CN108493998B - Robust power transmission network planning method considering demand response and N-1 expected faults - Google Patents

Robust power transmission network planning method considering demand response and N-1 expected faults Download PDF

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CN108493998B
CN108493998B CN201810334507.9A CN201810334507A CN108493998B CN 108493998 B CN108493998 B CN 108493998B CN 201810334507 A CN201810334507 A CN 201810334507A CN 108493998 B CN108493998 B CN 108493998B
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branch
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文劲宇
郑倩薇
艾小猛
方家琨
仉梦林
姚伟
刘巨
杨东俊
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Huazhong University of Science and Technology
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention discloses a robust power transmission network planning method considering demand response and N-1 expected faults, which includes the steps of acquiring historical data of renewable energy output by collecting conventional technical parameters of a main network, considering uncertainty of the renewable energy output and the power flow out-of-limit possibly caused by the N-1 expected faults, reducing the power flow level of the whole network by adopting the demand response, and reducing the line building requirements of a power transmission network. According to the method, a limit scene set is generated according to extreme values of renewable energy output historical data, the proposed model is solved by adopting mixed integer programming, and a power transmission network programming scheme capable of meeting the system safety requirements in all limit scenes is obtained through optimization. By setting the proportion of reasonable demand response resources at each load point, the line building cost and the demand response cost can be flexibly adjusted, and the robustness and the economy of the power transmission network planning scheme can be considered.

Description

Robust power transmission network planning method considering demand response and N-1 expected faults
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to a robust power transmission network planning method considering demand response and N-1 expected faults.
Background
The continuous increase of power load, the increasing severity of environmental problems and the wide attention to economic benefits make the planning and operation mode of the power system in China to be urgently changed. On the planning level, as the permeability of renewable energy sources such as wind power, photovoltaic and the like in a power grid is gradually increased, the fluctuation and intermittency of the output of the renewable energy sources affect the power supply reliability of the power grid; meanwhile, the N-1 expected fault verification is also a non-negligible content in the power transmission network planning, and the premise that the optimized power transmission network planning scheme is safe in operation through each N-1 verification is guaranteed. Therefore, in order to relieve the tidal current out-of-limit possibly caused by the output fluctuation or emergency fault of the renewable energy source, a capacity expansion power transmission network frame is needed. On the other hand, with the rising of the line building cost, the planning method for ensuring the safety and reliability of the power system by means of power transmission network extension planning or line disconnection optimization causes a power grid company to face high power transmission network investment cost.
In recent years, Demand Response (DR) has been regarded as a new measure that can achieve a plurality of benefits such as balance between supply and Demand, energy conservation and emission reduction. Demand response measures can be divided into incentive-based demand responses and price-based demand responses. The demand response based on the incentive comprises transferable loads, interruptible loads and the like, the consumption habits of power consumers are influenced through incentive measures, the power consumption in the peak period is reduced, and the tidal current level of the system is reduced. Therefore, if the uncertainty of emergency fault and renewable energy output can be responded by adopting demand response, so that the line building investment is reduced, the economic benefits of both a power grid company and a user can be obviously improved, and the method has a reference significance for the implementation of the current demand response project.
Disclosure of Invention
Aiming at the defects or the improved requirements of the prior art, the invention provides a robust power transmission network planning method considering the demand response and the N-1 expected faults, so that the problem of load flow out-of-limit caused by wind power uncertainty and the N-1 expected faults is solved.
To achieve the above object, the present invention provides a robust power transmission network planning method considering a demand response and an N-1 anticipated fault, comprising:
(1) generating limit scenes of the output of each renewable energy source, wherein the limit scenes are the vertexes of feasible domains formed by all the output scenes of the renewable energy sources, one renewable energy source output scene is composed of the target historical output of each renewable energy source, and the vertexes of the feasible domains represent that the output of each renewable energy source is the maximum value or the minimum value in the historical data;
(2) carrying out power grid line expansion under the premise of considering the uncertainty of renewable energy sources and N-1 expected faults to obtain a target model corresponding to the minimum total network line building cost so as to meet the requirements of total network load and line tide safety;
(3) and carrying out load flow calculation under a non-fault state and each N-1 expected fault in the limit scene of the output of each renewable energy source so as to plan the power transmission network.
Preferably, the target model is:
Figure BDA0001628876040000021
wherein the content of the first and second substances,
Figure BDA0001628876040000022
indicating the existing number of lines in corridor ij,
Figure BDA0001628876040000023
represents the maximum number of branches, N, of the corridor ijLRepresenting the total number of corridors, cijRepresenting the cost of building the individual branches of corridor ij,
Figure BDA0001628876040000024
representing the binary decision variable whether the kth branch in the corridor ij is erected or not, if so, erecting the line
Figure BDA0001628876040000025
Otherwise
Figure BDA0001628876040000026
i denotes the head end node of the corridor and j denotes the tail end node of the corridor.
Preferably, the first and second electrodes are formed of a metal,
Figure BDA0001628876040000027
the constraint satisfied is:
Figure BDA0001628876040000028
Figure BDA0001628876040000029
and
Figure BDA00016288760400000210
therein, constraining
Figure BDA00016288760400000211
Binary decision variable constraints for established branches in corridor ij, numbered 1 to
Figure BDA00016288760400000212
Contract initiationThe established line of the power transmission network always exists in the optimization process except for the occurrence of N-1 faults, and constraints are generated
Figure BDA0001628876040000031
A binary decision variable constraint for an unestablished branch in the corridor ij, numbered as
Figure BDA0001628876040000032
To
Figure BDA0001628876040000033
And agree on its number after the line has been established, constrain
Figure BDA0001628876040000034
The upper and lower limits of the total number of the overhead lines in the corridor ij are restricted.
Preferably, the constraint in the non-fault state in step (3) is: total branch power summation constraint in corridor ij
Figure BDA0001628876040000035
Upper and lower limit of branch tide
Figure BDA0001628876040000036
Branch tidal current-power angle constraint
Figure BDA0001628876040000037
Node power balance constraints
Figure BDA0001628876040000038
Upper and lower limit restraint of generator output
Figure BDA0001628876040000039
And node phase angle upper and lower bound constraints-thetamax≤θi,s≤θmaxWherein, in the step (A),
Figure BDA00016288760400000310
is the flow of the kth branch of the corridor ij under the scene s, Pij,sIs the total current, P, of each branch of the corridor ij under the scene sGi,sAnd thetai,sThe generator output and power angle values of the node i under the scene s,
Figure BDA00016288760400000311
representing the power flow capacity, theta, of the kth branch of the corridor ijj,sRepresenting the power angle value, x, of a node j under a scene sijRepresenting the reactance, theta, of each branch of the corridor ijmaxRepresents the upper limit of the node power angle, PRiRepresenting renewable energy history data, P, at the ith nodeDiRepresenting the active load at the ith node, N (i) representing the other nodes connected to node i,
Figure BDA00016288760400000312
represents the minimum output value of the generator at the ith node,
Figure BDA00016288760400000313
representing the maximum output value of the generator at the ith node, and superscript*To represent
Figure BDA00016288760400000314
The determined amount.
Preferably, each N-1 in step (3) envisions a fault with the constraint of: binary decision variable constraints for faulty branches
Figure BDA00016288760400000315
The established branch with the largest number of the corridor ij is selected as the fault branch of the corridor ij, and the binary decision variable constraint of the branch without fault
Figure BDA00016288760400000316
Total branch power summation constraint in corridor ij under fault state
Figure BDA00016288760400000317
Upper and lower limit constraint of branch tidal current under fault state
Figure BDA0001628876040000041
Branch power flow-power angle constraint under fault state
Figure BDA0001628876040000042
Node power balance constraints under fault conditions
Figure BDA0001628876040000043
Restraint of upper and lower output limits of generator in fault state
Figure BDA0001628876040000044
Node phase angle upper and lower limit constraint in fault state
Figure BDA0001628876040000045
Output climbing restraint of unit after fault
Figure BDA0001628876040000046
And the upper and lower limits of the demand response quantity of the node i
Figure BDA0001628876040000047
Wherein, the upper labelmnRepresents decision variables in a fault state, v represents the ramp rate of the unit after the fault, delta T represents the emergency allowable rescheduling time after the fault,
Figure BDA0001628876040000048
represents the load response, kiRepresenting the demand response resource fraction for each load point.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the method quantifies the potential of demand response in the aspects of alleviating the tidal current off-limit and reducing the line building cost. The balance between the line building cost and the demand response cost can be achieved by setting the reasonable proportion of the demand response resources at each load point, a solving strategy based on mixed integer linear programming is provided by combining a limit scene method, and the model can be efficiently solved.
(2) The invention selects the interruptible load strategy for modeling and analyzing, and compared with the transferable load, the interruptible load strategy can fundamentally and more remarkably reflect the economic benefit brought by load reduction.
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Fig. 1 is a schematic flowchart of a robust power transmission network planning method considering a demand response and an N-1 predicted fault according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an example topology of a master network 6 node according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a limit scene structure according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a schematic flow chart of a robust power transmission network planning method considering a demand response and an N-1 anticipated fault according to an embodiment of the present invention, where the method shown in fig. 1 includes:
(1) generating limit scenes of the output of each renewable energy source, wherein the limit scenes are the vertexes of feasible regions formed by all the output scenes of the renewable energy sources, one renewable energy source output scene is composed of the target historical output of each renewable energy source, and the vertexes of the feasible regions represent that the output of each renewable energy source is the maximum value or the minimum value in the historical data;
before the step (1), collecting main network conventional technical parameters and demand response parameters, and acquiring historical data of renewable energy output.
The main network conventional technical parameter comprises the number N of nodesBThe active load P at the ith nodeDiMaximum/minimum output of the generator at the ith node
Figure BDA0001628876040000051
Power angle theta at ith nodeiAngle of meritUpper limit of
Figure BDA0001628876040000052
The climbing speed v of the unit after the fault, the emergency allowable rescheduling time Delta T and the number N of power transmission corridorsLThe number of branches in each corridor (i.e. the minimum number of branches that can be built)
Figure BDA0001628876040000053
Maximum number of branches that can be constructed
Figure BDA0001628876040000054
Reactance x of each branch of each corridor head end node i, tail end node j and corridor ijijAnd tidal current capacity
Figure BDA0001628876040000055
And branch line construction cost cij。
The demand response parameter includes a cost of the demand response device per unit power
Figure BDA0001628876040000056
Cost of compensation of unit power demand response to customer response to grid dispatching requirements
Figure BDA0001628876040000057
Demand response resource occupation ratio k of each load pointi。
The historical data related to the renewable energy sources comprises a node where the renewable energy sources are located, and the historical data P of the renewable energy sources at the ith nodeRiAnd its maximum/small output
Figure BDA0001628876040000058
In the embodiment of the invention, the renewable energy uncertainty in the robust power transmission network planning method is described by adopting an extreme scenario method, a renewable energy output scenario consists of a certain determined historical output of the renewable energy of each node, all output scenarios jointly form a feasible domain of the renewable energy output scenario, and the extreme scenario is the top point of the feasible domain, namely the renewable energy output of each nodeThe force is the case of the maximum or minimum of its historical data. When there are n uncertainty variables, the number of extreme scenarios is 2nAnd (4) respectively. The method can prove that the solution adaptive to the extreme scenes can be adapted to all possible scenes, so that the calculation burden can be obviously reduced by selecting the extreme scenes as the test scene set on the premise of ensuring the solution accuracy.
(2) Carrying out power grid line expansion under the premise of considering the uncertainty of renewable energy sources and N-1 expected faults to obtain a target model corresponding to the minimum total network line building cost so as to meet the requirements of total network load and line tide safety;
wherein, the target model is:
Figure BDA0001628876040000061
wherein the content of the first and second substances,
Figure BDA0001628876040000062
indicating the existing number of lines in corridor ij,
Figure BDA0001628876040000063
represents the maximum number of branches, N, of the corridor ijLRepresenting the total number of corridors, cijRepresenting the cost of building the individual branches of corridor ij,
Figure BDA0001628876040000064
representing the binary decision variable whether the kth branch in the corridor ij is erected or not, if so, erecting the line
Figure BDA0001628876040000065
Otherwise
Figure BDA0001628876040000066
i denotes the head end node of the corridor and j denotes the tail end node of the corridor.
Wherein the content of the first and second substances,
Figure BDA0001628876040000067
the constraint satisfied is:
Figure BDA0001628876040000068
Figure BDA0001628876040000069
Figure BDA00016288760400000610
wherein constraint (2) is a binary decision variable constraint for a branch already established in corridor ij, numbered 1 to 1 in corridor
Figure BDA00016288760400000611
The established line of the appointed initial power transmission network always exists except the N-1 fault in the optimization process, the constraint (3) is a binary decision variable constraint of the branch which is not established in the corridor ij, and the number of the constraint in the corridor is
Figure BDA00016288760400000612
To
Figure BDA00016288760400000613
And appointing the number of the line after the line is established, and constraining (4) to be the upper and lower limits of the total number of the overhead lines in the corridor ij.
(3) And carrying out load flow calculation under a non-fault state and each N-1 expected fault in the limit scene of the output of each renewable energy source so as to plan the power transmission network.
Wherein, in the non-fault state in step (3), the constraint is:
Figure BDA0001628876040000071
Figure BDA0001628876040000072
Figure BDA0001628876040000073
Figure BDA0001628876040000074
Figure BDA0001628876040000075
-θmax≤θi,s≤θmax(10)
the constraint (5) is total branch power summation constraint in the corridor ij, the constraint (6) is upper and lower limit constraint of branch power flow, the constraint (7) is branch power flow-power angle constraint, the constraint (8) is node power balance constraint, the constraint (9) is upper and lower limit constraint of generator output, and the constraint (10) is upper and lower limit constraint of node phase angle.
Figure BDA0001628876040000076
Is the flow of the kth branch of the corridor ij under the scene s, Pij,sIs the total current, P, of each branch of the corridor ij under the scene sGi,sAnd thetai,sThe generator output and power angle values of the node i under the scene s,
Figure BDA0001628876040000077
representing the power flow capacity, theta, of the kth branch of the corridor ijj,sRepresenting the power angle value, x, of a node j under a scene sijRepresenting the reactance, theta, of each branch of the corridor ijmaxRepresents the upper limit of the node power angle, PRiRepresenting renewable energy history data, P, at the ith nodeDiRepresenting the active load at the ith node, N (i) representing the other nodes connected to node i,
Figure BDA0001628876040000078
represents the minimum output value of the generator at the ith node,
Figure BDA0001628876040000079
representing the maximum output value of the generator at the ith node, and superscript*To represent
Figure BDA00016288760400000710
The determined amount.
Wherein each N-1 predicted fault in step (3) is constrained to be:
Figure BDA00016288760400000711
Figure BDA0001628876040000081
Figure BDA0001628876040000082
Figure BDA0001628876040000083
Figure BDA0001628876040000084
Figure BDA0001628876040000085
Figure BDA0001628876040000086
Figure BDA0001628876040000087
Figure BDA0001628876040000088
Figure BDA0001628876040000089
wherein, the constraint (11) is a binary decision variable constraint of a fault branch, and the established branch with the maximum number of the corridor ij is selected as a fault branch of the corridor ijThe path, constraint (12) is a binary decision variable constraint of the non-fault branch, the definitions of the constraints (13) to (18) and all variables thereof can refer to the definitions of the constraints (5) to (10) and all variables thereof in the non-fault state, and the fault state decision variable adds a mark on the basis of the non-fault state decision variablemnAnd (3) distinguishing by indication, adding an asterisk to the binary decision variable of each branch to indicate the determined quantity transmitted by the step (2). And the constraint (19) is the output climbing constraint of the unit after the fault, and can be rescheduled on the basis of the output of the unit in a non-fault state for relieving the power flow out-of-limit. The constraint (20) is the constraint of the upper limit and the lower limit of the demand response quantity of the node i, and the percentage k of demand response resourcesiThe maximum available demand response of node i is limited,
Figure BDA00016288760400000810
indicating the load response.
In the embodiment of the present invention, because the model contains binary variables representing the wire-building decision, the model is a mixed integer linear programming problem, and can be solved by using the Cplex of MAT L AB software or the MI L P algorithm carried by a Gurobi solver.
The process of the present invention is described in detail below with reference to specific examples.
1. And collecting main network conventional technical parameters and demand response project cost parameters, and acquiring historical data of renewable energy output.
In this example, as shown in fig. 2, the main algorithm adopts an improved 6-node standard algorithm, which includes three generators, and the load and generator parameters of each node are shown in table 1 below:
TABLE 1 node parameter Table
Node numbering load/MW Lower limit of output/MW of generator Upper limit of output power/MW of generator
1 160 0 300
2 480 0 0
3 80 0 310
4 320 0 0
5 400 0 0
6 0 0 950
The parameters of each corridor are shown in the following table 2, and the unit line building cost is based on the actual line building cost of the national power grid:
TABLE 2 line parameter table
Figure BDA0001628876040000091
Cost per unit power demand response device
Figure BDA0001628876040000092
Setting the data of the demand response project of a certain provincial power grid as 20 ten thousand yuan/MW, and setting the total equipment investment cost based on the preset demand response proportion kiCalculated to obtain k for each load point in this exampleiSet to the same value, the unit power demand response compensates for the cost
Figure BDA0001628876040000093
Set to 20 RMB/kWh, the duration of DR performed when the N-1 fault occurred was set to 1 h.
2. And generating a limit scene of the output of the renewable energy.
As shown in fig. 3, in this example, the renewable energy is wind power, one wind power output scene is composed of a certain determined historical output of each wind farm, all the output scenes together form a feasible region of the wind power output scene, and the above-mentioned limit scene is the top point of the feasible region, that is, the renewable energy output of each node is the maximum value or the minimum value of the historical data. One fan is respectively arranged at the node 3 and the node 6, namely the number n of random quantities (namely wind power output) is 2, so that the limit scene is 2 in totalnThe resulting limit scenarios are shown in table 3 below for 4:
TABLE 3 extreme scenarios table
Random quantity Extreme scenario 1 Extreme scenario 2 Extreme scenario 3 Extreme scenario 4
Node 3 wind power output/MW 0 0 500 500
Node 6 wind power output/MW 0 500 0 500
3. Optimizing all limit scenes to obtain a power transmission network planning scheme meeting system safety, wherein the specific model is shown in step (2) and step (3), and different demand-response ratio k is setiThe costs were calculated and the results are shown in table 4:
TABLE 4 cost of each item at different demand response ratios
Figure BDA0001628876040000101
As can be seen from table 4, effective results can be obtained according to both the proposed robust power transmission network planning model and algorithm. Since the conclusion of "whether the total cost can be reduced by the demand response" will be different under different demand response ratios, a reasonable demand response ratio needs to be carefully selected to achieve the optimal economy of the power transmission network planning on the premise of ensuring the safe reliability of the system.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (4)

1. A robust power transmission network planning method considering demand response and N-1 anticipated faults, comprising:
(1) generating limit scenes of the output of each renewable energy source, wherein the limit scenes are the vertexes of feasible domains formed by all the output scenes of the renewable energy sources, one renewable energy source output scene is composed of the target historical output of each renewable energy source, and the vertexes of the feasible domains represent that the output of each renewable energy source is the maximum value or the minimum value in the historical data;
(2) carrying out power grid line expansion under the premise of considering the uncertainty of renewable energy sources and N-1 expected faults to obtain a target model corresponding to the minimum total network line building cost so as to meet the requirements of total network load and line tide safety;
(3) carrying out load flow calculation under a non-fault state and each N-1 expected fault in a limit scene of the output of each renewable energy source so as to plan a power transmission network; the constraint under each N-1 expected failure in step (3) is: binary decision variable constraints for faulty branches
Figure FDA0002422216790000011
The established branch with the largest number of the corridor ij is selected as the fault branch of the corridor ij, and the binary decision variable constraint of the branch without fault
Figure FDA0002422216790000012
Total branch power summation constraint in corridor ij under fault state
Figure FDA0002422216790000013
Upper and lower limit constraint of branch tidal current under fault state
Figure FDA0002422216790000014
Branch power flow-power angle constraint under fault state
Figure FDA0002422216790000015
Node power balance constraints under fault conditions
Figure FDA0002422216790000016
Restraint of upper and lower output limits of generator in fault state
Figure FDA0002422216790000017
Node phase angle upper and lower limit constraint in fault state
Figure FDA0002422216790000018
Output climbing restraint of unit after fault
Figure FDA0002422216790000019
And the upper and lower limits of the demand response quantity of the node i
Figure FDA00024222167900000110
Wherein the superscript mn represents a decision variable in a fault state, v represents a unit climbing speed after the fault, Delta T represents an emergency allowable rescheduling time after the fault,
Figure FDA00024222167900000111
represents the load response, kiRepresenting the demand response resource fraction for each load point.
2. The method of claim 1, wherein the target model is:
Figure FDA0002422216790000021
wherein the content of the first and second substances,
Figure FDA0002422216790000022
indicating the existing number of lines in corridor ij,
Figure FDA0002422216790000023
maximum constructable support for representing corridor ijNumber of passes, NLRepresenting the total number of corridors, cijRepresenting the cost of building the individual branches of corridor ij,
Figure FDA0002422216790000024
representing the binary decision variable whether the kth branch in the corridor ij is erected or not, if so, erecting the line
Figure FDA0002422216790000025
Otherwise
Figure FDA0002422216790000026
i denotes the head end node of the corridor and j denotes the tail end node of the corridor.
3. The method of claim 2,
Figure FDA0002422216790000027
the constraint satisfied is:
Figure FDA0002422216790000028
and
Figure FDA0002422216790000029
therein, constraining
Figure FDA00024222167900000210
Binary decision variable constraints for established branches in corridor ij, numbered 1 to
Figure FDA00024222167900000211
The established line of the appointed initial power transmission network always exists in the optimization process except the N-1 fault, and the constraint
Figure FDA00024222167900000212
A binary decision variable constraint for an unestablished branch in the corridor ij, numbered as
Figure FDA00024222167900000213
To
Figure FDA00024222167900000214
And agree on its number after the line has been established, constrain
Figure FDA00024222167900000215
The upper and lower limits of the total number of the overhead lines in the corridor ij are restricted.
4. The method of claim 3, wherein in the non-fault state in step (3) the constraint is: total branch power summation constraint in corridor ij
Figure FDA00024222167900000216
Upper and lower limit of branch tide
Figure FDA00024222167900000217
Branch tidal current-power angle constraint
Figure FDA00024222167900000218
Node power balance constraints
Figure FDA00024222167900000219
Upper and lower limit restraint of generator output
Figure FDA00024222167900000220
And node phase angle upper and lower bound constraints-thetamax≤θi,s≤θmaxWherein, in the step (A),
Figure FDA00024222167900000221
is the flow of the kth branch of the corridor ij under the scene s, Pij,sIs the total current, P, of each branch of the corridor ij under the scene sGi,sAnd thetai,sThe generator output and power angle values of the node i under the scene s,
Figure FDA00024222167900000222
representing the power flow capacity, theta, of the kth branch of the corridor ijj,sRepresenting the power angle value, x, of a node j under a scene sijRepresenting the reactance, theta, of each branch of the corridor ijmaxRepresents the upper limit of the node power angle, PRiRepresenting renewable energy history data, P, at the ith nodeDiRepresenting the active load at the ith node, N (i) representing the other nodes connected to node i,
Figure FDA0002422216790000031
represents the minimum output value of the generator at the ith node,
Figure FDA0002422216790000032
represents the maximum output force value of the generator at the ith node, and the upper mark represents
Figure FDA0002422216790000033
The determined amount.
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