CN115036923B - Electricity-gas comprehensive energy system state estimation method considering multi-energy flow time sequence - Google Patents

Electricity-gas comprehensive energy system state estimation method considering multi-energy flow time sequence Download PDF

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CN115036923B
CN115036923B CN202210965028.3A CN202210965028A CN115036923B CN 115036923 B CN115036923 B CN 115036923B CN 202210965028 A CN202210965028 A CN 202210965028A CN 115036923 B CN115036923 B CN 115036923B
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CN115036923A (en
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徐俊俊
张腾飞
吴巨爱
朱三立
邹花蕾
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Nanjing University of Posts and Telecommunications
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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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    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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
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    • 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
    • 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
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Abstract

The invention discloses a state estimation method of an electricity-gas comprehensive energy system considering a multi-energy flow time sequence, which comprises the following steps: step 1, establishing a multi-energy flow subsystem estimation model based on the physical characteristics of an electricity-gas integrated energy system; step 2, considering multi-time scale coupling and measurement delay, establishing a collaborative estimation strategy; and 3, optimizing an error propagation process and enhancing the stability of the real-time estimation numerical value. The real-time estimation strategy provided by the invention can effectively process the problem of measurement delay of the multi-source subsystem, and cooperates with the multi-energy flow subsystem, thereby effectively breaking the industry barrier, making up the defect of the real-time monitoring function of the current comprehensive energy system, and providing guarantee for the real-time observability of the electricity-gas comprehensive energy system.

Description

Electricity-gas comprehensive energy system state estimation method considering multi-energy flow time sequence
Technical Field
The invention relates to the technical field of comprehensive energy system state estimation, in particular to an electric-gas comprehensive energy system state estimation method considering a multi-energy flow time sequence.
Background
An Integrated Energy System (IES) integrates various Energy sources by adopting advanced physical information technology and innovative management mode, realizes coordinated planning and optimized operation, and realizes cooperative management, interactive response and complementation among a plurality of heterogeneous Energy subsystems, thus becoming an important carrier for low-carbon development of the Energy industry. Among them, gas-Electric Coupled IES (geees) coupling a natural Gas system and an Electric power system have a good multi-region energy transmission capability, and are widely paid attention by scholars at home and abroad due to their characteristics of economy, safety, flexible conversion, and the like.
The state estimation plays an important role in an Energy Management System (EMS), can perform limited detection on the operating state of the integrated energy system, and makes up for the problem of insufficient measurement of the conventional integrated energy system. However, the current research on energy network state estimation generally only needs to be considered in model design for information interaction between multi-energy flow networks of a single system, an electricity-gas integrated energy system.
At present, in order to realize reliable monitoring of an electric-gas integrated energy system, a State Estimation technology (geees-SE) for the electric-gas integrated energy system is already mentioned in some documents (for example, please refer to CN105958531A, CN113468797A, CN111695269 a), but none of the technologies can well coordinate inconsistent operation timing sequences of a multi-energy flow subsystem, and in the Estimation process of the multi-energy flow subsystem, if a proper Estimation sequence cannot be selected, system calculation power is wasted to a great extent, and Estimation efficiency is reduced. Meanwhile, the time lag deviation among the multi-source measuring devices contained in the multi-energy flow subsystem can also influence the reliable monitoring of the multi-energy flow comprehensive energy system.
Therefore, in the integrated energy system, how to coordinate the operation timing sequence of the multi-energy flow subsystem on the basis of establishing a multi-energy flow subsystem model conforming to the operation state, and meanwhile, a reliable processing method for reliably searching the time lag error between multi-source measurement information is a problem to be solved urgently at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides an electricity-gas integrated energy system state estimation method considering a multi-energy flow time sequence, which considers the multi-source measurement fusion problem of a multi-energy flow subsystem, can effectively process the measurement delay problem of the multi-source subsystem, cooperates with the multi-energy flow subsystem, and effectively breaks the industry barrier.
The invention relates to a state estimation method of an electricity-gas integrated energy system considering a multi-energy flow time sequence, which comprises the following steps of:
step 1, establishing a multi-energy flow subsystem estimation model based on physical characteristics of an electricity-gas integrated energy system, wherein the multi-energy flow subsystem estimation model comprises modeling of an electric power subsystem, a gas subsystem and a coupling element;
step 2, considering multi-time scale coupling and measurement delay in the established electricity-gas integrated energy system model, and establishing a collaborative estimation strategy;
and 3, optimizing an error propagation process in the collaborative estimation process, and enhancing the stability of the real-time estimation numerical value.
Further, in step 1, the voltage is used as a node variable and the current is used as a branch variable for modeling the power subsystem; the active and reactive power flow equation of the node is expressed as follows:
Figure 38345DEST_PATH_IMAGE001
Figure 194257DEST_PATH_IMAGE002
wherein the content of the first and second substances,V i and withV j Respectively represent nodesiAnd withjThe voltage of the node of (a) is,δ ij representing nodesiAndjthe phase angle difference between the two phases is different,G ij andB ij representing nodesiAndjinter-lineijAdmittance parameter, line ofijThe admittance of (d) is expressed as:Y ij =G ij +jB ij 。
further, in the step 1, natural gas energy flow is used as an energy flow carrier for modeling the gas subsystem, pipeline pressure is used as a node variable, and pipeline flow is used as a branch variable;
natural gas in a gas network can flow in a compressible fluid state and is influenced by a specific external environment, the natural gas has different properties, and parameters such as pressure, flow and the like in a gas pipeline are influenced; pressure of the pipelinep (KPa) Density of gasρ(kg/m 3 ) Flow rate of gasv (m/s) With gas temperatureT (K) Four main parameters for determining the state of the gas in the pipeline, all of which are timet (s) Distance from the pipex (m) A function of (a); the continuity equation, the motion equation and the state equation of the gas in the pipeline are respectively described as follows according to the parameters:
Figure 379382DEST_PATH_IMAGE003
Figure 292849DEST_PATH_IMAGE004
Figure 946857DEST_PATH_IMAGE005
wherein, the first and the second end of the pipe are connected with each other,GandFrespectively represents the gravity acceleration and the friction coefficient of the pipeline at the local part of the pipeline,
Figure 396424DEST_PATH_IMAGE006
which indicates the angle of inclination of the pipe,Dthe inner diameter of the pipe is shown,R、Zrespectively representing the molar gas constant
Figure 516565DEST_PATH_IMAGE007
And a compression factor;
considering the above process of the gas network as a constant temperature process, the temperature variation of the gas is not considered, and the standard density of the gas at the temperature is assumed to be
Figure 949951DEST_PATH_IMAGE008
Then, the gas flow equation of the gas transmission pipeline is expressed as:
Figure 915371DEST_PATH_IMAGE009
Figure 485024DEST_PATH_IMAGE010
wherein, the first and the second end of the pipe are connected with each other,findicating the flow of gas in a pipem 3 /s(ii) a Carrying out discretization treatment on a gas flow equation of a gas pipeline by adopting an implicit difference method:
Figure 776065DEST_PATH_IMAGE011
Figure 431169DEST_PATH_IMAGE012
wherein the content of the first and second substances,f a 、p a respectively representing slave ductsaEnd inflow gas flow rate and pipelineaGas pressure at the end;f b 、p b respectively representing slave ductsbEnd inflow gas flow rate, pipelinebGas pressure at the end;f t 、p t 、p t-1 respectively representtThe average flow rate of the gas in the pipeline at the moment,tTime pipelineabThe pressure at the midpoint,t-1 time pipeabPressure at the midpoint;
when gas flows through the inner wall, the pipeline is often influenced by gas pressure to generate certain deformation, and the volume of gas in the pipeline is not a simple pipeline theoretical volume; in order to ensure the accuracy of the established model, the influence of the stored natural gas in the pipeline of the gas network is considered, the stored gas model is established, and the stored gas model is establishedtTrapped gas volume in pipeline at timeV t Comprises the following steps:
Figure 200279DEST_PATH_IMAGE013
wherein the content of the first and second substances,p a,t andp b,t respectively representtTime pipelineaEnd and pipebThe pressure at the end of the tube is,p a,t-1 and p b,t-1 Respectively representt-1 moment pipelineaEnd and pipebThe pressure at the end of the tube is,Lindicating the length of pipe;
to facilitate discretization, the gas volume is manipulatedV t Expressed in a continuous fashion on the time axis:
Figure 952335DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,f a,t and f b,t Respectively representtConstantly flowing into the pipelineaEnd and outflow pipebGas volume at the end.
Further, in step 1, modeling the gas subsystem requires considering the physical characteristics of the coupling elements in the system;
the gas turbine set is used as an important coupling element between electric power and natural gas, bears energy conversion work among different networks, and has the characteristics of flexible start and stop and capability of utilizing energy in a gradient manner. The model of the coupling element gas turbine in the genies is expressed as:
Figure 650164DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,fandPrespectively representing the gas flow consumed by the gas turbine set and the output power of the gas turbine set,l、m、nthe energy consumption coefficients of the gas turbine set are all the energy consumption coefficients of the gas turbine set;
an electrical conversion device (Powerto Gas, P2G) produces hydrogen by electrolyzing water, and produces methane by using the hydrogen as a raw material, which is also an important energy conversion device in geees, and the chemical reaction principle is as follows:
Figure 25519DEST_PATH_IMAGE016
the mode of operation of the coupling element is represented by:
Figure 99785DEST_PATH_IMAGE017
in the formula (I), the compound is shown in the specification,η e,i andη f,i respectively shows the power generation efficiency and the gas production efficiency of the gas turbine set and the electrical conversion device,
Figure 408145DEST_PATH_IMAGE018
and
Figure 276875DEST_PATH_IMAGE019
respectively gas flow and electric power.
Further, in the step 2, multi-time scale coupling is considered, the response time of the power system in the ges is in the order of seconds to minutes, and the response time of the gas system is in the order of minutes to hours; a sequential estimation strategy is adopted to coordinate time scales of a power grid and a gas grid, and the method specifically comprises the following steps:
a, step a: initializing subsystem times
Figure 139526DEST_PATH_IMAGE020
；
Step b: inputting estimated time of power grid and gas grid
Figure 17483DEST_PATH_IMAGE021
；
Step c: performing gas net estimation
Figure 180349DEST_PATH_IMAGE022
；
Step d: judging the estimated node, if
Figure 219981DEST_PATH_IMAGE023
If not, the air network estimation is carried out continuously, otherwise, the power network estimation is carried out
Figure 569928DEST_PATH_IMAGE024
。
Furthermore, in the step 2, a delay estimation strategy of measurement delay is considered, and measurement devices of the gas network comprise an SCADA, a turbine flowmeter, a pressure transmitter and the like, and can provide gas network operation information of pressure and flow of nodes and pipelines; the turbine flowmeter obtains the measured data immediately, the delay is very low, and the measurement of the turbine flowmeter is regarded ast e A timestamp of the precise sampling of the time; SCADA is an asynchronous sample with time-lag error, and measurement delay of SCADA needs to be consideredτ s,d (ii) a In addition, both turbine flow meters and SCADA take into account the transmission delay associated with transmitting data to the control centerτ t,td Andτ s,td ；
further, the delay estimation strategy is based on the following assumptions:
1. the time delay error between different SCADA equipment in GEIES is not considered;
2. the measurement acquisition of the turbine flowmeter is assumed to be instantaneous, and the time lag error of the turbine flowmeter is not considered;
3. influence brought by abnormal communication states such as communication equipment faults, data attacks and the like is not considered;
estimating SCADA metrology delay by mapping SCADA sparse metrology information into dense turbine meter metrology informationτ s,d (ii) a According to the signal association degree theory, the turbine flowmeter acquisition information closest to the SCADA measurement acquisition time has the best information association degree with the turbine flowmeter acquisition information;
calculating theoretical delay of SCADA measurement at corresponding time by using sampling time stamp of turbine flowmeter as referencet e +τ s,d The method comprises the following specific steps:
the first step is as follows: inputting uploading speed measured by SCADAv s Uploading Rate of a turbine flowmeterv t And given a time periodT；
The second step is that: respectively generatensSCADA branch flow matrix
Figure 985997DEST_PATH_IMAGE025
Andxbranch flow data matrix of turbine flowmeter
Figure 3370DEST_PATH_IMAGE026
Among them are:
Figure 479481DEST_PATH_IMAGE027
；
the third step: computingf s And each one off t,m Pearson correlation coefficient of (a):
Figure 51146DEST_PATH_IMAGE028
in the formula (I), the compound is shown in the specification,
Figure 270906DEST_PATH_IMAGE029
and
Figure 142785DEST_PATH_IMAGE030
respectively representf s Andf t,m the standard deviation of (a) is determined,
Figure 789798DEST_PATH_IMAGE031
representf s Andf t,m the covariance of (a);
the fourth step: calculated by permutation from large to smallxA Pearson correlation coefficient, ifm=MTime of flightρThe maximum is obtained, the measurement delay of SCADA isτ s,d =M/v s 。
The above-described solution is equally applicable to delay estimation in power networks, in which the delay is estimated byμAnd calculating the delay estimation of the SCADA by taking the measurement time stamp of the PMU as a reference.
Further, the error propagation process in step 3 is optimized, the stability of the real-time estimation value is enhanced, the traditional UKF is adopted for real-time estimation, and if a non-positive condition occurs in the covariance propagation process, the estimation is diverged, which results in failure of real-time estimation.
Using a symmetric orthographic method based on modified alternative projection for preserving covarianceP k|k-1 The numerical stability of real-time estimation is enhanced by positive determination in the transmission process; the method is divided into two parts:
1) Projecting the matrix into a symmetrical and positive matrix set by adopting improved alternative projection; the method comprises the following specific steps:
initialization:
projected correction matrix deltaS=0 n,n
The matrix to be projectedXBy means of a temporary storage matrixYCircularly and alternately projecting:
Y=X, R=Y-ΔS
calculating matrixRCharacteristic value ofdAnd feature vectorV；
SelectingdMedium satisfaction is greater than characteristic decomposition amountτ eig max(d) Is composed of a set of elementsp；
Projection recalculationXAnd deltaS：
Figure 848758DEST_PATH_IMAGE032
ΔS=X-R
To:
Figure 934526DEST_PATH_IMAGE033
greater than a covariance thresholdτ conv Ending the cyclic alternative projection;
2) Forced positive-projection matrix: the method comprises the following specific steps:
computing post-projection matricesXCharacteristic value ofdAnd feature vectorV；
Forced positive determination:
Figure 660911DEST_PATH_IMAGE034
Figure 213246DEST_PATH_IMAGE035
regeneration ofX：
Figure 290662DEST_PATH_IMAGE036
Figure 852224DEST_PATH_IMAGE037
Figure 456553DEST_PATH_IMAGE038
]
Forced symmetry:
Figure 179790DEST_PATH_IMAGE039
。
the symmetric positive method based on the improved alternative projection effectively avoids the loss of the positive quality in the covariance transmission process, and can ensure the numerical stability of the real-time estimation strategy in the algorithm transmission principle.
The invention has the beneficial effects that: according to the method, the estimation model of the multi-energy flow subsystem fully considers the physical operation characteristics of the power subsystem and the gas subsystem, so that the defect that the physical modeling and actual operation condition deviation of the current technology to the electricity-gas comprehensive energy system is large is overcome; the provided multi-time scale coupled collaborative estimation strategy fully considers the deviation of the operation response speed among the multi-energy flow systems, the system computing power is greatly saved in the collaborative estimation process, and the operation efficiency is improved; the proposed cooperative estimation strategy for measurement delay handles time lag errors among multi-source measurement equipment, and provides estimation nodes with higher precision for estimation of a multi-energy flow subsystem; a symmetrical positive method for improving alternative projection is provided in the proposed error propagation process optimization strategy to improve the iterative process of the existing Kalman filtering algorithm and improve the numerical stability of an estimation result, so that the continuous and reliable monitoring of the running state of the electricity-gas integrated energy system is realized.
Drawings
FIG. 1 is a schematic diagram of the coupling situation on a time scale according to the present invention;
FIG. 2 is a flow chart of the sequential estimation of the present invention;
FIG. 3 is a schematic diagram of the estimated delay of the present invention;
FIG. 4 is a schematic diagram of a test electrical-to-gas interconnection integrated energy system topology;
FIG. 5 is a graph comparing the effect of voltage magnitude estimation for a power subsystem;
FIG. 6 is a graph comparing the effect of estimating the phase angle of the voltage of a power subsystem;
FIG. 7 is a schematic diagram comparing the effect of gas pressure estimation in a gas subsystem;
FIG. 8 is a comparison of the flow estimation effect of the gas subsystem;
fig. 9 is an overall step block diagram of the present invention.
Detailed Description
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
As shown in fig. 9, in the state estimation method of the electric-gas integrated energy system considering the multi-energy flow time sequence, firstly, an estimation model of the multi-energy flow subsystem is established based on physical characteristics of the electric-gas integrated energy system;
the multi-energy flow subsystem estimation model includes modeling of the power subsystem, the gas subsystem, and the coupling elements.
The modeling of the power subsystem in the multi-energy flow subsystem estimation model takes voltage as a node variable and current as a branch variable, and accords with energy flow laws such as kirchhoff current law, kirchhoff voltage law and the like. The active and reactive power flow equations of the nodes can be expressed as:
Figure 744501DEST_PATH_IMAGE040
wherein the content of the first and second substances,V i and withV j Respectively representing nodesiAndjthe voltage of the node of (a) is,δ ij representing nodesiAndjthe phase angle difference between the two phases is small,G ij andB ij representing nodesiAndjinter-lineijAdmittance parameter, line ofijThe admittance of (a) can be expressed as:Y ij =G ij +jB ij 。
the modeling of the fuel gas subsystem takes natural gas energy flow as an energy flow carrier, takes pipeline pressure as a node variable and pipeline flow as a branch variable, and accords with energy flow laws such as a pipeline pressure drop equation, a node flow equation, an annular energy equation and the like.
The energy flow natural gas in the gas network is in a compressible fluid state and is influenced by a specific external environment, and the natural gas has different properties, so that parameters such as pressure, flow and the like in a gas pipeline are influenced. Pressure in the pipelinep (KPa) Density of gasρ(kg/m 3 ) Flow rate of gasv (m/s) With gas temperatureT (K) Four main parameters for determining the state of the gas in the pipeline, all of which are timet (s) Distance from pipelinex (m) As a function of (c). The continuity equation, the motion equation and the state equation of the gas in the pipeline can be respectively described as follows according to the parameters:
Figure 640913DEST_PATH_IMAGE041
Figure 843355DEST_PATH_IMAGE042
Figure 501608DEST_PATH_IMAGE043
wherein the content of the first and second substances,GandFrespectively represents the gravity acceleration and the friction coefficient of the pipeline at the local part of the pipeline,
Figure 789501DEST_PATH_IMAGE006
which indicates the angle of inclination of the pipe,Dthe inner diameter of the pipe is shown,R、Zrespectively representing the molar gas constant
Figure 925822DEST_PATH_IMAGE044
And a compression factor.
Considering the above process of the gas network as a constant temperature process, the temperature variation of the gas is not considered, assuming that the standard density of the gas at this temperature is
Figure 982770DEST_PATH_IMAGE045
Then the gas flow equation of the gas transmission pipeline can be expressed as (whereinfRepresenting the gas flow in the pipe (m 3 /s))：
Figure 77503DEST_PATH_IMAGE046
Figure 587113DEST_PATH_IMAGE047
Carrying out discretization treatment on a gas flow equation of a gas pipeline by adopting an implicit difference method:
Figure 261546DEST_PATH_IMAGE048
Figure 766476DEST_PATH_IMAGE049
when gas flows through the inner wall, the pipeline is often influenced by gas pressure to generate certain deformation, and the volume of gas in the pipeline is not a simple theoretical volume of the pipeline. In order to ensure the accuracy of the established model, the invention considers the influence of the stored natural gas in the pipeline of the gas network, establishes a stored gas model, and then establishes the stored gas modeltTemporal trapped gas volume in pipelineV t Comprises the following steps:
Figure 766531DEST_PATH_IMAGE050
wherein, the first and the second end of the pipe are connected with each other,p a,t and p b,t Respectively representtTime pipelineaEnd and pipebPressure at the end.
To facilitate discretization, the gas volume is manipulatedV t Expressed in a continuous fashion on the time axis:
Figure 497858DEST_PATH_IMAGE051
in the formula (I), the compound is shown in the specification,f a,t and f b,t Respectively representtConstantly flowing into the pipelineaEnd and outflow pipebGas volume at the end.
The gas turbine set is used as an important coupling element between electric power and natural gas, bears energy conversion work among different networks, and has the characteristics of flexible start and stop and capability of utilizing energy in a gradient manner. The model of the coupling element gas turbine in geees can be expressed as:
Figure 507140DEST_PATH_IMAGE052
in the formula (I), the compound is shown in the specification,fandPrespectively representing the gas flow consumed by the gas turbine set and the output power of the gas turbine set,l、m、nare the energy consumption coefficients of the gas turbine set.
An electric conversion device (Powerto Gas, P2G) produces hydrogen by electrolyzing water, and produces methane by using the hydrogen as a raw material, which is also an important energy conversion device in geees; the chemical reaction principle is as follows:
Figure 538681DEST_PATH_IMAGE053
the mode of operation of the coupling element can be expressed by the following equation:
Figure 7840DEST_PATH_IMAGE054
in the formula (I), the compound is shown in the specification,η e,i andη f,i respectively show the efficiency of the gas turbine set and the efficiency of the electric conversion device for generating electricity and gas.
Secondly, considering multi-time scale coupling and measurement delay, establishing a collaborative estimation strategy;
the dynamic response time of the power system and the gas system in the giies is greatly different, the response time of the power system is in the order of seconds to minutes, the response time of the gas system is in the order of minutes to hours, and the coupling condition is as shown in fig. 1. And (3) coordinating the time scales of the power grid and the gas grid by adopting a sequence estimation strategy, wherein the specific steps are shown in figure 2.
The measuring equipment of the gas network comprises an SCADA, a turbine flowmeter, a pressure transmitter and the like, and can provide the operation information of the gas network such as the pressure and the flow of nodes and pipelines. The turbine flowmeter can obtain the measurement data immediately, the delay is low, and the measurement of the turbine flowmeter can be regarded ast e A timestamp of a precise sample of the time of day. SCADA is an asynchronous sample with time-lag error, and measurement delay of SCADA needs to be consideredτ s,d . In addition, the transmission delay brought by the data transmission to the control center is considered by the turbine flowmeter and the SCADAτ t,td Andτ s,td again, this need to be taken into account.
Estimating SCADA metrology delay by mapping SCADA sparse metrology information into dense turbine meter metrology informationτ s,d . According to the signal correlation theory, the information collected by the turbine flowmeter closest to the SCADA measurement collection time has the best information correlation with the information.
Using the sampling time stamp of the turbine flowmeter as a reference, as shown in fig. 3, the theoretical delay of the SCADA measurement at the time corresponding thereto is calculatedt e +τ s,d The method comprises the following specific steps:
the first step is as follows: inputting uploading speed measured by SCADAv s Uploading rate of turbine flowmeterv t And given a time periodT。
The second step is that: respectively generating SCADA branch flow matrixes
Figure 256156DEST_PATH_IMAGE055
Andxbranch flow data matrix of turbine flowmeter
Figure 508277DEST_PATH_IMAGE056
. Among them are:
Figure 627280DEST_PATH_IMAGE057
the third step: computingf s And each one off t,m Pearson correlation coefficient of (a):
Figure 267340DEST_PATH_IMAGE058
in the formula (I), the compound is shown in the specification,
Figure 737374DEST_PATH_IMAGE059
and
Figure 386661DEST_PATH_IMAGE060
respectively representf s Andf t,m the standard deviation of the (c) is,
Figure 94592DEST_PATH_IMAGE061
to representf s Andf t,m the covariance of (a).
The fourth step: calculated by permutation from large to smallxA Pearson correlation coefficient, ifm=MTime of flightρThe maximum is obtained, the measurement delay of SCADA isτ s,d =M/v s 。
And finally, optimizing an error propagation process and enhancing the stability of the real-time estimation numerical value.
The traditional UKF is adopted for real-time estimation, and if the covariance transmission process is not positive, the estimation is diverged, so that the real-time estimation fails.
Using a symmetric orthographic method based on modified alternative projection for preserving covarianceP k|k-1 Positive determination in the propagation process enhances the numerical stability of real-time estimation. The method is divided into two parts:
1) Projecting the matrix into a symmetrical and positive definite matrix set by adopting improved alternative projection; the method comprises the following specific steps:
initialization:
projected correction matrix deltaS=0 n,n
The matrix to be projectedXBy means of a temporary storage matrixYCircularly and alternately projecting:
Y=X, R=Y-ΔS
computing matricesRCharacteristic value ofdAnd feature vectorV；
SelectingdSatisfaction greater than characteristic decomposition amountτ eig max(d) Is composed of a set of elementsp；
Projection recalculationXAnd deltaS：
Figure 843236DEST_PATH_IMAGE032
ΔS=X-R
To:
Figure 800565DEST_PATH_IMAGE033
greater than a covariance thresholdτ conv Ending the cyclic alternative projection;
2) Forced positive-projection matrix: the method comprises the following specific steps:
computing a projected matrixXCharacteristic value ofdAnd feature vectorV；
Forced positive determination:
Figure 191226DEST_PATH_IMAGE034
Figure 81560DEST_PATH_IMAGE035
regeneration ofX:
Figure 1105DEST_PATH_IMAGE036
Figure 711310DEST_PATH_IMAGE037
Figure 843345DEST_PATH_IMAGE038
]
Forced symmetry:
Figure 57027DEST_PATH_IMAGE039
。
the symmetric positive method based on the improved alternative projection effectively avoids the loss of the positive quality in the covariance transmission process, and can ensure the numerical stability of the real-time estimation strategy in the algorithm transmission principle.
(1) Simulation verification model designed by invention
In order to verify the feasibility and the effectiveness of the state estimation method of the electricity-gas interconnection comprehensive energy system, the comprehensive energy system is formed by coupling an improved IEEE 33 node power network and a Belgian 20 node gas network, simulation analysis is carried out, and a system topological graph is shown in figure 4. The system is formed by coupling two generators and two gas turbines, wherein the gas turbine units are respectively positioned at a node 8 and a node 14 of a power network and are respectively connected with a node 4 and a node 12 in a Belgian natural gas system; the other nodes are normal power network nodes and natural gas network nodes;
in addition, in order to better characterize the proposed state estimation effect, a Root Mean Square Error (RMSE) performance index is designed as follows:
Figure 740949DEST_PATH_IMAGE062
in the formula (I), the compound is shown in the specification,xrepresents a system state value;
Figure 899571DEST_PATH_IMAGE063
a priori estimates representing the state of the system. Generally speaking, a smaller RMSE value indicates that the calculated state estimation value deviates less from the true value, and the estimation accuracy is higher.
(2) State estimation performance comparison analysis
Aiming at the test system, the simulation comparative analysis is carried out by adopting the traditional state estimation based on Weighted Least Square (WLS) algorithm, the state estimation based on Extended Kalman Filter (EKF) method, the state estimation based on the traditional Unscented Kalman Filter (UKF) method and four estimation methods provided by the invention. Table 1 shows the statistical state estimation effect and algorithm calculation efficiency of the four methods after 200 monte carlo simulations. As can be seen from the data in the table, the traditional WLS state estimation method has larger RMSE (remote measurement system) in the estimation result because of lack of prediction capability on the dynamic change of the electric-gas interconnected comprehensive energy system, which shows that the estimation result deviates from the real state more and is particularly obvious in estimating the operating state parameters such as pressure, flow and the like in a gas network. The state estimation of the electric-gas interconnected comprehensive energy system based on the EKF and UKF method is greatly improved in accuracy compared with static estimation, meanwhile, the UKF algorithm can better handle the problem of system nonlinearity due to the unscented transformation step, and the estimation result precision and the algorithm calculation efficiency are both superior to those of the EKF. The method provided by the invention improves the error propagation process on the basis of UKF, greatly improves the precision of the estimation result, and does not influence the algorithm calculation efficiency too much because the calculation burden of the improved alternative projection step is lighter;
Figure 163193DEST_PATH_IMAGE064
specifically, taking the 94 th simulation test as an example, fig. 5 to 8 show the estimation results of the four estimation methods on the operation state parameters of the electrical-gas interconnection energy system. Obviously, because of the large physical characteristic difference between part of source nodes and coupling nodes in the comprehensive energy and a single network, the WLS estimation method has large deviation on the estimation of the node parameters, and cannot be applied to occasions with high requirements on the monitoring performance of the system; the estimation precision of the other three estimation methods is obviously improved compared with the WLS estimation, particularly the node parameter estimation value and the true value of each subsystem of the comprehensive energy are most fit with each other by the scheme provided by the invention, and the precision of the comprehensive energy system state estimation method provided by the invention is fully embodied.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all equivalent variations made by using the contents of the present specification and the drawings are within the protection scope of the present invention.

Claims (5)

1. An electric-gas integrated energy system state estimation method considering multi-energy flow timing, characterized by comprising the following steps:
step 1, establishing a multi-energy flow subsystem estimation model based on physical characteristics of an electricity-gas integrated energy system, wherein the multi-energy flow subsystem estimation model comprises modeling of an electric power subsystem, a gas subsystem and a coupling element;
step 2, considering multi-time scale coupling and measurement delay in the established electricity-gas integrated energy system model, and establishing a collaborative estimation strategy;
in the step 2, multi-time scale coupling is considered, the response time of the power system in the GEIES is in the order of seconds to minutes, and the response time of the gas system is in the order of minutes to hours; a sequential estimation strategy is adopted to coordinate time scales of a power grid and a gas grid, and the method comprises the following specific steps:
step a: initializing subsystem times
Figure DEST_PATH_IMAGE001
；
Step b: inputting the estimated time of the power grid and the gas grid
Figure DEST_PATH_IMAGE002
；
Step c: performing gas network estimation
Figure DEST_PATH_IMAGE003
；
Step d: judging the estimated node, if
Figure DEST_PATH_IMAGE004
If not, the air network estimation is carried out continuously, otherwise, the power network estimation is carried out
Figure DEST_PATH_IMAGE005
；
In the step 2, a delay estimation strategy of measurement delay is considered, and measurement equipment of the gas subsystem comprises an SCADA (supervisory control and data acquisition), a turbine flowmeter and a pressure transmitter; the turbine flowmeter obtains the measured data immediately, the delay is very low, and the measurement of the turbine flowmeter is regarded ast e A timestamp of the precise sampling of the time; SCADA is an asynchronous sample with time-lag error, and measurement delay of SCADA needs to be consideredτ s,d (ii) a In addition, both turbine flow meters and SCADA take into account the transmission delay associated with transmitting data to the control centerτ t,td And withτ s,td ；
The delay estimation strategy is based on the following assumptions:
1) The time delay error between different SCADA devices in the GEIES is not considered;
2) The measurement acquisition of the turbine flowmeter is assumed to be instantaneous, and the time lag error of the turbine flowmeter is not considered;
3) Influence brought by abnormal communication states is not considered;
estimating SCADA metrology delay by mapping SCADA sparse metrology information into dense turbine meter metrology informationτ s,d (ii) a According to the signal association degree theory, the turbine flowmeter acquisition information closest to the SCADA measurement acquisition time has the best information association degree with the turbine flowmeter acquisition information;
calculating theoretical delay of SCADA measurement at corresponding time by using sampling time stamp of turbine flowmeter as referencet e +τ s,d The method comprises the following specific steps:
the first step is as follows: inputting uploading speed measured by SCADAv s Uploading rate of turbine flowmeterv t And given a time periodT；
The second step is that: respectively generatensSCADA branch flow matrix
Figure DEST_PATH_IMAGE006
Andxbranch flow data matrix of turbine flowmeter
Figure DEST_PATH_IMAGE007
Among them are:
Figure DEST_PATH_IMAGE008
；
the third step: computingf s And each one off t,m Pearson correlation coefficient of (a):
Figure DEST_PATH_IMAGE009
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE010
and
Figure DEST_PATH_IMAGE011
respectively representf s Andf t,m the standard deviation of (a) is determined,
Figure DEST_PATH_IMAGE012
to representf s Andf t,m the covariance of (a);
the fourth step: calculated by permutation from large to smallxA Pearson correlation coefficient, ifm=MTime of flightρThe maximum is obtained, the measurement delay of SCADA isτ s,d =M/v s ；
And 3, optimizing an error propagation process in the collaborative estimation process, and enhancing the stability of the real-time estimation numerical value.
2. The method for estimating the state of the electric-gas comprehensive energy system considering the multi-energy flow time sequence is characterized in that in the step 1, the voltage is used as a node variable and the current is used as a branch variable for modeling the electric subsystem; the active and reactive power flow equation of the node is expressed as follows:
Figure DEST_PATH_IMAGE013
Figure DEST_PATH_IMAGE014
wherein, the first and the second end of the pipe are connected with each other,V i andV j respectively representing nodesiAndjthe voltage of the node of (a) is,δ ij representing nodesiAndjthe phase angle difference between the two phases is small,G ij andB ij representing nodesiAndjinter-lineijAdmittance parameter, line ofijThe admittance of (a) is expressed as:Y ij =G ij +jB ij 。
3. the method for estimating the state of the electric-gas comprehensive energy system by considering the multi-energy-flow time sequence is characterized in that in the step 1, natural gas energy flow is used as an energy flow carrier, pipeline pressure is used as a node variable, and pipeline flow is used as a branch variable for modeling the gas subsystem;
the energy flow natural gas in the gas network is in a compressible fluid state and is influenced by a specific external environment, and the natural gas has different properties, so that pressure and flow parameters in a gas pipeline are influenced; pressure of the pipelinepDensity of gasρFlow rate of gasvWith gas temperatureTFor determining the state of the gas in the conduitFour main parameters, and they are all timetDistance from pipelinexA function of (a); the continuity equation, the motion equation and the state equation of the gas in the pipeline are respectively described as follows according to the parameters:
Figure DEST_PATH_IMAGE015
Figure DEST_PATH_IMAGE016
Figure DEST_PATH_IMAGE017
wherein the content of the first and second substances,Gand withFRespectively represents the gravity acceleration and the friction coefficient of the pipeline at the local part of the pipeline,
Figure DEST_PATH_IMAGE018
which represents the angle of inclination of the pipe,Dthe inner diameter of the pipe is shown,R、Zrespectively representing the molar gas constant
Figure DEST_PATH_IMAGE019
And a compression factor;
considering the above process of the gas network as a constant temperature process, the temperature variation of the gas is not considered, assuming that the standard density of the gas at this temperature is
Figure DEST_PATH_IMAGE020
kg/m 3 Then, the gas flow equation of the gas transmission pipeline is expressed as:
Figure DEST_PATH_IMAGE021
Figure DEST_PATH_IMAGE022
wherein the content of the first and second substances,findicating the flow of gas in a pipem 3 /s(ii) a Carrying out discretization treatment on a gas flow equation of a gas pipeline by adopting an implicit difference method:
Figure DEST_PATH_IMAGE023
Figure DEST_PATH_IMAGE024
wherein the content of the first and second substances,f a 、p a respectively representing slave ductsaEnd inflow gas flow rate and pipelineaGas pressure at the end;f b 、p b respectively representing slave ductsbEnd inflow gas flow rate and pipelinebGas pressure at the end;f t 、p t 、p t-1 respectively representtThe average flow rate of the gas in the pipeline at the moment,tTime pipelineabThe pressure at the midpoint,t-1 time pipeabPressure at the midpoint;
when gas flows through the inner wall, the pipeline is often influenced by gas pressure to generate certain deformation, and the volume of gas in the pipeline is not a simple pipeline theoretical volume; in order to ensure the accuracy of the established model, the influence of the stored natural gas in the pipeline of the gas network is considered, the stored gas model is established, and the stored gas model is establishedtTemporal trapped gas volume in pipelineV t Comprises the following steps:
Figure DEST_PATH_IMAGE025
wherein the content of the first and second substances,p a,t andp b,t respectively representtTime pipelineaEnd and pipebThe pressure at the end of the tube is,p a,t-1 and p b,t-1 Respectively representt-1 moment pipelineaEnd and pipebThe pressure at the end of the tube is,Lindicating the length of pipe;
to facilitate discretization, the gas volume is manipulatedV t Expressed in a continuous fashion on the time axis:
Figure DEST_PATH_IMAGE026
in the formula (I), the compound is shown in the specification,f a,t and f b,t Respectively representtConstantly flowing into the pipelineaEnd and outflow pipebGas volume at the tip.
4. The method for estimating the state of the electric-gas comprehensive energy system considering the multi-energy flow time sequence is characterized in that in the step 1, the physical characteristics of coupling elements in the system are considered in the modeling of a gas subsystem;
the gas turbine set is used as an important coupling element between electric power and natural gas, bears the energy conversion work among different networks,
the mode of operation of the coupling element is represented by:
Figure DEST_PATH_IMAGE027
in the formula (I), the compound is shown in the specification,η e,i and withη f,i Respectively shows the power generation efficiency and the gas production efficiency of the gas turbine set and the electrical conversion device,
Figure DEST_PATH_IMAGE028
and
Figure DEST_PATH_IMAGE029
respectively gas flow and electrical power.
5. The method of claim 1, wherein the method comprises estimating the state of the electric-gas integrated energy system based on the multi-energy flow timing sequenceIs characterized in that: in the step 3, a symmetric alignment method based on improved alternative projection is adopted for keeping covarianceP k|k-1 The numerical stability of real-time estimation is enhanced by positive determination in the transmission process; the method is divided into two parts:
1) Projecting the matrix into a symmetrical and positive definite matrix set by adopting improved alternative projection; the method comprises the following specific steps:
initialization:
projected correction matrix deltaS=0 n,n
The matrix to be projectedXBy means of a temporary storage matrixYCircularly and alternately projecting:
Y=X, R=Y-ΔS
calculating matrixRCharacteristic value ofdAnd feature vectorV；
SelectingdSatisfaction greater than characteristic decomposition amountτ eig max(d) Is composed of a set of elementsp；
Projection recalculationXAnd deltaS：
Figure DEST_PATH_IMAGE030
ΔS=X-R
To:
Figure DEST_PATH_IMAGE031
greater than a covariance thresholdτ conv Ending the cyclic alternative projection;
2) Forced positive-projection matrix: the method comprises the following specific steps:
computing post-projection matricesXCharacteristic value ofdAnd feature vectorV；
Forced positive determination:
Figure DEST_PATH_IMAGE032
Figure DEST_PATH_IMAGE033
regeneration ofX：
Figure DEST_PATH_IMAGE034
Figure DEST_PATH_IMAGE035
Figure DEST_PATH_IMAGE036
]
Forced symmetry:
Figure DEST_PATH_IMAGE037
。
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