CN105703368B - Multi-uncertainty energy flow modeling integrated with active power distribution network and transmission network - Google Patents

Multi-uncertainty energy flow modeling integrated with active power distribution network and transmission network Download PDF

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CN105703368B
CN105703368B CN201610077839.4A CN201610077839A CN105703368B CN 105703368 B CN105703368 B CN 105703368B CN 201610077839 A CN201610077839 A CN 201610077839A CN 105703368 B CN105703368 B CN 105703368B
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马瑞
吴瑕
颜宏文
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The present invention belongs toIn the technical field of energy flow calculation of an active power distribution network and transmission network integrated system, a multiple uncertain energy flow modeling method for integrating an active power distribution network and a transmission network is provided. The method comprises the steps of firstly simulating a random fuzzy space-time correlation sequence of multiple energy sources injected into EH, establishing an EH model under energy interconnection, and obtaining an interaction value of the EH model and a power distribution network. Active power distribution network and transmission network joint node PLi,tPositive/negative/zero values of (a) correspond to load/source/islanding operating modes of the ADN, respectively. The island mode power transmission network and the ADN are completely decoupled to operate and correspond to the N-1 state of the power system; the load/power mode takes the power and voltage of the power transmission network and the active distribution network at a joint node as shared variables, an SOS-based active power distribution network and power transmission network energy flow model is established, and an energy flow result is obtained. The method is suitable for the development trend of energy interconnection, and provides corresponding guidance basis for scheduling and dispatching of power generation plans of a large amount of new energy accessed to the power system in the future.

Description

Multi-uncertainty energy flow modeling integrated with active power distribution network and transmission network
Technical Field
The invention belongs to the technical field of multiple uncertain energy flow modeling of an active power distribution network and transmission network integrated system, and provides multiple uncertain energy flow modeling integrating an active power distribution network and a transmission network.
Background
The construction of multiple uncertain energy flow models for decomposing and coordinating the active power distribution network and the transmission network under the interconnection of extracted energy has important significance for system operation mode arrangement and safety check. With the increasing utilization range of electricity, gas AND heat comprehensive energy AND the continuous increase of capacity, a large number of DER, CCHP AND the like are connected to a power distribution network, AND a traditional power distribution network is developed into an active distribution network (AND) with the characteristics of bidirectional tide AND multi-energy coupling. The source and load uncertainty of the multi-energy coupling injection active distribution network is caused by the influence of uncertain natural factors, energy consumption behaviors, market price and the like of the injection of electricity, gas and heat comprehensive energy sources into the distribution network source and load. The uncertain features have randomness, and have ambiguity of cognitive significance due to the fact that probability distribution parameters with accurate significance are difficult to obtain based on limited data, and the effects of multi-energy complementary coupling and energy utilization space-time balance have space-time correlation, so that multiple complex uncertain features are presented. In the traditional power transmission network and power distribution network power flow analysis, the method for separating transmission and distribution without considering the competition of the power transmission network and the power distribution network and the random fuzzy injection of electricity, gas and heat into energy cannot adapt to the new reality. Mining the source and load multiple uncertain characteristics of the active power distribution network and the power transmission network under the condition of energy interconnection, and considering the integrated modeling and calculation of the power transmission network and the active power distribution network are key scientific problems to be researched urgently.
The existing research on multiple uncertain models for decomposition and coordination of the active power distribution network and the transmission network under energy interconnection at home and abroad obtains some achievements: for the processing of multiple uncertain characteristic data, a document of 'multiple uncertain model of daily wind speed' defines the wind speed as a random fuzzy variable, measured data of the random fuzzy variable is fitted to obtain probability distribution characteristics, and then a maximum likelihood method is used for obtaining a fuzzy membership function of distribution function parameters to obtain a random fuzzy model of the random fuzzy model. In the document "a novel random fuzzy neural networks for tracking unknown objects of electric load learning", the input, output, weight and deviation of a neural network are all represented in the form of random fuzzy variables, a model considering randomness and fuzziness simultaneously is established, and a load prediction method of the random fuzzy neural network is provided. The multiple uncertain models have the greatest advantages that the randomness and the ambiguity coexistence characteristics of a plurality of uncertain characteristics can be considered, and the research on the source load multiple uncertain modeling problems of multi-energy systems such as electricity, gas, heat and the like is very lacking. For the time-space correlation considering the uncertain features of the random fuzzy data, the literature, "wind speed-time correlation-considered dynamic random optimal power flow calculation of the power system including the wind power plant", considers the time-space correlation of the wind speed, establishes an autoregressive moving average model of a wind speed time sequence, obtains a time correlation coefficient matrix and a space correlation coefficient matrix of the wind speed sequence, and is effectively used for extracting the time-space correlation features of the random fuzzy time sequence. In the aspect of Energy flow research of an integrated Energy system of an active power distribution network and a transmission network under Energy interconnection, the coupling relation of an Energy Hub optimization planning and operation research overview and prospect in the Energy internet to an electricity, gas and heat multi-Energy system is described by using an Energy Hub (Energy Hub) to describe the proportion of the original input of the multi-Energy system and the output of the Energy converted into other forms, and the Energy Hub model of CHP and CCHP is obtained by considering the conversion efficiency. The literature, "regional integrated energy system electricity/gas/heat energy flow algorithm research" establishes an electricity/gas/heat energy flow model for the regional integrated energy system, and the model comprises nonlinear equations of an electricity/gas/heat mutual coupling power system, a natural gas system and an EH system, and judges and analyzes the interaction influence of the electricity/gas equation through a load flow equation partial derivative value. In terms of its power flow modeling and algorithm: the document Master-Slave-Splitting Based Distributed Global Power flow method for Integrated Transmission and Distribution Analysis establishes a Global Power flow equation which respectively represents a Power Transmission network, a coupling node and a main area, a boundary area and a subordinate area of a Distribution network, and coordinates the Global Power flow of the Power Transmission network and the active Distribution network in a closed loop by judging the node voltage convergence of the boundary part and a Distributed calculation method; the document "System of Systems Based Security-coordinated Unit Commission associating active distribution Grids" considers the market environment, Based on the System of Systems theory, the power transmission network operated by ISO and the active power distribution network operated by DSISO are taken as independent and coordinated subsystems, the interaction power is designed as a shared variable, and the coordination of the power transmission network and the active power distribution network is carried out by converging the shared variable at a certain precision.
In fact, the modeling and algorithm of the energy flow of the integrated active power distribution network and the power transmission network under the energy interconnection are not researched, and a plurality of uncertain energy flow modeling methods of the integrated active power distribution network and the power transmission network are provided. And disclosing the interaction rule of EH, the natural gas network, the active power distribution network and the power transmission network.
Disclosure of Invention
The load flow calculation aiming at the characteristics of electric/gas/heat and other multi-energy injection active distribution network sources and load uncertainty under energy interconnection is important basic work for arranging and checking the operation mode of an energy interconnection system, the existing research fails to consider the integration of a power transmission network and the active distribution network and the actual coupling situation of the electric/gas/heat and other multi-energy sources, the injected sources and loads have randomness and fuzziness, data mining analysis is necessary to be carried out based on actual measurement sources and load injection data, corresponding random fuzziness and multiple complex uncertainty models related to time and space are established, and the energy flow calculation of the electric, gas and heat multi-energy coupling active distribution network and the power transmission network based on an SOS theory is considered. And then arranging a power system operation and regulation plan under a reasonable risk level. The invention further considers an integrated energy flow calculation method of the active power distribution network and the transmission network with uncertain multi-energy coupling lower source and load injection on the basis of the traditional power distribution network load flow calculation.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention adopts the technical scheme for solving the problems as follows:
1. and establishing an uncertainty model of the source and load of the active power distribution network and the transmission network under the energy interconnection and extracting the time-space correlation characteristics of the uncertainty model. The method comprises the steps that source and load data of a multi-energy coupling original injection active power distribution network and a power transmission network are influenced by weather and energy consumption behaviors, random and fuzzy uncertain characteristics are provided, random fuzzy variables are defined according to an uncertain theory to obtain an opportunity measure function, 5000 times of random fuzzy simulation is carried out on the injection power of natural gas to distributed wind power and photovoltaic power generation according to the uncertain theory, the power of an electric, gas and heat injection active power distribution network with the random fuzzy characteristics is obtained, on the basis of a simulation value, the time correlation coefficient matrix of source and load injection of an energy hub in t time periods is obtained by an autoregressive sliding average model in consideration of the space-time correlation of prediction errors, and the space correlation coefficient matrix is obtained according to the geographic positions of different energy hubs.
2. And establishing an energy hub model with space-time related random fuzzy characteristics of power, gas and heat injection sources and loads under energy interconnection. According to the conversion supply and demand balance relationship between a power supply matrix formed by power grid power supply, natural gas energy and distributed energy and a load matrix formed by electric load, natural gas load and heat load when a natural gas grid and an active power distribution network are interconnected through an energy hub, a balance equation of an energy hub node set is established based on a historical data fitting coefficient matrix, an energy interaction value of the energy hub and the network is obtained, and the interaction influence of the electricity, the gas and the energy and the operation mode of a cogeneration system are judged through a Jacobi matrix of electricity, gas and heat energy flow equations.
3. And establishing judgment of three different operation modes of the active power distribution network based on system engineering theoretical energy interconnection. Solving the energy flow problem of the active power distribution network based on the system engineering theory, and obtaining a corresponding parent at the moment according to the resultLine switching power PLi,tIf P isLi,tIf 0, the transmission network and the active power distribution network are completely decoupled, the active power distribution network operates in an island mode and corresponds to the N-1 state of the power system, and if P is the stateLi,tIf the load is more than 0, the active power distribution network operates as a load relative to the power transmission network, and if P is greater than 0Li,tIf the active power distribution network runs as a power supply relative to the transmission network, P is less than 0Li,tAnd not equal to 0, adopting a method for calculating the energy flow of the integrated system of the transmission network and the active distribution network based on the system engineering theory.
4. And establishing an energy flow multiple uncertain model and calculation of the active power distribution network and transmission network integrated system based on system engineering theoretical energy interconnection. Based on a system engineering theory (System of systems), the interactive power of the active power distribution network and the power transmission network is regarded as a shared variable, an energy flow model of multi-energy coupling injection into the active power distribution network and the power transmission network is established, and the power and the voltage of the joint node of the power transmission network and the active power distribution network are expressed as a shared variable ztAnd power transmission network power flow calculation based on Newton Raphson algorithm is used for obtaining the energy η of the common nodetAnd calculating the load flow of the active power distribution network based on a forward-backward substitution algorithm to obtain the energy mu of the common nodetIteratively converging the energy of the common node until a convergence condition | η is reachedt-μtIf the value is less than the epsilon, wherein the epsilon is an error control coefficient, obtaining the energy flow result of the transmission network and the active power distribution network, wherein ηt，μtIs a shared variable ztIs measured.
Drawings
FIG. 1: schematic diagram of an example energy system of the invention;
FIG. 2: the invention discloses a multiple uncertain energy flow modeling flow chart integrating an active power distribution network and a power transmission network.
Detailed Description
The invention comprises the following steps:
1. and establishing an uncertainty model of the source and load of the active power distribution network and the transmission network under the energy interconnection and extracting the time-space correlation characteristics of the uncertainty model.
The source and load data originally injected into the active power distribution network and the power transmission network under the energy interconnection have randomness and model because of the influence of weather and energy consumption behaviorsAnd (4) defining random fuzzy variables according to an uncertain theory by using the fuzzy uncertain characteristics to obtain an opportunity measure function. Wherein a isi，biAnd ciBlurring parameters for density functions
Figure GDA0002294977230000031
Consider spatio-temporal correlations: after the predicted values of the sources and the loads of the multi-energy injection active distribution network and the transmission network are obtained through random fuzzy simulation, an autoregressive moving average (ARMA) model and an ARMA model of (p, q) order injection sources and load time series are utilized as follows:
Figure GDA0002294977230000041
wherein the content of the first and second substances,
Figure GDA0002294977230000042
the source and load sequence values at the time t;
Figure GDA0002294977230000043
and thetatα for the AR model and MA model, respectivelytObtaining an autocorrelation function rho of the time sequence corresponding to the ARMA model based on a Yule-Walker equation for a Gaussian white noise sequencetThen the time correlation coefficient matrix of the source and the load in the T time periods is
Figure GDA0002294977230000044
There is a strong spatial correlation between the electrical/gas/heat injection sources and loads between EHs in close geographical locations. Researching the correlation between the injection sources and loads among K EHs, the spatial correlation coefficient matrix of the injection sources and loads in the time period t of the K EHs is as follows: in the formula: t ═ 1,2, …, T; rhoij,tInjecting source, charge correlation coefficients for ith and jth EHs in the tth time period
Figure GDA0002294977230000045
2. And establishing an Energy Hub (EH) model with space-time related random fuzzy characteristics of power, gas and heat injection sources and loads under energy interconnection.
The main designations, variables, subscripts, etc. appearing in the model are first described as follows:
a T grid (TS);
d, Active Distribution Network (ADN);
an H Energy Hub (EH);
a period t;
i TS node number;
j ADN internal node number;
k, numbering natural gas network nodes;
ΦTa TS node set;
ΦT-Gthe node aggregation with the thermal power generating unit in the TS,
Figure GDA0002294977230000046
ΦT-tDthe set of nodes in the TS that are connected to the conventional distribution network,
Figure GDA0002294977230000047
ΦT-Dthe set of nodes in the TS that are connected to the ADN,
Figure GDA0002294977230000048
ΦDan ADN internal node set;
ΦD-Hnodes connected with the natural gas network inside the ADN, namely EH node sets;
ztTS and ADN share a variable vector.
Power supply by power grid when natural gas grid and active power distribution network are interconnected through EH
Figure GDA0002294977230000051
Natural gas energy source
Figure GDA0002294977230000052
And DER and other energy sources
Figure GDA0002294977230000053
Constructed energy supply matrix
Figure GDA0002294977230000054
And an electrical load
Figure GDA0002294977230000055
Natural gas load
Figure GDA0002294977230000056
Thermal load
Figure GDA0002294977230000057
Constructed load matrix
Figure GDA0002294977230000058
The transformation supply and demand balance relationship between the two is based on a historical data fitting coefficient matrix Cj,tEstablishing EH node set phiD-GThe equilibrium equation of (c). The source and the load of the electric/gas/heat multi-energy injection of the EH model are both expressed by random fuzzy variables (the superscript to represents that the variable is multiple uncertain quantity).
Figure GDA0002294977230000059
Namely:
Figure GDA00022949772300000510
for the AND other nodes:
Figure GDA00022949772300000511
and is
Figure GDA00022949772300000512
Figure GDA00022949772300000513
c) Natural gas network nodal tidal current equation
Figure GDA00022949772300000514
Wherein
Figure GDA00022949772300000515
Figure GDA00022949772300000516
The natural gas flow rate of the pipeline between the two natural gas nodes k and bk.
Figure GDA00022949772300000517
Figure GDA0002294977230000061
Wherein
Figure GDA0002294977230000062
Figure GDA0002294977230000063
And (5) establishing an electric, gas and heat comprehensive energy flow equation system combining the power flow equations (5), (7) and (10), and judging and analyzing the interactive influence of the electric and gas network equations by analyzing the power flow equation (7) of the power distribution network and the natural gas power flow equation (10). A partial derivative value of 0 indicates that there is no coupling between different types of energy, and a partial derivative value of 0 indicates that there is coupling between different types of energy. The EH operates in a heated and powered (FTL) mode when the power system and the natural gas system are completely decoupled or the natural gas system has an impact on the power system. If the power system affects the natural gas system or the electrical and gas systems affect each other, the EH operates in a heating and cooling (FEL) mode. And during the load flow calculation, the EH electrical interface is accessed into the electrical system as a PQ node when the FTL mode is operated, and the EH electrical interface is accessed into the electrical system as a V-f node when the FEL mode is operated, so that the load flow iteration is carried out on the electrical system.
3. And establishing judgment of three different operation modes of the active power distribution network based on system engineering theoretical energy interconnection.
First, based on the transmission network (node set Φ)M) Active power distribution network (node set phi)D) With the natural gas network (node set phi)G) Respective power flow equation and correction equation are considered, and the power transmission network and active power distribution network joint node set phi is consideredM-DThe power and voltage sharing variables of (a) are as follows:
zt＝{(PLi,t,Ui,t)|i∈ΦM-D}(15)
for nodes in ADN in common with the grid (i.e. sharing variable z)tNodes involved, i.e. ADN bus):
Figure GDA0002294977230000064
Figure GDA0002294977230000065
wherein P isESSj,tThe power of the energy storage device is positive with its charged state as a load. Adding the sum of all electric power loads in the ADN to all energy storage device powers and all network losses, and deducting all distributed energy output to obtain bus exchange power PL i,tThe positive and negative of which determine the mode of operation of the ADN.
If PLi,tIf 0, the transmission network and the active power distribution network are completely decoupled, the active power distribution network operates in an island mode and corresponds to the N-1 state of the power system, and if P is the stateLi,tNot equal to 0, adopting a method for calculating the energy flow of the integrated system of the transmission network and the active distribution network based on the SOS system theory, and if P is detected, judging whether the energy flow is equal to or less than the preset valueLi,tMore than 0, the relative power transmission network of the active power distribution network is used as the negativeRunning under load if PLi,tAnd if the frequency is less than 0, the active power distribution network operates as a power supply relative to the power transmission network.
4. Based on a system engineering theory, a multiple uncertain energy flow model integrating an active power distribution network and a transmission network under energy interconnection and calculation are established.
Based on a system engineering theory, an energy flow equation set of an integrated power transmission network and an active power distribution network containing shared variables is constructed as follows:
the node is connected with the active power distribution network in a sharing mode (namely, the sharing variable relates to the node):
Figure GDA0002294977230000071
for other nodes of the transmission network:
Figure GDA0002294977230000072
for other nodes of the active power distribution network:
Figure GDA0002294977230000073
wherein
Figure GDA0002294977230000074
Figure GDA0002294977230000075
In the energy flow calculation of the transmission network and the active power distribution network based on the system engineering theory, a power and voltage meter of a common node of the transmission network and the active power distribution network is a shared variable ztSolving the distribution of the energy flow of the power transmission network based on the Newton Raphson algorithm to obtain the shared variable η of the joint node of the power transmission network and the active power distribution networkt(ii) a Meanwhile, solving the energy flow distribution of the active power distribution network based on the forward-backward substitution algorithm to obtain the shared variable mu of the transmission network and the active power distribution networktWill be based on Newton's pullCarrying out iteration on power transmission network power flow calculation of the Frason algorithm and active power distribution network power flow calculation based on the forward-backward generation algorithm until the power transmission network power flow calculation reaches | ηt-μtIf | < epsilon, iterative convergence is carried out, wherein epsilon is an error control quantity, and an energy flow result of the transmission network and the active power distribution network is obtained, wherein ηt，μtIs a shared variable ztIs measured.
A flow chart for modeling multiple uncertain energy flows of an integrated active power distribution network and a transmission network is established as shown in fig. 2 and explained as follows.
1) Random fuzzy simulation is carried out on the basis of an uncertain theory to generate a random fuzzy time-space correlation sequence of electricity, gas and heat multi-energy injected into the active power distribution network;
2) and establishing an Energy Hub (EH) model under energy interconnection. And judging the operation mode of the EH to obtain an interaction value of the energy hub and the power distribution network.
3) Solving the energy flow problem of the active power distribution network to obtain a joint node P of the active power distribution network and the transmission networkLi,tThe positive/negative/zero of the value of (a) is respectively corresponding to three operation modes of load/power supply/island of ADN, if the operation mode is the load/power supply mode, the operation is switched to 5), and if the operation mode is the island mode, the operation is switched to 4);
4) the island mode is correspondingly disconnected with the connection feeder of the power transmission network and the ADN, the power transmission network and the ADN are completely decoupled to operate and correspond to the N-1 state of the power system;
5) solving the distribution of the energy flow of the power transmission network based on the Newton Raphson algorithm to obtain the shared variable η of the joint node of the power transmission network and the active power distribution networkt(ii) a Meanwhile, solving the energy flow distribution of the active power distribution network based on the forward-backward substitution algorithm to obtain the shared variable mu of the transmission network and the active power distribution networkt，
6) Determining whether an iteration condition | η is satisfiedt-μtIf not, turning to the step 5), and if so, outputting energy flow results of the power transmission network and the active power distribution network.

Claims (3)

1. A multiple uncertain energy flow modeling method for an integrated active power distribution network and a power transmission network comprises the following steps:
1) establishing an uncertainty model of the source and the load of the active power distribution network and the transmission network under the energy interconnection and extracting the time-space correlation characteristics of the uncertainty model;
2) establishing an energy hub model with energy interconnection power-off, gas-heat multi-energy injection sources and loads having space-time related random fuzzy characteristics;
3) judging three different operation modes of the active power distribution network based on the system engineering theory energy interconnection; solving the energy flow problem of the active power distribution network based on the system engineering theory, and acquiring corresponding bus exchange power P at the moment according to the resultLi,tIf P isLi,tIf 0, the transmission network and the active power distribution network are completely decoupled, the active power distribution network operates in an island mode and corresponds to the N-1 state of the power system, and if P is the stateLi,tIf the load is more than 0, the active power distribution network operates as a load relative to the power transmission network, and if P is greater than 0Li,t< 0, the active distribution network operates as a power source with respect to the transmission network, so that when P is presentLi,tWhen not equal to 0, adopting a method for calculating the energy flow of the integrated system of the transmission network and the active distribution network based on the system engineering theory;
4) establishing an energy flow multiple uncertain model and calculation of the active power distribution network and transmission network integrated system based on system engineering theoretical energy interconnection; based on a system engineering theory, the interactive power of an active power distribution network and a transmission network is regarded as a shared variable, an energy flow model of multi-energy coupling injection into the active power distribution network and the transmission network is established, and the power and the voltage of a joint node of the transmission network and the active power distribution network are expressed as a shared variable ztAnd power transmission network power flow calculation based on Newton Raphson algorithm is used for obtaining the energy η of the common nodetAnd calculating the load flow of the active power distribution network based on a forward-backward substitution algorithm to obtain the energy mu of the common nodetIteratively converging the energy of the common node until a convergence condition | η is reachedt-μtIf the value is less than the epsilon, wherein the epsilon is an error control quantity, obtaining the energy flow result of the transmission network and the active power distribution network, wherein ηt，μtIs a shared variable ztIs measured.
2. The method for modeling multiple uncertain energy flows of an integrated active power distribution network and transmission network according to claim l, characterized in that: the method comprises the following steps that 1) source and load data of a multi-energy coupling original injection active power distribution network and a power transmission network are influenced by weather and energy consumption behaviors and have uncertain characteristics of randomness and ambiguity, random fuzzy variables are defined according to an uncertain theory to obtain an opportunity measure function of the random fuzzy variables, 5000 times of random fuzzy simulation is carried out on the power of distributed wind power, photovoltaic power generation and natural gas injection according to the uncertain theory to obtain the power of the power, gas and heat injection active power distribution network with the random fuzzy characteristics, the time-space correlation of prediction errors is considered on the basis of a simulation value, an autoregressive sliding average model is used to obtain a time correlation coefficient matrix of source and load injection of hub energy in t time periods, and space correlation coefficient matrices are obtained according to geographic positions of hubs with different energies.
3. The method for modeling multiple uncertain energy flows of an integrated active power distribution network and transmission network according to claim l, characterized in that: and 2) establishing a balance equation of an energy junction node set based on a historical data fitting coefficient array according to a conversion supply and demand balance relationship between an energy supply matrix formed by power grid energy supply, natural gas energy and distributed energy and a load matrix formed by electric load, natural gas load and heat load when the natural gas grid and the active power distribution network are interconnected through an energy junction, obtaining an energy interaction value of the energy junction and the network, and judging the interaction influence of electricity, gas and energy and the operation mode of the cogeneration system through an electric, gas and heat energy flow equation Jacobi matrix.
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