阅读说明：本技术 一种电-气综合能源系统状态估计方法和系统 (State estimation method and system for electricity-gas comprehensive energy system ) 是由 张新鹤 李克成 王松岑 何桂雄 刘铠诚 李德智 钟鸣 黄伟 刘向向 卢婕 于 2021-05-07 设计创作，主要内容包括：本发明涉及一种电-气综合能源系统状态估计方法和系统,包括：获取电-气综合能源系统中多种量测量的采样值；基于电-气综合能源系统中多种量测量的采样值,利用粒子群算法对预先构建的电-气综合能源系统状态估计模型进行求解,确定电-气综合能源系统中多种状态量的估计值；其中,所述电-气综合能源系统状态估计模型以基于最小二乘法原理构建的多种量测量分别对应估计值的估计误差总和最小为目标确定多种状态量的估计值。本发明提供的技术方案能克服传统方法中因综合能源系统中多元化数据带来的估计精确性下降以及计算速度缓慢问题,能够实时反映综合能源系统内部运行状态,为综合能源态势感知工作奠定基础。(The invention relates to a state estimation method and a state estimation system for an electricity-gas integrated energy system, which comprise the following steps: obtaining a plurality of measured sampling values in the electricity-gas comprehensive energy system; on the basis of sampling values measured by various quantities in the electricity-gas integrated energy system, solving a pre-constructed state estimation model of the electricity-gas integrated energy system by utilizing a particle swarm algorithm to determine estimation values of various state quantities in the electricity-gas integrated energy system; the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target. The technical scheme provided by the invention can solve the problems of reduced estimation accuracy and slow calculation speed caused by diversified data in the comprehensive energy system in the traditional method, can reflect the internal running state of the comprehensive energy system in real time, and lays a foundation for the situation awareness work of the comprehensive energy.)

1. A method for estimating a state of an electric-gas integrated energy system, the method comprising:

obtaining a plurality of measured sampling values in the electricity-gas comprehensive energy system;

on the basis of sampling values measured by various quantities in the electricity-gas integrated energy system, solving a pre-constructed state estimation model of the electricity-gas integrated energy system by utilizing a particle swarm algorithm to determine estimation values of various state quantities in the electricity-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

2. The method of claim 1, wherein the measurements in the electric-gas integrated energy system include, but are not limited to:

the method comprises the steps that the voltage amplitude, the active load and the reactive load of a measurable node in a power system, the active power and the reactive power transmitted by a power transmission line in the power system, the pressure and the injection flow of the measurable node in a natural gas system and the transmission flow of a natural gas pipeline in the natural gas system can be measured;

the state quantities in the electric-gas integrated energy system include but are not limited to:

the voltage amplitude and the voltage phase angle of all nodes in the power system, and the pressure of all nodes in the natural gas system.

3. The method of claim 1, wherein the constructing of the state estimation model of the electric-gas integrated energy system comprises:

the method comprises the steps of enabling sampling values of various quantities of the electricity-gas integrated energy system to be input into a state estimation model of the electricity-gas integrated energy system, and enabling estimated values of various quantities of states in the electricity-gas integrated energy system to be output from the state estimation model of the electricity-gas integrated energy system;

based on the principle of least square method, constructing a target function by taking the sum of estimation errors of various quantity measurement respectively corresponding to the estimation values as the minimum;

constructing an electric-gas coupling power flow model based on the topological structure parameters of the electric-gas comprehensive energy system;

and taking the electric-gas coupling power flow model as the constraint of the objective function, and constructing the compressor operation constraint, the GPG electric-gas coupling element operation constraint and the P2G electric-gas coupling element operation constraint for the objective function according to the equipment characteristics of the electric-gas integrated energy system.

4. The method of claim 3, wherein the objective function is calculated as follows:

wherein J (x) is the value of the objective function, ε_eIs a measurement error matrix, epsilon, of the power system in the electricity-gas integrated energy system_gIs a measurement error matrix, R, of a natural gas system in an electricity-gas integrated energy system_eIs a covariance matrix of measurement errors, R, of an electric power system in an electric-gas integrated energy system_gThe measurement error covariance matrix of the natural gas system in the electricity-gas comprehensive energy system is obtained;

wherein the electricity-gas comprehensive energyMeasurement error matrix epsilon of power system in source system_eIs calculated as follows:

ε_e＝z_e-h_e(x)

in the formula, z_eMatrix of sampled values, h, for measurements of various quantities in an electrical power system in an electrical-gas integrated energy system_e(x) An estimated value matrix for measuring a plurality of quantities in an electric power system in the electricity-gas comprehensive energy system;

a measurement error matrix epsilon of a natural gas system in the electricity-gas integrated energy system_gIs calculated as follows:

ε_g＝z_g-h_g(x)

in the formula, z_gSampling value matrix h for multiple measurements in a natural gas system in an electric-gas integrated energy system_g(x) And the estimated value matrix is measured for various quantities in the natural gas system in the electricity-gas integrated energy system.

5. The method of claim 3, wherein the electrically-pneumatically coupled power flow model comprises: the power system power flow model and the natural gas system power flow model.

6. The method of claim 3, wherein the power system flow model is calculated as follows:

in the formula, V_iIs the voltage of node i, V, in the power system_jIs the voltage of node j in the power system, G_ijFor the conductance of lines ij in an electric power system, B_ijIs the susceptance, theta, of a line ij in an electric power system_ijIs the phase angle difference between node i and node j in the power system, K is the transformer transformation ratio, b_TFor the standard side susceptance, y, of the transformer_ijFor admittance, P, of lines ij in an electric power system_iFor the active load of node i, Q, in the power system_iIs reactive load of node i, P, in the power system_ijActive power, Q, transmitted for line ij in an electric power system_ijAnd j belongs to (1-N) for the reactive power transmitted by the line ij in the power system, wherein N is the sum of all nodes connected with the node i.

7. The method of claim 3, wherein the natural gas system flow model is calculated as follows:

in the formula, K_αβIs the pipeline constant, p, of the pipeline in a natural gas system_αIs the pressure of a node alpha in a natural gas system, p_βIs the pressure of a node beta, f in a natural gas system_αβFor the transmission flow of alpha beta in the pipeline in a natural gas system, f_αFor the injection flow rate of the node alpha in the natural gas system, beta belongs to N_α，N_αIs the set of all nodes connected to node alpha, s_αβIs the flow direction coefficient of the pipeline alpha beta in the natural gas system, if p_α＞p_βThen s_αβ＝1；p_α＝p_βThen s_αβ0; if p is_α＜p_βThen s_αβ＝-1。

8. The method of claim 1, wherein the fitness function of the PSO method is calculated as:

F(x)＝J(x)

wherein, F (x) is a fitness function value of a PSO method, and J (x) is an objective function value of the state estimation model of the electricity-gas integrated energy system.

9. The method of claim 3, wherein the electrical-gas integrated energy system topology parameters comprise: a grid structure parameter and a grid structure parameter;

the power grid structure parameters comprise line-to-ground susceptance B and line impedance Z;

the air network structure parameters comprise the length L of an air network pipeline, the diameter D of the air network pipeline and the friction coefficient lambda of the air network pipeline.

10. An electric-gas integrated energy system state estimation system, the system comprising:

the acquisition module is used for acquiring multiple measured sampling values in the electricity-gas integrated energy system;

the determining module is used for solving a pre-constructed state estimation model of the electric-gas integrated energy system by utilizing a particle swarm algorithm based on sampling values measured by various quantities in the electric-gas integrated energy system, and determining estimated values of various state quantities in the electric-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

Technical Field

The invention relates to the field of comprehensive energy operation, in particular to a state estimation method and system for an electricity-gas comprehensive energy system.

Background

In the existing increasingly huge integrated energy system, due to the relationship of coupling characteristics among subsystems, data gradually develops towards multi-scale and multi-azimuth directions, the complexity and the breadth of the data required to be processed by the integrated energy system also increase exponentially, and in addition, abnormal conditions such as measurement noise and bad data exist in the system, a dispatcher may not obtain real system operation data and be misled, so that the safety and the stability of the whole integrated energy system are damaged by inestimation. Therefore, the state estimation work of the comprehensive energy system is an important part in the development and construction of the comprehensive energy system, and can indicate the direction for the future development trend of the comprehensive energy system.

The role of state estimation work in an integrated energy system can be summarized as follows: the measurement data accuracy is improved by increasing the redundancy of the measurement; data which cannot be measured in the comprehensive energy system can be estimated; the state estimation can also carry out prediction analysis on the future system state according to the obtained data; under the actual operation condition, the measurement device configuration of the comprehensive energy system is limited, full measurement cannot be realized, the position of the measurement device in the comprehensive energy system can be reasonably distributed by state estimation, and both economy and high efficiency are realized.

It can be seen that the accuracy and speed of state estimation are of great importance to the development of the integrated energy system, and therefore, how to improve the accuracy and speed of state estimation is the focus of research.

Commonly used estimation methods are kalman filtering and maximum likelihood estimation. The Kalman filtering algorithm is a data processing technology for removing noise and restoring real data, and can update and process data acquired on site in real time; the principle of maximum likelihood estimation is to maximize the likelihood that a measurement value is observed by solving for a state quantity. The two methods are widely applied to the power system, but Kalman filtering is only suitable for a linear system and is not suitable for a nonlinear comprehensive energy system, and maximum likelihood estimation is only from the perspective of probability theory, does not consider the multi-energy flow coupling characteristic in the comprehensive energy system, and cannot describe the internal relation between the quantity measurement and the state quantity in the system.

Based on the above background, in order to perfect the evaluation of the state of the integrated energy system, it is urgently needed to research a new state estimation method of the integrated energy system.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a state estimation method of an electricity-gas integrated energy system, which comprises the following steps:

obtaining a plurality of measured sampling values in the electricity-gas comprehensive energy system;

on the basis of sampling values measured by various quantities in the electricity-gas integrated energy system, solving a pre-constructed state estimation model of the electricity-gas integrated energy system by utilizing a particle swarm algorithm to determine estimation values of various state quantities in the electricity-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

Preferably, the measurement of the quantity in the electric-gas integrated energy system includes but is not limited to:

the method comprises the steps that the voltage amplitude, the active load and the reactive load of a measurable node in a power system, the active power and the reactive power transmitted by a power transmission line in the power system, the pressure and the injection flow of the measurable node in a natural gas system and the transmission flow of a natural gas pipeline in the natural gas system can be measured;

the state quantities in the electric-gas integrated energy system include but are not limited to:

the voltage amplitude and the voltage phase angle of all nodes in the power system, and the pressure of all nodes in the natural gas system.

Further, the construction of the state estimation model of the electric-gas integrated energy system comprises the following steps:

the method comprises the steps of enabling sampling values of various quantities of the electricity-gas integrated energy system to be input into a state estimation model of the electricity-gas integrated energy system, and enabling estimated values of various quantities of states in the electricity-gas integrated energy system to be output from the state estimation model of the electricity-gas integrated energy system;

based on the principle of least square method, constructing a target function by taking the sum of estimation errors of various quantity measurement respectively corresponding to the estimation values as the minimum;

constructing an electric-gas coupling power flow model based on the topological structure parameters of the electric-gas comprehensive energy system;

and taking the electric-gas coupling power flow model as the constraint of the objective function, and constructing the compressor operation constraint, the GPG electric-gas coupling element operation constraint and the P2G electric-gas coupling element operation constraint for the objective function according to the equipment characteristics of the electric-gas integrated energy system.

Further, the objective function is calculated as follows:

wherein J (x) is the value of the objective function, ε_eIs a measurement error matrix, epsilon, of the power system in the electricity-gas integrated energy system_gIs a measurement error matrix, R, of a natural gas system in an electricity-gas integrated energy system_eIs a covariance matrix of measurement errors, R, of an electric power system in an electric-gas integrated energy system_gThe measurement error covariance matrix of the natural gas system in the electricity-gas comprehensive energy system is obtained;

wherein, the measurement error matrix epsilon of the electric power system in the electricity-gas integrated energy system_eIs calculated as follows:

ε_e＝z_e-h_e(x)

in the formula, z_eMatrix of sampled values, h, for measurements of various quantities in an electrical power system in an electrical-gas integrated energy system_e(x) An estimated value matrix for measuring a plurality of quantities in an electric power system in the electricity-gas comprehensive energy system;

a measurement error matrix epsilon of a natural gas system in the electricity-gas integrated energy system_gIs calculated as follows:

ε_g＝z_g-h_g(x)

in the formula, z_gSampling value matrix h for multiple measurements in a natural gas system in an electric-gas integrated energy system_g(x) And the estimated value matrix is measured for various quantities in the natural gas system in the electricity-gas integrated energy system.

Further, the electric-gas coupling power flow model comprises: the power system power flow model and the natural gas system power flow model.

Further, the calculation formula of the power system flow model is as follows:

in the formula, V_iIs the voltage of node i, V, in the power system_jIs the voltage of node j in the power system, G_ijFor the conductance of lines ij in an electric power system, B_ijIs the susceptance, theta, of a line ij in an electric power system_ijIs the phase angle difference between node i and node j in the power system, K is the transformer transformation ratio, b_TFor the standard side susceptance, y, of the transformer_ijFor admittance, P, of lines ij in an electric power system_iFor the active load of node i, Q, in the power system_iIs reactive load of node i, P, in the power system_ijActive power, Q, transmitted for line ij in an electric power system_ijAnd j belongs to (1-N) for the reactive power transmitted by the line ij in the power system, wherein N is the sum of all nodes connected with the node i.

Further, the calculation formula of the natural gas system flow model is as follows:

in the formula, K_αβIs the pipeline constant, p, of the pipeline in a natural gas system_αIs the pressure of a node alpha in a natural gas system, p_βIs the pressure of a node beta, f in a natural gas system_αβFor the transmission flow of alpha beta in the pipeline in a natural gas system, f_αFor the injection flow rate of the node alpha in the natural gas system, beta belongs to N_α，N_αIs the set of all nodes connected to node alpha, s_αβIs the flow direction coefficient of the pipeline alpha beta in the natural gas system, if p_α＞p_βThen s_αβ＝1；p_α＝p_βThen s_αβ0; if p is_α＜p_βThen s_αβ＝-1。

Preferably, the calculation formula of the fitness function of the PSO method is:

F(x)＝J(x)

wherein, F (x) is a fitness function value of a PSO method, and J (x) is an objective function value of the state estimation model of the electricity-gas integrated energy system.

Further, the topological structure parameters of the electric-gas integrated energy system comprise: a grid structure parameter and a grid structure parameter;

the power grid structure parameters comprise line-to-ground susceptance B and line impedance Z;

the air network structure parameters comprise the length L of an air network pipeline, the diameter D of the air network pipeline and the friction coefficient lambda of the air network pipeline.

Based on the same inventive concept, the invention also provides a state estimation system of the electricity-gas comprehensive energy system, which comprises:

the acquisition module is used for acquiring multiple measured sampling values in the electricity-gas integrated energy system;

the determining module is used for solving a pre-constructed state estimation model of the electric-gas integrated energy system by utilizing a particle swarm algorithm based on sampling values measured by various quantities in the electric-gas integrated energy system, and determining estimated values of various state quantities in the electric-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

Compared with the closest prior art, the invention has the following beneficial effects:

the invention provides a state estimation method and a state estimation system for an electricity-gas integrated energy system, which comprise the following steps: obtaining a plurality of measured sampling values in the electricity-gas comprehensive energy system; on the basis of sampling values measured by various quantities in the electricity-gas integrated energy system, solving a pre-constructed state estimation model of the electricity-gas integrated energy system by utilizing a particle swarm algorithm to determine estimation values of various state quantities in the electricity-gas integrated energy system; the state estimation model of the electricity-gas integrated energy system aims at determining the estimation values of various state quantities by taking the sum of estimation errors of various quantity measurement respectively corresponding to the estimation values constructed based on the principle of least square method as the minimum; the technical scheme provided by the invention can avoid the problems of reduced estimation accuracy and reduced calculation speed under the condition of existence of diversified data, reliably realize the state estimation of the comprehensive energy system, ensure the stable operation of the comprehensive energy system and lay a solid foundation for situation perception.

Drawings

FIG. 1 is a flow chart of a method for estimating the state of an electric-gas integrated energy system according to the present invention;

FIG. 2 is a schematic diagram illustrating the steps performed to estimate the state of the electric-gas integrated energy system according to an embodiment of the present invention;

fig. 3 is a structural diagram of a state estimation system of an electric-gas integrated energy system according to the present invention.

Detailed Description

The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.

Example 1:

in consideration of the problems of reduced estimation accuracy and slow calculation speed caused by diversified data in the integrated energy system, the invention provides a state estimation method of an electric-gas integrated energy system, as shown in fig. 1, comprising the following steps:

s1, obtaining multiple measured sampling values in an electricity-gas integrated energy system;

s2, solving a pre-constructed state estimation model of the electric-gas integrated energy system by utilizing a particle swarm algorithm based on sampling values measured by various quantities in the electric-gas integrated energy system, and determining estimated values of various state quantities in the electric-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

The specific steps of the present invention are shown in fig. 2, and before the step S1, the method further includes a step S0: a pre-constructed state estimation model of the electricity-gas integrated energy system;

the step S0 specifically includes:

step S0-1: initializing topological structure parameters of the electric-gas integrated energy system, and constructing an electric-gas coupling power flow model h (x) of the electric-gas integrated energy system based on the topological structure parameters of the electric-gas integrated energy system;

wherein, the topological structure parameters of the electricity-gas comprehensive energy system comprise: grid structure parameters such as line-to-ground susceptance B, line impedance Z, etc.; structural parameters of the air network, such as the length L of the air network pipeline, the diameter D of the air network pipeline, the friction coefficient lambda of the air network pipeline and the like;

h (x) h_e(x),h_g(x)]^T，h_e(x) For the power system flow model, h_g(x) A natural gas system trend model;

the power system power flow model is shown in the following formulas (1) to (6):

P_ij＝V_i ²G_ij-V_iV_jG_ijcosθ_ij-V_iV_jB_ijsinθ_ij (3)

Q_ij＝-V_i ²(B_ij+y_ij)-V_iV_jG_ijsinθ_ij+V_iV_jB_ijcosθ_ij (4)

in the formula, V_iIs the voltage of node i, V, in the power system_jIs the voltage of node j in the power system, G_ijFor the conductance of lines ij in an electric power system, B_ijIs the susceptance, theta, of a line ij in an electric power system_ijIs the phase angle difference between node i and node j in the power system, K is the transformer transformation ratio, b_TFor the standard side susceptance, y, of the transformer_ijFor admittance, P, of lines ij in an electric power system_iFor the active load of node i, Q, in the power system_iIs reactive load of node i, P, in the power system_ijActive power, Q, transmitted for line ij in an electric power system_ijFor the reactive power transmitted by the line ij in the power system, the case that the line ij corresponding to the equations (3) and (4) has no transformer, the case that the line ij corresponding to the equations (5) and (6) has a transformer, j ∈ (1 ∈ &)N), N being the sum of all nodes connected to node i.

The natural gas system power flow model is shown in the following formulas (7) to (8):

in the formula, K_αβIs the pipeline constant, s, of the pipeline alpha beta in a natural gas system_αβIs the flow direction coefficient of the pipeline alpha beta in the natural gas system, if p_α＞p_βThen s_αβ＝1；p_α＝p_βThen s_αβ0; if p is_α＜p_βThen s_αβ＝-1，p_αIs the pressure of a node alpha in a natural gas system, p_βIs the pressure of a node beta, f in a natural gas system_αβFor the transmission flow of alpha beta in the pipeline in a natural gas system, f_αFor the injection flow rate of the node alpha in the natural gas system, beta belongs to N_α，N_αIs the set of all nodes connected to node alpha.

The h (x) can reflect the relation between the quantity measurement and the state quantity in an electric power system and a natural gas system in the electricity-gas integrated energy system;

the measuring of the quantity of the power system comprises: the voltage amplitude, the active load and the reactive load of the node can be measured, and the active power and the reactive power transmitted by the power transmission line can be measured;

the state quantities of the power system include: the voltage amplitude and the voltage phase angle of the node;

the measuring of the quantity of the natural gas system comprises: the pressure intensity and the injection flow of the node can be measured, and the transmission flow of the natural gas pipeline can be measured;

the state quantity of the natural gas system comprises: the pressure at the node.

Step S0-2: constructing a state estimation objective function J (x) by adopting a weighted least square method (WLS) and taking a minimized model error as a target;

considering that the data error of the comprehensive energy system is more diversified and the nonlinear characteristic of the comprehensive energy system is more diversified, the negative influence on the state estimation is larger. Therefore, a weighted least square method (WLS) is adopted to construct a state estimation objective function J (x) with a minimized model error as a target;

the calculation of J (x) is as follows:

ε_e＝z_e-h_e(x) (10)

ε_g＝z_g-h_g(x) (11)

in the formula, z_eMatrix of sampled values for the measurement of quantities of an electric power system, h_e(x) Matrix of estimated values for measurements of an electrical power system, z_gSampling value matrix for natural gas system quantity measurement, h_g(x) Matrix of estimated values, epsilon, measured for natural gas systems_eIs a measurement error matrix of the power system, epsilon_gIs a measurement error matrix, R, of the natural gas system_eIs a covariance matrix of measurement errors, R, of the power system_gIs a covariance matrix of measurement errors of the natural gas system.

Step S0-3: establishing coupling element operation constraint according to the characteristics of the coupling elements in the electricity-gas comprehensive energy system, and establishing compressor operation constraint;

wherein the coupling element comprises: a gas generator group (GPG) and an electric gas generator group (P2G);

and (3) operation constraint of the compressor:

R_kp_l-p_o＝0 (13)

wherein l and o are the inlet and outlet of the compressor, respectively, K_olTo pressCompressor flow coefficient, s_olIs the compressor flow direction coefficient, p_oIs the outlet pressure of the compressor, p_lIs the inlet pressure, R, of the compressor_kTo the compression ratio of the compressor, f_comIs the natural gas flow entering the compressor;

GPG operation constraints:

in the formula (f)_g,GPGNatural gas flow consumed for GPG, P_e,GPGFor the power generated by GPG, HR is the heat rate of GPG, and C is the low heating value of natural gas;

P2G operating constraints:

f_g,P2G＝C_P2GP_d,P2G (15)

in the formula (f)_g,P2GAmount of Natural gas flow generated for P2G, C_P2GIs the energy conversion coefficient of P2G, P_d,P2GThe power consumed for P2G.

Step S0-4: taking J (x) as an objective function of the state estimation model of the electric-gas integrated energy system, and taking the electric-gas coupling power flow model, the compressor operation constraint, the GPG operation constraint and the P2G operation constraint of the electric-gas integrated energy system as constraint conditions of the state estimation model of the electric-gas integrated energy system to construct the state estimation model of the electric-gas integrated energy system;

the step S2 specifically includes:

substituting a sampling value measured at the moment to be estimated in the electricity-gas integrated energy system into the state estimation model of the electricity-gas integrated energy system, and solving by using a PSO (particle swarm optimization) method to obtain a value of the state quantity at the moment to be estimated in the electricity-gas integrated energy system;

wherein, a particle swarm algorithm (PSO) fitness function F (x) is set based on a state estimation objective function of the electricity-gas integrated energy system constructed according to the WLS;

F(x)＝J(x)(16)。

the solving process by using the PSO method comprises the following steps:

step A: setting the maximum iteration number n and the estimation precision requirement delta of the state estimation of the electricity-gas integrated energy system;

the maximum iteration number n of the state estimation of the comprehensive energy system is comprehensively selected according to the response time requirement and the precision requirement of the system, and the initialization iteration number n is 0;

the estimation accuracy requires that 10 of the state quantity is taken^-4～10^-6；

And B: generating an initial population by taking the state quantity of the electric-gas comprehensive energy system as particles, and initializing the speed and the position of the particles;

and C: calculating a fitness function value of each particle;

step D: if the current fitness function value of a certain particle i is smaller than the previous local optimal fitness function value, replacing pi with the position of the particle, and if the current fitness function value of the certain particle i is smaller than the previous global optimal fitness function value, replacing gb with the position of the particle;

step E: updating the speed and position of each particle;

step F: when the maximum iteration times or the estimation precision requirement is met, outputting the current optimal particle position, wherein the position is the state estimation result E of the electricity-gas integrated energy system; otherwise, returning to the step C.

The velocity versus position update formula can be written as:

wherein w is the inertial weight; c1 and c2 are both learning factors, typically set to 2; xi and eta are [0,1 ]]A random number in between; r is a constraint factor, typically set to 1; pi is the local optimum value of the particle; gb is the global optimum of the particle,for the k iteration bit of particle iThe device is placed in a water tank,for the (k + 1) th iteration position of particle i,for the kth iteration speed of the particle i,the (k + 1) th iteration speed of the particle i.

Example 2:

the present invention provides a state estimation system of an electricity-gas integrated energy system, as shown in fig. 3, comprising:

the acquisition module is used for acquiring multiple measured sampling values in the electricity-gas integrated energy system;

the determining module is used for solving a pre-constructed state estimation model of the electric-gas integrated energy system by utilizing a particle swarm algorithm based on sampling values measured by various quantities in the electric-gas integrated energy system, and determining estimated values of various state quantities in the electric-gas integrated energy system;

the state estimation model of the electric-gas integrated energy system aims at determining the estimation values of various state quantities by taking the minimum sum of estimation errors of various quantity measurement corresponding to the estimation values constructed based on the least square method as a target.

Specifically, the measurement of the quantity in the electricity-gas integrated energy system includes but is not limited to:

the method comprises the steps that the voltage amplitude, the active load and the reactive load of a measurable node in a power system, the active power and the reactive power transmitted by a power transmission line in the power system, the pressure and the injection flow of the measurable node in a natural gas system and the transmission flow of a natural gas pipeline in the natural gas system can be measured;

the state quantities in the electric-gas integrated energy system include but are not limited to:

the voltage amplitude and the voltage phase angle of all nodes in the power system, and the pressure of all nodes in the natural gas system.

Specifically, the system further comprises a building module for pre-building the state estimation model of the electric-gas integrated energy system, wherein the building module comprises:

the setting unit is used for enabling sampling values of various kinds of measured quantities in the electricity-gas integrated energy system to be input into the state estimation model of the electricity-gas integrated energy system, and enabling estimated values of various kinds of state quantities in the electricity-gas integrated energy system to be output from the state estimation model of the electricity-gas integrated energy system;

the target function construction unit is used for constructing a target function by taking the sum of the estimation errors of various quantity measurement respectively corresponding to the estimation values as the minimum on the basis of the principle of the least square method;

the electric-gas coupling power flow model building unit is used for building an electric-gas coupling power flow model based on the topological structure parameters of the electric-gas integrated energy system;

and the constraint condition construction unit is used for taking the electric-gas coupling power flow model as the constraint of the objective function and constructing the compressor operation constraint, the GPG electric-gas coupling element operation constraint and the P2G electric-gas coupling element operation constraint for the objective function according to the equipment characteristics of the electric-gas integrated energy system.

Specifically, the objective function is calculated as follows:

wherein J (x) is the value of the objective function, ε_eIs a measurement error matrix, epsilon, of the power system in the electricity-gas integrated energy system_gIs a measurement error matrix, R, of a natural gas system in an electricity-gas integrated energy system_eIs a covariance matrix of measurement errors, R, of an electric power system in an electric-gas integrated energy system_gThe measurement error covariance matrix of the natural gas system in the electricity-gas comprehensive energy system is obtained;

wherein, the measurement error matrix epsilon of the electric power system in the electricity-gas integrated energy system_eIs calculated as follows:

ε_e＝z_e-h_e(x)

in the formula (I), the compound is shown in the specification,z_ematrix of sampled values, h, for measurements of various quantities in an electrical power system in an electrical-gas integrated energy system_e(x) An estimated value matrix for measuring a plurality of quantities in an electric power system in the electricity-gas comprehensive energy system;

a measurement error matrix epsilon of a natural gas system in the electricity-gas integrated energy system_gIs calculated as follows:

ε_g＝z_g-h_g(x)

in the formula, z_gSampling value matrix h for multiple measurements in a natural gas system in an electric-gas integrated energy system_g(x) And the estimated value matrix is measured for various quantities in the natural gas system in the electricity-gas integrated energy system.

Further, the electric-gas coupling power flow model comprises: the power system power flow model and the natural gas system power flow model.

Further, the calculation formula of the power system flow model is as follows:

in the formula, V_iIs the voltage of node i, V, in the power system_jIs the voltage of node j in the power system, G_ijFor the conductance of lines ij in an electric power system, B_ijIs the susceptance, theta, of a line ij in an electric power system_ijIs the phase angle difference between node i and node j in the power system, K is the transformer transformation ratio, b_TFor the standard side of the transformerSusceptance, y_ijFor admittance, P, of lines ij in an electric power system_iFor the active load of node i, Q, in the power system_iIs reactive load of node i, P, in the power system_ijActive power, Q, transmitted for line ij in an electric power system_ijAnd j belongs to (1-N) for the reactive power transmitted by the line ij in the power system, wherein N is the sum of all nodes connected with the node i.

Further, the calculation formula of the natural gas system flow model is as follows:

in the formula, K_αβIs the pipeline constant, p, of the pipeline in a natural gas system_αIs the pressure of a node alpha in a natural gas system, p_βIs the pressure of a node beta, f in a natural gas system_αβFor the transmission flow of alpha beta in the pipeline in a natural gas system, f_αFor the injection flow rate of the node alpha in the natural gas system, beta belongs to N_α，N_αIs the set of all nodes connected to node alpha, s_αβIs the flow direction coefficient of the pipeline alpha beta in the natural gas system, if p_α＞p_βThen s_αβ＝1；p_α＝p_βThen s_αβ0; if p is_α＜p_βThen s_αβ＝-1。

Further, the calculation formula of the fitness function of the PSO method is:

F(x)＝J(x)

wherein, F (x) is a fitness function value of a PSO method, and J (x) is an objective function value of the state estimation model of the electricity-gas integrated energy system.

Further, the topological structure parameters of the electric-gas integrated energy system comprise: a grid structure parameter and a grid structure parameter;

the power grid structure parameters comprise line-to-ground susceptance B and line impedance Z;

the air network structure parameters comprise the length L of an air network pipeline, the diameter D of the air network pipeline and the friction coefficient lambda of the air network pipeline.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.