determining and setting a plurality of risk early warning lines with different levels according to the confidence level, the corresponding risk value and a preset early warning requirement.
And S3, acquiring a gas-electricity real-time price data set, calculating a real-time price conduction coefficient between natural gas and electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and carrying out price risk early warning and gas-electricity coordinated dispatching according to the risk early warning line and the real-time price conduction coefficient.
In practical application, the risk value of the Ut matrix in the historical period is calculated by a historical data method, M pieces of Ut result data are sequentially arranged from a low level to a high level, and the critical value at the position is the estimated value of the risk value VaR. And taking the risk values VaR under different confidence degrees as the risk early warning lines for the gas-electricity price conduction, and setting the VaR under the confidence levels of 95%, 90%, 85% and 80% as a first-level early warning line, a second-level early warning line, a third-level early warning line and a fourth-level early warning line respectively. And when the gas-electricity price fluctuation conduction level of the adjacent time period exceeds the gas-electricity price conduction risk early warning line under a certain grade, sending out early warning of a corresponding grade.
The embodiment of the invention describes a natural gas and power coordinated scheduling method, which constructs a BEKK-GARCH-based gas price and electricity price fluctuation overflow model, measures fluctuation correlation of a power market and a natural gas market through a multivariate GARCH model, can specifically calculate a price conduction coefficient between the two markets, continuously performs BEKK-GARCH fitting modeling by using historical data through a preset rolling window prediction method, can analyze the gas and power price fluctuation overflow degree in different periods through multiple groups of parameter estimation results, and sets a gas and power price conduction risk early warning line under different confidence levels based on VaR.
Detailed description of the invention
Besides, the embodiment of the invention also discloses a natural gas and electric power coordinated dispatching device. Fig. 2 is a schematic structural diagram of a coordinated scheduling apparatus for natural gas and electric power according to an embodiment of the present invention.
As shown in fig. 2, the coordinated scheduling apparatus includes a model building unit 11, a risk early warning unit 12, and a coordinated scheduling unit 13.
The model establishing unit 11 is configured to obtain a natural gas historical price time sequence and an electric power historical price time sequence from a preset gas-electricity historical data set, perform stationarity check and ARCH effect check on the natural gas historical price time sequence and the electric power historical price time sequence, and construct a gas price and electricity price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect check passes.
In one embodiment, the model building unit 11 is further configured to: carrying out logarithm taking and difference processing on the natural gas historical price time sequence and the electric power historical price time sequence to obtain a first natural gas historical price change rate sequence and a first electric power historical price change rate sequence; according to an ADF (automatic document delivery) inspection method, performing stationarity inspection on the first natural gas historical price change rate sequence and the first power historical price change rate sequence, and judging whether the stationarity inspection is passed; and if the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power pass stationarity check, calculating a fitting coefficient of a preset check auxiliary equation according to the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power by a Lagrange multiplier check method so as to carry out ARCH check, and when the fitting coefficient is calculated to be remarkably zero, considering that the ARCH effect check is passed.
The risk early warning unit 12 is configured to calculate a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determine a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model.
In one embodiment, the risk pre-warning unit 12 is further configured to: estimating a model parameter group of the gas price and electricity price fluctuation overflow model according to a preset log likelihood function, and substituting the model parameter group into the gas price and electricity price fluctuation overflow model to obtain a first gas price and electricity price fluctuation overflow model; and setting a first length of a rolling time window, and rolling and predicting the historical gas and electricity price conduction level according to the first length, a first gas price and electricity price fluctuation overflow model, the historical natural gas price time series and the historical electricity price time series to calculate a historical gas and electricity price conduction level data set.
In one embodiment, the risk pre-warning unit 12 is further configured to: calculating the risk value and the corresponding confidence level of the gas-electricity historical price conduction level data corresponding to each rolling time window period according to a historical data method, the gas-electricity historical price conduction level data set and a preset risk value model; and determining and setting a plurality of risk early warning lines with different levels according to the confidence level, the corresponding risk value and a preset early warning requirement.
The coordination scheduling unit 13 is configured to obtain a gas-electricity real-time price data set, calculate a real-time price conduction coefficient between the natural gas and the electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and perform price risk early warning and gas-electricity coordination scheduling according to the risk early warning line and the real-time price conduction coefficient.
In one embodiment, the coordinated scheduling apparatus further includes an autocorrelation analyzing unit, the autocorrelation analyzing unit is configured to: and when the ARCH effect is not detected, fitting the autocorrelation relation in the residual square sequence of the power price change rate and the natural gas price change rate according to a preset regression model, the natural gas historical price time sequence and the power historical price time sequence.
The unit integrated by the coordinated scheduling device can be stored in a computer readable storage medium if the unit is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-described embodiments of the method may be implemented. That is, another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for coordinated scheduling of natural gas and electric power as described above.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described embodiments of the apparatus are merely illustrative, where the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relationship between the units indicates that the units have communication connection therebetween, and the connection relationship can be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the invention describes a natural gas and power coordinated scheduling device and a computer readable storage medium, wherein a BEKK-GARCH-based gas price and electricity price fluctuation overflow model is constructed, fluctuation correlation of a power market and a natural gas market is measured through a multi-element GARCH model, a price conduction coefficient between the two markets can be specifically calculated, BEKK-GARCH fitting modeling is continuously carried out through a preset rolling window prediction method by utilizing historical data, the fluctuation overflow degree of the gas and power prices in different periods can be analyzed through multiple groups of parameter estimation results, gas and power price conduction risk early warning lines under different confidence levels are formulated based on VaR, and the efficiency and the effect of the coordinated scheduling of the natural gas and the power are improved by the coordinated scheduling device and the computer readable storage medium.
Detailed description of the invention
In addition to the above method and apparatus, the embodiment of the present invention also describes a coordinated scheduling system for natural gas and electricity.
The coordinated scheduling system comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the coordinated scheduling method of natural gas and electric power as described above when executing the computer program.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center for the device and that connects the various parts of the overall device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the apparatus by executing or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment of the invention describes a natural gas and power coordinated dispatching system, which constructs a BEKK-GARCH-based gas price and electricity price fluctuation overflow model, measures fluctuation correlation of a power market and a natural gas market through a multivariate GARCH model, can specifically calculate a price conduction coefficient between the two markets, continuously performs BEKK-GARCH fitting modeling by using historical data through a preset rolling window prediction method, can analyze the gas and power price fluctuation overflow degree in different periods through multiple groups of parameter estimation results, and sets a gas and power price conduction risk early warning line under different confidence levels based on VaR.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A coordinated scheduling method of natural gas and electric power is characterized by comprising the following steps:
acquiring a natural gas historical price time sequence and an electric power historical price time sequence from a preset gas-electricity historical data set, performing stationarity test and ARCH effect check on the natural gas historical price time sequence and the electric power historical price time sequence, and constructing a gas price and electricity price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect test is passed;
calculating a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model;
and acquiring a gas-electricity real-time price data set, calculating a real-time price conduction coefficient between natural gas and electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and carrying out price risk early warning and gas-electricity coordinated dispatching according to the risk early warning line and the real-time price conduction coefficient.
2. The coordinated scheduling method of natural gas and electric power as claimed in claim 1, further comprising:
and when the ARCH effect is not detected, fitting the autocorrelation relation in the residual square sequence of the power price change rate and the natural gas price change rate according to a preset regression model, the natural gas historical price time sequence and the power historical price time sequence.
3. The natural gas and power coordinated scheduling method according to claim 2, wherein the calculation of the gas and power historical price conducting level data set according to a maximum likelihood estimation method, the gas price and power price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time series and a power historical price time series specifically comprises:
estimating a model parameter group of the gas price and electricity price fluctuation overflow model according to a preset log-likelihood function, and substituting the model parameter group into the gas price and electricity price fluctuation overflow model to obtain a first gas price and electricity price fluctuation overflow model;
and setting a first length of a rolling time window, and rolling and predicting the historical gas and electricity price conduction level according to the first length, a first gas price and electricity price fluctuation overflow model, the historical natural gas price time series and the historical electricity price time series to calculate a historical gas and electricity price conduction level data set.
4. The natural gas and power coordinated scheduling method according to claim 3, wherein a risk early warning line is determined according to the gas-electric historical price conduction level data set and a preset risk value model, and specifically comprises:
calculating the risk value and the corresponding confidence level of the gas-electricity historical price conduction level data corresponding to each rolling time window period according to a historical data method, the gas-electricity historical price conduction level data group and a preset risk value model;
and determining and setting a plurality of risk early warning lines with different levels according to the confidence level, the corresponding risk value and a preset early warning requirement.
5. The natural gas and power coordinated scheduling method according to claim 4, wherein performing stationarity check and ARCH effect check on the natural gas historical price time series and the power historical price time series specifically comprises:
carrying out logarithm taking and difference processing on the natural gas historical price time sequence and the electric power historical price time sequence to obtain a first natural gas historical price change rate sequence and a first electric power historical price change rate sequence;
according to an ADF (automatic document delivery) inspection method, performing stationarity inspection on the first natural gas historical price change rate sequence and the first power historical price change rate sequence, and judging whether the stationarity inspection is passed;
and if the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power pass stationarity check, calculating a fitting coefficient of a preset check auxiliary equation according to the first historical price change rate sequence of the natural gas and the first historical price change rate sequence of the electric power by a Lagrange multiplier check method so as to carry out ARCH check, and when the fitting coefficient is calculated to be remarkably zero, considering that the ARCH effect check is passed.
6. The coordinated scheduling method of natural gas and electricity according to claim 5, wherein the risk value model is:
P u (U t >V aR |Φ t-1 )＝1-α；
U t ＝[a 12,t a 21,t b 12,t b 21,t ] T ；
in the formula, P u Representing a probability statistic, U t Obtaining a price fluctuation coefficient for the BEKK-GARCH model in the t stage; v aR The risk value at a confidence level of α; phi t-1 Is the collection of all previous information. The formula indicates that the market price risk is greater than V within delta t aR Has a probability of 1-alpha, or a market price risk of not more than V within delta t aR The probability of (a) is alpha.
7. The coordinated scheduling method of natural gas and electricity according to claim 6, wherein said inspection assistance equation is:
in the formula, epsilon t Residual errors of the power price change rate and the natural gas price change rate at the time t are alpha, assuming that the variance of the residual errors is the same and changes along with the time i As fitting coefficient, ω t Is an error term.
8. The device for coordinately dispatching natural gas and electric power is characterized by comprising a model establishing unit, a risk early warning unit and a coordinately dispatching unit,
the model establishing unit is used for acquiring a natural gas historical price time sequence and an electric power historical price time sequence from a preset gas-electricity historical data set, performing stationarity test and ARCH effect check on the natural gas historical price time sequence and the electric power historical price time sequence, and establishing a gas price and electricity price fluctuation overflow model according to a fluctuation overflow effect and a generalized autoregressive condition difference variance model when the ARCH effect check is passed;
the risk early warning unit is used for calculating a gas-electricity historical price conduction level data set according to a maximum likelihood estimation method, the gas price and electricity price fluctuation overflow model, a preset rolling window prediction method, a natural gas historical price time sequence and an electric power historical price time sequence, and determining a risk early warning line according to the gas-electricity historical price conduction level data set and a preset risk value model;
the coordination scheduling unit is used for acquiring a gas-electricity real-time price data set, calculating a real-time price conduction coefficient between natural gas and electric power according to the gas-electricity and electricity price fluctuation overflow model and the gas-electricity real-time price data set, and performing price risk early warning and gas-electricity coordination scheduling according to the risk early warning line and the real-time price conduction coefficient.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method of coordinated scheduling of natural gas and electricity according to any one of claims 1 to 7.
10. A coordinated scheduling system for natural gas and electricity, characterized in that the coordinated scheduling system comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the coordinated scheduling method for natural gas and electricity according to any one of claims 1 to 7 when executing the computer program.
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