CN113742917A - Comprehensive energy system toughness improvement method considering multi-stage recovery process - Google Patents
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Abstract
The invention discloses a comprehensive energy system toughness improvement method considering a multi-stage recovery process, which comprises the following specific steps: firstly, constructing a pre-disaster preparation stage model; secondly, constructing a disaster attack stage model, and identifying fault and non-fault areas in the comprehensive energy system after the disaster happens; then, constructing a fault isolation stage model and reducing the area of a fault region; then, constructing an energy supply recovery stage model based on rapid reconstruction of the net rack, and realizing energy supply recovery of users in a non-fault area; and finally, decomposing the original model into a series of fault scene sub-models capable of being solved in parallel by adopting a step-by-step hedging algorithm, and realizing efficient and rapid solving of the model. The invention provides a comprehensive energy system toughness improvement method considering a multi-stage recovery process and multi-energy flow coordination from the viewpoints of pre-disaster active defense, rapid post-disaster fault isolation and energy supply recovery, and provides a theoretical basis for extreme disaster response capability construction of a toughness comprehensive energy system.
Description
Technical Field
The invention relates to a comprehensive energy system toughness improvement method considering a multi-stage recovery process, and belongs to the technical field of comprehensive energy system optimization.
Background
With the increasingly prominent global energy and environmental problems, the construction of a cleaner and more efficient comprehensive energy system becomes an important development direction for the energy structure optimization in China. The multi-energy interconnected comprehensive energy system realizes mutual coupling, substitution and supplement of multi-energy forms and promotes the diversified utilization of energy. In recent years, various extreme weather events occur, and the energy supply safety of the comprehensive energy system is seriously threatened. The operation optimization of the current comprehensive energy system does not sufficiently consider the extreme climate risk, on one hand, the influence of extreme weather on the comprehensive energy system can be divided into a plurality of stages, certain coupling relation exists among different stages, and the whole energy supply recovery process needs to be comprehensively considered; on the other hand, protection against its risks cannot rely on only a single energy system. Therefore, it is important to propose a strategy for toughness restoration that takes into account a multi-stage restoration process to construct an integrated energy system with climate toughness.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a comprehensive energy system toughness improvement method considering a multi-stage recovery process, and aims at the toughness improvement problem of the comprehensive energy system under the impact of extreme disasters, the comprehensive energy system toughness improvement method comprehensively considering multi-stage recovery processes of pre-disaster active defense, post-disaster fault rapid isolation, energy supply recovery based on rapid net rack reconstruction and the like and coordination of multi-energy flow systems such as a power distribution network, an air distribution network, an energy concentrator and the like is established, and a theoretical basis is provided for the construction of the extreme disaster response capability of the tough comprehensive energy system.
The invention adopts the following technical scheme for solving the technical problems:
a comprehensive energy system toughness improvement method considering a multi-stage recovery process comprises the following steps:
step 1, constructing a pre-disaster preparation stage model with the purposes of reducing the damage degree of a disaster to a system and improving the post-disaster energy supply recovery speed of the system;
step 2, constructing a disaster attack stage model, identifying fault and non-fault areas in the system after the disaster occurs, and providing a basis for a fault isolation stage;
step 3, constructing a fault isolation stage model, reducing the area of a fault area, and preparing for realizing system energy supply recovery based on rapid network frame reconstruction;
step 4, constructing an energy supply recovery stage model based on rapid net rack reconstruction, and realizing energy supply recovery of a non-fault area;
and 5, converting the model into a series of fault scene sub-models capable of being solved in parallel by adopting a step-by-step hedging algorithm, and realizing rapid solving.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. most of the existing researches on the comprehensive energy system toughness recovery strategy are considered for a single recovery process, and the coordination capacity among different energy subsystems is not considered enough. Considering that a certain coupling relation exists among different stages such as a preparation stage before a disaster, a disaster attack stage, a fault isolation stage and an energy supply recovery stage, meanwhile, energy supply recovery processes can be assisted through multi-energy complementation among different energy subsystems, and comprehensively considering the multi-stage recovery process is very important for enhancing the resistance capability of the comprehensive energy system to extreme weather events. The method considers the multi-stage recovery process and takes the coupling relation among different stages into account, so that the toughness of the comprehensive energy system is effectively improved.
2. The established model is a stochastic programming problem under the condition of considering multiple uncertain scenes, and when the number of scenes is large and the system scale is large, the model has long calculation time and low solving efficiency. The method adopts a step-by-step hedging algorithm to decompose the model into a series of disaster scene subproblems which can be solved in parallel for iterative solution, thereby greatly improving the solving speed of the problems.
Drawings
FIG. 1 is a flow chart of an integrated energy system toughness boosting method of the present invention that contemplates a multi-stage recovery process.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, a flowchart of the method for improving toughness of an integrated energy system considering a multi-stage recovery process according to the present invention includes the following steps:
step 1, aiming at reducing the damage degree of the system caused by the disaster and improving the energy supply recovery speed of the system after the disaster, constructing a pre-disaster preparation stage model
Step 101, the deployment quantity of the remote switches and the remote valves is restricted as follows:
in the formula (II)Respectively representing a line set in a power distribution network and a pipeline set in a gas distribution network; ij. mn respectively represents the serial numbers of the lines and the pipelines; z is a radical ofij、zmnRespectively indicating whether the lines ij and mn are provided with 0-1 variables of a remote control switch and a remote control valve, wherein 1 represents installation, and 0 represents non-installation; n is a radical ofRCS、NRCVRespectively showing the maximum configuration quantity of remote control switches in the power distribution network and remote control valves in the gas distribution network.
Step 102, the net rack topology is constrained as follows:
in the formula (II)Respectively representing a transformer substation node set and a distribution network gate station node set; collection Respectively representing a power distribution network node set and a gas distribution network node set; ε represents a set of energy hub nodes; pi (i) and delta (i) respectively represent a line head section node set taking a node i as a tail end node and a line tail end node set taking the node i as a head end node in the power distribution network; pi (m) and delta (m) respectively represent a pipeline first section node set taking the node m as a tail end node and a pipeline tail end node set taking the node m as a head end node in the gas distribution network; the superscript Pre represents the Pre-disaster preparation stage;virtual variables respectively representing whether the distribution line ij is put into operation in the forward direction and the reverse direction, wherein 1 represents putting into operation, and 0 represents not putting into operation;a virtual variable representing the connection state of the distribution line ij, wherein 1 represents connection and 0 represents disconnection;the method comprises the following steps of respectively representing commissioning state variables of a distribution line ij and a distribution pipeline mn, wherein 1 represents connection, and 0 represents disconnection;respectively representing virtual power flow variables of the distribution lines ki and ij;respectively representing the virtual airflow variables of km and mn of the gas distribution pipeline; di、DmRespectively representing the virtual load of a power distribution network node i and a gas distribution network node m, 1 tableThe electric load and the gas load are not 0, and 0 represents the electric load and the gas load is 0; m represents a larger positive integer.
Step 103, power flow constraint of the power distribution network is as follows:
in the formula (II)Representing a set of gas turbine nodes in a power distribution grid;respectively showing the active power and the reactive power flowing through the distribution line ij,respectively representing active power and reactive power flowing through the distribution line ki;respectively representing active power and reactive power of a gas turbine at a node i;respectively representing active power and reactive power of a node i transformer substation;respectively representing active power and reactive power of the node i flowing to the power distribution network from the energy concentrator;respectively representing the voltage square values of the nodes i and j; pD,i、QD,iRespectively representing the power values of active and reactive loads of the node i; rij、XijRespectively representing the resistance and reactance values of the line ij;respectively representing the minimum value and the maximum value of the voltage of the node i;represents the power capacity of line ij;respectively representing the power factors of the gas turbine and the substation.
Step 104, the power flow constraint of the distribution network is as follows:
in the formula (II)Representing a set of gas turbine nodes in a gas distribution network;respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn;the gas density of the node m and the node n is represented;respectively representing the gas pressure of the nodes m and n;representing the mass flow of the air stream at the gate station node m;representing the gas consumption of the gas turbine at the node m;representing the gas mass flow of the node m from the energy concentrator to the gas distribution network; Δ tPreRepresenting the duration of a preparation stage before a disaster; l ismn、Amn、dmn、ψmnRespectively representing the length, cross-sectional area, diameter, friction coefficient and average gas flow rate of the pipe mn; c represents the speed of sound;respectively representing the initial values of the gas mass flow of the first end node and the tail end node of the pipeline mn;respectively representing the initial values of the gas density of the nodes m and n;respectively representing the initial values of the gas pressure of the nodes m and n;respectively represents the minimum and maximum gas mass flow of the head end node of the mn pipeline,respectively representing the minimum and maximum gas mass flow of the mn tail end node of the pipeline;representing the maximum gas mass flow at the gate station;representing the maximum gas consumption of the gas turbine.
Step 105, the energy hub power flow constraint is as follows:
wherein set ε represents a set of energy hub nodes;respectively representing the active power of photovoltaic, energy storage charging, energy storage discharging, electric gas conversion equipment, a gas turbine and a heat pump in the energy concentrator e;respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;respectively showing the thermal power of a gas turbine, a heat pump and a gas boiler;the method comprises the steps of representing the active power of equipment chi, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump;respectively representing the active power transmitted to the power distribution network by the energy concentrator e and the gas mass flow transmitted to the gas distribution network;representing the energy storage charging and discharging state, wherein the charging is 1, and the discharging is 0;representing the state of charge of the energy storage battery; pD,e、HD,eRespectively representing the power of an active load and the power of a heat load in the energy concentrator e;respectively representing the maximum values of the stored energy charging power and the discharge power; etaES+、ηES-representing the charging and discharging efficiency of the stored energy, respectively;representing an initial value of the state of charge of the battery;respectively representing the minimum value and the maximum value of the charge state of the energy storage battery;respectively representing the maximum active power values of the electric gas conversion equipment, the gas turbine and the heat pump;respectively representing the maximum values of the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;respectively representing the maximum thermal power values of the gas boiler and the electric heat pump;representing the initial value of the active power of equipment chi; RDχ、RUχRespectively representing the maximum power values of downward and upward climbing of equipment chi, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump; etaPtG、ηGT、ηGBRespectively indicating electric gas-converting equipment, gas-fired boiler and electric heaterThe conversion efficiency of the pump; etaGT,gp、ηGT,ghRespectively representing the gas-to-electricity and gas-to-heat efficiency of the gas turbine;respectively representing the upper limit of active power transmitted from the energy concentrator to the distribution grid and the upper limit of gas mass flow transmitted to the distribution grid.
In step 106, the coupling constraints between systems are as follows:
in the formula (I), the compound is shown in the specification,representing a distribution network node i gas turbine gas source node set in a distribution network,and respectively representing a connection node set of the energy concentrator node e and the power distribution network and a connection node set of the energy concentrator node e and the power distribution network.
Step 2, constructing a disaster attack stage model, identifying fault and non-fault areas in the system after the disaster happens, and providing a basis for a fault isolation stage
Step 201, the net rack topology is constrained as follows:
in the formula, subscript s represents a disaster scene;respectively representing state variables of nodes at two ends of a line or a pipeline ij after being attacked in a disaster scene s, wherein the fault state is 1, and the fault state is 0;and (3) indicating whether the line or the pipeline ij is damaged or not under the disaster scene s, wherein the damage is 1 and the undamaged damage is 0.
Step 202, power flow constraint of the power distribution network is as follows:
in the formula (I), the compound is shown in the specification,representing a set of disaster scenarios; the superscript Dis represents the disaster attack stage;representing a commissioning state variable of a distribution line ij under a disaster scene s;respectively representing active power and reactive power flowing through the distribution line ki under a disaster scene s,respectively representing active power and reactive power flowing through a distribution line ij under a disaster scene s;respectively representing active power and reactive power of a gas turbine at a node i under a disaster scene s;respectively representing active power and reactive power of a node i transformer substation in a disaster scene s; respectively representing that the node i flows to the power distribution network from the energy concentrator under the disaster scene sActive and reactive power of;respectively representing the active load shedding power and the reactive load shedding power of a node i under a disaster scene s;respectively representing the voltage square values of the nodes i and j under the disaster scene s.
Step 203, the power flow constraint of the distribution network is as follows:
in the formula (I), the compound is shown in the specification,representing the commissioning state variable of the gas distribution pipeline mn in a disaster scene s, wherein 1 represents connection and 0 represents disconnection;respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn in a disaster scene s;respectively representing the gas density of nodes m and n under a disaster scene s;respectively representing the gas pressure of the nodes m and n under the disaster scene s;representing the airflow mass flow of a node m of a lower gate station under a disaster scene s;representing the gas consumption of a gas turbine at a node m under a disaster scene s;representing the gas mass flow of the node m flowing to the gas distribution network from the energy concentrator under the disaster scene s;representing disaster areasThe gas cutting load of a node m under a scene s; Δ tDisRepresenting the duration of the pre-disaster attack phase.
In step 204, the power hub flow constraints are as follows:
in the formula (I), the compound is shown in the specification,respectively representing active power of photovoltaic, energy storage charging, energy storage discharging, electric gas conversion equipment, a gas turbine and a heat pump in an energy concentrator e under a disaster scene s;respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler under the disaster scene s;respectively representing the thermal power of a gas turbine, a heat pump and a gas boiler under a disaster scene s;representing the magnitude of the tangential active load power and the tangential thermal load power in the energy concentrator e;the method comprises the steps of representing the active power of equipment chi under a disaster scene s, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump;respectively representing active power transmitted to a power distribution network by an energy concentrator e under a disaster scene s and gas mass flow transmitted to a gas distribution network;representing the energy storage charging and discharging state under the disaster scene s, wherein the charging is 1, and the discharging is 0;and representing the charge state of the energy storage battery in a disaster scene s.
In step 205, the coupling constraints between systems are as follows:
the variables in the formulae are as described above.
Step 3, constructing a fault isolation stage model, reducing the area of a fault area, and preparing for system energy supply recovery based on rapid grid reconstruction
Step 301, the rack topology constraint is as follows:
in the formula, the upper mark Iso represents a fault isolation stage;representing the running state variable of the line or pipeline ij under the fault scene s, wherein 1 represents that the pipeline is in a running state, and otherwise, the running state variable is 0;and respectively representing state variables of nodes at two ends of the line or pipeline ij after being attacked in a disaster scene s, wherein the fault state is 1, and otherwise, the fault state is 0.
Step 302, power flow constraints and intersystem coupling constraints of the power distribution network, the gas distribution network and the energy concentrator are as follows:
the fault isolation phase related constraints are the same as the disaster attack phase.
Step 4, constructing an energy supply recovery stage model based on rapid net rack reconstruction, and realizing energy supply recovery of a non-fault area
Step 401, the net rack topology constraint in the energy supply recovery stage is the same as the net rack topology constraint in the preparation stage before the disaster.
And step 402, related constraints such as a power distribution network, a gas distribution network, an energy concentrator current constraint and an intersystem coupling constraint in the energy supply recovery stage are the same as those in the disaster attack stage.
Step 5, converting the model into a series of fault scene sub-models capable of being solved in parallel by adopting a step-by-step hedging algorithm to realize rapid solving
Step 501, considering the objective functions of the multi-energy flow coordination energy supply recovery model of the preparation stage before disaster, the disaster attack stage, the fault isolation stage and the energy supply recovery stage as follows:
in the formula, PrsRepresenting the probability of occurrence, ω, of a disaster scene si、ωm、ωeωiRespectively representing the weight coefficients of the power distribution network node i electric load, the distribution network node m gas load, the energy concentrator e electric load and the heat load,representing the conversion factor of gas mass flow to electrical power.
Step 502, expressing the appellation model in a matrix form as follows:
wherein x represents a decision variable in a pre-disaster preparation stage, ysRepresenting decision variables of a disaster attack stage, a fault isolation stage and an energy supply recovery stage under a disaster scene s,a transposed matrix representing the coefficients of the variables under the disaster scenario s,representing a set of constraints under a disaster scenario s.
Step 503, converting the model into a fault scene sub-model capable of being solved in parallel by adopting a step-by-step hedging algorithm, and performing iterative solution specifically comprises the following steps:
(1) setting initial values of a penalty factor upsilon and a convergence coefficient epsilon, setting the iteration times k to be 0, and setting the initial fixed variable quantity sigmak0, initial value of Lagrange multiplier matrix
(9) if K is less than or equal to K3Or σk+1-σkEntering the step (10) when the value is more than or equal to 1; otherwise, entering the step (15);
(10) if K is more than or equal to K1Entering the step (11); otherwise, entering a step (12);
(12) If K is more than or equal to K2Entering step (13); otherwise, entering a step (14);
(14) Taking k as k +1, and returning to the step (5);
Taking a testing system as an example, the established method for improving the toughness of the comprehensive energy system considering the multi-stage recovery process is verified. Five comparative cases were set, which were:
1) case 1: consider a multi-stage recovery process;
2) case 2: considering multi-energy flow coordination, only considering a preparation stage before disaster;
3) case 3: considering multi-energy flow coordination and not considering a preparation stage before disaster;
4) case 4: considering a multi-stage recovery process, independently optimizing a power distribution network, a gas distribution network and an energy concentrator;
5) case 5: the multi-energy flow recovery process is considered, without considering the energy hub.
The percentage of energy recovery during the multi-stage recovery in cases 1-5 is shown in table 1.
TABLE 1 percentage energy recovery in cases 1-5 of the Multi-stage recovery Process
The result shows that the toughness of the comprehensive energy system in consideration of the multi-stage recovery process is effectively improved by the method for improving the toughness of the comprehensive energy system in response to the uncertain extreme disaster scene.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (6)
1. A comprehensive energy system toughness improvement method considering a multi-stage recovery process is characterized by comprising the following steps:
step 1, constructing a pre-disaster preparation stage model with the purposes of reducing the damage degree of a disaster to a system and improving the post-disaster energy supply recovery speed of the system;
step 2, constructing a disaster attack stage model, identifying fault and non-fault areas in the system after the disaster occurs, and providing a basis for a fault isolation stage;
step 3, constructing a fault isolation stage model, reducing the area of a fault area, and preparing for realizing system energy supply recovery based on rapid network frame reconstruction;
step 4, constructing an energy supply recovery stage model based on rapid net rack reconstruction, and realizing energy supply recovery of a non-fault area;
and 5, converting the model into a series of fault scene sub-models capable of being solved in parallel by adopting a step-by-step hedging algorithm, and realizing rapid solving.
2. The method for improving the toughness of the integrated energy system in consideration of the multi-stage recovery process according to claim 1, wherein the step 1 of constructing the pre-disaster preparation stage model is as follows:
step 1.1, the deployment quantity of the remote control switches and the remote control valves is restricted as follows:
in the formula: collectionRespectively representing a line set in a power distribution network and a pipeline set in a gas distribution network; ij. mn respectively represents the serial numbers of the lines and the pipelines; z is a radical ofij、zmnRespectively indicating whether the lines ij and mn are provided with 0-1 variables of a remote control switch and a remote control valve, wherein 1 represents installation, and 0 represents non-installation; n is a radical ofRCS、NRCVRespectively representing the maximum configuration quantity of remote control switches in the power distribution network and remote control valves in the gas distribution network;
step 1.2, the net rack topology constraint is as follows:
in the formula: collectionRespectively representing a transformer substation node set and a distribution network gate station node set; collection Respectively representing a power distribution network node set and a gas distribution network node set; ε represents a set of energy hub nodes; pi (i) and delta (i) respectively represent a line head section node set taking a node i as a tail end node and a line tail end node set taking the node i as a head end node in the power distribution network; pi (m) and delta (m) respectively represent a pipeline first section node set taking the node m as a tail end node and a pipeline tail end node set taking the node m as a head end node in the gas distribution network; the superscript Pre represents the Pre-disaster preparation stage;virtual variables respectively representing whether the distribution line ij is put into operation in the forward direction and the reverse direction, wherein 1 represents putting into operation, and 0 represents not putting into operation;a virtual variable representing the connection state of the distribution line ij, wherein 1 represents connection and 0 represents disconnection;the method comprises the following steps of respectively representing commissioning state variables of a distribution line ij and a distribution pipeline mn, wherein 1 represents connection, and 0 represents disconnection;respectively representing virtual power flow variables of the distribution lines ki and ij;respectively representing the virtual airflow variables of km and mn of the gas distribution pipeline; di、DmRespectively representing the virtual load quantities of a power distribution network node i and a gas distribution network node m, wherein 1 represents that the electric load and the gas load are not 0, and 0 represents that the electric load and the gas load are 0; m represents a larger positive integer;
step 1.3, power flow constraint of the power distribution network is as follows:
in the formula: collectionRepresenting a set of gas turbine nodes in a power distribution grid;respectively showing the active power and the reactive power flowing through the distribution line ij,respectively representing active power and reactive power flowing through the distribution line ki;respectively representing active power and reactive power of a gas turbine at a node i;respectively representing active power and reactive power of a node i transformer substation;respectively representing active power and reactive power of the node i flowing to the power distribution network from the energy concentrator;respectively representing the voltage square values of the node i and the node j; pD,i、QD,iRespectively representing the power values of active and reactive loads of the node i; rij、XijRespectively representing the resistance and reactance values of the line ij;respectively representing the minimum value and the maximum value of the voltage of the node i;represents the power capacity of line ij;respectively representing power factors of a gas turbine and a transformer substation;
step 1.4, the power flow constraint of the gas distribution network is as follows:
in the formula: collectionRepresenting a set of gas turbine nodes in a gas distribution network;respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn;representing the gas density at nodes m, n; respectively representing the gas pressure of the nodes m and n;representing the mass flow of the air stream at the gate station node m;representing the gas consumption of the gas turbine at the node m;representing the gas mass flow of the node m from the energy concentrator to the gas distribution network; Δ tPreRepresenting the duration of a preparation stage before a disaster; l ismn、Amn、dmn、ψmnRespectively representing the length and cross section of the pipe mnArea, diameter, coefficient of friction and average gas flow rate; c represents the speed of sound; respectively representing the initial values of the gas mass flow of the nodes at the head end and the tail end of the mn pipeline;respectively representing the initial values of the gas density of the nodes m and n;respectively representing the initial values of the gas pressure of the nodes m and n;respectively represents the minimum and maximum gas mass flow of the head end node of the mn pipeline, respectively representing the minimum and maximum gas mass flow of the mn tail end node of the pipeline;representing the maximum gas mass flow at the gate station;representing a maximum gas consumption of the gas turbine;
step 1.5, the power flow constraint of the energy concentrator is as follows:
in the formula: set ε represents a set of energy hub nodes;respectively representing the active power of photovoltaic, energy storage charging, energy storage discharging, electric gas conversion equipment, a gas turbine and a heat pump in the energy concentrator e;respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;respectively showing the thermal power of a gas turbine, a heat pump and a gas boiler;the method comprises the steps of representing the active power of equipment chi, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump;respectively representing the active power transmitted to the power distribution network by the energy concentrator e and the gas mass flow transmitted to the gas distribution network;representing the energy storage charging and discharging state, wherein the charging is 1, and the discharging is 0;representing the state of charge of the energy storage battery; pD,e、HD,eRespectively representing the power of an active load and the power of a heat load in the energy concentrator e;respectively representing the maximum values of the stored energy charging power and the discharge power; etaES+、ηES-Respectively representing the charging efficiency and the discharging efficiency of the stored energy;representing an initial value of the state of charge of the battery;respectively representing the minimum value and the maximum value of the charge state of the energy storage battery;respectively representing the maximum active power values of the electric gas conversion equipment, the gas turbine and the heat pump;respectively representing the maximum values of the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;respectively representing the maximum thermal power values of the gas boiler and the electric heat pump;representing the initial value of the active power of equipment chi; RDχ、RUχRespectively representing the maximum power values of downward and upward climbing of equipment chi, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump; etaPtG、ηGT、ηGBRespectively showing the conversion efficiency of the electric gas conversion equipment, the gas boiler and the electric heat pump; etaGT,gp、ηGT,ghRespectively representing the gas-to-electricity and gas-to-heat efficiency of the gas turbine;respectively representing the upper limit of active power transmitted from the energy concentrator to the distribution network and the mass flow of gas transmitted to the distribution networkAn upper limit;
step 1.6, the coupling constraint between systems is as follows:
in the formula:representing a power distribution network node i, namely a gas source node set of a gas turbine in a gas distribution network;and respectively representing a connection node set of the energy concentrator node e and the power distribution network and a connection node set of the energy concentrator node e and the power distribution network.
3. The method for improving the toughness of the integrated energy system in consideration of the multi-stage restoration process according to claim 1, wherein the step 2 of constructing the disaster attack stage model is as follows:
step 2.1, the net rack topology constraint is as follows:
in the formula: subscript s denotes the disaster scenario;respectively representing that nodes at two ends of a line or a pipeline ij are attacked in a disaster scene sThe later state variable has a fault state of 1 and a non-fault state of 0; lij,sWhether a line or a pipeline ij is damaged or not under a disaster scene s is represented, the damage is 1, and the undamaged damage is 0;
step 2.2, power flow constraint of the power distribution network is as follows:
in the formula:representing a set of disaster scenarios; the superscript Dis represents the disaster attack stage;representing a commissioning state variable of a distribution line ij under a disaster scene s;respectively representing active power and reactive power flowing through the distribution line ki under a disaster scene s,respectively representing active power and reactive power flowing through a distribution line ij under a disaster scene s;respectively representing active power and reactive power of a gas turbine at a node i under a disaster scene s;respectively representing active power and reactive power of a node i transformer substation in a disaster scene s; respectively representing active power and reactive power of a node i flowing from the energy concentrator to the power distribution network in a disaster scene s;respectively representing the active load shedding power and the reactive load shedding power of a node i under a disaster scene s;respectively representing voltage square values of a node i and a node j under a disaster scene s;
step 2.3, the power flow constraint of the gas distribution network is as follows:
in the formula:representing the commissioning state variable of the gas distribution pipeline mn in a disaster scene s, wherein 1 represents connection and 0 represents disconnection;respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn in a disaster scene s;respectively representing the gas density of nodes m and n under a disaster scene s;respectively representing the gas pressure of the nodes m and n under the disaster scene s;representing the airflow mass flow of a node m of a lower gate station under a disaster scene s;representing the gas consumption of a gas turbine at a node m under a disaster scene s;representing the gas mass flow of the node m flowing to the gas distribution network from the energy concentrator under the disaster scene s;representing the gas cutting load of a node m under a disaster scene s; Δ tDisRepresenting the duration of the attack stage before the disaster;
step 2.4, the power flow constraint of the energy concentrator is as follows:
in the formula:respectively representing active power of photovoltaic, energy storage charging, energy storage discharging, electric gas conversion equipment, a gas turbine and a heat pump in an energy concentrator e under a disaster scene s;respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler under the disaster scene s;respectively representing the thermal power of a gas turbine, a heat pump and a gas boiler under a disaster scene s;representing the magnitude of the tangential active load power and the tangential thermal load power in the energy concentrator e;the method comprises the steps of representing the active power of equipment chi under a disaster scene s, wherein the equipment chi comprises electric gas conversion equipment, a gas turbine and an electric heat pump;respectively representing active power transmitted to a power distribution network by an energy concentrator e under a disaster scene s and gas mass flow transmitted to a gas distribution network;representing the energy storage charging and discharging state under the disaster scene s, wherein the charging is 1, and the discharging is 0;representing the charge state of the energy storage battery under a disaster scene s;
step 2.5, the coupling constraint between systems is as follows:
the variables in the formulae are as described above.
4. The method for improving the toughness of the integrated energy system in consideration of the multi-stage recovery process according to claim 1, wherein the step 3 of constructing the fault isolation stage model is as follows:
step 3.1, the net rack topology constraint is as follows:
in the formula: the superscript Iso represents the fault isolation phase;representing the running state variable of the line or pipeline ij under the fault scene s, wherein 1 represents that the pipeline is in a running state, and otherwise, the running state variable is 0;respectively representing state variables of nodes at two ends of a line or a pipeline ij after being attacked in a disaster scene s, wherein the fault state is 1, and otherwise, the fault state is 0;
and 3.2, the power distribution network, the gas distribution network, the energy concentrator and the inter-system coupling constraint are the same as the disaster attack stage.
5. The method for improving the toughness of the integrated energy system in consideration of the multi-stage recovery process according to claim 1, wherein the step 4 of constructing the energy supply recovery stage model is as follows:
and 4.1, the net rack topological constraint in the energy supply recovery stage is the same as that in the preparation stage before the disaster.
And 4.2, the power distribution network, the gas distribution network, the energy concentrator and the inter-system coupling constraint are the same as the disaster attack stage.
6. The method for improving the toughness of the integrated energy system in consideration of the multi-stage recovery process according to claim 1, wherein the step 5 adopts a step-by-step hedging algorithm to perform an iterative solution process on the model as follows:
step 5.1, considering the objective functions of the multi-energy flow coordination energy supply recovery model in the preparation stage before disaster, the disaster attack stage, the fault isolation stage and the energy supply recovery stage as follows:
in the formula: pr (Pr) ofsRepresenting the occurrence probability of a disaster scenario s; omegai、ωm、ωeRespectively representing the weight coefficients of an electrical load of a node i of the power distribution network, an electrical load of a node m of the gas distribution network, an electrical load of an energy concentrator e and a thermal load;a conversion coefficient representing a gas mass flow rate and an electric power;
step 5.2, expressing the appellation model in a matrix form as follows:
in the formula: x represents a decision variable in a preparation stage before a disaster; y issThe decision variables represent a disaster attack stage, a fault isolation stage and an energy supply recovery stage under a disaster scene s;a transposed matrix representing a variable coefficient in a disaster scene s;representing a set of constraints under a disaster scenario s;
step 5.3, converting the model into a fault scene sub-model capable of being solved in parallel by adopting a step-by-step hedging algorithm, and carrying out iterative solution specifically comprises the following steps:
(1) setting initial values of a penalty factor upsilon and a convergence coefficient epsilon, setting the iteration times k to be 0, and setting the initial fixed variable quantity sigmak0, initial value of Lagrange multiplier matrix
(9) if K is less than or equal to K3Or σk+1-σkEntering the step (10) when the value is more than or equal to 1; otherwise, entering the step (15);
(10) if K is more than or equal to K1Entering the step (11); otherwise, entering a step (12);
(12) If K is more than or equal to K2Entering step (13); otherwise, entering a step (14);
(14) Taking k as k +1, and returning to the step (5);
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