CN112886573B - Power system recovery method and device considering operation performance - Google Patents

Power system recovery method and device considering operation performance Download PDF

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CN112886573B
CN112886573B CN202110097959.1A CN202110097959A CN112886573B CN 112886573 B CN112886573 B CN 112886573B CN 202110097959 A CN202110097959 A CN 202110097959A CN 112886573 B CN112886573 B CN 112886573B
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庞凯元
文福拴
王崇宇
李鹏
袁智勇
于力
徐全
林心昊
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Zhejiang University ZJU
CSG Electric Power Research Institute
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Abstract

The application discloses a method and a device for recovering a power system considering operation performance. Taking the load recovery capacity in the maximum recovery period as a target function, and considering the starting of a generator, the network frame topology and the power balance constraint to establish an optimal recovery submodel; establishing an optimal operation sub-model by taking one or more of voltage balance degree, network loss and power generation cost as objective functions and considering alternating current power flow and safety constraints; after the fault occurs, the optimal recovery submodel is solved, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; and if the recovery strategy obtained by solving the optimal operation submodel has voltage or line load flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint.

Description

Power system recovery method and device considering operation performance
Technical Field
The present application relates to the field of power system technologies, and in particular, to a method and an apparatus for recovering a power system with consideration of operation performance.
Background
Due to equipment failure, natural disasters or artificial attacks, a plurality of blackout accidents of a power system occur worldwide in recent years, so that huge economic loss and serious social influence are caused. For example, a large-area power failure accident in south China caused by ice and snow weather in 2008 causes electric quantity loss of approximately 6209 GW.h; julian Sangdi caused about 800 million households in east coast of the United states to have a power failure in 2012, and the economic loss is about $ 500 million. The research on the recovery strategy of the power system is beneficial to rapidly recovering the power failure load, thereby reducing the power failure loss.
The existing power system recovery method mainly focuses on how to reduce the power failure loss, namely, the load is quickly recovered through operations such as recovering a power failure unit and commissioning a power line, but the operation performance of the power system after recovery, such as voltage balance degree, network loss, power generation cost and the like, is neglected. In addition, the current power system recovery strategy is generally established off-line, and after a fault occurs, a power dispatching personnel is required to select a proper recovery strategy according to experience. Because the specific fault type is difficult to determine before the fault occurs, the recovery strategy established off-line is not necessarily completely matched with the actual fault; under the condition of cascading failure, the recovery strategy established off-line cannot be updated in real time according to the newly-occurring failure; these two factors can lead to a non-optimal recovery process, and even cause secondary failure in severe cases, further enlarging the power outage range.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for recovering an electric power system in consideration of operation performance, wherein the method and the device decompose an electric power system recovery model into a two-stage optimization model, the first stage is an optimal recovery sub-model, and the second stage is an optimal operation sub-model, so that the defect of the existing electric power system recovery strategy in consideration of the operation performance after recovery is overcome, and the operation performance after recovery of the electric power system can be improved under the condition of ensuring the optimal recovery performance of the electric power system; in addition, the optimal operation submodel is gradually solved on line, the power system recovery strategy is updated on line, the defect that the recovery strategy established off line is not matched with the actual fault can be overcome, and therefore the power failure load is quickly recovered.
According to a first aspect of embodiments of the present application, there is provided a power system recovery method considering operation performance, including:
establishing an optimal recovery submodel by taking the load recovery capacity in the maximized recovery period as a target function and considering the constraint conditions of generator starting, network frame topology and power balance;
taking one or more operation performance indexes representing voltage balance degree, network loss and power generation cost as a target function, considering alternating current power flow and safety constraints in a recovery process, and establishing an optimal operation sub-model;
after the fault occurs, the optimal recovery submodel is solved, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; and if the recovery strategy obtained by solving the optimal operation submodel has voltage or line load flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint.
According to a second aspect of embodiments of the present application, there is provided a power system restoration device taking operating performance into account, including:
the first model building module is used for building an optimal recovery submodel by taking the load recovery capacity in the maximum recovery period as an objective function and considering the constraint conditions of generator starting, grid topology and power balance;
the second model building module is used for building an optimal operation sub-model by taking one or more operation performance indexes representing the voltage balance degree, the network loss and the power generation cost as a target function and considering the alternating current power flow and the safety constraint in the recovery process;
the calculation module is used for solving the optimal recovery submodel firstly after the fault occurs, and then gradually solving the optimal operation submodel on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; and if the recovery strategy obtained by solving the optimal operation submodel has voltage or line load flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the embodiment, the optimal recovery submodel and the optimal operation submodel are respectively constructed, the optimal recovery submodel is firstly solved after a fault occurs, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; if the recovery strategy obtained by solving the optimal operation submodel has voltage or line power flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint can improve the operation performance of the power system after recovery, including the voltage balance degree, the network loss and the power generation cost, under the condition of ensuring the recovery performance of the power system, namely the recovery load capacity in a given time and optimizing; in addition, the two-stage power system recovery strategy that this application provided can online real-time operation, can update power system recovery strategy in real time after the trouble takes place, is favorable to recovering the load fast to reduce the power failure loss.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a block diagram illustrating a power system recovery method that accounts for operational performance, according to an example embodiment.
FIG. 2 is a schematic diagram of an online operation system of an optimal recovery submodel and an optimal operation submodel, according to an exemplary embodiment.
FIG. 3 is a flow diagram illustrating two model solutions according to an exemplary embodiment.
FIG. 4 is a schematic diagram of an IEEE 30 node standard power system shown in accordance with an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating operational performance over time during an IEEE 30 node standard power system recovery process in accordance with an exemplary embodiment.
FIG. 6 is a block diagram of a power system restoration device that accounts for operational performance, according to an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Fig. 1 is a block diagram of a power system recovery method that accounts for operational performance, according to an example embodiment. The method may comprise the steps of:
step S101, establishing an optimal recovery submodel by taking load recovery capacity in a maximized recovery period as an objective function and considering constraint conditions of generator starting, grid topology and power balance;
step S102, establishing an optimal operation sub-model by taking one or more operation performance indexes representing voltage balance degree, network loss and power generation cost as a target function and considering alternating current power flow and safety constraints in a recovery process;
step S103, solving the optimal recovery submodel after the fault occurs, and then gradually solving the optimal operation submodel on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved step by step on line; and if the recovery strategy obtained by solving the optimal operation submodel has voltage or line load flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint.
According to the embodiment, the optimal recovery submodel and the optimal operation submodel are respectively constructed, the optimal recovery submodel is firstly solved after a fault occurs, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; if the recovery strategy obtained by solving the optimal operation submodel has voltage or line tidal current out-of-limit, delaying the recovery of the line or the load to the next step, then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint can improve the operation performance of the power system after recovery, including the voltage balance degree, the network loss and the power generation cost, under the condition of ensuring the recovery performance of the power system, namely the recovery load capacity in a given time and optimizing; in addition, the two-stage power system recovery strategy that this application provided can online real-time operation, can update power system recovery strategy in real time after the trouble takes place, is favorable to recovering the load fast to reduce the power failure loss.
In the step S101, with the load recovery capacity in the maximum recovery period as an objective function, considering the constraint conditions of generator starting, network frame topology, and power balance, an optimal recovery submodel is established, which specifically includes:
(1) an objective function: maximizing the load recovery capacity in a given recovery period as a function of the cost of the outage based on loads of different grades, i.e.
Figure GDA0003667295130000051
In the formula: omegaBA set of nodes that are power systems;
Figure GDA0003667295130000052
is the load set of node i;
Figure GDA0003667295130000053
and
Figure GDA0003667295130000054
respectively representing the active power requirements of the kth important load and the common load of the node i; epsilon is the weight coefficient of the common load, and the value of epsilon is less than 1;
Figure GDA0003667295130000055
the time of the kth load recovery of node i.
(2) And (3) generator recovery constraint: based on the required starting time, starting power, climbing speed and maximum active power of the non-black starting generator, establishing the constraint of the active power output and recovery time of the non-black starting generator; and establishing the constraint of the active power output of the black-start generator based on the climbing rate and the maximum active power of the black-start generator.
For a non-black start generator, its output power can be expressed as:
Figure GDA0003667295130000056
in the formula: omegaNBSA set of non-black start generators; t is a set of recovery time intervals;
Figure GDA0003667295130000061
the active power of the generator g at the moment t is started in a non-black mode; vTAn actual duration for each recovery time interval;
Figure GDA0003667295130000062
the required starting time for the generator g; rgIs the ramp rate of the generator g;
Figure GDA0003667295130000063
and
Figure GDA0003667295130000064
the maximum active power and the starting power of the generator g are obtained; w is ag,1,t、wg,2,t、wg,3,t、vg,1,t、vg,2,tAnd vg,3,tTo characterize the auxiliary variables of the start-up process of the non-black start generator g, it satisfies the following constraints:
Figure GDA0003667295130000065
Figure GDA0003667295130000066
Figure GDA0003667295130000067
Figure GDA0003667295130000068
Figure GDA0003667295130000069
Figure GDA00036672951300000610
Figure GDA00036672951300000611
Figure GDA00036672951300000612
Figure GDA00036672951300000613
in the formula
Figure GDA00036672951300000614
The moment of recovery of the generator g.
For a black start generator, its output power can be expressed as:
Figure GDA00036672951300000615
in the formula: omegaBSA set of black start generators;
Figure GDA00036672951300000616
the active power of the generator g at the moment t is started in black; w is ag,1,tAnd wg,2,tTo characterize the auxiliary variables of the black start generator g start-up process, it satisfies the following constraints:
Figure GDA00036672951300000617
Figure GDA00036672951300000618
Figure GDA0003667295130000071
Figure GDA0003667295130000072
(3) and power balance constraint: establishing a power balance constraint, i.e. based on that the recovered load active demand at any recovery moment should not exceed the active power generated by all generators
Figure GDA0003667295130000073
In the formula:
Figure GDA0003667295130000074
for the recovery state of the kth load of the node i at the time t, 1 and 0 respectively represent recovered state and unrecovered state; and gamma is a network loss coefficient.
(4) And (3) carrying out topological constraint on the net rack: constructing recovery constraint of the non-black start generator based on that the non-black start generator can be recovered after any adjacent line is recovered; constructing a recovery constraint of the adjacent lines of the black-start generator based on that the adjacent lines of the black-start generator can be recovered after the black-start generator starts to output power; constructing a restoration constraint of the line based on that the line can be restored only after any adjacent line of the line is restored; and constructing the recovery constraint of the load node based on that the load node can not be recovered after any adjacent line of the load node is recovered.
According to the topological relation of the net rack, operations such as generator starting, line commissioning, load recovery and the like need to meet a series of precedence relations in the recovery process.
Non-black start generators can only be restored after any adjacent line is restored, i.e.
Figure GDA0003667295130000075
Figure GDA0003667295130000076
In the formula:
Figure GDA0003667295130000077
the adjacent lines are the nodes where the generator g is located;
Figure GDA0003667295130000078
the recovery state of the non-black start generator g at the time t is shown, and 1 and 0 respectively represent recovered state and unrecovered state;
Figure GDA0003667295130000079
for the recovered state of line l at time t, 1 and 0 indicate recovered and unrecovered, respectively.
The line adjacent to the black-start generator can only be restored after the black-start generator starts to output power, i.e.
Figure GDA0003667295130000081
The remaining lines can only be recovered after any adjacent line is recovered, i.e.
Figure GDA0003667295130000082
In the formula: psi is the line set of the power system;
Figure GDA0003667295130000083
is a set of adjacent lines to line l.
The load being restored after any adjacent line is restored, i.e.
Figure GDA0003667295130000084
Figure GDA0003667295130000085
In the formula
Figure GDA0003667295130000086
Is a set of adjacent lines of node i.
The amount of load recovered in each recovery interval must not exceed the maximum recovery load, i.e.
Figure GDA0003667295130000087
In the formula,. DELTA.PmaxThe maximum amount of recovery load allowed by the system.
Any line or load will not be disconnected again after recovery, i.e.
Figure GDA0003667295130000088
Figure GDA0003667295130000089
And constructing and obtaining an optimal recovery sub-model based on the objective function and the constraint condition. The optimal recovery submodel is an integer linear programming problem and can be solved by a branch cut set method. And solving the optimal recovery submodel to obtain a primary recovery scheme, wherein the primary recovery scheme comprises the recovery time of the non-black start generator, the commissioning time of the line and the recovery time of the load.
In the step S102, an optimal operation submodel is established by taking one or more operation performance indexes representing voltage balance degree, network loss and power generation cost as an objective function, considering the alternating current power flow and safety constraints in the recovery process;
specifically, the objective function and constraint conditions of the model are represented as follows:
(1) objective function characterizing the degree of voltage equalization: minimizing the sum of the squares of the differences between the voltage amplitudes of all nodes and the reference value as an objective function characterizing the degree of voltage equalization, i.e.
Figure GDA0003667295130000091
In the formula Vi,tThe voltage magnitude at node i at time t.
(2) Objective function characterizing the loss of the network: with minimization of active loss on all lines as an objective function to characterize the loss of the network, i.e.
Figure GDA0003667295130000092
In the formula: omegaGIs a collection of generators;
Figure GDA0003667295130000093
the active power of the generator g at the moment t.
(3) Objective function characterizing the cost of power generation: minimizing the cost of power generation for all generators as an objective function characterizing the cost of power generation, i.e.
Figure GDA0003667295130000094
In the formula ag0、ag1And ag2Is a coefficient representing the cost of generating electricity.
The objective function of the optimal operational submodel may be a combination of one or more of the above objective functions. The method can flexibly select proper performance indexes according to the actual operation experience of the power system so as to achieve the purpose of improving one or more operation performances of the recovered power system.
(4) The equation constrains: building equality constraints based on power flow constraints in the power system, i.e.
Figure GDA0003667295130000095
Figure GDA0003667295130000096
In the formula:
Figure GDA0003667295130000097
the reactive power of the generator g at the moment t is obtained; theta.theta.ijIs the voltage phase angle difference between node i and node j; gijAnd BijRespectively are the ith row and the jth column element Y in the power system node admittance matrixijReal and imaginary parts of (c).
(5) The inequality constrains: based on the fact that the output of the generator cannot exceed the maximum and minimum output power and the range limited by the climbing rate, the node voltage amplitude cannot exceed the allowable range, the active power flowing through the line cannot exceed the allowable maximum value, and a series of safety constraints, namely inequality constraints, need to be met in the recovery process are established.
The generator output is limited by maximum and minimum output power and ramp rate, i.e.
Figure GDA0003667295130000098
Figure GDA0003667295130000101
Figure GDA0003667295130000102
In the formula:
Figure GDA0003667295130000103
and with
Figure GDA0003667295130000104
The minimum active power and the maximum active power of the generator g are respectively;
Figure GDA0003667295130000105
and
Figure GDA0003667295130000106
the minimum reactive power and the maximum reactive power of the generator g are respectively.
The node voltage amplitude cannot exceed the permissible range, i.e.
Figure GDA0003667295130000107
In the formula Vi,minAnd Vi,maxRespectively, allowed minimum of node iVoltage amplitude and maximum voltage amplitude.
The active power flowing through the line must not exceed the maximum permitted, i.e.
Figure GDA0003667295130000108
Figure GDA0003667295130000109
In the formula: pl,tThe active power flowing through the line l at the moment t; pl,maxThe maximum active power allowed to flow for line i.
And constructing and obtaining an optimal operation sub-model based on the objective function and the constraint condition. Similar to the optimal power flow model, the optimal operation submodel is a nonlinear programming problem and can be solved by adopting an interior point method. The optimal operation submodel is solved to obtain the active power output and the reactive power output of the generator, and the generator output and the initial recovery scheme provided by the optimal recovery submodel jointly form a complete power system recovery scheme.
In the above step S103, fig. 2 is a schematic diagram of an online operation system of the optimal recovery submodel and the optimal operation submodel according to an exemplary embodiment. FIG. 3 is a flow diagram illustrating two model solutions according to an exemplary embodiment. After the fault occurs, the optimal recovery submodel is solved, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; if the recovery strategy obtained by solving the optimal operation submodel has voltage or line load flow out-of-limit, delaying the recovery of the line or the load to the next step, and then solving the optimal operation submodel again until the obtained power system recovery strategy meeting the safety constraint; compared with the traditional power system recovery strategy, the recovery model is decomposed into the optimal recovery submodel and the optimal operation submodel, the scale of each submodel is greatly reduced, and a foundation is provided for the online operation of the recovery strategy. The optimal recovery submodel is an integer linear programming problem, the optimal operation submodel is a nonlinear programming problem, and the optimal recovery submodel and the optimal operation submodel can be quickly solved by adopting a branch cut set method and an interior point method respectively. The online operation system provided by the application is shown as the attached drawing 2, and the specific solving process is shown as the attached drawing 3, and the online operation system can be divided into the following 5 steps:
(1) initializing parameters of the power system, including power system network frame topology, generator starting parameters and load active and reactive requirements;
(2) collecting power failure state information including a fault type, a fault position, a power failure range and a generator state, and executing an optimal recovery submodel to obtain a primary recovery scheme, wherein the primary recovery scheme comprises a recovery moment of a non-black-start generator, a commissioning moment of a line and a recovery moment of a load;
(3) the initial recovery scheme is used as input and provided for the optimal operation submodel, the optimal operation submodel is executed gradually on line, and the obtained generator output and the initial recovery scheme provided by the optimal recovery submodel jointly form a complete power system recovery scheme; if the solving result of the optimal operation submodel has a voltage amplitude or the line active power flow exceeds the limit, delaying the line operation and load recovery to the next step; otherwise, continuously executing the optimal operation submodel on line step by step;
(4) if a new fault occurs in the recovery process, after collecting and updating new fault information, re-executing the optimal recovery submodel to obtain a new initial recovery scheme, and then gradually executing the optimal operation submodel to update the active and reactive power output of the generator on line; repeating the steps after each new failure;
(5) and if no new fault occurs, the optimal operation submodel is executed on line until the recovery ending time, and the recovery time of the non-black start generator, the commissioning time of the line, the recovery time of the load and the generator output provided by the optimal operation submodel form a complete power system recovery scheme together.
According to the embodiment of the application, a power system recovery model is decomposed into a two-stage optimization model, the first stage is an optimal recovery submodel, and the second stage is an optimal operation submodel; in the optimal recovery submodel, taking load recovery capacity in a maximized recovery period as a target function, considering constraint conditions such as generator starting, network frame topology, power balance and the like, establishing an integer linear programming model, and solving by adopting a branch cut set method; in the optimal operation submodel, one or more operation performance indexes representing voltage balance degree, network loss and power generation cost are used as objective functions, alternating current power flow and safety constraints in a recovery process are considered, a nonlinear programming model is established, and an interior point method is adopted for solving. And then, providing an online operation system of a two-stage power system recovery optimization model and a solving algorithm thereof, and improving the operation performance of the power system after recovery under the condition of ensuring the optimal recovery performance of the power system. In addition, the two-stage power system recovery strategy that this application provided can online real-time operation, can update power system recovery strategy in real time after the trouble takes place, is favorable to recovering the load fast to reduce the power failure loss.
For further understanding of the present application, the practical application of the present application is explained below by taking an IEEE 30 node standard power system as an example, and a schematic diagram of the IEEE 30 node standard power system is shown in fig. 4.
In an IEEE 30 node standard power system, the discrete load number of each node is set to be 3, and the discrete load number respectively accounts for 30%, 30% and 40% of the original load requirement; the weight coefficient epsilon of the ordinary load is set to 0.3; the network loss coefficient gamma is set to 1.05; the allowable minimum and maximum voltage amplitudes are set to 0.9 and 1.1p.u., respectively; actual duration V of each recovery time intervalTThe setting was 5 min. Assuming that the IEEE 30 node standard power system is in a system-wide power failure state, the generator G6 at the node 13 is a black-start generator, and the other generators are non-black-start generators. The generator and load parameters are shown in tables 1 and 2, respectively.
TABLE 1 Generator parameters for IEEE 30 node Standard Power System
Figure GDA0003667295130000121
TABLE 2 important load parameters for IEEE 30 node standard power system
Figure GDA0003667295130000122
Figure GDA0003667295130000131
The two-stage power system recovery optimization model established by the method is solved, the obtained recovery schemes are shown in tables 3 and 4, and the generator, the important load and the line in the IEEE 30 node standard power system are all recovered within 1 hour.
TABLE 3 recovery moments of the generator and important loads
Figure GDA0003667295130000132
TABLE 4 recovery time of line
Figure GDA0003667295130000133
Figure GDA0003667295130000141
The operation performance of the obtained power system recovery scheme is shown in fig. 5, wherein the change of the voltage equalization degree index with time is shown in (a) in fig. 5, the change of the grid loss index with recovery time is shown in (b) in fig. 5, and the change of the power generation cost index with recovery time is shown in (c) in fig. 5. If only one of the voltage balance degree, the network loss and the power generation cost index is selected as the target function of the optimal operation submodel, the voltage balance degree, the network loss or the power generation cost of the power system after the recovery operation is finished (all loads are recovered) are very close to those before the fault and are all more than 95% before the fault; if a proper weight coefficient is selected, the weighted values of the voltage balance degree, the network loss and the power generation cost are used as the target function of the optimal operation submodel, and the three operation performances of the power system after the recovery operation are all about 90% before the fault. Therefore, by adopting the two-stage power system recovery strategy provided by the application, the operation performance of the power system after recovery can be improved to a level close to that before the fault.
The average time required for solving the two-stage power system recovery strategy provided by the application on the standard power system of IEEE 30 and 118 nodes is shown in table 5, wherein the adopted simulation platform is a Windows 10 computer with an Intel Core i 56 Core processor (2.8GHz) and an 8GB memory.
TABLE 5 mean solution time for IEEE 30 and 118 node standard power system
Figure GDA0003667295130000142
Taking the IEEE 118 node standard power system with a large scale as an example, the program average running time of the optimal recovery sub-model does not exceed 20s, and the program average running time of the optimal running sub-model is 1.56 s. For minute-level power system recovery operation, the time required by the two-stage power system recovery strategy provided by the application is short, and the requirement of online operation is met.
Through the analysis, the two-stage power system recovery method considering the operation performance can improve the operation performance (including but not limited to voltage balance degree, network loss and power generation cost) of the power system after recovery, also meets the requirement of online real-time operation, can update the power system recovery strategy in real time after a fault occurs, is favorable for rapidly recovering the load, and accordingly reduces the power failure loss.
Corresponding to the foregoing embodiment of the method for recovering an electric power system in consideration of operation performance, the present application also provides an embodiment of an apparatus for recovering an electric power system in consideration of operation performance.
FIG. 6 is a block diagram illustrating a power system restoration device that accounts for operational performance, according to an example embodiment. Referring to fig. 6, the apparatus includes:
the first model establishing module 21 is configured to establish an optimal recovery submodel by taking load recovery capacity in a maximum recovery period as an objective function and considering constraint conditions of generator starting, grid topology and power balance;
the second model establishing module 22 is used for establishing an optimal operation submodel by taking one or more operation performance indexes representing the voltage balance degree, the network loss and the power generation cost as an objective function and considering the alternating current load flow and the safety constraint in the recovery process;
the calculation module 23 is used for solving the optimal recovery submodel after the fault occurs, and then gradually solving the optimal operation submodel on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved step by step on line; and if the recovery strategy obtained by solving the optimal operation submodel has voltage or line power flow out-of-limit, delaying the recovery of the line or the load to the next step, and then re-solving the optimal operation submodel until obtaining the power system recovery strategy meeting the safety constraint.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
For the device embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof.

Claims (8)

1. A power system recovery method in view of operational performance, comprising:
establishing an optimal recovery submodel by taking the load recovery capacity in the maximized recovery period as a target function and considering the constraint conditions of generator starting, network frame topology and power balance;
establishing an optimal operation sub-model by taking a plurality of operation performance indexes representing voltage balance degree, network loss and power generation cost as objective functions and considering alternating current power flow and safety constraints in a recovery process;
after the fault occurs, the optimal recovery submodel is solved, and then the optimal operation submodel is gradually solved on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved step by step on line; if the recovery strategy obtained by solving one step in the optimal operation submodel step by step has voltage or line power flow out-of-limit, canceling the line and load recovery operation of the step, delaying the operation to the next step, and then solving the optimal operation submodel again until obtaining the power system recovery strategy meeting the safety constraint;
the method comprises the following steps of establishing an optimal recovery submodel by taking load recovery capacity in a maximized recovery period as an objective function and considering constraint conditions of generator starting, network frame topology and power balance, and specifically comprises the following steps:
based on the power failure cost of loads of different grades, the load recovery capacity in a given recovery period is maximized to be used as a target function;
based on the required starting time, starting power, climbing speed and maximum active power of the non-black starting generator, establishing the constraint of the active power output and recovery time of the non-black starting generator; establishing constraint of active power output of the black-start generator based on the climbing rate and the maximum active power of the black-start generator;
establishing a power balance constraint based on that the recovered load active demand at any recovery moment should not exceed the active power generated by all the generators;
constructing recovery constraint of the non-black start generator based on that the non-black start generator can be recovered after any adjacent line is recovered; constructing a recovery constraint of the adjacent lines of the black-start generator based on that the adjacent lines of the black-start generator can be recovered after the black-start generator starts to output power; constructing a restoration constraint of the line based on that the line can be restored only after any adjacent line of the line is restored; constructing a recovery constraint of the load node based on that the load node can be recovered after any adjacent line of the load node is recovered;
and constructing and obtaining an optimal recovery submodel based on the objective function and the constraint condition.
2. The method for recovering the power system with the operation performance taken into consideration according to claim 1, wherein a plurality of operation performance indexes representing voltage balance degree, network loss and power generation cost are used as an objective function, an optimal operation sub-model is established by considering alternating current power flow and safety constraints in a recovery process, and the method specifically comprises the following steps:
taking the sum of squares of the differences between the voltage amplitudes of all the nodes and the reference value as an objective function for representing the voltage equalization degree;
minimizing active loss on all lines as an objective function for representing network loss;
minimizing the power generation cost of all generators as an objective function representing the power generation cost;
the objective function of the optimal operation submodel can be any combination of the above objective functions;
constructing an equality constraint based on a power flow constraint in the power system;
based on the fact that the output of the generator cannot exceed the maximum and minimum output power and the range limited by the climbing rate, the node voltage amplitude cannot exceed the allowable range, and the active power flowing through a line cannot exceed the allowable maximum value, a series of safety constraints, namely inequality constraints, need to be met in the recovery process are established;
and constructing and obtaining an optimal operation sub-model based on the objective function and the constraint condition.
3. The power system recovery method considering the operation performance as claimed in claim 1, wherein the optimal recovery submodel is solved after the fault occurs, and then the optimal operation submodel is gradually solved on line to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved step by step on line; if the recovery strategy obtained by solving one step of the optimal operation submodel step by step has voltage or line load flow out-of-limit, canceling the line and load recovery operation of the step, delaying the operation to the next step, and then solving the optimal operation submodel again until obtaining the power system recovery strategy meeting the safety constraint, which specifically comprises the following steps:
initializing parameters of the power system, including power system network frame topology, generator starting parameters and load active and reactive requirements;
collecting power failure state information, and executing an optimal recovery submodel to obtain a primary recovery scheme;
providing the initial recovery scheme as input to an optimal operation submodel, gradually executing the optimal operation submodel on line, if a voltage amplitude value or line active power flow out-of-limit exists in a solving result, cancelling the line and load recovery operation of the step, and delaying the operation to the next step; otherwise, updating the active and reactive power output of the generator on line;
if a new fault occurs in the recovery process, after collecting and updating new fault information, re-executing the optimal recovery submodel to obtain a new initial recovery scheme, and then gradually executing the optimal operation submodel to update the active and reactive power output of the generator on line; repeating the steps after each new failure;
and if no new fault occurs, the optimal operation submodel is executed on line until the recovery ending time, and the recovery time of the non-black start generator, the commissioning time of the line, the recovery time of the load and the generator output provided by the optimal operation submodel form a complete power system recovery scheme together.
4. The method of claim 3, wherein the outage status information comprises a fault type, a fault location, an outage range, and a generator status.
5. The method of claim 3, wherein the preliminary recovery scheme comprises a recovery time of a non-black start generator, an operation time of a line and a recovery time of a load.
6. An electric power system recovery apparatus taking into account operation performance, characterized by comprising:
the first model building module is used for building an optimal recovery submodel by taking the load recovery capacity in the maximum recovery period as a target function and considering the constraint conditions of generator starting, grid topology and power balance;
the second model establishing module is used for establishing an optimal operation sub-model by taking a plurality of operation performance indexes representing voltage balance degree, network loss and power generation cost as objective functions and considering the alternating current power flow and safety constraints in the recovery process;
the calculation module is used for solving the optimal recovery submodel firstly after the fault occurs, and then gradually solving the optimal operation submodel on line so as to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; if the recovery strategy obtained by solving one step in the optimal operation submodel step by step has voltage or line load flow out-of-limit, canceling the line and load recovery operation of the step, delaying the operation to the next step, and then solving the optimal operation submodel again until obtaining the power system recovery strategy meeting the safety constraint;
the method comprises the following steps of establishing an optimal recovery submodel by taking load recovery capacity in a maximized recovery period as an objective function and considering constraint conditions of generator starting, network frame topology and power balance, and specifically comprises the following steps:
based on the power failure cost of loads of different grades, the load recovery capacity in a given recovery period is maximized to be used as a target function;
based on the required starting time, starting power, climbing speed and maximum active power of the non-black starting generator, establishing the constraint of the active power output and recovery time of the non-black starting generator; establishing constraint of active power output of the black-start generator based on the climbing rate and the maximum active power of the black-start generator;
establishing a power balance constraint based on that the recovered load active demand at any recovery moment should not exceed the active power generated by all the generators;
constructing recovery constraint of the non-black start generator based on that the non-black start generator can be recovered after any adjacent line is recovered; constructing recovery constraints of adjacent lines of the black-start generator based on that the adjacent lines of the black-start generator can be recovered after the black-start generator starts to output power; constructing a restoration constraint of the line based on that the line can be restored only after any adjacent line of the line is restored; constructing a recovery constraint of the load node based on that the load node can be recovered after any adjacent line of the load node is recovered;
and constructing and obtaining an optimal recovery sub-model based on the objective function and the constraint condition.
7. The power system recovery device considering the operational performance of claim 6, wherein a plurality of operational performance indexes representing voltage balance degree, network loss and power generation cost are used as an objective function, and an optimal operational submodel is established by considering the alternating current power flow and safety constraints in the recovery process, and specifically comprises:
taking the sum of squares of the differences between the voltage amplitudes of all the nodes and the reference value as an objective function for representing the voltage balance degree;
minimizing active loss on all lines as an objective function for representing network loss;
minimizing the power generation cost of all generators as an objective function representing the power generation cost;
the objective function of the optimal operation submodel can be any combination of the above objective functions;
constructing an equality constraint based on a power flow constraint in the power system;
based on the fact that the output of the generator cannot exceed the maximum and minimum output power and the range limited by the climbing rate, the node voltage amplitude cannot exceed the allowable range, and the active power flowing through a line cannot exceed the allowable maximum value, a series of safety constraints, namely inequality constraints, need to be met in the recovery process are established;
and constructing and obtaining an optimal operation sub-model based on the objective function and the constraint condition.
8. The power system recovery device considering the operation performance as claimed in claim 6, wherein the optimal recovery submodel is solved after the fault occurs, and then the optimal operation submodel is gradually solved on line to update the recovery operation in real time; if a new fault occurs, the optimal recovery submodel is solved again after the fault information is updated, and then the optimal operation submodel is solved gradually on line; if the recovery strategy obtained by solving one step of the optimal operation submodel step by step has voltage or line load flow out-of-limit, canceling the line and load recovery operation of the step, delaying the operation to the next step, and then solving the optimal operation submodel again until obtaining the power system recovery strategy meeting the safety constraint, which specifically comprises the following steps:
initializing parameters of the power system, including power system network frame topology, generator starting parameters and load active and reactive requirements;
collecting power failure state information, and executing an optimal recovery submodel to obtain a primary recovery scheme;
providing the initial recovery scheme as input to an optimal operation submodel, gradually executing the optimal operation submodel on line, if the solving result has voltage amplitude or line active power flow out-of-limit, cancelling the line and load recovery operation of the step, and delaying the operation to the next step; otherwise, updating the active and reactive power output of the generator on line;
if a new fault occurs in the recovery process, after collecting and updating new fault information, re-executing the optimal recovery submodel to obtain a new initial recovery scheme, and then gradually executing the optimal operation submodel to update the active and reactive power output of the generator on line; repeating the steps after each new failure;
if no new fault occurs, the optimal operation submodel is executed on line until the recovery ending time, and the recovery time of the non-black-start generator, the commissioning time of the line, the recovery time of the load and the generator output provided by the optimal operation submodel jointly form a complete power system recovery scheme.
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