CN117154852A - Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint - Google Patents

Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint Download PDF

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CN117154852A
CN117154852A CN202311403718.0A CN202311403718A CN117154852A CN 117154852 A CN117154852 A CN 117154852A CN 202311403718 A CN202311403718 A CN 202311403718A CN 117154852 A CN117154852 A CN 117154852A
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张俊勃
谢志刚
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South China University of Technology SCUT
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
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Abstract

The application discloses a power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint. According to the method, all base state power flow constraints and N-1 power flow constraints in a power grid safety economic dispatching problem are ignored, an optimization target for minimizing the maximum line load rate is introduced into a model, and an economic better solution meeting safety requirements is obtained through solving the model in a hopeful and efficient manner and is used as an initial point for a follow-up further optimization dispatching plan; the follow-up optimization process adopts the idea of alternate iterative optimization, firstly, based on a safe economic dispatch model with simplified constraint, a large number of constraint conditions covered in the power grid safe economic dispatch model are processed by alternate iterative execution of a model solving-constraint checking-constraint supplementing-model perfecting mode, the problem of solving difficulty caused by considering a large number of constraints in the model at one time is avoided, the overall solving speed is accelerated, and the requirement of efficiently making a safe economic dispatch plan in a power system containing wind power is met.

Description

Power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint
Technical Field
The application belongs to the technical field of economic dispatch of power systems, and particularly relates to a power grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint.
Background
In order to adjust the energy structure, the permeability of new energy, represented by wind power generation, in the electric power system is continuously increasing. Unlike conventional thermal power generation, wind power output has strong randomness and intermittence, and wind power output can be accurately predicted only in a short-term time scale, for example, wind power output after one hour in the future is predicted, and when the prediction period is increased, the prediction error is obviously increased. In order to reduce the influence of wind power output uncertainty on safe and economic operation of a power grid, safe and economic scheduling decisions can be made based on relatively accurate wind power output prediction data obtained in a short-term time scale. However, since a certain time is required for collecting the wind power output prediction data, and a certain time margin is required for scheduling decision, namely, a formulated scheduling plan is required to be issued after a certain time is extracted, the time left for scheduling decision after the wind power prediction data under a short-term time scale is acquired is very short, and the efficiency of scheduling decision is very important.
The power grid safety economic dispatching decision-making problem belongs to the large-scale complex nonlinear programming problem. The existing solving method is concentrated on a direct optimization method, namely, the running cost of a power grid is taken as an objective function, a dispatching optimization model is constructed by taking constraints such as the base state power flow of the power grid into consideration, and then, the solution is carried out through nonlinear programming or heuristic algorithm and the like, so that an economic dispatching strategy meeting the constraints is obtained.
In the prior art, an economic scheduling method of an active power distribution network based on opportunity constraint
CN 110336308B), comprising: an active power distribution network economic dispatching model considering wind power output uncertainty is established, the active power distribution network economic dispatching model takes the minimized power distribution network running cost as an objective function, and active power distribution network tide constraint, unit output constraint, unit climbing constraint, system maximum rotation standby constraint, energy storage constraint and wind power output opportunity constraint are taken as constraint conditions; and solving an economic dispatch model of the active power distribution network.
In the prior art, a regional power grid dispatching optimization method and system containing source load uncertainty utilizes an improved Gaussian fitting model to generate scenes, builds scenes of wind power, photovoltaic and load double-side uncertainty, combines a quantile regression method, and performs scene reduction according to dispatching requirements of different regions; and taking the system operation cost into consideration, constructing an interval objective function and constraint conditions, optimizing the objective function to be the minimum system operation cost, taking constraints such as unit constraint, start-stop constraint, wind-light output constraint, power flow constraint and the like as constraint conditions, and solving the model by adopting a random programming method to obtain a final scheduling optimization strategy (CN 113852069A).
However, when considering the N-1 power flow constraint, the constraint condition scale in the model is greatly increased, and at this time, all the constraints in the disposable processing model become difficult to process in terms of calculation, so that the algorithm convergence speed is slow, the solving time is long, and the requirement of efficient decision cannot be met.
In view of the above, there is a need to solve the technical problems in the related art.
Disclosure of Invention
In order to solve the problem that the safety economic dispatch decision efficiency is low after N-1 power flow constraint is considered, the application provides a power grid economic dispatch plan optimization method based on alternate iteration under N-1 power flow constraint.
The object of the application is achieved by at least one of the following technical solutions.
The power grid economic dispatching method based on alternate iteration under the consideration of N-1 tide constraint comprises the following steps:
s1, constructing a double-target optimization model M1 by taking minimized conventional unit power generation cost and minimized maximum line load rate as objective functions and considering conventional unit output constraint and power balance constraint;
s2, establishing a ground state power flow constraint and an N-1 power flow constraint which need to be considered in power grid economic dispatch, and forming a constraint set C1;
s3, solving the model M1 by adopting a multi-objective genetic algorithm to obtain a group of optimal solution setsPS；
S4, correspondingly generating the power at the cost from small to largePSIs a solution to the ordering of (2);
s5, sequentially taking out the optimal solution setPSSolution in (a)S；
S6, judging solutionSWhether the constraints in constraint set C1 are violated,if yes, recording the violated constraint, forming a key constraint set C2, and entering a step S7, otherwise, entering a step S8;
s7, judging whether the traversal is finishedPSIf yes, the step S8 is carried out, if not, the step S5 is returned;
s8, outputting solutionS；
S9, taking the minimized conventional unit power generation cost as an objective function, and constructing an optimization model M2 taking the key constraint set into consideration by taking the conventional unit output constraint, the power balance constraint and the N-1 constraint in the key constraint set C2 into consideration;
s10, bySFor initial solution，Obtaining an optimal solution by solving an optimization model M2 considering a key constraint set through nonlinear programmingS’；
S11, judging the optimal solutionS’If yes, executing step S13, and if no, executing step S12;
s12, judging the optimal solutionS’Whether the constraint in the constraint set C1 is violated, if yes, adding the violated constraint into the key constraint set C2 and returning to the step S9, and if not, outputting an optimal solutionS’Obtaining an economic optimal scheduling plan meeting all constraints, and performing power grid scheduling according to the economic optimal scheduling plan;
s13, finishing searching, and outputting that no feasible solution exists.
Further, in step S1, the objective function in the dual-objective optimization model M1 is expressed as follows:
wherein,f 1 the output cost of a conventional unit is represented;gnumbering the conventional units;NGthe number of the conventional units;P g for conventional units in dispatch plansgIs an active force of (a);a g 、b g 、c g are all conventional unitsgIs a coefficient of force;f 2 indicating maximum line load factorkThe sum of the load rates of the lines;F l representation linelThe specific expression of the line flow of the (C) is as follows：
Wherein,G l-g is a conventional unitgLine-to-linelA power injection transfer profile of (2);wnumbering the wind farm;NWthe number of wind farms in the grid;P w is a power generation fieldwIs a predicted force of (1);G l-w for wind farmswLine-to-linelA power injection transfer profile of (2);nnumbering the load nodes;G l-n is a load nodenLine-to-linelNode load transfer distribution factors of (a);d n is a nodenIs a load of (2);
the double-objective optimization model M1 includes the following constraint conditions:
1) Power balance constraint:
2) Conventional unit output constraint:
wherein,P g,min andP g,max respectively conventional unitsgAnd a maximum output.
Further, in step S2, the specific expression of the ground state power flow constraint is as follows:
wherein,F l,max is a circuitlIs a trend limit value of (2);
the specific expression of the N-1 tide constraint is as follows:
wherein,cpredicting a faulty line for the N-1 under consideration;F l,c representation linecLine after N-1 faultlIs a trend of (2);is a circuitcLine-to-linelIs a power flow transfer factor of (1).
Further, the specific implementation flow of step S3 is as follows:
s3.1, setting parameters of a multi-target genetic algorithm: population numberHCross probability factorp m Variation probability factorp c Maximum number of iterationsN max ；
S3.2, randomly generating a parent population;
s3.3, performing cross mutation operation on the parent population to obtain a child population, and combining the child population and the parent population;
s3.4, calculating the fitness of all individuals of the population, comparing the merits of the individuals according to the Pareto dominant relationship, and reserving the superior individuals to form a next generation parent population;
s3.5, removing individuals with smaller crowding degree distance from the parent population to control the parent population scale to be kept asHThe crowding degree distance formula is:
wherein,D i representing individuals in a populationiIs used for the distance of the degree of congestion,D i smaller, indicate individualiThe more crowded the distribution of (2);hrepresenting the number of optimization targets;representing individualsiAlong the first edgemThe horizontal distance between two sides of each optimization target and adjacent individuals;
and S3.6, judging whether the iteration times meet the termination condition, if so, outputting the solved set, otherwise, returning to the step S3.3.
Further, in step S2, the N-1 contemplated faulty linecIs preassigned and satisfies the disconnection linecThe lower network has no island node condition.
Further, in step S6, by checking the optimal solution setPSIdentifying and extracting a key constraint set C2 in the power grid ground state power flow constraint and the N-1 power flow constraint;
checking optimal solution setPSIn the process of (1), violation conditions of a plurality of constraints are analyzed simultaneously in a parallel computing mode.
Further, in step S9, the constraint conditions of the optimization model M2 constructed by considering the key constraint set only consider the conventional unit output constraint, the power balance constraint and the key constraint set C2, and the ground state power flow constraint and the N-1 power flow constraint outside the key constraint set C2 are ignored.
Further, in step S10, the solution obtained in step S8 is always usedSAs a starting point for the optimization model M2 taking into account the set of key constraints by non-linear programming solution.
Further, in step S10, if the solution isSAnd if the constraint in the constraint set C1 is not violated, solving the optimization model M2 considering the key constraint set by an interior point method, otherwise solving the optimization model M2 considering the key constraint set by an exterior point method.
Further, in step S11, if the test finds the optimal solution of the optimization model M2 considering the key constraint setS’If all constraints are not satisfied, recording the optimal solutionS’Violated constraints and supplement these into the optimization model M2, which considers the set of key constraints.
Compared with the prior art, the application has the following beneficial effects:
aiming at the problem that the safety economic dispatching decision efficiency is low after N-1 power flow constraint is considered, the application provides a power grid economic dispatching method based on alternate iteration under N-1 power flow constraint. According to the application, all ground state power flow constraints and N-1 power flow constraints in the power grid safety economic dispatching problem are ignored, an optimization target for minimizing the maximum line load rate is introduced into the model, the complexity of the model is reduced, meanwhile, the economical efficiency and the safety requirements are considered, and the model is solved to obtain an economical efficiency preferred solution meeting the safety requirements in a high-efficiency manner, and the economic efficiency preferred solution is used as an initial point for further optimizing a dispatching plan. In the subsequent optimization process, the method adopts the idea of alternate iterative optimization, firstly, the safe economic dispatch model after constraint simplification is based, and then, a large number of constraint conditions covered in the power grid safe economic dispatch model are processed by means of alternate iterative execution of model solving-constraint checking-constraint supplementing-model perfecting, so that the problem of solving difficulty caused by considering a large number of constraints in the model at one time is avoided, the overall solving speed is increased, and the requirement of efficiently making the safe economic dispatch plan in the wind power system is met.
Drawings
FIG. 1 is a schematic flow diagram of a power grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint provided by an embodiment of the application;
FIG. 2 is a diagram of an improved IEEE30 node system test grid structure in a grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint provided by an embodiment of the application;
fig. 3 is a schematic flow chart of solving a double-objective optimization model M1 by adopting a multi-objective genetic algorithm in the power grid economic dispatching method based on alternate iteration under consideration of N-1 tide constraint provided by the embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Examples:
considering the power grid economic dispatching method based on alternate iteration under the constraint of N-1 tide, as shown in fig. 1, the method comprises the following steps:
s1, constructing a double-target optimization model M1 by taking minimized conventional unit power generation cost and minimized maximum line load rate as objective functions and considering conventional unit output constraint and power balance constraint;
the objective function in the dual-objective optimization model M1 is expressed as follows:
wherein,f 1 the output cost of a conventional unit is represented;gnumbering the conventional units;NGthe number of the conventional units;P g for conventional units in dispatch plansgIs an active force of (a);a g 、b g 、c g are all conventional unitsgIs a coefficient of force;f 2 indicating maximum line load factorkThe sum of the load rates of the lines;F l representation linelThe specific expression of the line flow is as follows:
wherein,G l-g is a conventional unitgLine-to-linelA power injection transfer profile of (2);wnumbering the wind farm;NWthe number of wind farms in the grid;P w is a power generation fieldwIs a predicted force of (1);G l-w for wind farmswLine-to-linelA power injection transfer profile of (2);nnumbering the load nodes;G l-n is a load nodenLine-to-linelNode load transfer distribution factors of (a);d n is a nodenIs a load of (2);
the double-objective optimization model M1 includes the following constraint conditions:
1) Power balance constraint:
2) Conventional unit output constraint:
wherein,P g,min andP g,max respectively conventional unitsgIs the minimum of (2)Output and maximum output.
In one embodiment, in the test system, the conventional crew at node 22 in the original IEEE30 node system is replaced with a wind farm and assumed to be predicted to have an output of 25MW, as shown in FIG. 2. The load of each node in the system is shown in table 1.
Table 1 load table for each node in test system
S2, establishing a ground state power flow constraint and an N-1 power flow constraint which need to be considered in power grid economic dispatch, and forming a constraint set C1;
the specific expression of the ground state tide constraint is as follows:
wherein,F l,max is a circuitlIs a power flow limit value of (2).
The specific expression of the N-1 tide constraint is as follows:
wherein,cpredicting a faulty line for the N-1 under consideration;F l,c representation linecLine after N-1 faultlIs a trend of (2);is a circuitcLine-to-linelIs a power flow transfer factor of (1).
N-1 contemplated fault linecIs preassigned and satisfies the disconnection linecThe lower network has no island node condition.
In one embodiment, N-1 expected failure lines for improved IEEE39 node system considerations are shown in Table 2.
Table 2N-1 expected failure line set table for improved IEEE39 node system considerations
S3, solving the model M1 by adopting a multi-objective genetic algorithm to obtain a group of optimal solution setsPSAs shown in fig. 3, the specific implementation flow is as follows:
s3.1, setting parameters of a multi-target genetic algorithm: population numberHCross probability factorp m Variation probability factorp c Maximum number of iterationsN max The method comprises the steps of carrying out a first treatment on the surface of the In one embodiment, a settingHAt the point of 50 a,p m the total number of the components is 0.9,p c the content of the acid in the solution is 0.1,N max 100;
s3.2, randomly generating a parent population;
s3.3, performing cross mutation operation on the parent population to obtain a child population, and combining the child population and the parent population;
s3.4, calculating the fitness of all individuals of the population, comparing the merits of the individuals according to the Pareto dominant relationship, and reserving the superior individuals to form a next generation parent population;
s3.5, removing individuals with smaller crowding degree distance from the parent population to control the parent population scale to be kept asHThe crowding degree distance formula is:
wherein,D i representing individuals in a populationiIs used for the distance of the degree of congestion,D i smaller, indicate individualiThe more crowded the distribution of (2);hrepresenting the number of optimization targets;representing individualsiAlong the first edgemThe horizontal distance between two sides of each optimization target and adjacent individuals;
and S3.6, judging whether the iteration times meet the termination condition, if so, outputting the solved set, otherwise, returning to the step S3.3.
S4, correspondingly generating the power at the cost from small to largePSIs a solution to the ordering of (2);
s5, sequentially taking out the optimal solution setPSSolution in (a)S；
S6, judging solutionSIf yes, recording the violated constraint in the constraint set C1, forming a key constraint set C2, and entering a step S7, otherwise, entering a step S8;
by checking the optimal solution setPSIdentifying and extracting a key constraint set C2 in the power grid ground state power flow constraint and the N-1 power flow constraint;
checking optimal solution setPSIn the process of (1), violation conditions of a plurality of constraints are analyzed simultaneously in a parallel computing mode.
S7, judging whether the traversal is finishedPSIf yes, the step S8 is carried out, if not, the step S5 is returned;
s8, outputting solutionS；
S9, taking the minimized conventional unit power generation cost as an objective function, and constructing an optimization model M2 taking the key constraint set into consideration by taking the conventional unit output constraint, the power balance constraint and the N-1 constraint in the key constraint set C2 into consideration;
the constraint conditions of the constructed optimization model M2 considering the key constraint set only consider the conventional unit output constraint, the power balance constraint and the key constraint set C2, and the ground state power flow constraint and the N-1 power flow constraint outside the key constraint set C2 are ignored.
S10, bySFor initial solution，Obtaining an optimal solution by solving an optimization model M2 considering a key constraint set through nonlinear programmingS’；
Always with the solution obtained in step S8SAs a starting point for the optimization model M2 taking into account the set of key constraints by non-linear programming solution.
If solveSAnd if the constraint in the constraint set C1 is not violated, solving the optimization model M2 considering the key constraint set by an interior point method, otherwise solving the optimization model M2 considering the key constraint set by an exterior point method.
S11, judging the optimal solutionS’If yes, executing step S13, and if no, executing step S12;
if the test finds that the optimization model M2 considers the key constraint setOptimal solutionS’If all constraints are not satisfied, recording the optimal solutionS’Violated constraints and supplement these into the optimization model M2, which considers the set of key constraints.
S12, judging the optimal solutionS’Whether the constraint in the constraint set C1 is violated, if yes, adding the violated constraint into the key constraint set C2 and returning to the step S9, and if not, outputting an optimal solutionS’Obtaining an economical optimal scheduling plan meeting all constraints;
s13, finishing searching, and outputting that no feasible solution exists.
In one embodiment, the resulting solution is shown in Table 3.
Table 3 table of the solution results using the method provided by the present application in the example
To better verify the effectiveness of the present application, the alternate optimization method provided by the present application is compared with a direct optimization method that considers all constraints in the model once. The solution results of the direct optimization method are shown in table 4.
Table 4 Table of the solution results using the direct optimization method in the example
Comparing tables 3 and 4, the application is verified to be capable of efficiently obtaining the economic dispatch plan meeting the safety constraint, and meeting the requirement of high-efficiency decision of the safety economic dispatch in the wind power system.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. The power grid economic dispatching method based on alternate iteration under the consideration of N-1 tide constraint is characterized by comprising the following steps:
s1, constructing a double-target optimization model M1 by taking minimized conventional unit power generation cost and minimized maximum line load rate as objective functions and considering conventional unit output constraint and power balance constraint;
s2, establishing a ground state power flow constraint and an N-1 power flow constraint which need to be considered in power grid economic dispatch, and forming a constraint set C1;
s3, solving the model M1 by adopting a multi-objective genetic algorithm to obtain a group of optimal solution setsPS；
S4, correspondingly generating the power at the cost from small to largePSIs a solution to the ordering of (2);
s5, sequentially taking out the optimal solution setPSSolution in (a)S；
S6, judging solutionSIf yes, recording the violated constraint in the constraint set C1, forming a key constraint set C2, and entering a step S7, otherwise, entering a step S8;
s7, judging whether the traversal is finishedPSIf yes, the step S8 is carried out, if not, the step S5 is returned;
s8, outputting solutionS；
S9, taking the minimized conventional unit power generation cost as an objective function, and constructing an optimization model M2 taking the key constraint set into consideration by taking the conventional unit output constraint, the power balance constraint and the N-1 constraint in the key constraint set C2 into consideration;
s10, bySFor initial solution，Obtaining an optimal solution by solving an optimization model M2 considering a key constraint set through nonlinear programmingS’；
S11, judging the optimal solutionS’If yes, executing step S13, and if no, executing step S12;
s12, judging the optimal solutionS’Whether the constraint in the constraint set C1 is violated, if yes, adding the violated constraint into the key constraint set C2 and returning to the step S9, and if not, outputting an optimal solutionS’Obtaining an economic optimal scheduling plan meeting all constraints, and performing power grid scheduling according to the economic optimal scheduling plan;
s13, finishing searching, and outputting that no feasible solution exists.
2. The grid economic dispatch method based on alternate iteration under consideration of N-1 power flow constraint of claim 1, wherein in step S1, the objective function in the dual objective optimization model M1 is expressed as follows:
wherein,f 1 the output cost of a conventional unit is represented;gnumbering the conventional units;NGthe number of the conventional units;P g for conventional units in dispatch plansgIs an active force of (a);a g 、b g 、c g are all conventional unitsgIs a coefficient of force;f 2 indicating maximum line load factorkThe sum of the load rates of the lines;F l representation linelThe specific expression of the line flow is as follows:
wherein,G l-g is a conventional unitgLine-to-linelA power injection transfer profile of (2);wnumbering the wind farm;NWthe number of wind farms in the grid;P w is a power generation fieldwIs a predicted force of (1);G l-w for wind farmswLine-to-linelA power injection transfer profile of (2);nnumbering the load nodes;G l-n is a load nodenLine-to-linelNode load transfer distribution factors of (a);d n is a nodenIs a load of (2);
the double-objective optimization model M1 includes the following constraint conditions:
1) Power balance constraint:
2) Conventional unit output constraint:
wherein,P g,min andP g,max respectively conventional unitsgAnd a maximum output.
3. The grid economic dispatch method based on alternate iteration under consideration of N-1 power flow constraint according to claim 1, wherein in step S2, the specific expression of the ground state power flow constraint is as follows:
wherein,F l,max is a circuitlIs a trend limit value of (2);
the specific expression of the N-1 tide constraint is as follows:
wherein,cpredicting a faulty line for the N-1 under consideration;F l,c representation linecLine after N-1 faultlIs a trend of (2);is a circuitcLine-to-linelIs a power flow transfer factor of (1).
4. The grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint according to claim 1, wherein the specific implementation flow of step S3 is as follows:
s3.1, setting parameters of a multi-target genetic algorithm: population numberHCross probability factorp m Variation probability factorp c Maximum number of iterationsN max ；
S3.2, randomly generating a parent population;
s3.3, performing cross mutation operation on the parent population to obtain a child population, and combining the child population and the parent population;
s3.4, calculating the fitness of all individuals of the population, comparing the merits of the individuals according to the Pareto dominant relationship, and reserving the superior individuals to form a next generation parent population;
s3.5, removing individuals with smaller crowding degree distance from the parent population to control the parent population scale to be kept asHThe crowding degree distance formula is:
wherein,D i representing individuals in a populationiIs used for the distance of the degree of congestion,D i smaller, indicate individualiThe more crowded the distribution of (2);hrepresenting the number of optimization targets;representing individualsiAlong the first edgemThe horizontal distance between two sides of each optimization target and adjacent individuals;
and S3.6, judging whether the iteration times meet the termination condition, if so, outputting the solved set, otherwise, returning to the step S3.3.
5. The grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint of claim 1, wherein in step S2, N-1 considered envisions a faulty linecIs preassigned and satisfies the disconnection linecThe lower network has no island node condition.
6. The grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint of claim 1, wherein in step S6, the optimal solution set is verifiedPSIs to identify the solution in (a)Extracting a key constraint set C2 in the power grid ground state power flow constraint and the N-1 power flow constraint;
checking optimal solution setPSIn the process of (1), violation conditions of a plurality of constraints are analyzed simultaneously in a parallel computing mode.
7. The grid economic dispatch method based on alternate iteration under consideration of N-1 power flow constraint according to claim 1, wherein in step S9, the constraint condition of the constructed optimization model M2 considering the key constraint set only considers the conventional unit output constraint, the power balance constraint and the key constraint set C2, and the ground state power flow constraint and the N-1 power flow constraint outside the key constraint set C2 are ignored.
8. The grid economic dispatch method based on alternate iteration under consideration of N-1 tide constraint according to claim 1, wherein in step S10, the solution obtained in step S8 is always usedSAs a starting point for the optimization model M2 taking into account the set of key constraints by non-linear programming solution.
9. The grid economic dispatch method based on alternate iteration under consideration of N-1 tidal current constraint of claim 1, wherein in step S10, if the solution isSAnd if the constraint in the constraint set C1 is not violated, solving the optimization model M2 considering the key constraint set by an interior point method, otherwise solving the optimization model M2 considering the key constraint set by an exterior point method.
10. The grid economic dispatch method based on alternate iteration under consideration of N-1 tidal current constraint according to claim 1, wherein in step S11, if the test finds the optimal solution of the optimization model M2 considering the key constraint setS’If all constraints are not satisfied, recording the optimal solutionS’Violated constraints and supplement these into the optimization model M2, which considers the set of key constraints.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103473604A (en) * 2013-08-22 2013-12-25 国家电网公司 Method for evaluating comprehensive property of day-ahead power generation plan
US20150378387A1 (en) * 2013-05-14 2015-12-31 Rensselaer Polytechnic Institute Methods of computing steady-state voltage stability margins of power systems
US20150378384A1 (en) * 2014-06-29 2015-12-31 International Business Machines Corporation Reducing corrective actions in security-constrained optimal power flow via sparsity regularization
CN106786529A (en) * 2016-12-23 2017-05-31 国网山西省电力公司电力科学研究院 A kind of distributed static security analysis method
CN110880072A (en) * 2019-11-15 2020-03-13 国网湖南省电力有限公司 Real-time power grid static security risk disposal optimization method and device and storage medium
CN114142530A (en) * 2021-09-23 2022-03-04 重庆大学 Risk scheduling method considering N-1 security constraint based on near-end policy optimization algorithm
CN116934021A (en) * 2023-07-17 2023-10-24 西安理工大学 Electric-thermal comprehensive energy system optimal scheduling method considering N-1 safety constraint

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150378387A1 (en) * 2013-05-14 2015-12-31 Rensselaer Polytechnic Institute Methods of computing steady-state voltage stability margins of power systems
CN103473604A (en) * 2013-08-22 2013-12-25 国家电网公司 Method for evaluating comprehensive property of day-ahead power generation plan
US20150378384A1 (en) * 2014-06-29 2015-12-31 International Business Machines Corporation Reducing corrective actions in security-constrained optimal power flow via sparsity regularization
CN106786529A (en) * 2016-12-23 2017-05-31 国网山西省电力公司电力科学研究院 A kind of distributed static security analysis method
CN110880072A (en) * 2019-11-15 2020-03-13 国网湖南省电力有限公司 Real-time power grid static security risk disposal optimization method and device and storage medium
CN114142530A (en) * 2021-09-23 2022-03-04 重庆大学 Risk scheduling method considering N-1 security constraint based on near-end policy optimization algorithm
CN116934021A (en) * 2023-07-17 2023-10-24 西安理工大学 Electric-thermal comprehensive energy system optimal scheduling method considering N-1 safety constraint

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
肖逸 等: "基于微服务的大电网在线安全稳定分析系统数据中台与架构设计", 中国电机工程学报, vol. 42, pages 126 - 135 *

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