CN113742917A - Comprehensive energy system toughness improvement method considering multi-stage recovery process - Google Patents

Comprehensive energy system toughness improvement method considering multi-stage recovery process Download PDF

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CN113742917A
CN113742917A CN202111037647.8A CN202111037647A CN113742917A CN 113742917 A CN113742917 A CN 113742917A CN 202111037647 A CN202111037647 A CN 202111037647A CN 113742917 A CN113742917 A CN 113742917A
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孙琦润
吴志
顾伟
陆于平
周苏洋
刘鹏翔
何品泉
熊钰
罗李子
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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

Comprehensive energy system toughness improvement method considering multi-stage recovery process
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:
Figure RE-GDA0003299827250000031
Figure RE-GDA0003299827250000032
in the formula (II)
Figure RE-GDA0003299827250000033
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:
Figure RE-GDA0003299827250000034
Figure RE-GDA0003299827250000035
Figure RE-GDA0003299827250000036
Figure RE-GDA0003299827250000037
Figure RE-GDA0003299827250000038
Figure RE-GDA0003299827250000039
Figure RE-GDA00032998272500000310
in the formula (II)
Figure RE-GDA00032998272500000311
Respectively representing a transformer substation node set and a distribution network gate station node set; collection
Figure RE-GDA00032998272500000312
Figure RE-GDA0003299827250000041
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;
Figure RE-GDA0003299827250000042
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;
Figure RE-GDA0003299827250000043
a virtual variable representing the connection state of the distribution line ij, wherein 1 represents connection and 0 represents disconnection;
Figure RE-GDA0003299827250000044
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;
Figure RE-GDA0003299827250000045
respectively representing virtual power flow variables of the distribution lines ki and ij;
Figure RE-GDA0003299827250000046
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:
Figure RE-GDA0003299827250000047
Figure RE-GDA0003299827250000048
Figure RE-GDA0003299827250000049
Figure RE-GDA00032998272500000410
Figure RE-GDA00032998272500000411
Figure RE-GDA00032998272500000412
Figure RE-GDA00032998272500000413
Figure RE-GDA00032998272500000414
in the formula (II)
Figure RE-GDA00032998272500000415
Representing a set of gas turbine nodes in a power distribution grid;
Figure RE-GDA00032998272500000416
respectively showing the active power and the reactive power flowing through the distribution line ij,
Figure RE-GDA0003299827250000051
respectively representing active power and reactive power flowing through the distribution line ki;
Figure RE-GDA0003299827250000052
respectively representing active power and reactive power of a gas turbine at a node i;
Figure RE-GDA0003299827250000053
respectively representing active power and reactive power of a node i transformer substation;
Figure RE-GDA0003299827250000054
respectively representing active power and reactive power of the node i flowing to the power distribution network from the energy concentrator;
Figure RE-GDA0003299827250000055
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;
Figure RE-GDA0003299827250000056
respectively representing the minimum value and the maximum value of the voltage of the node i;
Figure RE-GDA0003299827250000057
represents the power capacity of line ij;
Figure RE-GDA0003299827250000058
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:
Figure RE-GDA0003299827250000059
Figure RE-GDA00032998272500000510
Figure RE-GDA00032998272500000511
Figure RE-GDA00032998272500000512
Figure RE-GDA00032998272500000513
Figure RE-GDA00032998272500000514
Figure RE-GDA00032998272500000515
Figure RE-GDA00032998272500000516
Figure RE-GDA00032998272500000517
in the formula (II)
Figure RE-GDA00032998272500000518
Representing a set of gas turbine nodes in a gas distribution network;
Figure RE-GDA00032998272500000519
respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn;
Figure RE-GDA00032998272500000520
the gas density of the node m and the node n is represented;
Figure RE-GDA00032998272500000521
respectively representing the gas pressure of the nodes m and n;
Figure RE-GDA00032998272500000522
representing the mass flow of the air stream at the gate station node m;
Figure RE-GDA00032998272500000523
representing the gas consumption of the gas turbine at the node m;
Figure RE-GDA00032998272500000524
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、
Figure RE-GDA00032998272500000620
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;
Figure RE-GDA0003299827250000061
respectively representing the initial values of the gas mass flow of the first end node and the tail end node of the pipeline mn;
Figure RE-GDA0003299827250000062
respectively representing the initial values of the gas density of the nodes m and n;
Figure RE-GDA0003299827250000063
respectively representing the initial values of the gas pressure of the nodes m and n;
Figure RE-GDA0003299827250000064
respectively represents the minimum and maximum gas mass flow of the head end node of the mn pipeline,
Figure RE-GDA0003299827250000065
respectively representing the minimum and maximum gas mass flow of the mn tail end node of the pipeline;
Figure RE-GDA0003299827250000066
representing the maximum gas mass flow at the gate station;
Figure RE-GDA0003299827250000067
representing the maximum gas consumption of the gas turbine.
Step 105, the energy hub power flow constraint is as follows:
Figure RE-GDA0003299827250000068
Figure RE-GDA0003299827250000069
Figure RE-GDA00032998272500000610
Figure RE-GDA00032998272500000611
Figure RE-GDA00032998272500000612
Figure RE-GDA00032998272500000613
Figure RE-GDA00032998272500000614
Figure RE-GDA00032998272500000615
Figure RE-GDA00032998272500000616
Figure RE-GDA00032998272500000617
Figure RE-GDA00032998272500000618
wherein set ε represents a set of energy hub nodes;
Figure RE-GDA00032998272500000619
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;
Figure RE-GDA0003299827250000071
respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;
Figure RE-GDA0003299827250000072
respectively showing the thermal power of a gas turbine, a heat pump and a gas boiler;
Figure RE-GDA00032998272500000718
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;
Figure RE-GDA0003299827250000073
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;
Figure RE-GDA0003299827250000074
representing the energy storage charging and discharging state, wherein the charging is 1, and the discharging is 0;
Figure RE-GDA0003299827250000075
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;
Figure RE-GDA0003299827250000076
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;
Figure RE-GDA0003299827250000077
representing an initial value of the state of charge of the battery;
Figure RE-GDA0003299827250000078
respectively representing the minimum value and the maximum value of the charge state of the energy storage battery;
Figure RE-GDA0003299827250000079
respectively representing the maximum active power values of the electric gas conversion equipment, the gas turbine and the heat pump;
Figure RE-GDA00032998272500000710
respectively representing the maximum values of the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;
Figure RE-GDA00032998272500000711
respectively representing the maximum thermal power values of the gas boiler and the electric heat pump;
Figure RE-GDA00032998272500000712
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;
Figure RE-GDA00032998272500000713
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:
Figure RE-GDA00032998272500000714
Figure RE-GDA00032998272500000715
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00032998272500000716
representing a distribution network node i gas turbine gas source node set in a distribution network,
Figure RE-GDA00032998272500000717
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:
Figure RE-GDA0003299827250000081
Figure RE-GDA0003299827250000082
in the formula, subscript s represents a disaster scene;
Figure RE-GDA0003299827250000083
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;
Figure RE-GDA00032998272500000816
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:
Figure RE-GDA0003299827250000084
Figure RE-GDA0003299827250000085
Figure RE-GDA0003299827250000086
Figure RE-GDA0003299827250000087
Figure RE-GDA0003299827250000088
Figure RE-GDA0003299827250000089
Figure RE-GDA00032998272500000810
Figure RE-GDA00032998272500000811
Figure RE-GDA00032998272500000812
Figure RE-GDA00032998272500000813
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00032998272500000817
representing a set of disaster scenarios; the superscript Dis represents the disaster attack stage;
Figure RE-GDA00032998272500000814
representing a commissioning state variable of a distribution line ij under a disaster scene s;
Figure RE-GDA00032998272500000815
respectively representing active power and reactive power flowing through the distribution line ki under a disaster scene s,
Figure RE-GDA0003299827250000091
respectively representing active power and reactive power flowing through a distribution line ij under a disaster scene s;
Figure RE-GDA0003299827250000092
respectively representing active power and reactive power of a gas turbine at a node i under a disaster scene s;
Figure RE-GDA0003299827250000093
respectively representing active power and reactive power of a node i transformer substation in a disaster scene s;
Figure RE-GDA0003299827250000094
Figure RE-GDA0003299827250000095
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;
Figure RE-GDA0003299827250000096
respectively representing the active load shedding power and the reactive load shedding power of a node i under a disaster scene s;
Figure RE-GDA0003299827250000097
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:
Figure RE-GDA0003299827250000098
Figure RE-GDA0003299827250000099
Figure RE-GDA00032998272500000910
Figure RE-GDA00032998272500000911
Figure RE-GDA00032998272500000912
Figure RE-GDA00032998272500000913
Figure RE-GDA00032998272500000914
Figure RE-GDA00032998272500000915
Figure RE-GDA00032998272500000916
Figure RE-GDA00032998272500000917
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00032998272500000918
representing the commissioning state variable of the gas distribution pipeline mn in a disaster scene s, wherein 1 represents connection and 0 represents disconnection;
Figure RE-GDA00032998272500000919
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;
Figure RE-GDA00032998272500000920
respectively representing the gas density of nodes m and n under a disaster scene s;
Figure RE-GDA00032998272500000921
respectively representing the gas pressure of the nodes m and n under the disaster scene s;
Figure RE-GDA00032998272500000922
representing the airflow mass flow of a node m of a lower gate station under a disaster scene s;
Figure RE-GDA0003299827250000101
representing the gas consumption of a gas turbine at a node m under a disaster scene s;
Figure RE-GDA0003299827250000102
representing the gas mass flow of the node m flowing to the gas distribution network from the energy concentrator under the disaster scene s;
Figure RE-GDA0003299827250000103
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:
Figure RE-GDA0003299827250000104
Figure RE-GDA0003299827250000105
Figure RE-GDA0003299827250000106
Figure RE-GDA0003299827250000107
Figure RE-GDA0003299827250000108
Figure RE-GDA0003299827250000109
Figure RE-GDA00032998272500001010
Figure RE-GDA00032998272500001011
Figure RE-GDA00032998272500001012
Figure RE-GDA00032998272500001013
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00032998272500001014
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;
Figure RE-GDA00032998272500001015
respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler under the disaster scene s;
Figure RE-GDA00032998272500001016
respectively representing the thermal power of a gas turbine, a heat pump and a gas boiler under a disaster scene s;
Figure RE-GDA00032998272500001017
representing the magnitude of the tangential active load power and the tangential thermal load power in the energy concentrator e;
Figure RE-GDA00032998272500001018
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;
Figure RE-GDA0003299827250000111
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;
Figure RE-GDA0003299827250000112
representing the energy storage charging and discharging state under the disaster scene s, wherein the charging is 1, and the discharging is 0;
Figure RE-GDA0003299827250000113
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:
Figure RE-GDA0003299827250000114
Figure RE-GDA0003299827250000115
Figure RE-GDA0003299827250000116
Figure RE-GDA0003299827250000117
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:
Figure RE-GDA0003299827250000118
Figure RE-GDA0003299827250000119
Figure RE-GDA00032998272500001110
in the formula, the upper mark Iso represents a fault isolation stage;
Figure RE-GDA00032998272500001111
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;
Figure RE-GDA00032998272500001112
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:
Figure RE-GDA0003299827250000121
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,
Figure RE-GDA0003299827250000129
representing the conversion factor of gas mass flow to electrical power.
Step 502, expressing the appellation model in a matrix form as follows:
Figure RE-GDA0003299827250000122
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,
Figure RE-GDA0003299827250000123
a transposed matrix representing the coefficients of the variables under the disaster scenario s,
Figure RE-GDA0003299827250000124
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
Figure RE-GDA0003299827250000125
(2) For any fault scene
Figure RE-GDA00032998272500001210
Solving sub-problems
Figure RE-GDA0003299827250000126
(3) Averaging
Figure RE-GDA0003299827250000127
(4) For any fault scene
Figure RE-GDA00032998272500001211
Computing
Figure RE-GDA0003299827250000128
(5) For any fault scene
Figure RE-GDA00032998272500001311
Solving sub-problems
Figure RE-GDA0003299827250000131
(6) Averaging
Figure RE-GDA0003299827250000132
(7) For any fault scene
Figure RE-GDA00032998272500001312
Computing
Figure RE-GDA0003299827250000133
(8) If it satisfies
Figure RE-GDA0003299827250000134
The iteration is terminated; otherwise, entering the step (9);
(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);
(11) if it satisfies
Figure RE-GDA0003299827250000135
Then is fixed
Figure RE-GDA0003299827250000136
Take sigmak+1＝σk+1；
(12) If K is more than or equal to K2Entering step (13); otherwise, entering a step (14);
(13) if it satisfies
Figure RE-GDA0003299827250000137
Then is fixed
Figure RE-GDA0003299827250000138
Take sigmak+1＝σk+1；
(14) Taking k as k +1, and returning to the step (5);
(15) solving model
Figure RE-GDA0003299827250000139
And (6) ending.
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
Figure RE-GDA00032998272500001310
Figure RE-GDA0003299827250000141
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:
Figure RE-FDA0003299827240000011
Figure RE-FDA0003299827240000012
in the formula: collection
Figure RE-FDA0003299827240000013
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 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:
Figure RE-FDA0003299827240000014
Figure RE-FDA0003299827240000021
Figure RE-FDA0003299827240000022
Figure RE-FDA0003299827240000023
Figure RE-FDA0003299827240000024
Figure RE-FDA0003299827240000025
Figure RE-FDA0003299827240000026
in the formula: collection
Figure RE-FDA0003299827240000027
Respectively representing a transformer substation node set and a distribution network gate station node set; collection
Figure RE-FDA0003299827240000028
Figure RE-FDA0003299827240000029
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;
Figure RE-FDA00032998272400000210
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;
Figure RE-FDA00032998272400000211
a virtual variable representing the connection state of the distribution line ij, wherein 1 represents connection and 0 represents disconnection;
Figure RE-FDA00032998272400000212
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;
Figure RE-FDA00032998272400000213
respectively representing virtual power flow variables of the distribution lines ki and ij;
Figure RE-FDA00032998272400000214
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:
Figure RE-FDA00032998272400000215
Figure RE-FDA00032998272400000216
Figure RE-FDA0003299827240000031
Figure RE-FDA0003299827240000032
Figure RE-FDA0003299827240000033
Figure RE-FDA0003299827240000034
Figure RE-FDA0003299827240000035
Figure RE-FDA0003299827240000036
in the formula: collection
Figure RE-FDA0003299827240000037
Representing a set of gas turbine nodes in a power distribution grid;
Figure RE-FDA0003299827240000038
respectively showing the active power and the reactive power flowing through the distribution line ij,
Figure RE-FDA0003299827240000039
respectively representing active power and reactive power flowing through the distribution line ki;
Figure RE-FDA00032998272400000310
respectively representing active power and reactive power of a gas turbine at a node i;
Figure RE-FDA00032998272400000311
respectively representing active power and reactive power of a node i transformer substation;
Figure RE-FDA00032998272400000312
respectively representing active power and reactive power of the node i flowing to the power distribution network from the energy concentrator;
Figure RE-FDA00032998272400000313
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;
Figure RE-FDA00032998272400000314
respectively representing the minimum value and the maximum value of the voltage of the node i;
Figure RE-FDA00032998272400000315
represents the power capacity of line ij;
Figure RE-FDA00032998272400000316
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:
Figure RE-FDA00032998272400000317
Figure RE-FDA00032998272400000318
Figure RE-FDA00032998272400000319
Figure RE-FDA00032998272400000320
Figure RE-FDA0003299827240000041
Figure RE-FDA0003299827240000042
Figure RE-FDA0003299827240000043
Figure RE-FDA0003299827240000044
Figure RE-FDA0003299827240000045
in the formula: collection
Figure RE-FDA0003299827240000046
Representing a set of gas turbine nodes in a gas distribution network;
Figure RE-FDA0003299827240000047
respectively representing the gas mass flow of the head end node and the tail end node of the pipeline mn;
Figure RE-FDA0003299827240000048
representing the gas density at nodes m, n;
Figure RE-FDA0003299827240000049
Figure RE-FDA00032998272400000410
respectively representing the gas pressure of the nodes m and n;
Figure RE-FDA00032998272400000411
representing the mass flow of the air stream at the gate station node m;
Figure RE-FDA00032998272400000412
representing the gas consumption of the gas turbine at the node m;
Figure RE-FDA00032998272400000413
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、
Figure RE-FDA00032998272400000414
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;
Figure RE-FDA00032998272400000415
Figure RE-FDA00032998272400000416
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;
Figure RE-FDA00032998272400000417
respectively representing the initial values of the gas density of the nodes m and n;
Figure RE-FDA00032998272400000418
respectively representing the initial values of the gas pressure of the nodes m and n;
Figure RE-FDA00032998272400000419
respectively represents the minimum and maximum gas mass flow of the head end node of the mn pipeline,
Figure RE-FDA00032998272400000420
Figure RE-FDA00032998272400000421
respectively representing the minimum and maximum gas mass flow of the mn tail end node of the pipeline;
Figure RE-FDA00032998272400000422
representing the maximum gas mass flow at the gate station;
Figure RE-FDA00032998272400000423
representing a maximum gas consumption of the gas turbine;
step 1.5, the power flow constraint of the energy concentrator is as follows:
Figure RE-FDA00032998272400000424
Figure RE-FDA00032998272400000425
Figure RE-FDA00032998272400000426
Figure RE-FDA00032998272400000427
Figure RE-FDA00032998272400000428
Figure RE-FDA00032998272400000429
Figure RE-FDA0003299827240000051
Figure RE-FDA0003299827240000052
Figure RE-FDA0003299827240000053
Figure RE-FDA0003299827240000054
Figure RE-FDA0003299827240000055
in the formula: set ε represents a set of energy hub nodes;
Figure RE-FDA0003299827240000056
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;
Figure RE-FDA0003299827240000057
respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;
Figure RE-FDA0003299827240000058
respectively showing the thermal power of a gas turbine, a heat pump and a gas boiler;
Figure RE-FDA0003299827240000059
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;
Figure RE-FDA00032998272400000510
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;
Figure RE-FDA00032998272400000511
representing the energy storage charging and discharging state, wherein the charging is 1, and the discharging is 0;
Figure RE-FDA00032998272400000512
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;
Figure RE-FDA00032998272400000513
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;
Figure RE-FDA00032998272400000514
representing an initial value of the state of charge of the battery;
Figure RE-FDA00032998272400000515
respectively representing the minimum value and the maximum value of the charge state of the energy storage battery;
Figure RE-FDA00032998272400000516
respectively representing the maximum active power values of the electric gas conversion equipment, the gas turbine and the heat pump;
Figure RE-FDA00032998272400000517
respectively representing the maximum values of the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler;
Figure RE-FDA00032998272400000518
respectively representing the maximum thermal power values of the gas boiler and the electric heat pump;
Figure RE-FDA00032998272400000519
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;
Figure RE-FDA0003299827240000061
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:
Figure RE-FDA0003299827240000062
Figure RE-FDA0003299827240000063
in the formula:
Figure RE-FDA0003299827240000064
representing a power distribution network node i, namely a gas source node set of a gas turbine in a gas distribution network;
Figure RE-FDA0003299827240000065
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:
Figure RE-FDA0003299827240000066
Figure RE-FDA0003299827240000067
in the formula: subscript s denotes the disaster scenario;
Figure RE-FDA0003299827240000068
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:
Figure RE-FDA0003299827240000069
Figure RE-FDA00032998272400000610
Figure RE-FDA0003299827240000071
Figure RE-FDA0003299827240000072
Figure RE-FDA0003299827240000073
Figure RE-FDA0003299827240000074
Figure RE-FDA0003299827240000075
Figure RE-FDA0003299827240000076
Figure RE-FDA0003299827240000077
Figure RE-FDA0003299827240000078
in the formula:
Figure RE-FDA0003299827240000079
representing a set of disaster scenarios; the superscript Dis represents the disaster attack stage;
Figure RE-FDA00032998272400000710
representing a commissioning state variable of a distribution line ij under a disaster scene s;
Figure RE-FDA00032998272400000711
respectively representing active power and reactive power flowing through the distribution line ki under a disaster scene s,
Figure RE-FDA00032998272400000712
respectively representing active power and reactive power flowing through a distribution line ij under a disaster scene s;
Figure RE-FDA00032998272400000713
respectively representing active power and reactive power of a gas turbine at a node i under a disaster scene s;
Figure RE-FDA00032998272400000714
respectively representing active power and reactive power of a node i transformer substation in a disaster scene s;
Figure RE-FDA00032998272400000715
Figure RE-FDA00032998272400000716
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;
Figure RE-FDA00032998272400000717
respectively representing the active load shedding power and the reactive load shedding power of a node i under a disaster scene s;
Figure RE-FDA00032998272400000718
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:
Figure RE-FDA00032998272400000719
Figure RE-FDA00032998272400000720
Figure RE-FDA0003299827240000081
Figure RE-FDA0003299827240000082
Figure RE-FDA0003299827240000083
Figure RE-FDA0003299827240000084
Figure RE-FDA0003299827240000085
Figure RE-FDA0003299827240000086
Figure RE-FDA0003299827240000087
Figure RE-FDA0003299827240000088
in the formula:
Figure RE-FDA0003299827240000089
representing the commissioning state variable of the gas distribution pipeline mn in a disaster scene s, wherein 1 represents connection and 0 represents disconnection;
Figure RE-FDA00032998272400000810
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;
Figure RE-FDA00032998272400000811
respectively representing the gas density of nodes m and n under a disaster scene s;
Figure RE-FDA00032998272400000812
respectively representing the gas pressure of the nodes m and n under the disaster scene s;
Figure RE-FDA00032998272400000813
representing the airflow mass flow of a node m of a lower gate station under a disaster scene s;
Figure RE-FDA00032998272400000814
representing the gas consumption of a gas turbine at a node m under a disaster scene s;
Figure RE-FDA00032998272400000815
representing the gas mass flow of the node m flowing to the gas distribution network from the energy concentrator under the disaster scene s;
Figure RE-FDA00032998272400000816
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:
Figure RE-FDA00032998272400000817
Figure RE-FDA00032998272400000818
Figure RE-FDA00032998272400000819
Figure RE-FDA00032998272400000820
Figure RE-FDA00032998272400000821
Figure RE-FDA00032998272400000822
Figure RE-FDA0003299827240000091
Figure RE-FDA0003299827240000092
Figure RE-FDA0003299827240000093
Figure RE-FDA0003299827240000094
in the formula:
Figure RE-FDA0003299827240000095
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;
Figure RE-FDA0003299827240000096
respectively representing the gas mass flow of the electric gas conversion equipment, the gas turbine and the gas boiler under the disaster scene s;
Figure RE-FDA0003299827240000097
respectively representing the thermal power of a gas turbine, a heat pump and a gas boiler under a disaster scene s;
Figure RE-FDA0003299827240000098
representing the magnitude of the tangential active load power and the tangential thermal load power in the energy concentrator e;
Figure RE-FDA0003299827240000099
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;
Figure RE-FDA00032998272400000910
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;
Figure RE-FDA00032998272400000911
representing the energy storage charging and discharging state under the disaster scene s, wherein the charging is 1, and the discharging is 0;
Figure RE-FDA00032998272400000912
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:
Figure RE-FDA00032998272400000913
Figure RE-FDA00032998272400000914
Figure RE-FDA00032998272400000915
Figure RE-FDA00032998272400000916
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:
Figure RE-FDA0003299827240000101
Figure RE-FDA0003299827240000102
Figure RE-FDA0003299827240000103
in the formula: the superscript Iso represents the fault isolation phase;
Figure RE-FDA0003299827240000104
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;
Figure RE-FDA0003299827240000105
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:
Figure RE-FDA0003299827240000106
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;
Figure RE-FDA0003299827240000107
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:
Figure RE-FDA0003299827240000111
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;
Figure RE-FDA0003299827240000112
a transposed matrix representing a variable coefficient in a disaster scene s;
Figure RE-FDA0003299827240000113
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
Figure RE-FDA0003299827240000114
(2) For any fault scene
Figure RE-FDA0003299827240000115
Solving sub-problems
Figure RE-FDA0003299827240000116
(3) Averaging
Figure RE-FDA0003299827240000117
(4) For any fault scene
Figure RE-FDA0003299827240000118
Computing
Figure RE-FDA0003299827240000119
(5) For any fault scene
Figure RE-FDA00032998272400001110
Solving sub-problems
Figure RE-FDA00032998272400001111
(6) Averaging
Figure RE-FDA00032998272400001112
(7) For any fault scene
Figure RE-FDA00032998272400001113
Computing
Figure RE-FDA00032998272400001114
(8) If it satisfies
Figure RE-FDA00032998272400001115
The iteration is terminated; otherwise, entering the step (9);
(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);
(11) if it satisfies
Figure RE-FDA00032998272400001116
Then is fixed
Figure RE-FDA00032998272400001117
Take sigmak+1＝σk+1；
(12) If K is more than or equal to K2Entering step (13); otherwise, entering a step (14);
(13) if it satisfies
Figure RE-FDA00032998272400001118
Then is fixed
Figure RE-FDA00032998272400001119
Take sigmak+1＝σk+1；
(14) Taking k as k +1, and returning to the step (5);
(15) solving model
Figure RE-FDA0003299827240000121
And (6) ending.
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