CN116388291A - Large power grid new energy consumption capability calculation method, system, device and medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a medium for calculating new energy consumption capacity of a large power grid, which comprise the following steps: and determining decision variables, objective functions and constraint conditions according to the power grid parameters. Sending all power grid parameters into a model for improving an NSGA-III algorithm, setting algorithm parameters, and solving the future state new energy absorbing capacity of the large power grid to obtain a Pareto optimal solution set; and calculating the subjective and objective weight coefficients of the indexes by using an analytic hierarchy process and a variation coefficient process, obtaining a final comprehensive weight coefficient by using a combined weighting, evaluating a Pareto optimal solution set by using a gray correlation ideal solution, and optimizing an optimal new energy consumption capacity scheduling scheme. The invention has the advantages that: the result can more truly reflect the running state of the power system, the efficiency of obtaining the result is higher, the result is more accurate, and the rationality and scientificity of the comprehensive evaluation of the power system are improved.
Description
Technical Field
The invention relates to the technical field of power system dispatching, in particular to a method, a system, a device and a medium for calculating new energy consumption capacity of a large power grid by adopting an improved NSGA-III algorithm.
Background
The current energy consumption structure is faced with the problem of resource exhaustion, the climate and environment problems are also increasingly aggravated, and the transformation of the energy structure is imperative. The energy supply and demand situation conversion and the pressure of new energy consumption are faced, the high-quality, economical, reliable and environment-friendly supply of the electric power demand and the electric energy is realized by clean energy, the down-regulating space of the unit in the area is mastered timely and accurately, the fine management of the spare capacity of the power grid is enhanced, and the method has profound significance for quantitatively evaluating the energy consumption capability of the power grid.
However, with the addition of the extra-high voltage direct current line and the increase of the new energy duty ratio, the power grid faces huge consumption pressure, and the current intelligent power grid optimization scheduling control system (D5000 system) real-time balance capacity monitoring function can only simply evaluate the energy consumption capacity, particularly when the power consumption load and the new energy output fluctuate greatly, the balance capacity of the whole power system can be influenced, so that the power grid consumption capacity is accurately evaluated, an optimal scheduling scheme is given, a spare capacity optimization adjustment strategy is provided, and the method has important significance in improving the power system scheduling automation level and stability.
The currently common calculation method for new energy consumption capacity of the electric power system mainly comprises a single-target optimization algorithm and a multi-target optimization algorithm. For single-objective optimization, the selection of the optimization objective can be considered from various angles, and the power system accessed by the high-proportion new energy is considered to have the maximum consumption of the new energy: with the access of wind power generation, photovoltaic power generation, electric vehicle charging and replacing facilities, distributed energy storage and other multi-type power supplies to a power system, the operation form and the functional form of the power system are changed greatly, the safety operation risk of a power grid is increased continuously, and the operation requirement cannot be met completely only by considering a single-objective optimal scheduling model.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method, a system, a device and a medium for calculating new energy consumption capability of a large power grid, which adopt a comprehensive evaluation method of a power system to analyze the obtained scheduling scheme set and select an optimal scheduling scheme.
In order to achieve the above object, the present invention adopts the following technical scheme:
a calculation method for new energy consumption capacity of a large power grid comprises the following steps:
s1: and determining an objective function and constraint conditions according to the power grid parameters. The grid parameters include: the current power of the section, the stability limit, the real-time output of a unit in each zone, the sensitivity information, the positive and negative standby of the power grid of each zone, the power receiving plan of a direct current connecting line, the power receiving plan of an alternating current connecting line, the prediction data of the new energy power generation power and the like.
S2: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
s3: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the two weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
Preferably, the determining of the objective function in step S1 is specifically:
s11: calculating the maximum value of the new energy consumption power, wherein the maximum value is represented by the following formula:
wherein NRC q Representing the negative spare capacity of zone q, ΔP q,i The power value of the tile q is consumed by the tile i, and n represents the number of the trimming tiles.
The subarea spare capacity is the sum of spare capacities of all thermal power, hydroelectric power and pumping and accumulating units in the area; the positive and negative spare capacities of the tiles are calculated as follows:
PRC＝P r -P us -P (2)
NRC＝P-P ls -P t (3)
wherein PRC represents a positive spare capacity; p (P) r Indicating rated capacity; p (P) us Represents an upward blocking capacity; p represents real-time output; p (P) ls Represents a downward blocked capacity; p (P) t Representing the lowest technical output.
S12, the receiving end power grid is expected to be maximum in the upper margin and the lower margin of each important power transmission (transformation) section after the adoption of the cross-region, the province, the inter-slice region and the slice region, and the function expression is as follows:
wherein P is G,up 、P G,down Respectively show that in a prescribed positive directionIn the direction, the upper limit and the lower limit of the stable limit of the section G are recorded as the section power value after each iteration adjustment of the algorithm
The calculation formula is as follows:
wherein S is q-G The sensitivity coefficient of the selected power plant to the section G in the slice area q is represented; ΔP q Representing the power supply quantity of the patch q to other patches or the total power consumption quantity of other patches; ΔL i Representing the power adjustment quantity of the absorption slice area, namely the absorption electric quantity value; n represents the number of adjustment of the absorption slice.
S13, calculating the minimum number of the participated tab areas in the scheduling scheme, wherein the formula is as follows:
min f 3 ＝nonzero(ΔP) (6)
the S14 constraint is as follows:
wherein: ΔP q,i Representing the power value of the patch i to absorb the patch q; i is a fragment set with digestion capability; s is S q-G The sensitivity coefficient of the selected power plant to the section G in the slice area q is represented; s is S i-G The sensitivity coefficient of the absorption slice region i to the section G is represented; p (P) G Representing the section G real-time power; p (P) G,up 、P G,down The upper and lower limits of the steady limit of the cross section G are shown in a predetermined positive direction, respectively.
Preferably, the model modeling method for improving the NSGA-III algorithm in the step S2 is as follows:
s201: reading power grid parameters, including: the current power of the section and the real-time output and sensitivity information of the unit in each slice area are stabilized;
s202: setting algorithm parameters; generating or giving H reference points;
the reference points may be set according to the decision maker's preferences, or may be generated using a structured approach. Initializing a reference point by a boundary crossing construction weighting method. On a normalized plane, for M optimization targets with the equal score of p, calculating the number H of the reference points through a formula (8):
s203: initializing an initial parent population P by decision variable upper and lower limits k And calculating a fitness value;
s204: crossover and mutation operations to generate a new offspring population Q k ；
S205: merging Q k And P k Obtaining a new recombinant population R k ,R k ＝Q k UP k At this time, the population scale is 2N, and the fitness value is obtained;
s206: r is R k Performing rapid non-dominant sorting to obtain different non-dominant layers F 1 、F 2 、…、F i …, wherein F i Is a collection for holding individuals of layer i;
s207: creating an empty set S k For storing the next generation population, the individuals are put into S from the lower layer number of the non-dominant layer k Until S k The number of the medium elements is larger than or equal to N (population scale); if the last added non-dominant layer is F i This layer is also called a critical layer, and then step S208 is performed;
s208: carrying out normalization treatment on individuals in front of the critical layer;
s209: finding out a reference point corresponding to each individual, and calculating the niche number;
s210: from critical layer F i Selecting an appropriate number of individuals to enter S k In such a way that S k The number of the medium individuals is exactly N;
s211: the iteration number increases, k=k+1;
s212: judging whether the k meets the standard, if so, outputting a result; otherwise, the process proceeds to step S205.
Preferably, the specific steps of step S3 are as follows:
s301: determining index weights
Respectively constructing an n-order judgment matrix between optimization targets of each model, and then solving a feature vector and a maximum feature root lambda max The feature vector is the subjective weight coefficient corresponding to the optimization target, and consistency test is carried out according to the formula (10), namely consistency ratio judgment C R With a size relationship of 0.1.
Wherein CI represents a consistency index; RI represents an average uniformity random index and is found in table 1.
TABLE 1RI values
S302: constructing an evaluation matrix
The scheduling model with n indexes works out m schemes in a scheme set, and then an evaluation matrix X= (X) is established through calculation result data ij ) m*n (i=1, 2,3,) m, j=1, 2,3,) n, each index in the evaluation matrix X is calculated by formulas (11), (12) according to the meaning of the evaluation index to obtain a normalized evaluation matrix y= (Y ij ) m*n 。
If the index value is smaller, the better, using a formula (10); if the index value is larger, the formula (11) is used.
S303: constructing a weighted standardized evaluation matrix, calculating an optimal ideal solution and a worst ideal solution according to a formula (12), and obtaining a planned evaluation matrix Y= (Y) in the step (2) ij ) m*n And comprehensive index weight w= { W 1 ,w 2 ,...,w m Multiplying to obtain a weighted normalized matrix z= (Z) ij ) m*n Determining optimal ideal solutions among all evaluation indexes in a multi-objective optimal scheduling scheme of the power system according to a formula (13)
Worst ideal solution +.>
Is a set of (3).
Z＝(z ij ) m*n ＝(y ij w j ) m*n (12)
S304: calculating gray correlation between the evaluation object and two optimal solutions using equation (14) solution
Where ρ represents the resolution factor and ρ ε [0,1].
Then, the gray correlation obtained by the formula (15) is normalized.
S305: calculating Euclidean distance;
the Euclidean distance is calculated from equation (16), i.e. eachDistance of scheduling scheme to optimal and worst ideal solutions
Then, the obtained euclidean distance is normalized using equation (17).
S306: constructing an improved gray-Euclidean distance;
based on the principle of GRA-TOPSIS method
When both are larger, the closer the target is to the optimal ideal solution, < +.>
The larger the target is, the closer the target is to the worst ideal solution. And respectively calculating the closeness degree between the target and the two optimal solutions through the formula (18).
Where α and β are decision maker preference coefficients, and α+β=1, α and β e 0,1, if there is no subjective preference for both methods, 0.5 is taken.
S307: constructing relative closeness;
the relative closeness value phi of each scheduling scheme is calculated by the formula (19) to form a decision basis, namely, the larger phi is the better the scheme is.
The invention also provides a large power grid new energy consumption capability calculation system which can be used for implementing the large power grid new energy consumption capability calculation method, and specifically comprises the following steps: the system comprises a power grid parameter reading module, a decision variable determining module, a calculating module and an optimal scheme comprehensive evaluating module;
the power grid parameter reading module: reading power grid parameters, including: the current power of the section, the steady quota, the real-time output of the unit in each section, the sensitivity information, the positive and negative standby of the power grid in each section, the power receiving plan of the direct current connecting line, the power receiving plan of the alternating current connecting line and the prediction data of the new energy power generation power,
decision variable determining module: and determining decision variables, objective functions and constraint conditions according to the power grid parameters.
The calculation module: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
and the optimal scheme comprehensive evaluation module is used for: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the subjective and objective weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
The invention also discloses a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for calculating the new energy consumption capacity of the large power grid when executing the program.
The invention also discloses a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the method for calculating the new energy consumption capacity of the large power grid.
Compared with the prior art, the invention has the advantages that:
and selecting an optimal scheduling method by using a comprehensive evaluation method of optimal scheduling of the power system. In order to reduce the interference of subjective factors, the weight coefficient is divided into a main part and an objective part, and a subjective weight coefficient of different indexes is obtained by using an Analytic Hierarchy Process (AHP) to be combined with a gray correlation ideal solution (GRA-TOPSIS) to evaluate a model, so that the result can more truly reflect the running state of the electric power system, the efficiency of obtaining the result is higher, the result is more accurate, and the rationality and scientificity of the comprehensive evaluation of the electric power system are improved.
Drawings
FIG. 1 is a schematic diagram of a large power grid new energy consumption capacity flow chart according to an embodiment of the invention;
FIG. 2 is a Pareto three-dimensional scatter diagram of a new energy consumption model according to an embodiment of the present invention;
FIG. 3 is a graph showing maximum absorption versus minimum section margin for an embodiment of the present invention;
fig. 4 shows the adjustment amounts of each segment of the new energy consumption model according to the embodiment of the invention.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
The invention provides a power distribution network fault recovery method considering wind-light output uncertainty, and a specific flow is shown in figure 1.
The specific application steps are as follows:
the new energy is greatly increased in areas other than the existing province network A, a large amount of new energy surplus occurs, the conventional unit output of each area in the province network A is required to be adjusted to consume the surplus new energy, under the condition that the power consumption of each area in the province network A is orderly not adopted and the starting and stopping modes of the unit are unchanged, the new energy consumption capability of the province network is estimated according to the standby condition (of units such as water power, thermal power and pumping and accumulating) of the current load level, the new energy power prediction information and the load prediction information, the maximum new energy consumption capability of the province network is estimated under the condition that the safe and stable operation of the province network A is ensured, in order to reduce the complexity of a model, the new energy to be consumed by each area is firstly allocated according to a fixed proportion (the proportion adopts the new energy power ratio at the current moment of each area), and then the optimal scheduling of the area is carried out.
Step one: and reading power grid parameters, current power and stability quota, real-time output and sensitivity information of units in each zone, positive and negative standby of the power grid of each zone, a direct current tie line power receiving plan, a power receiving plan of an alternating current tie line, new energy power generation power prediction data and the like, and setting algorithm parameters.
Step two: and (3) constructing a multi-objective optimal scheduling model for new energy consumption capacity of the large power grid by adopting an improved NSGA-III algorithm, and outputting a Patero optimal solution set.
Step three: and (3) evaluating the Patero optimal solution set in the second step by adopting a gray correlation ideal solution (GRA-TOPSIS) and screening an optimal scheduling scheme.
Table 1-1 is a new energy scheme that a certain provincial network can also want to eliminate by adjusting the output of other segment units when the current load increases in equal proportion to the limit. The data in the table can know that the new energy source is at most 500.37MW and the new energy source is at least 375.75MW; namely, the 3026.00MW new energy which is absorbed by the falling point area through the negative standby is added, and the Hubei province network can absorb 3526.37MW at most and 3401.75MW power at least by making more passes through the direct current circuit; the minimum section margin of the 4 important concerned sections is 27.40MW at the maximum and 0.03MW at the minimum; the number of participating tab areas is at most 9 and at least 2.
TABLE 1-1 New energy consumption scenario optimization scheduling scheme non-inferior solution
From the data in Table 1-1, a Pareto three-dimensional scatter diagram of the maximum consumption, the minimum section margin and the slice adjustment number of the new energy consumption scene optimization scheduling can be drawn as shown in FIG. 2, wherein f 1 、f 2 、f 3 Respectively representing the maximum consumption, the minimum section margin and the slice adjustment quantity.
To analyze the relationship between the targets, pareto two-dimensional scattergrams are drawn using the maximum consumption data and the section margin data. The number change interval of the participated adjustment sheet area is too small, so that the change interval is not considered independently, and the correlation between the maximum consumption and the section margin is shown in fig. 3.
As can be clearly seen from fig. 3, the minimum section margin and the maximum new energy consumption are inversely related, and as the new energy consumption increases, the relevant slice adjustment increases, the section margin of the focused section is inevitably reduced, the section tide distribution is uneven, the section with the highest participation degree is in a heavy load state for a long time, and the section out-of-limit hidden trouble exists. The best scheduling scheme needs to be selected among a plurality of schemes in consideration of various factors.
Tables 1-2 are comprehensive evaluation results of the multi-objective new energy consumption scene based on the GRA-TOPSIS method, and the obtained results are ranked, so that the scheme 14 with the largest relative closeness in 91 optimized scheduling schemes can be known. Therefore, from the standpoint of comprehensive evaluation, it can be known that the scheme 14 is the optimal scheduling scheme for the new energy consumption scenario.
TABLE 1-2 New energy consumption scheme comprehensive evaluation related parameter values
The evaluation results of the more reasonable power system optimization scheduling scheme obtained by the new energy consumption scheme comprehensive evaluation model constructed by the GRA-TOPISIS method are shown in tables 1-3.
Tables 1-3 Power System optimal scheduling scheme selection results
When the load is increased in equal proportion and reaches the load limit, new energy is transmitted to a certain power saving network through a certain extra-high voltage line, and the drop point areas transmit the new energy which cannot be absorbed by the system to 2 areas through four focused sections, wherein the minimum section margin of the 4 sections is 27.37MW; that is, adding 3026.00MW new energy which is consumed by the falling point area in the target provincial network through negative standby, the target provincial network can totally consume 3401.75MW of new energy through adjusting the output of 3 area units and eliminating the power of the new energy through the line, and the adjustment amount of each area is shown in fig. 4, wherein the adjustment amount of the area 3 is too small, and is ignored in actual scheduling.
In still another embodiment of the present invention, a system for calculating new energy consumption capability of a large power grid is provided, where the system can be used to implement the method for calculating new energy consumption capability of a large power grid, and specifically includes: the system comprises a power grid parameter reading module, a decision variable determining module, a calculating module and an optimal scheme comprehensive evaluating module;
the power grid parameter reading module: reading power grid parameters, including: the current power of the section, the steady quota, the real-time output of the unit in each section, the sensitivity information, the positive and negative standby of the power grid in each section, the power receiving plan of the direct current connecting line, the power receiving plan of the alternating current connecting line and the prediction data of the new energy power generation power,
decision variable determining module: and determining decision variables, objective functions and constraint conditions according to the power grid parameters.
The calculation module: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
and the optimal scheme comprehensive evaluation module is used for: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the subjective and objective weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
In yet another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the large power grid new energy consumption capability calculation method, and comprises the following steps:
s1: and determining decision variables, objective functions and constraint conditions according to the power grid parameters. The grid parameters include: the current power of the section, the stability limit, the real-time output of a unit in each zone, the sensitivity information, the positive and negative standby of the power grid of each zone, the power receiving plan of a direct current connecting line, the power receiving plan of an alternating current connecting line, the prediction data of the new energy power generation power and the like.
S2: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
s3: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the two weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for calculating new energy consumption capacity of a large power grid in the above embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:
s1: and determining decision variables, objective functions and constraint conditions according to the power grid parameters. The grid parameters include: the current power of the section, the stability limit, the real-time output of a unit in each zone, the sensitivity information, the positive and negative standby of the power grid of each zone, the power receiving plan of a direct current connecting line, the power receiving plan of an alternating current connecting line, the prediction data of the new energy power generation power and the like.
S2: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
s3: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the two weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (7)
1. The method for calculating the new energy consumption capacity of the large power grid is characterized by comprising the following steps of:
s1: determining decision variables, objective functions and constraint conditions according to the power grid parameters; the grid parameters include: the current power of the section, the stability limit, the real-time output of a unit in each zone, the sensitivity information, the positive and negative standby of a power grid of each zone, the power receiving plan of a direct current connecting line, the power receiving plan of an alternating current connecting line and the prediction data of the new energy power generation power;
s2: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
s3: and calculating the subjective and objective weight coefficients of the indexes by using an analytic hierarchy process and a variation coefficient process, reasonably distributing the two weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution, and optimizing an optimal new energy consumption capacity scheduling scheme.
2. The method for calculating new energy consumption capacity of a large power grid according to claim 1, wherein the method comprises the following steps: the determination of the objective function in step S1 is specifically:
s11: calculating the maximum value of the new energy consumption power, wherein the maximum value is represented by the following formula:
wherein NRC q Representing the negative spare capacity of zone q, ΔP q,i The power value of the patch area q is absorbed by the patch area i, and n represents the number of the patch areas;
the subarea spare capacity is the sum of spare capacities of all thermal power, hydroelectric power and pumping and accumulating units in the area; the positive and negative spare capacities of the tiles are calculated as follows:
PRC＝P r -P us -P (2)
NRC＝P-P ls -P t (3)
wherein PRC represents a positive spare capacity; p (P) r Indicating rated capacity; p (P) us Represents an upward blocking capacity; p represents real-time output; p (P) ls Represents a downward blocked capacity; p (P) t Representing a minimum technical output;
s12, the receiving end power grid is expected to be maximum in the upper margin and the lower margin of each important power transmission (transformation) section after the adoption of the cross-region, the province, the inter-slice region and the slice region, and the function expression is as follows:
wherein P is G,up 、P G,down Respectively representing the upper limit and the lower limit of the stable limit of the section G under the prescribed positive direction, and recording the section power value after each iteration adjustment of the algorithm as
The calculation formula is as follows:
wherein S is q-G The sensitivity coefficient of the selected power plant to the section G in the slice area q is represented; ΔP q Representing the power supply quantity of the patch q to other patches or the total power consumption quantity of other patches; ΔL i Representing the power adjustment quantity of the absorption slice area, namely the absorption electric quantity value; n represents the adjustment quantity of the absorption slice area;
s13, calculating the minimum number of the participated tab areas in the scheduling scheme, wherein the formula is as follows:
min f 3 ＝nonzero(ΔP) (6)
the S14 constraint is as follows:
wherein: ΔP q,i Representing the power value of the patch i to absorb the patch q; i is a fragment set with digestion capability; s is S q-G The sensitivity coefficient of the selected power plant to the section G in the slice area q is represented; s is S i-G The sensitivity coefficient of the absorption slice region i to the section G is represented; p (P) G Representing the section G real-time power; p (P) G,up 、P G,down The upper and lower limits of the steady limit of the cross section G are shown in a predetermined positive direction, respectively.
3. The method for calculating new energy consumption capacity of a large power grid according to claim 1, wherein the method comprises the following steps: the model modeling method for improving the NSGA-III algorithm in the step S2 is as follows:
s201: reading power grid parameters, including: the current power of the section and the real-time output and sensitivity information of the unit in each slice area are stabilized;
s202: setting algorithm parameters; generating or giving H reference points;
initializing a reference point by a boundary crossing construction weight method; on a normalized plane, for M optimization targets with the equal score of p, calculating the number H of the reference points through a formula (8):
s203: initializing an initial parent population P by decision variable upper and lower limits k And calculating a fitness value;
s204: crossover and mutation operations to generate a new offspring population Q k ；
S205: merging Q k And P k Obtaining a new recombinant population R k ,R k ＝Q k UP k At this time, the population scale is 2N, and the fitness value is obtained;
s206: r is R k Performing rapid non-dominant sorting to obtain different non-dominant layers F 1 、F 2 、…、F i …, wherein F i Is a collection for holding individuals of layer i;
s207: creating an empty set S k For storing the next generation population, the individuals are put into S from the lower layer number of the non-dominant layer k Until S k The number of the medium elements is larger than or equal to N (population scale); if the last added non-dominant layer is F i This layer is also called a critical layer, and then step S208 is performed;
s208: carrying out normalization treatment on individuals in front of the critical layer;
s209: finding out a reference point corresponding to each individual, and calculating the niche number;
s210: from critical layer F i Selecting an appropriate number of individuals to enter S k In such a way that S k The number of the medium individuals is exactly N;
s211: the iteration number increases, k=k+1;
s212: judging whether the k meets the standard, if so, outputting a result; otherwise, the process proceeds to step S205.
4. The method for calculating new energy consumption capacity of a large power grid according to claim 1, wherein the method comprises the following steps: the specific steps of step S3 are as follows:
s301: determining index weights
Respectively constructing an n-order judgment matrix between optimization targets of each model, and then solving a feature vector and a maximum feature root lambda max The feature vector is the subjective weight coefficient corresponding to the optimization target, and consistency test is carried out according to the formula (10), namely consistency ratio judgment C R A size relationship with 0.1;
wherein CI represents a consistency index; RI represents an average uniformity random index, as found in table 1;
table 1RI values
S302: constructing an evaluation matrix
The scheduling model with n indexes works out m schemes in a scheme set, and then an evaluation matrix X= (X) is established through calculation result data ij ) m*n (i=1, 2,3,) m, j=1, 2,3,) n, each index in the evaluation matrix X is calculated by formulas (11), (12) according to the meaning of the evaluation index to obtain a normalized evaluation matrix y= (Y ij ) m*n ；
If the index value is smaller, the better, using a formula (10); if the index value is larger, the better, using a formula (11);
s303: constructing a weighted standardized evaluation matrix, calculating an optimal ideal solution and a worst ideal solution according to a formula (12), and obtaining a planned evaluation matrix Y= (Y) in the step (2) ij ) m*n And comprehensive index weight w= { W 1 ,w 2 ,...,w m Multiplying to obtain a weighted normalized matrix z= (Z) ij ) m*n Determining optimal ideal solutions among all evaluation indexes in a multi-objective optimal scheduling scheme of the power system according to a formula (13)
Worst ideal solution +.>
Is a collection of (3);
Z＝(z ij ) m*n ＝(y ij w j ) m*n (12)
s304: calculating gray correlation between the evaluation object and two optimal solutions using equation (14) solution
Wherein ρ represents a resolution coefficient, and ρ ε [0,1];
then, normalizing the obtained gray correlation degree by using a formula (15);
s305: calculating Euclidean distance;
the Euclidean distance is calculated by equation (16), i.e. the distance from each scheduling scheme to the optimal and worst ideal solutions
Then, normalization processing is performed on the obtained Euclidean distance by using the formula (17);
s306: constructing an improved gray-Euclidean distance;
based on the principle of GRA-TOPSIS method
When both are larger, the closer the target is to the optimal ideal solution, < +.>
When the two solutions are bigger, the closer the target is to the worst ideal solution; calculating the target and two optimal solutions by equation (18) respectivelyThe degree of closeness between the two;
wherein, alpha and beta are decision maker preference coefficients, alpha+beta=1, alpha and beta are epsilon [0,1], if no subjective preference exists for both methods, 0.5 is taken;
s307: constructing relative closeness;
calculating the relative closeness value phi of each scheduling scheme by a formula (19) to form a decision basis, wherein the larger phi is the better the scheme is;
5. a large power grid new energy consumption capability computing system is characterized in that: the system can be used for implementing the large power grid new energy consumption capability calculation method according to one of claims 1 to 4, and specifically comprises the following steps: the system comprises a power grid parameter reading module, a decision variable determining module, a calculating module and an optimal scheme comprehensive evaluating module;
the power grid parameter reading module: reading power grid parameters, including: the current power of the section, the steady quota, the real-time output of the unit in each section, the sensitivity information, the positive and negative standby of the power grid in each section, the power receiving plan of the direct current connecting line, the power receiving plan of the alternating current connecting line and the prediction data of the new energy power generation power,
decision variable determining module: determining decision variables, objective functions and constraint conditions according to the power grid parameters;
the calculation module: according to the objective function and the constraint condition, sending each power grid parameter into a model for improving an NSGA-III algorithm, setting algorithm parameters, solving the future state new energy absorbing capacity of the large power grid, and outputting a Pareto optimal solution set;
and the optimal scheme comprehensive evaluation module is used for: and calculating the subjective and objective weight coefficients of the indexes by using an Analytic Hierarchy Process (AHP) and a coefficient of variation method, reasonably distributing the subjective and objective weight coefficients by using a combined weighting to obtain a final comprehensive weight coefficient, evaluating Pareto optimal solution sets of a plurality of new energy consumption scenes obtained by solving an improved NSGA-III algorithm by using a gray correlation ideal solution (GRA-TOPSIS), and optimizing an optimal new energy consumption capacity scheduling scheme.
6. A computer device, characterized by: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the method for calculating new energy consumption capacity of a large power grid according to one of claims 1 to 4 when said program is executed.
7. A computer-readable storage medium, characterized by: a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the large grid new energy consumption capability calculation method of one of claims 1 to 4.
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