CN117060403B - Method, device, equipment and medium for adjusting black start sequence of power grid - Google Patents
Method, device, equipment and medium for adjusting black start sequence of power grid Download PDFInfo
- Publication number
- CN117060403B CN117060403B CN202311308458.9A CN202311308458A CN117060403B CN 117060403 B CN117060403 B CN 117060403B CN 202311308458 A CN202311308458 A CN 202311308458A CN 117060403 B CN117060403 B CN 117060403B
- Authority
- CN
- China
- Prior art keywords
- load
- starting
- black start
- power grid
- start sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 241001672018 Cercomela melanura Species 0.000 title claims abstract description 129
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 claims abstract description 55
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 31
- 230000005540 biological transmission Effects 0.000 claims abstract description 26
- 230000006870 function Effects 0.000 claims description 59
- 230000009466 transformation Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 11
- 230000035772 mutation Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 230000005611 electricity Effects 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 6
- 108090000623 proteins and genes Proteins 0.000 description 4
- 230000002068 genetic effect Effects 0.000 description 3
- 238000013139 quantization Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000013016 damping Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention discloses a method, a device, equipment and a medium for adjusting a black start sequence of a power grid, wherein the method comprises the following steps: acquiring a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one, determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one, constructing a class routing model according to the plurality of load priorities, the power supply relation of the plurality of loads in the power grid system and the plurality of power transmission losses between adjacent loads in the power grid system, generating a black start sequence optimization model according to the plurality of starting parameters and the class routing model, and solving the black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence. And generating a black start sequence of the power grid, which is matched with the current start scene, according to the start parameters of the current generator set and the start priority of the current load.
Description
Technical Field
The invention relates to the technical field of black start of power grids, in particular to a method, a device, equipment and a medium for adjusting black start sequence of a power grid.
Background
In recent years, large-capacity, long-distance transmission and alternating current/direct current hybrid connection become a normal operation state of a power grid, a high-voltage large-power grid system brings significant effects, meanwhile, potential threats are objectively existed, the stable operation margin of the power grid is smaller, the safety problem of the multi-feed direct current system exists in the power grid, the voltage regulation in local areas is difficult, the reactive support capacity of a receiving end is insufficient, the distribution points of a power grid safety automatic device are multiple, the types of the safety automatic device are multiple, the control strategy is complex, the system damping is relatively weak, the system stability can fluctuate due to the intrinsic factors of the power grid and the interference of the external environment, once serious faults such as multiple complex faults, the receiving end loses a large power supply or an important power transmission line, the safety automatic device is in misoperation and refusal operation are caused, the disaster which is difficult to control is caused, the linkage reaction is caused by the fact that each subsystem is closely connected, the whole body is pulled, the influence range of the accident is further aggravated, the stable operation of the power system is seriously threatened, and the serious consequences of regional grid disconnection are possibly caused. The existing black start schemes are all pre-stored start schemes, and cannot be well adapted to different power grids and start conditions.
Disclosure of Invention
Aiming at the technical problem of low black start adaptability of a power grid in the prior art, the invention provides a method, a device, equipment and a medium for adjusting the black start sequence of the power grid.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect of the embodiment of the present invention, a method for adjusting a black start sequence of a power grid is provided, where the method includes:
acquiring a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one, wherein the starting parameters comprise starting time, starting power and starting stability;
determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one, wherein the load priorities can be determined by the following formula:
wherein, said->Is->Load priority of class load, said +.>For the first weight coefficient, said +.>Is->Load importance of class load, said +.>For the second weight coefficient, said +.>Is->Load emergency degree of class load, said +.>For the third weight coefficient, said +.>Is->The degree of grid influence of the class load;
constructing a class routing model according to the plurality of load priorities, the power supply relation of the plurality of loads in the power grid system and the plurality of power transmission losses between adjacent loads in the power grid system;
generating a black start sequence optimization model according to the plurality of start parameters and the class routing model;
and solving the black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence.
Optionally, the generating a black start sequence optimization model according to the plurality of start parameters and the class routing model includes:
acquiring a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities which are in one-to-one correspondence with the plurality of generator sets;
generating an objective function according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities;
determining a path constraint condition according to the plurality of starting parameters and the class routing model;
and generating the black start sequence optimization model according to the path constraint condition and the objective function.
Optionally, the generating an objective function according to the plurality of shortest start-up times, the plurality of maximum start-up powers, and the plurality of maximum start-up stabilities includes:
generating a plurality of linear programming functions according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities;
and generating the objective function according to the plurality of linear programming functions.
Optionally, the solving the black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence includes:
generating a plurality of initial black start sequences according to the black start sequence optimization model;
generating an initial population according to the initial black start sequences, wherein the initial population comprises a plurality of individuals, each individual represents an initial black start sequence, and each individual comprises a plurality of generator sets and the black start sequences of the plurality of generator sets;
determining a plurality of advantages of the plurality of individuals in one-to-one correspondence based on a preset fitness function;
and determining a black start sequence corresponding to a target individual with the largest superiority from the plurality of superiorities, wherein the black start sequence is the target black start sequence.
Optionally, the determining, based on a preset fitness function, a plurality of advantages of the plurality of individuals in one-to-one correspondence includes:
performing iterative transformation on the initial population based on a preset transformation rule, and generating a target population under the condition that the number of times of the iterative transformation reaches a set threshold, wherein the preset transformation rule comprises cross transformation and/or mutation transformation;
and determining the plurality of advantages of the plurality of individuals in the target population in one-to-one correspondence according to the preset fitness function.
Optionally, the constructing a class routing model according to the priorities of the loads, the power supply relation of the loads in the power grid system and the power transmission loss between adjacent loads in the power grid system includes:
constructing an initial graph model according to the power supply relation, wherein the initial graph model comprises a plurality of graph nodes used for representing the plurality of loads, and the weight of each graph node is the load priority of the corresponding load;
generating a plurality of weight values between adjacent graph nodes in the initial graph model according to the plurality of power transmission losses;
and constructing the class routing model according to the plurality of weight values and the initial graph model.
Optionally, the determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one includes:
acquiring a plurality of importance parameters, a plurality of emergency degree parameters and a plurality of starting influence parameters which are in one-to-one correspondence with the plurality of loads;
and determining the load priorities corresponding to the loads one by one according to the importance parameters, the emergency degree parameters and the starting influence parameters.
In a second aspect of the embodiment of the present invention, there is provided an adjustment device for a black start sequence of a power grid, the device including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one, and the starting parameters comprise starting time, starting power and starting stability;
the determining module is used for determining a plurality of load priorities corresponding to the loads in the power grid system one by one, and the load priorities can be determined through the following formula:
wherein, said->Is->Load priority of class load, said +.>For the first weight coefficient, said +.>Is->Load importance of class load, said +.>For the second weight coefficient, said +.>Is->Load emergency degree of class load, said +.>For the third weight coefficient, said +.>Is->The degree of grid influence of the class load;
the construction module is used for constructing a class routing model according to the plurality of load priorities, the power supply relation of the plurality of loads in the power grid system and the plurality of power transmission losses between adjacent loads in the power grid system;
the first generation module is used for generating a black start sequence optimization model according to the plurality of start parameters and the class routing model;
and the second generation module is used for solving the black start sequence optimization model based on a preset optimization algorithm so as to generate a target black start sequence.
In a third aspect of the embodiment of the present invention, there is provided an electronic device, including:
a memory having a computer program stored thereon;
and the processor is used for executing the computer program in the memory to realize the steps of the method for adjusting the black start sequence of the power grid in any one of the first aspect of the disclosure.
In a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of the first aspects of the present disclosure.
The invention provides a method, a device, equipment and a medium for adjusting a black start sequence of a power grid. Compared with the prior art, the method has the following beneficial effects:
by the method, a plurality of starting parameters corresponding to the generator sets in the power grid system one by one are obtained, wherein the starting parameters comprise starting time, starting power and starting stability, a plurality of load priorities corresponding to the loads in the power grid system one by one are determined, a class routing model is built according to the load priorities, the power supply relation of the loads in the power grid system and the power transmission loss among adjacent loads in the power grid system, a black start sequence optimization model is generated according to the starting parameters and the class routing model, and the black start sequence optimization model is solved based on a preset optimization algorithm to generate a target black start sequence. And then solving the black start sequence optimizing model based on an optimizing algorithm to generate a target black start sequence, and generating a power grid black start sequence which is matched with the current start scene according to the start parameter of the current generator set and the start priority of the current load.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating a method for adjusting a black start sequence of a power grid according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating another method of adjusting a grid black start sequence according to an exemplary embodiment.
Fig. 3 is a block diagram illustrating an adjustment device for a black start sequence of a power grid according to an exemplary embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart illustrating a method for adjusting a black start sequence of a power grid according to an exemplary embodiment, and the method includes the following steps as shown in fig. 1.
Step S11, a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one are obtained, wherein the starting parameters comprise starting time, starting power and starting stability.
The method and the system are applied to a power grid system, the power grid system comprises a plurality of generator sets and a plurality of loads, starting parameters of the generator sets and priorities of the plurality of loads are monitored through the power grid system, wherein the starting parameters of the generator sets comprise starting time, starting power and starting stability, and the starting capability comprises parameters such as starting time, starting power and starting speed of a power plant.
Step S12, determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one.
For example, in this embodiment, the priority of each load in the power grid needs to be analyzed. The judgment basis of the load priority comprises the importance of the load, the emergency degree and the influence of recovery on the whole power grid. The loads in the power grid are required to be classified, the loads can be classified into several types according to the application of the loads, such as domestic electricity, industrial electricity, commercial electricity and the like, and the priority of each load is determined according to the electricity utilization rule under the current power grid application scene. Each type of load has its particular importance and degree of urgency. For example, the importance and urgency of life electricity may be high because it is directly related to people's life. While the emergency level of industrial electricity may be relatively low, its impact on the whole grid may be large. Load priorities of various types of electric loads are specified based on the setting rule.
For example, in a grid system, the priorities of the same type of loads are the same, and thus, the load priorities of the same type of loads may be determined based on the same priority function. In this embodiment, different types of loads may be split based on the different types of loads, and for each type of load, a priority function of the type of load may be definedWhere i represents the type of load. />The function may be dependent on the importance of the type of load +.>Degree of urgency->And restoring the influence of the entire network>To calculate the priority:
wherein->、/>、/>Is a weight coefficient representing the relative importance of importance, urgency and recovery to the impact of the whole grid in the priority calculation, respectively. These weight coefficients may be obtained by expert evaluation or historical data analysis.
Then, each type of load needs to be quantitatively evaluated, and a quantitative evaluation function can be definedWhere i represents the type of load. />The function may be based on the actual power consumption of the load +.>Expected power consumption->And electric wave power->To calculate a quantization evaluation value:
wherein->、/>And->Is a weight coefficient representing the relative importance of the actual power usage, the expected power usage, and the power usage in the quantitative evaluation. These weighting coefficients may also be obtained by expert evaluation or historical data analysis.
Finally, the priority function can be usedAnd quantization evaluation function->Combining, get the final priority of each type of load +.>：/>Wherein->Is a coefficient between 0 and 1, indicating the relative importance of the priority function and the quantization evaluation function in the final priority calculation. This coefficient can be adjusted according to the actual situation.
Through the steps, the priority of each load in the power grid can be obtained, so that basis is provided for the optimization of the subsequent black start sequence.
Optionally, in some embodiments, step S12 above includes:
and acquiring a plurality of importance parameters, a plurality of emergency degree parameters and a plurality of starting influence parameters which are in one-to-one correspondence with the loads.
And determining a plurality of load priorities corresponding to the loads one by one according to the importance parameters, the emergency degree parameters and the starting influence parameters.
And step S13, constructing a class routing model according to the priorities of the loads, the power supply relation of the loads in the power grid system and the power transmission loss between adjacent loads in the power grid system.
In this embodiment, a plurality of loads are built into a class routing model according to a power supply relationship of the plurality of loads in a power grid system, each coincidence is set as a point in the class routing model, each node in the class routing model is connected based on a plurality of power transmission losses between adjacent loads in the power grid system and a connection relationship of each load in the power supply system, and weights between two nodes with the connection relationship are built based on each load priority and the power transmission losses, so that the class routing model is built. All loads in the power grid are classified into a graph based on the routing model, a weight relation between connection nodes in the graph is constructed according to the load priority and the power transmission loss, and the shortest path traversing all the nodes is found based on the graph and the corresponding relation, so that the optimal black start sequence of the power grid system is determined.
Optionally, in one embodiment, the step S13 includes:
and constructing an initial graph model according to the power supply relation, wherein the initial graph model comprises a plurality of graph nodes used for representing a plurality of loads, and the weight of each graph node is the load priority of the corresponding load.
And generating a plurality of weight values between adjacent graph nodes in the initial graph model according to the plurality of power transmission losses.
And constructing a class routing model according to the plurality of weight values and the initial graph model.
In this embodiment, a class routing model needs to be established according to the priorities of all loads in the power grid system, each load is set as a point, all loads in the power grid are classified into a graph, optimal path planning is achieved for the graph, a route is formed, the shortest path traversed in all is found to serve as a starting point, and the whole path serves as a starting load priority. This step mainly involves the application of graph theory and shortest path algorithms.
First, a graph model needs to be built to represent the load in the grid. In this graph model, each load can be considered as a node, and the weight of each node i is the priority of the load. If there is a direct power relationship between the two loads, an edge is drawn between the two nodes. The weight of each edge can be set to the power transmission loss between two nodes, i.e.>Wherein->。
For example, in some embodiments, a path from a certain node, passing through all other nodes and returning to the original node needs to be found from the graph corresponding to the class routing model based on the class routing model, so that the total weight of the path is minimum. This problem can be solved using a method called approximation algorithm. The approximation algorithm may find an approximately optimal solution in a limited time, and although this solution may not be the optimal solution, in practical applications the effect of the approximation algorithm is often already good enough.
Specifically, TSP (Traveling Salesman Problem, tourist problem) can be solved using an approximation algorithm called Christofides algorithm. The basic idea of the Christofides algorithm is to find the minimum spanning tree of all nodes in the graph, then find an euler loop based on the minimum spanning tree, and finally convert the euler loop into a hamiltonian loop. The hamiltonian loop is the shortest path required. The Christofides algorithm can be formulated as follows, find the minimum spanning tree for graph G：/>;
Determination ofNode set with medium number of odd numbers +.>：/>;
Determination ofMinimum perfect match of all nodes in +.>：/>;
MergingAnd->Obtaining an Euler diagram->：/>;
At the position ofIs determined by a Euler loop->：/>;
Will beConversion to Hamiltonian Loop->：/>;
Finally, the obtained Hamiltonian loopA black start sequence for the grid. This sequence may be used to guide the scheduling control of the power grid during a black start.
And S14, generating a black start sequence optimization model according to the plurality of start parameters and the class routing model.
By way of example, a black start sequence optimization model will be built based on the start parameters, start-up capabilities, start-up starting points, and load priorities of the power plant. The model mainly comprises an objective function with shortest starting time, maximum starting power and optimal starting stability, and constraint conditions of power plant starting parameters and load priorities.
Optionally, in one embodiment, step S14 includes:
and acquiring a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities which are in one-to-one correspondence with the plurality of generator sets.
And generating an objective function according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities.
And determining path constraint conditions according to the plurality of starting parameters and the class routing model.
And generating a black start sequence optimization model according to the path constraint condition and the objective function.
For example, an objective function needs to be defined. The aim in this embodiment is to find a sequence of grid black starts such that the start time is the shortest, the start power is the greatest and the start stability is the best. An objective function is determined based on the objective.
For example, in another embodiment, the step of generating the objective function according to the plurality of shortest start-up times, the plurality of maximum start-up powers, and the plurality of maximum start-up stabilities includes:
and generating a plurality of linear programming functions according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities.
An objective function is generated from the plurality of linear programming functions.
By way of example, the objective function may be implemented by the following linear programming model:
wherein n represents the number of power plants, +.>Indicating the start-up sequence of the ith power plant +.>The start-up time, start-up power and start-up stability of the i-th power plant are indicated, respectively. Constraint conditions are defined based on preset rules. In this embodiment, the constraints mainly include the start-up parameters and load priorities of the power plant.
For the start-up parameters of the power plants, the start-up time, the start-up power and the start-up stability of each power plant are set such that they cannot exceed their maximum start-up capability, i.e.:
wherein->、/>And->The maximum start-up time, maximum start-up power and maximum start-up stability of the i-th power plant are indicated, respectively. As for the load priority, the priority of each load is set so as not to exceed its maximum priority, that is: />Wherein->Indicating the maximum priority that is met, and finally, this optimization problem needs to be solved. This can be achieved by using an optimization algorithm called linear programming. Linear programming is an algorithm for solving an optimal solution of a linear objective function under a set of linear equation or inequality constraints. In the present invention, this problem can be solved using a simple x-algorithm or an interior point method. In practical applications, this optimization model may be applied to the actual operation of the power grid to guide the scheduling control of the power grid during black start. This will greatly increase the operation of the gridEfficiency and stability.
And S15, solving a black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence.
For example, the black start sequence optimization model will be solved using an optimization algorithm to obtain an optimal black start sequence scheme. In practice, various optimization algorithms may be used, including genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, and the like.
In some embodiments, because the load in the power grid system may be started by multiple generator sets, the black start sequence optimization model may be solved based on a preset optimization algorithm, the obtained black start sequence scheme may be multiple, the black start sequence optimization model generates a black start sequence scheme sequence, the multiple black start sequence schemes are sequentially arranged from top to bottom based on a preset evaluation criterion in the sequence, and the first N black start sequences may be output as target black start sequences during output.
By the method, a plurality of starting parameters corresponding to the generator sets in the power grid system one by one are obtained, wherein the starting parameters comprise starting time, starting power and starting stability, a plurality of load priorities corresponding to the loads in the power grid system one by one are determined, a class routing model is built according to the load priorities, the power supply relation of the loads in the power grid system and the power transmission loss between adjacent loads in the power grid system, a black start sequence optimization model is generated according to the starting parameters and the class routing model, and the black start sequence optimization model is solved based on a preset optimization algorithm to generate a target black start sequence. And then solving the black start sequence optimizing model based on an optimizing algorithm to generate a target black start sequence, and generating a power grid black start sequence which is matched with the current start scene according to the start parameter of the current generator set and the start priority of the current load.
Fig. 2 is a flowchart illustrating another method for adjusting the black start sequence of the power grid according to an exemplary embodiment, as shown in fig. 2, in some embodiments, the step S15 described above includes the following steps.
Step S151, generating a plurality of initial black start sequences according to the black start sequence optimization model.
By way of example, in the present embodiment, a plurality of initial black start sequences traversing a plurality of gensets and a plurality of loads are generated based on a black start sequence optimization model, wherein the plurality of initial black start sequences include all start possibilities across the plurality of gensets and the plurality of loads.
In step S152, an initial population is generated according to a plurality of initial black start sequences, where the initial population includes a plurality of individuals, each individual represents an initial black start sequence, and each individual includes a plurality of generator sets and a black start sequence of the plurality of generator sets.
A population needs to be initialized. Each individual in the initial population represents one possible black start sequence scheme. The genetic code for each individual may be an arrangement wherein the location of the genes represents the start-up sequence and the value of the genes represents the specific power plant. For example, the individual p= (3, 1, 2) means that the 3 rd power plant is started first, then the 1 st power plant is started, and finally the 2 nd power plant is started.
Step S153, determining a plurality of advantages of one-to-one correspondence of a plurality of individuals based on a preset fitness function.
An fitness function f (P) needs to be defined to evaluate the superiority of each individual. The fitness function f (P) may be the value of the objective function, i.e. the shortest start time, the greatest start power and the optimal start stability. Since there may be multiple objective functions, it is necessary to convert these objective functions into a single objective function. Assume that the shortest starting time weight isThe maximum weight of the starting power is +.>The weight for optimal starting stability is +.>The fitness function can be determinedThe meaning is as follows:
wherein->、/>And->The functions of start-up time, start-up power and start-up stability are shown, respectively. And calculating the superiority of each individual according to the fitness function, and generating a plurality of superiorities corresponding to the individuals one by one.
Optionally, in some embodiments, step S153 includes:
and carrying out iterative transformation on the initial population based on a preset transformation rule, and generating a target population under the condition that the number of iterative transformation reaches a set threshold, wherein the preset transformation rule comprises cross transformation and/or mutation transformation.
And determining a plurality of advantages of one-to-one correspondence of the individuals in the target population according to a preset fitness function.
In order to improve diversity of initial populations and thus obtain a more compatible black start sequence, in this embodiment, a plurality of initial populations are transformed to generate multiple types of populations, and the black start sequence is determined based on the multiple types of populations. Selection, crossover and mutation operations are required. The selecting operation is to select a good individual according to the value of the fitness function f (P). Crossover operations are random selection of two individuals for gene exchange to create new individuals. The mutation operation is to randomly change a certain gene of a certain individual so as to increase the diversity of the population. Finally, termination conditions need to be defined. The termination condition may be that the maximum number of iterations is reached, or that the fitness value of the optimal individual reaches a preset threshold.
Step S154, determining the black start sequence corresponding to the target individual with the largest superiority from the plurality of superiorities as the target black start sequence.
For example, in this embodiment, after determining the values of the plurality of superiorities through the above steps, the target individual with the highest superiority is selected from the plurality of superiorities, and the black start sequence of the target individual is read as the target black start sequence. After the optimal black start sequence scheme is obtained, the optimal black start sequence scheme is applied to actual operation of the power grid so as to guide scheduling control of the power grid in the black start process.
By adopting the mode, the black start sequence is obtained by initializing the population, defining the fitness function, selecting, intersecting and mutating, defining the termination condition, adjusting the parameters of the genetic algorithm, and applying the optimal start sequence scheme, so that the obtained black start sequence is more in line with the current power grid system, and the corresponding adaptation period is stronger.
Fig. 3 is a block diagram of an adjustment device for a black start sequence of a power grid according to an exemplary embodiment, and as shown in fig. 3, the device 100 includes: the system comprises an acquisition module 110, a determination module 120, a construction module 130, a first generation module 140 and a second generation module 150.
The acquiring module 110 is configured to acquire a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system, where the starting parameters include a starting time, a starting power and a starting stability;
the determining module 120 is configured to determine a plurality of load priorities corresponding to a plurality of loads in the power grid system, where the load priorities may be determined by the following formula:
wherein->Is->Load priority of class load, +.>For the first weight coefficient, +.>Is->Load importance of class load,/->For the second weight coefficient, +.>Is->Load emergency of class load, +.>For the third weight coefficient, +.>Is->The degree of grid impact of the class load.
A construction module 130 is configured to construct a class routing model according to the priorities of the loads, the power supply relationships of the loads in the power grid system, and the power transmission losses between adjacent loads in the power grid system.
The first generation module 140 is configured to generate a black start sequence optimization model according to the plurality of start parameters and the class routing model.
The second generating module 150 is configured to solve the black start sequence optimization model based on a preset optimization algorithm, so as to generate a target black start sequence.
Optionally, the first generating module 140 includes:
the acquisition sub-module is used for acquiring a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities which are in one-to-one correspondence with the plurality of generator sets.
The first generation sub-module is used for generating an objective function according to a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities.
And the first determining submodule is used for determining the path constraint condition according to the plurality of starting parameters and the class routing model.
And the second generation sub-module is used for generating a black start sequence optimization model according to the path constraint condition and the objective function.
Optionally, the first generating sub-module is configured to:
generating a plurality of linear programming functions according to a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities; an objective function is generated from the plurality of linear programming functions.
Optionally, the second generating module 150 includes:
and the third generation sub-module is used for generating a plurality of initial black start sequences according to the black start sequence optimization model.
And the fourth generation sub-module is used for generating an initial population according to a plurality of initial black start sequences, wherein the initial population comprises a plurality of individuals, each individual represents an initial black start sequence, and each individual comprises a plurality of generator sets and black start sequences of the plurality of generator sets.
And the second determining submodule is used for determining a plurality of advantages corresponding to the individuals one by one based on a preset fitness function.
And the third determining submodule is used for determining the black start sequence corresponding to the target individual with the largest superiority from the plurality of superiorities, and the black start sequence is the target black start sequence.
Optionally, the second determining submodule is configured to:
performing iterative transformation on the initial population based on a preset transformation rule, and generating a target population under the condition that the number of iterative transformation reaches a set threshold, wherein the preset transformation rule comprises cross transformation and/or mutation transformation; and determining a plurality of advantages of one-to-one correspondence of the individuals in the target population according to a preset fitness function.
Optionally, the construction module 130 is configured to:
according to the power supply relation, an initial graph model is built, wherein the initial graph model comprises a plurality of graph nodes used for representing a plurality of loads, and the weight of each graph node is the load priority of the corresponding load; generating a plurality of weight values between adjacent graph nodes in an initial graph model according to the plurality of power transmission losses; and constructing a class routing model according to the plurality of weight values and the initial graph model.
Optionally, the determining module 120 is configured to:
acquiring a plurality of importance parameters and a plurality of emergency degree parameters corresponding to a plurality of loads one by one and a plurality of starting influence parameters; and determining a plurality of load priorities corresponding to the loads one by one according to the importance parameters, the emergency degree parameters and the starting influence parameters.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
In another exemplary embodiment, there is also provided an electronic device including: a memory having a computer program stored thereon; and the processor is used for executing the computer program in the memory to realize the steps of the method for adjusting the black start sequence of any power grid in the first aspect of the disclosure.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having means for performing the above-mentioned method when being executed by the programmable apparatus.
With the above-described preferred embodiments according to the present application as a teaching, the related workers can make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the contents of the specification, and must be determined according to the scope of claims.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A method for adjusting a black start sequence of a power grid, the method comprising:
acquiring a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one, wherein the starting parameters comprise starting time, starting power and starting stability;
determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one, wherein the load priorities are determined by the following formula:
wherein the saidIs->Load priority of class load, said +.>For the first weight coefficient, said +.>Is->Load importance of class load, said +.>For the second weight coefficient, said +.>Is->Load tightening of class loadUrgency, said->For the third weight coefficient, said +.>Is->The degree of grid influence of the class load;
constructing a class routing model according to the plurality of load priorities, the power supply relation of the plurality of loads in the power grid system and the plurality of power transmission losses between adjacent loads in the power grid system;
generating a black start sequence optimization model according to the plurality of start parameters and the class routing model;
solving the black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence,
the generating a black start sequence optimization model according to the plurality of start parameters and the class routing model comprises the following steps:
acquiring a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities which are in one-to-one correspondence with the plurality of generator sets;
generating an objective function according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities;
determining a path constraint condition according to the plurality of starting parameters and the class routing model;
generating the black start sequence optimization model according to the path constraint condition and the objective function;
constructing a class routing model according to the power supply relation of a plurality of loads in a power grid system, setting each coincidence as a point in the class routing model, connecting each node in the class routing model based on a plurality of power transmission losses between adjacent loads in the power grid system and the connection relation of each load in the power supply system, and building a weight between two nodes with the connection relation based on each load priority and the power transmission loss, thereby constructing the class routing model; all loads in the power grid are classified into a graph based on the routing model, a weight relation between connection nodes in the graph is constructed according to the load priority and the power transmission loss, and the shortest path traversing all the nodes is found based on the graph and the corresponding relation, so that the optimal black start sequence of the power grid system is determined.
2. The method of claim 1, wherein the generating an objective function based on the plurality of shortest start-up times, the plurality of maximum start-up powers, and the plurality of maximum start-up stabilities comprises:
generating a plurality of linear programming functions according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities;
and generating the objective function according to the plurality of linear programming functions.
3. The method of claim 1, wherein the solving the black start sequence optimization model based on a preset optimization algorithm to generate a target black start sequence comprises:
generating a plurality of initial black start sequences according to the black start sequence optimization model;
generating an initial population according to the initial black start sequences, wherein the initial population comprises a plurality of individuals, each individual represents an initial black start sequence, and each individual comprises a plurality of generator sets and the black start sequences of the plurality of generator sets;
determining a plurality of advantages of the plurality of individuals in one-to-one correspondence based on a preset fitness function;
and determining a black start sequence corresponding to a target individual with the largest superiority from the plurality of superiorities, wherein the black start sequence is the target black start sequence.
4. The method of claim 3, wherein determining a plurality of superiorities for the plurality of individuals one-to-one based on a preset fitness function comprises:
performing iterative transformation on the initial population based on a preset transformation rule, and generating a target population under the condition that the number of times of the iterative transformation reaches a set threshold, wherein the preset transformation rule comprises cross transformation and/or mutation transformation;
and determining the plurality of advantages of the plurality of individuals in the target population in one-to-one correspondence according to the preset fitness function.
5. The method of claim 1, wherein constructing the class routing model based on the plurality of load priorities, a power supply relationship of the plurality of loads in the power grid system, and a plurality of power delivery losses between adjacent loads in the power grid system comprises:
constructing an initial graph model according to the power supply relation, wherein the initial graph model comprises a plurality of graph nodes used for representing the plurality of loads, and the weight of each graph node is the load priority of the corresponding load;
generating a plurality of weight values between adjacent graph nodes in the initial graph model according to the plurality of power transmission losses;
and constructing the class routing model according to the plurality of weight values and the initial graph model.
6. The method of claim 1, wherein determining a plurality of load priorities for a plurality of loads in the grid system one-to-one comprises:
acquiring a plurality of importance parameters, a plurality of emergency degree parameters and a plurality of starting influence parameters which are in one-to-one correspondence with the plurality of loads;
and determining the load priorities corresponding to the loads one by one according to the importance parameters, the emergency degree parameters and the starting influence parameters.
7. An adjustment device for a black start sequence of a power grid, the device comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of starting parameters corresponding to a plurality of generator sets in a power grid system one by one, and the starting parameters comprise starting time, starting power and starting stability;
the determining module is used for determining a plurality of load priorities corresponding to a plurality of loads in the power grid system one by one, wherein the load priorities are determined through the following formula:
wherein the saidIs->Load priority of class load, said +.>For the first weight coefficient, said +.>Is->Load importance of class load, said +.>For the second weight coefficient, said +.>Is->Load emergency degree of class load, said +.>For the third weight coefficient, said +.>Is->The degree of grid influence of the class load;
the construction module is used for constructing a class routing model according to the plurality of load priorities, the power supply relation of the plurality of loads in the power grid system and the plurality of power transmission losses between adjacent loads in the power grid system;
the first generation module is used for generating a black start sequence optimization model according to the plurality of start parameters and the class routing model; comprising the following steps:
acquiring a plurality of shortest starting times, a plurality of maximum starting powers and a plurality of maximum starting stabilities which are in one-to-one correspondence with the plurality of generator sets;
generating an objective function according to the plurality of shortest starting times, the plurality of maximum starting powers and the plurality of maximum starting stabilities;
determining a path constraint condition according to the plurality of starting parameters and the class routing model;
generating the black start sequence optimization model according to the path constraint condition and the objective function;
the second generating module is configured to solve the black start sequence optimization model based on a preset optimization algorithm, so as to generate a target black start sequence, and includes: constructing a class routing model according to the power supply relation of a plurality of loads in a power grid system, setting each coincidence as a point in the class routing model, connecting each node in the class routing model based on a plurality of power transmission losses between adjacent loads in the power grid system and the connection relation of each load in the power supply system, and building a weight between two nodes with the connection relation based on each load priority and the power transmission loss, thereby constructing the class routing model; all loads in the power grid are classified into a graph based on the routing model, a weight relation between connection nodes in the graph is constructed according to the load priority and the power transmission loss, and the shortest path traversing all the nodes is found based on the graph and the corresponding relation, so that the optimal black start sequence of the power grid system is determined.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing said computer program in said memory for implementing the steps of the method for adjusting a grid black start sequence according to any one of claims 1-6.
9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311308458.9A CN117060403B (en) | 2023-10-11 | 2023-10-11 | Method, device, equipment and medium for adjusting black start sequence of power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311308458.9A CN117060403B (en) | 2023-10-11 | 2023-10-11 | Method, device, equipment and medium for adjusting black start sequence of power grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117060403A CN117060403A (en) | 2023-11-14 |
| CN117060403B true CN117060403B (en) | 2024-02-02 |
Family
ID=88655738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311308458.9A Active CN117060403B (en) | 2023-10-11 | 2023-10-11 | Method, device, equipment and medium for adjusting black start sequence of power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117060403B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108281965A (en) * | 2018-02-08 | 2018-07-13 | 国网黑龙江省电力有限公司电力科学研究院 | Black starting-up service restoration method |
| CN108964027A (en) * | 2018-07-02 | 2018-12-07 | 清华大学 | Power method for routing and device based on electric energy router networking |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3471231A1 (en) * | 2017-10-13 | 2019-04-17 | Ørsted Wind Power A/S | A method for black-starting an electrical grid |
-
2023
- 2023-10-11 CN CN202311308458.9A patent/CN117060403B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108281965A (en) * | 2018-02-08 | 2018-07-13 | 国网黑龙江省电力有限公司电力科学研究院 | Black starting-up service restoration method |
| CN108964027A (en) * | 2018-07-02 | 2018-12-07 | 清华大学 | Power method for routing and device based on electric energy router networking |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117060403A (en) | 2023-11-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Farzin et al. | A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids | |
| Das et al. | A computationally efficient optimization approach for battery systems in islanded microgrid | |
| CN110518580A (en) | A kind of active distribution network running optimizatin method for considering microgrid and actively optimizing | |
| Pappala et al. | A variable-dimension optimization approach to unit commitment problem | |
| Zhou et al. | Deep learning-based rolling horizon unit commitment under hybrid uncertainties | |
| Korab et al. | Application of particle swarm optimization for optimal setting of Phase Shifting Transformers to minimize unscheduled active power flows | |
| CN110994598A (en) | Multi-target power grid fault recovery method and device | |
| CN117060403B (en) | Method, device, equipment and medium for adjusting black start sequence of power grid | |
| CN116154791B (en) | Power angle stable control method, system and terminal for cooperative multiple controllable resources | |
| CN109038546B (en) | AC/DC series-parallel power grid load recovery method and system based on VSC-HVDC system | |
| Miranda et al. | PAR/PST location and sizing in power grids with wind power uncertainty | |
| Pourghasem et al. | Reliable economic dispatch of microgrids by exchange market algorithm | |
| Torres et al. | Security constrained transmission expansion planning for smart transmission grids based on the AC network model | |
| Keko et al. | A multiple scenario security constrained reactive power planning tool using EPSO | |
| Mansour et al. | Dynamic economic load dispatch of thermal power system using genetic algorithm | |
| CN110571791B (en) | Optimal configuration method for power transmission network planning under new energy access | |
| Amer et al. | Modified evolutionary particle swarm optimization for AVR-PID tuning | |
| Han et al. | Distributionally robust generation expansion planning model considering res integrations | |
| Nakayama et al. | Complete automation of future grid for optimal real-time distribution of renewables | |
| Tian et al. | Sensitivity guided genetic algorithm for placement of distributed energy resources | |
| Bent et al. | Randomized discrepancy bounded local search for transmission expansion planning | |
| CN114204613A (en) | Reactive compensation method and system for offshore wind farm access power system | |
| Trivedi et al. | A multiobjective evolutionary algorithm based on decomposition for unit commitment problem with significant wind penetration | |
| Alvarez et al. | Ant Colony Optimization Algorithm for the Multiyear Transmission Network Expansion Planning | |
| Gu et al. | Investigation on extended black-start schemes of power system considering reasonable load restoration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |