CN111414675A - Double-layer robust state estimation method and system for electric heating comprehensive energy system - Google Patents

Double-layer robust state estimation method and system for electric heating comprehensive energy system Download PDF

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CN111414675A
CN111414675A CN202010123240.6A CN202010123240A CN111414675A CN 111414675 A CN111414675 A CN 111414675A CN 202010123240 A CN202010123240 A CN 202010123240A CN 111414675 A CN111414675 A CN 111414675A
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node
power system
hydraulic
thermodynamic
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杨晓楠
郎燕生
孙博
齐洋洋
韩锋
李森
焦永刚
陈艳波
姚远
朱承治
刘晔
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention provides a double-layer robust state estimation method of an electric heating comprehensive energy system, which comprises the following steps: acquiring measurement of an electric power system quantity and measurement of a thermodynamic system quantity of an electric heating comprehensive energy system; the thermodynamic system quantity measurement comprises hydraulic network quantity measurement and thermodynamic network quantity measurement; measuring the quantity of a power system and the quantity of a hydraulic network, inputting the measured quantities into a hydraulic network state estimation model of the power system, and obtaining a state variable estimation value and a hydraulic network state variable estimation value of the power system, and branch flow and node injection flow estimation values of the hydraulic network; the method comprises the steps of measuring branch flow of a hydraulic network, estimated value of node injection flow and a thermal network quantity, inputting a thermal network state estimation model, and obtaining estimated value of state variable of the thermal network.

Description

Double-layer robust state estimation method and system for electric heating comprehensive energy system
Technical Field
The invention belongs to the field of comprehensive energy state evaluation, and particularly relates to a double-layer robust state estimation method and system for an electric heating comprehensive energy system.
Background
The integrated energy system is considered to be the main form of energy application for future human society. With the application and development of various energy conversion devices (such as cogeneration CHP, which is the simultaneous generation of electric energy and available heat by using various ways such as fossil fuel, residual energy, renewable energy, and electric energy), the coupling of various forms of energy (such as electric energy, heat energy, and natural gas energy) is gradually enhanced. The coupling of electric energy and thermal energy is one of the main application forms of the comprehensive energy system. In order to realize comprehensive and accurate sensing of an electric-thermal integrated energy System (IEHS), an IEHS-oriented State Estimation (SE) method is required, so as to provide credible mature data for a corresponding Energy Management System (EMS) and realize unified management and scientific scheduling of the IEHS.
The state estimation and the technology thereof are well researched in an electric power system, and an application thereof in a thermodynamic system is still an ongoing research, namely a bilinear robust state estimation method facing an electric-thermal integrated energy system, wherein the bilinear robust state estimation method facing an IEHS is provided.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a double-layer robust state estimation method of an electric heating comprehensive energy system, which comprises the following steps:
acquiring the measurement of an electric power system quantity and the measurement of a thermodynamic system quantity of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement;
measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a pre-constructed power system hydraulic network state estimation model, and obtaining a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value;
measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, and inputting the measured values into a thermodynamic network state estimation model which is constructed in advance to obtain a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value;
the thermodynamic network state estimation model is constructed by considering temperature constraint among nodes of the thermodynamic network and thermal power measurement.
Preferably, the construction of the power system hydraulic network state estimation model comprises the following steps:
determining a linear measurement equation of the power system based on the set first auxiliary state variable and the first auxiliary quantity measurement;
determining a hydraulic network linear measurement equation based on the set second auxiliary state variable and the second auxiliary quantity measurement;
constructing a unified linear measurement model of the electric power system and the hydraulic network based on the electric power system linear measurement equation, the hydraulic network linear measurement equation and the coupling mode of the coupling nodes of the electric power system and the thermal system;
constructing a state estimation model of a linear weighted minimum absolute value unified by the power system and the hydraulic network based on the unified linear measurement model of the power system and the hydraulic network;
constructing a power system hydraulic network state estimation model based on the relation of the power system auxiliary state variables and the state estimation model of the uniform linear weighted minimum absolute value of the power system and the hydraulic network;
wherein the first auxiliary state variable is set by a power system state variable; the first auxiliary quantity is measured and set by the electric power system quantity; the second auxiliary state variable is set by a hydraulic network state variable; the second auxiliary quantity measurement is set by hydraulic network quantity measurement.
Preferably, the first auxiliary state variable and the first auxiliary quantity are measured as follows:
Figure BDA0002393640970000021
Figure BDA0002393640970000022
wherein the content of the first and second substances,
Figure BDA0002393640970000023
is the first auxiliary state variable to be used,
Figure BDA0002393640970000024
in order to introduce the first auxiliary quantity,
Figure BDA0002393640970000025
for first auxiliary quantity measurement, UiIs the voltage amplitude of node i, PiInjecting active power, Q, for node iiReactive power, P, injected for node iijActive power, Q, for branch ijijIs the reactive power of branch ij;
the first auxiliary quantity is calculated as follows:
Figure BDA0002393640970000026
wherein, UjMagnitude of voltage at node j, θijIs the phase angle difference between node i and node j.
Preferably, the power system linear measurement equation is as follows:
Figure BDA0002393640970000031
in the formula of UiAmplitude of voltage at node i, PiInjecting active power, Q, for node iiReactive power, P, injected for node iijIs a branchActive power of ij, QijIs the reactive power of the branch ij, Ni is the number of nodes of the power system,
Figure BDA0002393640970000032
is a first auxiliary amount, gsiIs the equivalent resistance to ground of node i, bsiIs the equivalent ground reactance of node i, gijIs the equivalent resistance of branch ij, bijIs the equivalent reactance of branch ij, GijDetermined by the equivalent resistance of branch ij, BijDetermined by the equivalent reactance of branch ij.
Preferably, the second auxiliary state variable and the second auxiliary quantity are measured as follows:
Figure BDA0002393640970000033
Figure BDA0002393640970000034
wherein the content of the first and second substances,
Figure BDA0002393640970000035
is a second auxiliary state variable, αaIs the second auxiliary quantity to be used,
Figure BDA0002393640970000036
the second amount of assistance is measured and,
Figure BDA0002393640970000037
the water flow rate of the branch ij,
Figure BDA0002393640970000038
injecting water flow into the nodes;
the αaThe second auxiliary quantity is calculated as follows:
Figure BDA0002393640970000039
wherein the content of the first and second substances,
Figure BDA00023936409700000310
is αaElement of (5), pijFor pressure head loss of the pipe, sijDetermined by the relationship of the node i pressure head and the node j pressure head.
Preferably, the hydraulic network linearity measurement equation is as follows:
Figure BDA00023936409700000311
in the formula (I), the compound is shown in the specification,
Figure BDA00023936409700000312
the water flow rate of the branch ij,
Figure BDA00023936409700000313
injection of water flow into the node, KijThe impedance coefficient of the conduit, p, for branch ijijFor pressure head loss of the pipe, sijDetermined by the relationship of the node i pressure head and the node j pressure head,
Figure BDA00023936409700000314
is the second auxiliary amount αaOf (1).
Preferably, the unified linear measurement model of the power system and the hydraulic network is shown as the following formula:
Figure BDA0002393640970000041
in the formula, xaUnified auxiliary state variable, z, for electric power systems, hydraulic networksaFor the uniform auxiliary quantity measurement of an electric power system and a hydraulic power network,
Figure BDA0002393640970000042
is the first auxiliary state variable to be used,
Figure BDA0002393640970000043
is the second auxiliary state variable and is,
Figure BDA0002393640970000044
for the measurement of the first auxiliary quantity,
Figure BDA0002393640970000045
for the purpose of the second auxiliary quantity measurement,
Figure BDA0002393640970000046
for the heat energy generated by adopting a gas turbine or an internal combustion engine in a coupling mode, N1 is the number of nodes which are coupled by adopting the gas turbine or the internal combustion engine,
Figure BDA0002393640970000047
for the heat energy generated by the coupling of the turbine, N2 is the number of nodes coupled by the turbine, HaIs a uniform constant coefficient matrix of an electric power system and a hydraulic power network, raThe method is a unified measurement error of a power system and a hydraulic network.
Preferably, the power system and the hydraulic network are unified, and the state estimation model of the linear weighted minimum absolute value is as follows:
minw(u+v)
Figure BDA0002393640970000048
wherein w is a unified measurement weight matrix of the power system and the hydraulic network, u and v are two types of nonnegative variables introduced by a unified model of the power system and the hydraulic network, and zaFor uniform auxiliary quantity measurement of electric power systems, hydraulic networks, HaIs a uniform constant coefficient matrix, x, of an electric power system and a hydraulic power networkaThe method is a unified auxiliary state variable of a power system and a hydraulic network.
Preferably, the constructing a power system and hydraulic network state estimation model based on the relationship of the power system auxiliary state variables and the state estimation model of the linear weighted minimum absolute value unified by the power system and the hydraulic network includes:
constructing a relational expression among auxiliary state variables of the power system;
constructing a second-order cone inequality constraint based on a relational expression between the auxiliary state variables of the power system;
and constructing a state estimation model of the hydraulic network of the power system based on the second-order cone inequality constraint and a state estimation model of the uniform linear weighted minimum absolute value of the power system and the hydraulic network.
Preferably, the second order cone inequality constraint is as follows:
Figure BDA0002393640970000049
in the formula (I), the compound is shown in the specification,
Figure BDA00023936409700000410
is the first auxiliary amount.
Preferably, the power system hydraulic network state estimation model is as follows:
min∑λijRij-w(u+v)
Figure BDA00023936409700000411
wherein w is a unified measurement weight matrix of the power system and the hydraulic network, u and v are two types of nonnegative variables introduced by a unified model of the power system and the hydraulic network, and zaFor uniform auxiliary quantity measurement of electric power systems, hydraulic networks, HaIs a uniform constant coefficient matrix, x, of an electric power system and a hydraulic power networkaIs a unified auxiliary state variable of a power system and a hydraulic network,
Figure BDA0002393640970000051
is a first auxiliary quantity, λijFor adjusting the parameters, the magnitude is equal to the weighted value of the power measurement of the corresponding branch ij in the power system.
Preferably, the measuring of the power system quantity and the measuring of the hydraulic network quantity are input into a pre-constructed power system hydraulic network state estimation model to obtain a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value, and the method comprises the following steps:
measuring the electric power system quantity and the hydraulic network quantity, inputting the electric power system hydraulic network state estimation model which is constructed in advance, and calculating a first auxiliary state variable estimation value and a second auxiliary state variable estimation value;
calculating a branch phase angle difference estimation value, a node voltage amplitude estimation value and a hydraulic network pipeline pressure head loss estimation value of the power system through nonlinear transformation based on the first auxiliary state variable estimation value and the second auxiliary state variable estimation value;
calculating a node phase angle estimation value and a hydraulic network node pressure head estimation value of the electric power system through linear transformation based on the branch phase angle difference estimation value of the electric power system and the hydraulic network pipeline pressure head loss estimation value;
and calculating estimated values of branch flow and node injection flow of the hydraulic network based on the estimated value of the pressure head of the hydraulic network node.
Preferably, the construction of the thermodynamic network state estimation model comprises the following steps:
constructing a thermodynamic network linear measurement equation based on thermodynamic network state variables and thermodynamic network quantity measurement;
constructing a state estimation model of a linear weighted minimum absolute value of the thermodynamic network based on the thermodynamic network linear measurement equation;
setting the flow estimation value of the hydraulic network branch and the node injection flow estimation value as pseudo-quantity measurement, and determining the temperature constraint among nodes of the thermodynamic network;
and constructing a thermodynamic network state estimation model based on the state estimation model of the linear weighted minimum absolute value of the thermodynamic network and the constraint.
Preferably, the thermodynamic network state variables and thermodynamic network quantity are measured according to the following formulas:
xt＝[Ts；Tr]
zt＝[φ；Ts；Tr]
wherein x istBeing state variables of the thermodynamic network, TsSupply of heat temperature, T, to the noderIs the node regenerative temperature; z is a radical oftFor thermodynamic network measurements, φ is the node thermal power.
Preferably, the thermodynamic network linearity measurement equation is as follows:
Figure BDA0002393640970000061
in the formula, phiiIs the thermal power of the node i,
Figure BDA0002393640970000062
injection water flow rate, C, for node ipIs the specific heat capacity of water, TsiTemperature of heat supply to node i, TriIs the regenerative temperature of node i.
Preferably, the linear weighted least absolute state estimation model of the thermal network is as follows:
minwt(ut+vt)
Figure BDA0002393640970000063
in the formula, wtIs a measured weight matrix, u, of the thermal networktAnd vtTwo types of non-negative variables, z, introduced for thermodynamic networkstFor thermodynamic network quantity measurement, xtIs a state variable of the thermodynamic network, HtA constant coefficient matrix of the thermal network.
Preferably, the constant coefficient matrix H of the thermodynamic networktIs represented as follows:
Figure BDA0002393640970000064
wherein, CpIs the specific heat capacity of the water,
Figure BDA0002393640970000065
and injecting the water flow estimation value for the node.
Preferably, the temperature constraints between nodes of the thermodynamic network are as follows:
Figure BDA0002393640970000066
Figure BDA0002393640970000067
in the formula, TendIs the temperature at the end of the pipe, TstartFor the joint temperature, T, of the head end of the pipelineaIs the ambient temperature, λhHeat transfer coefficient per unit length of pipe, L length of pipe, CpIs the specific heat capacity of the water,
Figure BDA0002393640970000068
for the estimated injection water flow at node i,
Figure BDA0002393640970000069
for an estimate of the total flow of a particular pipe out of the node,
Figure BDA00023936409700000610
flow estimate, T, for a pipe injected into a nodeoutIs a node mixing temperature, TinIs the pipe head end node temperature.
Preferably, the thermodynamic network state estimation model is as follows:
minwt(ut+vt)
Figure BDA00023936409700000611
in the formula, wtIs a measured weight matrix, u, of the thermal networktAnd vtTwo types of non-negative variables, z, introduced for thermodynamic networkstFor thermodynamic network quantity measurement, xtIs a state variable of the thermodynamic network, HtConstant coefficient matrix, T, of a thermodynamic networkendIs the temperature at the end of the pipe, TstartFor the joint temperature, T, of the head end of the pipelineaIs the ambient temperature, λhHeat transfer coefficient per unit length of pipe, L length of pipe, CpIs the specific heat capacity of the water,
Figure BDA0002393640970000071
for the estimated injection water flow at node i,
Figure BDA0002393640970000072
for an estimate of the total flow of a particular pipe out of the node,
Figure BDA0002393640970000073
flow estimate, T, for a pipe injected into a nodeoutIs a node mixing temperature, TinIs the pipe head end node temperature.
Based on the same conception, the invention also provides a double-layer robust state estimation system of the electric-heat comprehensive energy system, which comprises the following components:
the data acquisition module is used for acquiring the electric power system quantity measurement and the thermal power system quantity measurement of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement;
the power system hydraulic network state estimation module is used for measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a power system hydraulic network state estimation model which is constructed in advance, and obtaining a power system state variable node voltage amplitude value estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value;
the thermodynamic network state estimation module is used for measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, inputting the measured values into a thermodynamic network state estimation model which is constructed in advance, and obtaining a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value;
the thermodynamic network state estimation model is constructed by considering temperature constraint among nodes of the thermodynamic network and thermal power measurement.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a double-layer robust state estimation method of an electric heating comprehensive energy system, which comprises the following steps: acquiring the measurement of an electric power system quantity and the measurement of a thermodynamic system quantity of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement; measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a pre-constructed power system hydraulic network state estimation model, and obtaining a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value; the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermal network quantity are measured and input into a thermal network state estimation model which is constructed in advance to obtain a thermal network state variable node heat supply temperature estimation value and a node heat return temperature estimation value, and the thermal network state estimation model considers the temperature constraint and the thermal power measurement construction among the thermal network nodes.
Meanwhile, second-order cone constraint is introduced in the process of establishing the hydraulic network state estimation model of the power system, loss of measurement redundancy caused by introduction of auxiliary variables in the power system is made up, and accuracy of state variable estimation of the power system is improved.
Drawings
FIG. 1 is a schematic diagram of a double-layer robust state estimation method for an electric-thermal integrated energy system according to the present invention;
FIG. 2 is a schematic diagram of a double-layer robust state estimation system of an electric-thermal integrated energy system according to the present invention;
fig. 3 is a schematic diagram of a solving process of a double-layer robust state estimation model provided in the embodiment of the present invention;
FIG. 4 is a graph illustrating an average value of estimation errors of state variables of a power system according to an embodiment of the present invention;
fig. 5 is an average value of estimation errors of state variables of a thermodynamic system provided in an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the invention provides a double-layer robust state estimation method of an electrothermal comprehensive energy system, a schematic diagram of which is shown in figure 1 and comprises the following steps: acquiring the measurement of an electric power system quantity and the measurement of a thermodynamic system quantity of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement; measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a pre-constructed power system hydraulic network state estimation model, and obtaining a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value; and measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, inputting the measured values into a thermodynamic network state estimation model which is constructed in advance, and obtaining a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value.
S1, obtaining electric power system quantity measurement and thermodynamic system quantity measurement of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: hydraulic network and thermal network, the thermal system measurements include hydraulic network measurements and thermal network measurements.
S2, measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a pre-constructed power system hydraulic network state estimation model, and obtaining a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value.
S2-1 construction of power system hydraulic network state estimation model
Measuring model of S2-1-1IEHS
Measurement equation of S2-1-1-1 power system
In an electric power system, a state variable xeIncluding nodal phase angle θiAnd node voltage amplitude Ui. Measuring zeGenerally comprising: node voltage amplitude UiNode injection power Pi、QiAnd branch power Pij、Qij. The measurement equation is shown in formula (1):
Figure BDA0002393640970000091
for simplicity, measurement noise is ignored here. Meanwhile, the formula (1) adopts a pi-type equivalent circuit, and the parameters of the equivalent circuit are as follows: gij＝gs/k，bij＝bs/k，gsi＝(1-k)gs/k2，bsi＝(1-k)bs/k2+bc/2，Gij＝-gij，Bij＝-bij，gs+jbsIs a series susceptance, bcFor charging susceptance, k stands for the branch transformation ratio (for a branch without transformer, k is 1, b)c＝0)。
In the formula of Ui、UjThe voltage amplitudes, P, of nodes i, j, respectivelyi、QiActive and reactive power injected for node i, Pij、QijActive power, reactive power, N, of branch ij respectivelyiIs the number of nodes, θ, of the power systemijIs the phase angle difference, g, between node i and node js、bsThe actual resistance, the actual reactance, g, of branch ijij、bijEquivalent resistance, equivalent reactance, g, of branch ijsi、bsiRespectively, the equivalent resistance to ground and the equivalent reactance to the node i.
Measurement equation of S2-1-1-2 thermodynamic system
The thermodynamic system consists of a hydraulic network and a thermodynamic network. The heat is transferred between the heat source and the user in the form of water or steam through the heat supply pipeline and the heat return pipeline. When a thermodynamic system is analyzed, it is usually modeled as a hydraulic network model and a thermodynamic network model, respectively.
Measurement equation of S2-1-1-2-1 hydraulic network
Since the variables in a hydraulic network generally include pressure and flow, the state variable x in the hydraulic network of the present inventionhFor node pressure head pi. Measuring zhComprising a nodal pressure head piFlow of branch ij
Figure BDA0002393640970000092
And node i injection flow
Figure BDA0002393640970000093
The expression of the metrology equation is (for simplicity, metrology noise is ignored here):
Figure BDA0002393640970000094
in the formula (I), the compound is shown in the specification,
Figure BDA0002393640970000095
water flow, K, for branch ijijIs the pipe impedance coefficient of branch ij, when pi≥pjWhen s isij1 is ═ 1; otherwise, sijIs-1. K is the pipe impedance coefficient, generally given by the Colebrook-White equation, expressed as:
Figure BDA0002393640970000096
wherein L represents the length of the pipe (m), D represents the diameter of the pipe (m), and ρ is the density of water (kg/m)3) G is the acceleration of gravity (kg. m/s)2). f represents the friction factor, expressed as:
Figure BDA0002393640970000097
Figure BDA0002393640970000098
wherein, the roughness (m) of the pipeline, Re is Reynolds number, v is pipeline flow velocity (m/s), and mu is kinematic viscosity (m/s) of water2In s). The formula (4) belongs to an implicit equation, and the Haaland formula is adopted in the invention[19]Approximate solution f:
Figure BDA0002393640970000101
measurement equation of S2-1-1-2-2 thermodynamic network
State variable x in a thermodynamic networktIncluding node heating temperature TsiAnd node regenerative temperature Tri. Measuring ztTypically including the node heating temperature TsiTemperature of regenerative node heatriNode injection flow
Figure BDA0002393640970000102
Thermal power phi of nodei. The measurement equation is expressed (for simplicity, measurement noise is ignored here):
Figure BDA0002393640970000103
wherein, CpThe specific heat capacity (J/(kg. K)) is shown. At the same time, the temperature T of the head and tail ends of the pipelinestartAnd TendThe following relationships exist:
Figure BDA0002393640970000104
wherein, TaRepresents the ambient temperature and is a constant; lambda [ alpha ]hThe heat transfer coefficient per unit length of the pipe (W/(m.K)) is shown. When a node connects multiple injection lines simultaneously, the mixed temperature of the node is expressed as:
Figure BDA0002393640970000105
wherein, ToutRepresenting the temperature of the mixture at that node,
Figure BDA0002393640970000106
is the total flow out of the node.
Figure BDA0002393640970000107
TinThe flow rate of a pipe injected into the node and the temperature of the node at the head end corresponding to the pipe are measured.
Measurement model of S2-1-1-3 coupling unit
The coupling unit of the present invention mainly considers Combined Heat and Power (CHP) equipment, and its main types include gas turbine, internal combustion engine and steam turbine. For gas turbines and internal combustion engines, the heat energy generated therefrom is phiCHPWith electric energy PCHPThe relationship of (1) is:
Figure BDA0002393640970000108
for steam turbines, [ phi ]CHPAnd PCHPThe relationship of (c) is then expressed as:
Figure BDA0002393640970000109
wherein, cmZ represents the heat-electricity generation ratio, PconFor maximum output of electric power from the turbine, FinFor input fuel power, ηeThe conversion efficiency of electric energy.
The operation modes of the IEHS comprise island operation and grid-connected operation. 1) Island operation: the relaxation node of the heat supply network is connected with a certain node (except the relaxation node) of the power grid through a certain CHP, and the relaxation node of the power grid is connected with a certain node (except the relaxation node) of the heat supply network through another CHP. 2) Grid-connected operation: the relaxation node of the heat supply network is connected with a certain node (except the relaxation node) of the power grid through a certain CHP, and the relaxation node of the power grid is connected with a certain large power grid.
Therefore, the power grid and the heat supply network under the island operation have closer coupling relationship. The invention mainly researches IEHS under island operation. In addition, other types of nodes (e.g., PQ, PV nodes, heat load nodes, etc.) may establish connectivity through the CHP in addition to the relaxation nodes. Therefore, the coupling of the grid and the heat supply grid can be regarded as a zero measurement constraint, which is expressed as:
Figure BDA00023936409700001010
wherein the content of the first and second substances,
Figure BDA0002393640970000111
respectively representing the heat energy and the electric energy generated by the CHP satisfying the coupling relations (10) and (11), N1 and N2 respectively representing the number of the coupling nodes satisfying the coupling relations (10) and (11), 0N1、0N2Column vectors of dimensions N1 and N2, respectively.
In summary, the measurement model of IEHS is expressed as:
Figure BDA0002393640970000112
wherein h ise(xe),hh(xh) And ht(xt) Are respectively expressed by the formulas (1), (2) and (7). r ise，rhAnd rtRespectively representing the measurement errors of the power system, the hydraulic network and the thermal network.
S2-1-2 first-layer Robust State Estimation (RSE) model establishment based on second-order cone programming
The main purpose of modeling is to convert a traditional non-linear non-convex SE model into a linear convex optimized RSE model, including linearizing a measurement equation, establishing a W L AV model and introducing a second-order cone inequality constraint.
Linearization of S2-1-2-1 measurement equation
The solution result of the conventional non-linear non-convex SE method may be to obtain a locally optimal solution, or may not converge. The fundamental problem that arises in the above-described problem is that the metrology equations in the SE model are non-linear. The invention realizes the linearization of the measurement equation by introducing proper auxiliary state variables and auxiliary quantity measurement.
Linearization of S2-1-2-1-1 power system measurement equation
Make the square of the node voltage amplitude
Figure BDA00023936409700001113
Substitute node voltage amplitude UiAs a new quantity measurement, the rest is unchanged. At the same time, auxiliary variables are selected
Figure BDA0002393640970000113
Wherein the content of the first and second substances,
Figure BDA0002393640970000114
then measure the auxiliary quantity
Figure BDA0002393640970000115
The measurement equation with linearization is expressed (for simplicity, measurement noise is ignored here):
Figure BDA0002393640970000116
Figure BDA0002393640970000117
linearization of S2-1-2-1-2 hydraulic network measurement equation
Square root of pressure head loss of pipe i-j
Figure BDA0002393640970000118
(pij＝pi-pj) Pressure head p instead of nodes i and jiAnd pj. Selecting
Figure BDA0002393640970000119
As an auxiliary variable, wherein
Figure BDA00023936409700001110
Then measure the auxiliary quantity
Figure BDA00023936409700001111
The measurement equation with linearization is expressed as:
Figure BDA00023936409700001112
Figure BDA0002393640970000121
s2-1-2-1-3 building unified linear measurement model of electric power system and hydraulic power network
Heat supply network node thermal power measurement considering active power correspondence of power system coupling node
Figure BDA0002393640970000122
Then the unified linear measurement model of the power system and the hydraulic network is expressed as:
Figure BDA0002393640970000123
wherein r isa＝[re；rh；rcp1；rcp2]，rcp1、rcp2Are respectively as
Figure BDA0002393640970000124
Corresponding measurement error. HaThe constant coefficient matrix is composed of the following parts:
Figure BDA0002393640970000125
establishment of S2-1-2-2W L AV model
The above-described linearization measurement model (18) can be constructed as a W L AV based RSE model:
minw|ra|
s.t.za-Haxa＝ra(20)
introducing two types of non-negative variables u and v into a measurement weight matrix W, so as to obtain an equivalent linear W L AV (L initial-W L AV, &lttttranslation = L "&tttl &ltt/t &ttt-W L AV) model, which is expressed as:
Figure BDA0002393640970000126
establishment of S2-1-2-3 linear second-order cone programming model
The model (21) already belongs to a linear convex optimization RSE model. However, for the power system, the introduction of the auxiliary state variables increases the number of the state variables by BeWherein B iseThe number of branches in the power system. Therefore, the measurement redundancy of the power system part is reduced, and the estimation precision is influenced.
Further, auxiliary state variables in the power system
Figure BDA0002393640970000127
And
Figure BDA0002393640970000128
they have the following relationships:
Figure BDA0002393640970000129
and (5) relaxing the (22) to convert the quadratic equation into a second-order cone inequality constraint to obtain:
Figure BDA00023936409700001210
adding (23) to the model (21) resulting in a second order cone planning based RSE model:
min∑λijRij-w(u+v)
Figure BDA00023936409700001211
wherein λ isijFor adjusting the parameters, the magnitude is equal to the weighted value of the power measurement of the corresponding branch ij in the power system. It can be seen that the number of inequality constraints (23) is BeThus it equivalently makes the model(24) The quantity of the medium power system part is increased by BeThereby compensating for the partial measurement redundancy of the model (21) loss.
S2-2 solving method of hydraulic network state estimation model of power system
The first layer of the power system hydraulic network state estimation model (24) belongs to a linear SOCP model, and optimization software MOSEK is adopted to solve. The method comprises the following steps:
s2-2-1 solving the model (24) to obtain auxiliary variables in the power system
Figure BDA0002393640970000131
And auxiliary variables in hydraulic networks
Figure BDA0002393640970000132
An estimate of (d).
S2-2-2 nonlinear transformation. For hydraulic networks, the pipe pressure head loss pijAnd auxiliary variables
Figure BDA0002393640970000133
The relationship of (1) is:
Figure BDA0002393640970000134
for an electric power system, the phase angle difference theta of the branchesijState variable UiAnd auxiliary variables
Figure BDA0002393640970000135
The following relationships exist:
Figure BDA0002393640970000136
therefore, by the nonlinear changes (28) and (29), p can be obtainedij、θijAnd UiAnd (6) estimating the value.
S2-2-3 linear transformation. For power system and hydraulic network, the phase angle difference theta of branchbPressure head loss p of pipelinebThe relationships with the state variables θ, p are:
Figure BDA0002393640970000137
wherein, theta and p are column vectors of a node phase angle and a node pressure head respectively; thetab、pbColumn vectors of branch phase angle difference and pipeline pressure head loss are respectively; a. thee、AhReduced order node-branch incidence matrices (containing no slack nodes) in the power system and the hydro network, respectively.
Therefore, in the first-stage state estimation, the estimation values of the state variables of the power system and the hydraulic network can be obtained by solving the model (24) and performing the first nonlinear transformation and the first linear transformation. Further, from the estimated value of p obtained, the pipe flow rate m and the node injection flow rate m can be obtainedqIs estimated value of
Figure BDA0002393640970000138
And
Figure BDA0002393640970000139
both will be applied as a pseudo-quantity measurement in the second layer state estimation.
S3, measuring the branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity of the hydraulic network, inputting the measured values into a thermodynamic network state estimation model which is constructed in advance, and obtaining a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value.
S3-1 thermodynamic network state estimation model construction
The modeling object of the state estimation of the second layer is a heat network, and the modeling idea is the same as that of the first layer. Wherein, the branch flow estimated value is obtained through the first layer state estimation
Figure BDA00023936409700001310
And node injection flow estimate
Figure BDA00023936409700001311
Both are considered as pseudo-metrology for state estimation of the second layer.
Construction of S3-1-1 thermodynamic network L-W L AV model
The measurement equation of the thermal network is itself a linear equation, where xt＝[Ts；Tr]，zt＝[φ；Ts；Tr]Accordingly, the L-W L AV based RSE model can be constructed directly:
minwt(ut+vt)
Figure BDA0002393640970000141
wherein, wtIs a measured weight matrix, u, of the thermal networktAnd vtTwo types of non-negative variables, z, introduced for thermodynamic networkstFor thermodynamic network quantity measurement, xtIs a state variable of the thermodynamic network, HtThe constant coefficient matrix of the thermodynamic network has the specific structure as follows:
Figure BDA0002393640970000142
s3-1-1, building a thermodynamic network state estimation model based on a thermodynamic network L-W L AV model and node temperature constraints
The thermodynamic system itself has low measurement redundancy (about 1.5). Therefore, the state estimation result of the thermal system is susceptible to a large influence when bad data is generated in the measurement. From the first analysis, it can be seen that there are equality constraints (8), (9) between the temperatures of the nodes. Measuring and estimating values obtained by estimating state of the first layer
Figure BDA0002393640970000143
And
Figure BDA0002393640970000144
the corresponding linear equality constraints are obtained as pseudo-measures and added to the model (25) to obtain:
minwt(ut+vt)
Figure BDA0002393640970000145
to this end, RSE modeling of thermal systems based on L-W L AV was completed.
S3-1-2 solution of second-layer thermodynamic network state estimation model
The RSE model (27) of the second layer belongs to a linear programming model, the invention adopts CP L EX to solve, and the state variable T in the thermodynamic network can be directly obtainedsAnd TrAn estimate of (d).
The IEHS-oriented double-layer robust state estimation model is characterized in that a hydraulic system and a thermodynamic system in a heat supply network are separated substantially, state estimation of a power system and the hydraulic system is firstly carried out, and therefore measurement estimation values of pipeline flow and node injection flow are obtained and are used as pseudo-quantity measurement to be used for state estimation of the thermodynamic system of a second layer. The specific solving flow is shown in fig. 3.
Advantages of the two-layer robust state estimation model
1) Make up for the loss of the measurement redundancy of the power system
Suppose that in the regional IEHS, the number of nodes of the power system is NeThe number of branches is Be(ii) a The number of the nodes in the heat supply network (including hydraulic and thermal networks) is NhThe number of the pipelines is Bh. And N ise＝Be-1，Nh＝Bh-1。
The invention firstly realizes the linearization of the measurement equation of the power system by introducing auxiliary variables. Compared with the nonlinear power system measurement model (13), the number of the measurement of the linearized measurement model is unchanged, but the number of the state variables is 2Ne-1 is increased to Ne+2BeI.e. increase Ne-1, resulting in loss of measurement redundancy. The second-order cone inequality constraint (23) can be obtained by relaxing the equation (22), and the number of the constraint is Ne-1. Therefore, introducing a second order cone constraint (23) increases N equivalently as the number of measurementse-1, thereby compensating for the loss of measurement redundancy of the power system section.
2) The measurement redundancy of the thermodynamic system is increased
In the process ofWhen the thermal network measurement model is established, in the prior art, only the node thermal power measurement, the node heating temperature measurement and the node regenerative temperature measurement are considered, which results in lower measurement redundancy of the thermal network part. The invention considers the constraint relation existing between the node temperatures and equivalently increases the measurement redundancy of the thermodynamic network part. Meanwhile, when the constraint between the node temperatures is constructed, the method does not directly adopt the pipeline flow m and the node injection flow mqInstead, a more reliable measurement estimation value is obtained by the first layer state estimation
Figure BDA0002393640970000151
And
Figure BDA0002393640970000152
the constraint conditions are made more accurate.
3) No need of initial value and good poor resistance
Document [12 ]]In general, W L S is an iterative solution of a nonlinear measurement equation by using a Newton method, therefore, W L S needs to give an initial value of a state variable, and the initial value needs to be close enough to a real value, and in a hydraulic network, as can be seen from equation (2), since a square root term
Figure BDA0002393640970000153
The model (24) is linear, so the two-layer state estimation of the invention does not need to consider the initial value of the state variable, and moreover, the two-layer state estimation model is built based on W L AV, so the method has good identification capability on bad data.
Example 2
The electric-thermal comprehensive energy system adopted by the invention adopts an island operation mode. Wherein the modified relaxed node 1 in the IEEE14 node power system is connected with the heat source node 31 in the Bali island 32 node thermodynamic system through a gas turbine, and the proportionality coefficient cm11.3; PV node 6 in the power system and loose node 1 in the thermodynamic system pass through a turbineIn phase relation, where Z is 8.1, Pcon0.2 p.u; PV node 2 in the power system is connected with heat source node 32 in the thermodynamic system through an internal combustion engine, and proportionality coefficient cm2＝1.266。
Test analysis under Normal measurements
The measurement value of the IEHS under the normal condition is formed by adding Gaussian noise on the basis of the true value of the power flow, and the standard deviation of the measurement noise of the power system and the thermal system is set to be 10-3Among them, the IEHS trend truth adopts the document [15 ]]In this section, the proposed double-layer robust state estimation method is combined with the traditional nonlinear W L S[12]And document [9 ]]The proposed BRSE method performs a comparative analysis.
The invention selects the average value of the estimation error of the state variable
Figure BDA0002393640970000154
Maximum estimation error of state variablemaxAnd a measurement error statistic SMEstimating the error statistic SHAnd λ as an indicator for state estimation performance analysis [21]. Respectively expressed as:
Figure BDA0002393640970000155
Figure BDA0002393640970000156
Figure BDA0002393640970000157
Figure BDA0002393640970000158
λ＝SH/SM(34)
wherein x istrue、
Figure BDA0002393640970000159
Respectively the true value and the estimated value of the state variable; x is the number ofi,true、
Figure BDA00023936409700001510
Are respectively xtrue、
Figure BDA00023936409700001511
Calculating a true value and an estimated value of the power flow of the ith state variable; z is a radical ofi,j、hi(xtrue) And
Figure BDA00023936409700001512
respectively calculating a measurement value, a load flow calculation truth value and a measurement estimation value of the ith quantity measurement of the jth experiment; sigmaiIs the standard deviation of the measurement error of the ith quantity measurement.
λ is used to evaluate the filtering effect of the state estimation method, the smaller λ is, the better the filtering effect is, m is the total number of measurements, T is the number of monte carlo simulation experiments performed, the present invention performs the monte carlo simulation experiments T1000 times, the average values of the estimation errors of the state variables of the power system and the thermal system obtained from W L S, BRSE and T L-RSE are shown in fig. 4 and 5, respectively:
from FIGS. 4 and 5, it can be concluded that 1) the mean value of the estimation errors of the state variables from W L S is minimal for the power system and better than BRSE for the T L-RSE, and 2) the mean value of the estimation errors from W L S is small for the node pressure head in the thermodynamic system and T for the node heating temperaturesAnd node regenerative temperature TrThe mean of the estimation errors obtained by T L-RSE is significantly smaller than the estimation results obtained by W L S and BRSE.
The performance analysis indexes (31) to (34) obtained by the three state estimation methods are respectively shown in tables 1 and 2, and as can be seen from the table 1, in the power system, the maximum estimation error of the state variables U and theta obtained by W L S is the minimum of the three methods, while the estimation accuracy of T L-RSE is superior to BRSE, in the thermodynamic system, the maximum estimation error of the state variable p obtained by W L S is the minimum, and the state variable T obtained by T L-RSE is the minimumsAnd TrThe maximum error of (a) is the smallest,wherein T issThe maximum error of (2) is significantly reduced.
TABLE 1 maximum estimation error of state variables obtained by three state estimation methods
Figure BDA0002393640970000161
As can be seen from Table 2, the estimated error statistic S of the T L-RSE partHIs obviously smaller than W L S and BRSE, so the obtained lambda is obviously reduced, which means that the filtering effect of T L-RSE is better than that of W L S and BRSE.
TABLE 2 results of Performance analysis indexes (31) to (34) obtained by the three State estimation methods
Figure BDA0002393640970000162
In conclusion, under normal measurement, 1) for an electric power system, the estimation accuracy of the T L-RSE is higher than BRSE and is identical with the theoretical analysis in the foregoing, and 2) for a thermodynamic system, due to the consideration of the physical constraint between the node temperatures, the estimation accuracy of the T L-RSE for the node heating temperature and the node regenerative temperature is obviously better than that of W L S and BRSE.
Resistance to differential test analysis
Since W L S has no tolerance, a maximum normalized residual error (L argetnormalized residual, L NR) identification link is added after W L S in the analysis of the section, namely W L S + L NR. carries out comparative analysis on W L S + L NR, BRSE and the double-layer robust state estimation method provided by the invention.
Resistance to deterioration of general bad data
General bad data include: 1) the bad data without interaction, namely the bad data i in the residual sensitivity matrix S and the corresponding element S (i, j) of j are approximately equal to 0; 2) there are bad data that interact (S (i, j) is large), but do not vary in accordance with each other.
The invention respectively sets general bad data in the power system and the thermodynamic system for test analysis.
For the power system, the test results of the setup of 10 bad data and the three state estimation methods are shown in table 3, where (P)10,P10-11) And (P)14,P13-14) The specific identification process of W L S + L NR, belonging to the second category of general bad data, is shown in Table 4.
TABLE 3 robust test results for three state estimation methods in power systems
Figure BDA0002393640970000171
Figure BDA0002393640970000172
TABLE 4W L S + L NR identification Process for general bad data in Power systems
Figure BDA0002393640970000173
In Table 4, zideRepresenting the normalized residual r from each recognitionN,maxThe maximum measurement is measured, and the W L S is removed before the next time, and tables 3 and 4 show that W L S + L NR successfully identifies 10 general bad data set by the previous 10 identifications, and BRSE and T L-RSE are not influenced by the bad data, so that a measurement estimated value close to the real value is obtained.
For the thermal system, the results of the test for the setting of 15 bad data and the three state estimation methods are shown in table 5, where (m)q3,Φ3) And (m)q18,Φ18) Belonging to the second category of general bad data, Table 6 describes the specific identification process of W L S + L NR, wherein the shaded portions in Table 5 represent estimated values that are significantly different from the true values, and the shaded portions in Table 6 represent misrecognized quantity measurements.
It can be seen that 1) for W L S + L NR, within the first 14 recognitions, W L S + L NR measured Φ incorrectly1、Tr3And phi2As bad data go onCulling, thus in Table 5, the bad data Φ3、Tr1And Tr1The estimated value of (1) generates larger deviation with the true value, (2) for BRSE, when bad data occurs in the node temperature measurement, larger error is generated between the corresponding estimated result and the true value, and (3) for T L-RSE, the general bad data occurring in the heat supply network can be well resisted.
TABLE 5 results of the resistance to error test of the three state estimation methods in the thermodynamic system
Figure BDA0002393640970000181
Figure BDA0002393640970000182
Figure BDA0002393640970000183
TABLE 6W L S + L NR identification of general bad data in a thermal system
Figure BDA0002393640970000184
Figure BDA0002393640970000191
Robust testing of strongly correlated poor data
Poor data with strong correlation refers to poor data that interact closely and vary in agreement with each other. Measure P the quantity in the electric power system in this section1、P12And P15Set as bad data while measuring the quantity P21And P51Bad data set and estimation of BRSE and T L-RSE are shown in Table 7. the specific identification process of W L S + L NR is shown in Table 8.
TABLE 7 statistics of each type obtained by three state estimation methods
Figure BDA0002393640970000192
TABLE 8 statistics of each type obtained by three state estimation methods
Figure BDA0002393640970000193
As can be seen from tables 7 and 8, BRSE and T L-RSE can identify the strong correlation bad data appearing in IEHS, while W L S + L NR identified the bad data P only for the second time in the first 5 identifications15W L S + L NR therefore cannot identify strongly correlated bad data in IEHS.
Example 3:
based on the same inventive concept, the invention also provides a double-layer robust state estimation system of the electric-thermal comprehensive energy system, as shown in fig. 2, comprising:
the data acquisition module is used for acquiring the electric power system quantity measurement and the thermal power system quantity measurement of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement;
the power system hydraulic network state estimation module is used for measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a power system hydraulic network state estimation model which is constructed in advance, and obtaining a power system state variable node voltage amplitude value estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value;
the thermodynamic network state estimation module is used for measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, inputting the measured values into a thermodynamic network state estimation model which is constructed in advance, and obtaining a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value;
the thermodynamic network state estimation model is constructed by considering temperature constraint among nodes of the thermodynamic network and thermal power measurement.
Preferably, the building module of the power system hydraulic network state estimation model comprises:
the electric power system linear measurement equation building module is used for determining an electric power system linear measurement equation based on the set first auxiliary state variable and the first auxiliary quantity measurement;
the hydraulic network linear measurement equation building module is used for determining a hydraulic network linear measurement equation based on the set second auxiliary state variable and the second auxiliary quantity measurement;
the unified linear measurement model building module of the power system and the hydraulic network is used for building a unified linear measurement model of the power system and the hydraulic network based on the linear measurement equation of the power system, the linear measurement equation of the hydraulic network and the coupling mode of the coupling nodes of the power system and the thermal system;
the linear weighted minimum absolute value state estimation model building module is used for building a linear weighted minimum absolute value state estimation model unified for the power system and the hydraulic network based on the unified linear measurement model for the power system and the hydraulic network;
and the electric power system hydraulic network state estimation model building module is used for building an electric power system hydraulic network state estimation model based on the relation of the auxiliary state variables of the electric power system and the state estimation model of the uniform linear weighted minimum absolute value of the electric power system and the hydraulic network.
Wherein the first auxiliary state variable is set by a power system state variable; the first auxiliary quantity is measured and set by the electric power system quantity; the second auxiliary state variable is set by a hydraulic network state variable; the second auxiliary quantity measurement is set by hydraulic network quantity measurement.
Preferably, the power system hydraulic network state estimation module includes:
the calculation module 1 is used for inputting the measurement of the power system quantity and the measurement of the hydraulic network quantity into a pre-constructed power system hydraulic network state estimation model and calculating a first auxiliary state variable estimation value and a second auxiliary state variable estimation value;
the calculation module 2 is used for calculating a branch phase angle difference estimation value, a node voltage amplitude estimation value and a hydraulic network pipeline pressure head loss estimation value of the power system through nonlinear transformation based on the first auxiliary state variable estimation value and the second auxiliary state variable estimation value;
the calculation module 3 is used for calculating a phase angle estimation value of a node of the electric power system and a pressure head estimation value of a node of the hydraulic network through linear transformation based on the phase angle difference estimation value of the branch of the electric power system and the pressure head loss estimation value of the hydraulic network pipeline;
and the calculating module 4 is used for calculating the estimated values of the branch flow and the node injection flow of the hydraulic network based on the estimated value of the pressure head of the hydraulic network node.
Preferably, the building module of the thermodynamic network state estimation model includes:
the system comprises a thermodynamic network linear measurement equation construction module, a thermodynamic network state variable measurement module and a thermodynamic network measurement module, wherein the thermodynamic network linear measurement equation construction module is used for constructing a thermodynamic network linear measurement equation based on thermodynamic network state variables and thermodynamic network measurement;
the state estimation model building module of the linear weighted minimum absolute value of the thermal network is used for building a state estimation model of the linear weighted minimum absolute value of the thermal network based on the thermal network linear measurement equation;
the constraint construction module is used for setting the flow estimation value of the hydraulic network branch and the node injection flow estimation value as pseudo quantity measurement and determining the temperature constraint among nodes of the thermodynamic network;
and the thermodynamic network state estimation model construction module is used for constructing a thermodynamic network state estimation model based on the state estimation model of the linear weighted minimum absolute value of the thermodynamic network and the constraint.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.

Claims (20)

1. A double-layer robust state estimation method of an electric heating comprehensive energy system is characterized by comprising the following steps:
acquiring the measurement of an electric power system quantity and the measurement of a thermodynamic system quantity of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement;
measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a pre-constructed power system hydraulic network state estimation model, and obtaining a power system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value;
measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, and inputting the measured values into a thermodynamic network state estimation model which is constructed in advance to obtain a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value;
the thermodynamic network state estimation model is constructed by considering temperature constraint among nodes of the thermodynamic network and thermal power measurement.
2. The method of claim 1, wherein the constructing of the power system hydraulic network state estimation model comprises:
determining a linear measurement equation of the power system based on the set first auxiliary state variable and the first auxiliary quantity measurement;
determining a hydraulic network linear measurement equation based on the set second auxiliary state variable and the second auxiliary quantity measurement;
constructing a unified linear measurement model of the electric power system and the hydraulic network based on the electric power system linear measurement equation, the hydraulic network linear measurement equation and the coupling mode of the coupling nodes of the electric power system and the thermal system;
constructing a state estimation model of a linear weighted minimum absolute value unified by the power system and the hydraulic network based on the unified linear measurement model of the power system and the hydraulic network;
constructing a power system hydraulic network state estimation model based on the relation of the power system auxiliary state variables and the state estimation model of the uniform linear weighted minimum absolute value of the power system and the hydraulic network;
wherein the first auxiliary state variable is set by a power system state variable; the first auxiliary quantity is measured and set by the electric power system quantity; the second auxiliary state variable is set by a hydraulic network state variable; the second auxiliary quantity measurement is set by hydraulic network quantity measurement.
3. The method of claim 2, wherein the first auxiliary state variable and the first auxiliary quantity are measured as follows:
Figure FDA0002393640960000011
Figure FDA0002393640960000012
wherein the content of the first and second substances,
Figure FDA0002393640960000013
is a first auxiliary state variable, Vi a、
Figure FDA0002393640960000014
In order to introduce the first auxiliary quantity,
Figure FDA0002393640960000015
for first auxiliary quantity measurement, UiIs the voltage amplitude of node i, PiInjecting active power, Q, for node iiReactive power, P, injected for node iijActive power, Q, for branch ijijIs a supportReactive power of way ij;
the first auxiliary quantity is calculated as follows:
Figure FDA0002393640960000021
wherein, UjMagnitude of voltage at node j, θijIs the phase angle difference between node i and node j.
4. The method of claim 2, wherein the power system linearity measurement equation is as follows:
Figure FDA0002393640960000022
in the formula of UiAmplitude of voltage at node i, PiInjecting active power, Q, for node iiReactive power, P, injected for node iijActive power, Q, for branch ijijIs the reactive power of branch ij, Ni is the node number of the power system, Vi a、
Figure FDA0002393640960000023
Is a first auxiliary amount, gsiIs the equivalent resistance to ground of node i, bsiIs the equivalent ground reactance of node i, gijIs the equivalent resistance of branch ij, bijIs the equivalent reactance of branch ij, GijDetermined by the equivalent resistance of branch ij, BijDetermined by the equivalent reactance of branch ij.
5. The method of claim 2, wherein the second auxiliary state variable and the second auxiliary quantity are measured as follows:
Figure FDA0002393640960000024
Figure FDA0002393640960000025
wherein the content of the first and second substances,
Figure FDA0002393640960000026
is a second auxiliary state variable, αaIs the second auxiliary quantity to be used,
Figure FDA0002393640960000027
the second amount of assistance is measured and,
Figure FDA0002393640960000028
the water flow rate of the branch ij,
Figure FDA0002393640960000029
injecting water flow into the nodes;
the αaThe second auxiliary quantity is calculated as follows:
Figure FDA00023936409600000210
wherein the content of the first and second substances,
Figure FDA00023936409600000211
is αaElement of (5), pijFor pressure head loss of the pipe, sijDetermined by the relationship of the node i pressure head and the node j pressure head.
6. The method of claim 2, wherein the hydraulic network linearity measurement equation is as follows:
Figure FDA0002393640960000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002393640960000032
the water flow rate of the branch ij,
Figure FDA0002393640960000033
injection of water flow into the node, KijThe impedance coefficient of the conduit, p, for branch ijijFor pressure head loss of the pipe, sijDetermined by the relationship of the node i pressure head and the node j pressure head,
Figure FDA0002393640960000034
is the second auxiliary amount αaOf (1).
7. The method of claim 2, wherein the unified linear metrology model of the power system, the hydraulic network is represented by the following equation:
Figure FDA0002393640960000035
in the formula, xaUnified auxiliary state variable, z, for electric power systems, hydraulic networksaFor the uniform auxiliary quantity measurement of an electric power system and a hydraulic power network,
Figure FDA0002393640960000036
is the first auxiliary state variable to be used,
Figure FDA0002393640960000037
is the second auxiliary state variable and is,
Figure FDA0002393640960000038
for the measurement of the first auxiliary quantity,
Figure FDA0002393640960000039
for the purpose of the second auxiliary quantity measurement,
Figure FDA00023936409600000310
for the heat energy generated by adopting a gas turbine or an internal combustion engine in a coupling mode, N1 is the number of nodes which are coupled by adopting the gas turbine or the internal combustion engine,
Figure FDA00023936409600000311
for the heat energy generated by the coupling of the turbine, N2 is the number of nodes coupled by the turbine, HaIs a uniform constant coefficient matrix of an electric power system and a hydraulic power network, raThe method is a unified measurement error of a power system and a hydraulic network.
8. The method of claim 2, wherein the power system, hydraulic network unified linear weighted least absolute value state estimation model is as follows:
minw(u+v)
Figure FDA00023936409600000312
wherein w is a unified measurement weight matrix of the power system and the hydraulic network, u and v are two types of nonnegative variables introduced by a unified model of the power system and the hydraulic network, and zaFor uniform auxiliary quantity measurement of electric power systems, hydraulic networks, HaIs a uniform constant coefficient matrix, x, of an electric power system and a hydraulic power networkaThe method is a unified auxiliary state variable of a power system and a hydraulic network.
9. The method of claim 2, wherein constructing a power system and hydraulic network state estimation model based on the relationship of the power system auxiliary state variables and the power system and hydraulic network unified linear weighted minimum absolute value state estimation model comprises:
constructing a relational expression among auxiliary state variables of the power system;
constructing a second-order cone inequality constraint based on a relational expression between the auxiliary state variables of the power system;
and constructing a state estimation model of the hydraulic network of the power system based on the second-order cone inequality constraint and a state estimation model of the uniform linear weighted minimum absolute value of the power system and the hydraulic network.
10. The method of claim 9, wherein the second order cone inequality constraint is represented by:
Figure FDA0002393640960000041
in the formula, Vi a、Vj a、
Figure FDA0002393640960000042
Is the first auxiliary amount.
11. The method of claim 2, wherein the power system hydraulic network state estimation model is as follows:
min∑λijRij-w(u+v)
Figure FDA0002393640960000043
wherein w is a unified measurement weight matrix of the power system and the hydraulic network, u and v are two types of nonnegative variables introduced by a unified model of the power system and the hydraulic network, and zaFor uniform auxiliary quantity measurement of electric power systems, hydraulic networks, HaIs a uniform constant coefficient matrix, x, of an electric power system and a hydraulic power networkaIs a unified auxiliary state variable, V, of an electric power system, a hydraulic networki a、Vj a、
Figure FDA0002393640960000044
Figure FDA0002393640960000045
Is a first auxiliary quantity, λijFor adjusting the parameters, the magnitude is equal to the weighted value of the power measurement of the corresponding branch ij in the power system.
12. The method of claim 1, wherein the inputting the electrical system quantity measurement and the hydraulic network quantity measurement into a pre-constructed electrical system hydraulic network state estimation model to obtain an electrical system state variable node voltage amplitude estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value comprises:
measuring the electric power system quantity and the hydraulic network quantity, inputting the electric power system hydraulic network state estimation model which is constructed in advance, and calculating a first auxiliary state variable estimation value and a second auxiliary state variable estimation value;
calculating a branch phase angle difference estimation value, a node voltage amplitude estimation value and a hydraulic network pipeline pressure head loss estimation value of the power system through nonlinear transformation based on the first auxiliary state variable estimation value and the second auxiliary state variable estimation value;
calculating a node phase angle estimation value and a hydraulic network node pressure head estimation value of the electric power system through linear transformation based on the branch phase angle difference estimation value of the electric power system and the hydraulic network pipeline pressure head loss estimation value;
and calculating estimated values of branch flow and node injection flow of the hydraulic network based on the estimated value of the pressure head of the hydraulic network node.
13. The method of claim 1, wherein the building of the thermodynamic network state estimation model comprises:
constructing a thermodynamic network linear measurement equation based on thermodynamic network state variables and thermodynamic network quantity measurement;
constructing a state estimation model of a linear weighted minimum absolute value of the thermodynamic network based on the thermodynamic network linear measurement equation;
setting the flow estimation value of the hydraulic network branch and the node injection flow estimation value as pseudo-quantity measurement, and determining the temperature constraint among nodes of the thermodynamic network;
and constructing a thermodynamic network state estimation model based on the state estimation model of the linear weighted minimum absolute value of the thermodynamic network and the constraint.
14. The method of claim 13, wherein the thermodynamic network state variables, thermodynamic network quantity measurements are as follows:
xt＝[Ts；Tr]
zt＝[φ；Ts；Tr]
wherein x istBeing state variables of the thermodynamic network, TsSupply of heat temperature, T, to the noderIs the node regenerative temperature; z is a radical oftFor thermodynamic network measurements, φ is the node thermal power.
15. The method of claim 13, wherein the thermodynamic network linearity measurement equation is as follows:
Figure FDA0002393640960000051
in the formula, phiiIs the thermal power of the node i,
Figure FDA0002393640960000052
injection water flow rate, C, for node ipIs the specific heat capacity of water, TsiTemperature of heat supply to node i, TriIs the regenerative temperature of node i.
16. The method of claim 13, wherein the linear weighted least absolute state estimation model of the thermal network is as follows:
minwt(ut+vt)
Figure FDA0002393640960000053
in the formula, wtIs a measured weight matrix, u, of the thermal networktAnd vtTwo types of non-negative variables, z, introduced for thermodynamic networkstFor thermodynamic network quantity measurement, xtIs a state variable of the thermodynamic network, HtA constant coefficient matrix of the thermal network.
17. The method of claim 13, wherein the constant coefficient matrix H of the thermodynamic networktIs represented as follows:
Figure FDA0002393640960000054
wherein, CpIs the specific heat capacity of the water,
Figure FDA0002393640960000055
and injecting the water flow estimation value for the node.
18. The method of claim 13, wherein the temperature constraints between nodes of the thermodynamic network are as follows:
Figure FDA0002393640960000056
Figure FDA0002393640960000061
in the formula, TendIs the temperature at the end of the pipe, TstartFor the joint temperature, T, of the head end of the pipelineaIs the ambient temperature, λhHeat transfer coefficient per unit length of pipe, L length of pipe, CpIs the specific heat capacity of the water,
Figure FDA0002393640960000062
for the estimated injection water flow at node i,
Figure FDA0002393640960000063
for an estimate of the total flow of a particular pipe out of the node,
Figure FDA0002393640960000064
for a pipe into which the joint is injectedFlow rate estimation value, ToutIs a node mixing temperature, TinIs the pipe head end node temperature.
19. The method of claim 13, wherein the thermodynamic network state estimation model is represented by the following equation:
minwt(ut+vt)
Figure FDA0002393640960000065
in the formula, wtIs a measured weight matrix, u, of the thermal networktAnd vtTwo types of non-negative variables, z, introduced for thermodynamic networkstFor thermodynamic network quantity measurement, xtIs a state variable of the thermodynamic network, HtConstant coefficient matrix, T, of a thermodynamic networkendIs the temperature at the end of the pipe, TstartFor the joint temperature, T, of the head end of the pipelineaIs the ambient temperature, λhHeat transfer coefficient per unit length of pipe, L length of pipe, CpIs the specific heat capacity of the water,
Figure FDA0002393640960000066
for the estimated injection water flow at node i,
Figure FDA0002393640960000067
for an estimate of the total flow of a particular pipe out of the node,
Figure FDA0002393640960000068
flow estimate, T, for a pipe injected into a nodeoutIs a node mixing temperature, TinIs the pipe head end node temperature.
20. A double-layer robust state estimation system of an electric-thermal comprehensive energy system is characterized by comprising:
the data acquisition module is used for acquiring the electric power system quantity measurement and the thermal power system quantity measurement of the electric heating comprehensive energy system; wherein the thermodynamic system comprises: the system comprises a hydraulic network and a thermal network, wherein the thermal system quantity measurement comprises hydraulic network quantity measurement and thermal network quantity measurement;
the power system hydraulic network state estimation module is used for measuring the power system quantity and the hydraulic network quantity, inputting the measured quantities into a power system hydraulic network state estimation model which is constructed in advance, and obtaining a power system state variable node voltage amplitude value estimation value, a node phase angle estimation value, a hydraulic network state variable node pressure head estimation value, a hydraulic network branch flow estimation value and a node injection flow estimation value;
the thermodynamic network state estimation module is used for measuring the hydraulic network branch flow estimation value, the node injection flow estimation value and the thermodynamic network quantity, inputting the measured values into a thermodynamic network state estimation model which is constructed in advance, and obtaining a thermodynamic network state variable node heat supply temperature estimation value and a node heat return temperature estimation value;
the thermodynamic network state estimation model is constructed by considering temperature constraint among nodes of the thermodynamic network and thermal power measurement.
CN202010123240.6A 2020-02-27 2020-02-27 Double-layer robust state estimation method and system for electric heating comprehensive energy system Pending CN111414675A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112699513A (en) * 2020-12-22 2021-04-23 广东电网有限责任公司广州供电局 Robust state estimation method and device for comprehensive energy system
CN113673853A (en) * 2021-08-12 2021-11-19 华北电力大学 Electricity-heat comprehensive energy system state estimation method based on data driving

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112699513A (en) * 2020-12-22 2021-04-23 广东电网有限责任公司广州供电局 Robust state estimation method and device for comprehensive energy system
CN112699513B (en) * 2020-12-22 2024-02-02 广东电网有限责任公司广州供电局 Robust state estimation method and device for comprehensive energy system
CN113673853A (en) * 2021-08-12 2021-11-19 华北电力大学 Electricity-heat comprehensive energy system state estimation method based on data driving
CN113673853B (en) * 2021-08-12 2024-03-05 华北电力大学 Data-driven-based electric-thermal comprehensive energy system state estimation method

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