Hydropower station generator set active power output optimal distribution method based on genetic algorithm

Abstract

The invention relates to the technical field of hydroelectric power generation, in particular to a hydropower station generator set active power output optimal distribution method based on a genetic algorithm, and the method comprises the steps: obtaining basic data of different types of generator sets of a hydropower station, and determining different power generation branch plant start-up combination modes according to a power generation plan; solving the minimum power generation flow of the hydropower station by integrating the operation constraint condition, constructing an active power output optimal distribution mathematical model of the hydropower station generator set according to the operation constraint condition and a power generation flow calculation formula, calculating the fitness of each chromosome in a population, determining population chromosomes, carrying out crossover and mutation operation, and calculating the optimal power generation flow of the hydropower station. And eliminating the chromosomes with low fitness and keeping the chromosomes with high fitness as a new generation of population. According to the method, the active output of each water-turbine generator set is reasonably arranged, and the mutual influence effect among the output, the hair head and the power generation flow of the set is coordinated, so that the overall power generation flow of the hydropower station is minimum, the power generation utilization efficiency of water is improved, and the power generation benefit of water resources is maximized.

Description

Technical Field

[0001] The present application relates to the technical field of hydropower generation, and in particular to a method for optimizing the active output distribution of generator sets in a hydropower station based on a genetic algorithm. Background Art

[0002] With the continuous growth of global energy demand and the accelerated transformation of energy structure, hydropower, as a clean and renewable energy form, plays an increasingly important role in the energy supply system. As the main conversion facility of hydropower energy, the operating efficiency and economy of hydropower stations are directly related to the stability and sustainable development of the entire energy system. A large amount of data at home and abroad show that the implementation of economic operation in hydropower stations can improve economic benefits by 1% to 3%. Therefore, the proper distribution of system load among operating units is of great significance to achieving the optimal operation of hydropower stations to obtain the maximum economic benefits.

[0003] At present, the solutions to the problem of optimal distribution of hydropower unit output mainly include traditional optimization algorithms and intelligent algorithms. The structure of traditional optimization algorithms is relatively simple, but they cannot adapt to the current nonlinear high-dimensional optimization problem. Intelligent algorithms rely on initial solutions and search strategies in the solution process. When there are multiple local optimal solutions in the search space, the algorithm may converge to one of the local optimal solutions too early, thus missing the global optimal solution. At the same time, the performance of intelligent algorithms is often affected by their parameter settings. For example, the settings of parameters such as crossover probability and mutation probability in genetic algorithms, inertia weight and learning factor in particle swarm algorithms will directly affect the algorithm's search efficiency and solution quality. Inappropriate parameter settings may lead to a decline in algorithm performance or even failure to find a satisfactory solution. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a method for optimizing active output allocation of hydropower station generator sets based on genetic algorithm, comprising:

[0006] S1, obtain the basic data of the power generation flow characteristic curve, head loss curve, stable operation curve, and downstream water level flow relationship curve of different types of generating units in the hydropower station.

[0007] S2, determining different startup combinations of power plants according to a given power generation plan of a hydropower station power plant;

[0008] S3, under different power plant startup combinations, taking the power generation plan of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, taking the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the limit of the generator set output by the power grid safety and stability control system as the operation constraint condition, arranging the active output of each hydro-generator set, and performing objective function optimization to solve the minimum power generation flow of the hydropower station;

[0009] S4, randomly assigning the active output of the first N-1 generator sets of each power plant of the hydropower station, the active output of the Nth generator set is determined by the power generation plan of the power plant and the active output of the first N-1 generator sets, and the binary codes corresponding to the active output of the first N-1 generator sets of each power plant are used as the initial population;

[0010] S5, constructing a mathematical model for optimizing the active output allocation of the hydropower station generator set according to the operation constraint conditions and the power generation flow calculation formula in combination with the penalty function, and calculating the fitness of each chromosome in the population;

[0011] S6, performing crossover on the population chromosomes according to a roulette wheel algorithm, and performing a mutation operation on offspring of the crossover chromosomes;

[0012] S7, based on the principle of survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation, and retain chromosomes with high fitness as the new generation population;

[0013] S8, repeat S3-S7 until the group iteration reaches the specified number of times.

[0014] Furthermore, the S2 further includes:

[0015] According to the power generation plan of each power plant of the hydropower station, the rated output of the generator set is taken as the upper limit of the output of a single generator set, and the minimum number of units to be started in the power plant is preliminarily determined using formula (1):

[0016]

[0017] Where: N k，min is the minimum number of units in operation for the kth power plant of a hydropower station; CP k The power generation plan for the power plant; dP k，max is the rated output of the generator set;

[0018] Taking the minimum output corresponding to the minimum water head in the stable operation area as the lower limit of the output of a single generator set, the maximum number of units in operation of the branch is preliminarily determined using formula (2):

[0019]

[0020] Where: N k，maxThe maximum number of units in operation in the power plant; dP k，min B is the minimum output corresponding to the minimum water head in the stable operation area of ​​the unit; k The number of installed capacity of the power plant.

[0021] Furthermore, the S4 further includes:

[0022] The active output of the first (n-1) generator sets is calculated according to σ k Discrete, then each generator has an active output P k，i Total Z k =(dP k，max -dP k，min ) / σ k +1 solution:

[0023] P k，i ∈(dP k，min , dP k，min +σ k ,dP k，min +2σ k ,…,dP k,max ) (3).

[0024] Furthermore, the S4 further includes:

[0025] For each of the first (n-1) generators, select a value less than or equal to Z k The integer I k，i , then the active output of the i-th generator set is:

[0026] P k，i =dP k,min +I k，i ×σ k (4)

[0027] The last generator set has an active output P k，n The difference between the power generation plan of the power plant and the active output of the first (n-1) generator sets is:

[0028]

[0029] If the active output of the last generator set meets the maximum and minimum output constraints, then the generator set sequence (I1, I2, ..., I n-1 ) can be used as the chromosome of the power plant of the hydropower station.

[0030] Furthermore, the S4 further includes:

[0031] The chromosomes of all power plants are combined to form a complete chromosome. The total number of genes contained in each chromosome is:

[0032]

[0033] Where: j k Z k The corresponding binary encoding length.

[0034] Furthermore, the S5 further includes:

[0035] The fitness F is used to measure the degree to which each chromosome in the population finds the optimal solution in the optimization calculation;

[0036] Chromosomes with high fitness have a high probability of being inherited to the next generation, while chromosomes with low fitness have a low probability of being inherited to the next generation;

[0037] In order to find the minimum power generation flow of the hydropower station, the objective function is linearly transformed accordingly, and the fitness function is:

[0038]

[0039] Where: const1 and const2 are constants; c1 is the output penalty factor required for stable operation of the hydropower station generator set; f1(P k，i ) is the slack variable of the stable operation output requirement of the hydropower station generator set; c2 is the output penalty factor required by the power grid security and stability control system; f2(P k，i ) is the required output relaxation variable of the power grid security and stability control system; c3 is the maximum and minimum required output penalty factor of the hydropower station generator set; f3(P k，i ) is the maximum and minimum required output relaxation variable of the hydropower station generator set.

[0040] Furthermore, the S6 further includes:

[0041] Pair the chromosomes generated by the selection operation in pairs and perform gene crossover operation according to the uniform crossover principle:

[0042] Randomly generate a floating point number greater than or equal to 0 and less than 1. If it is less than the crossover probability P c The genes are exchanged, the good genes are passed on to the next generation, and new excellent chromosomes are produced.

[0043] Crossover probability P c The larger the setting, the faster new chromosomes are generated, and the greater the possibility that the genetic pattern will be disrupted, destroying excellent gene combinations;

[0044] Crossover probability P c If the setting is too small, the search process becomes slow;

[0045] Adaptive crossover probability is used during chromosome crossover to increase the crossover probability of chromosomes with low fitness in the population and speed up the convergence;

[0046] For chromosomes with high fitness in the population, the crossover probability is reduced to protect the excellent chromosomes in the population from being destroyed. The crossover probability is set as follows:

[0047]

[0048] Where: P c1 , P c2 is a constant; F′ is the larger fitness value of the two chromosomes selected for crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population.

[0049] If the active output of the generator set corresponding to the next generation chromosome after crossover does not meet the minimum output requirements of less than or equal to the rated output of the generator set and greater than or equal to the minimum water head in the stable operation area, the crossover operation is performed again until the maximum and minimum output requirements are met.

[0050] Furthermore, the S6 further includes:

[0051] In order to prevent the genetic algorithm from falling into a local optimal solution during the optimization process, the chromosome is mutated during the search process, 0 in the binary code is changed to 1, 1 is changed to 0, and a floating point number greater than or equal to 0 and less than 1 is randomly generated. If it is less than the mutation probability, the gene is mutated;

[0052] Adaptive mutation probability is used during chromosome mutation. For offspring chromosomes generated after chromosome crossover, if the fitness is low, the mutation probability is increased to increase population diversity and jump out of the local solution.

[0053] For chromosomes with high fitness in the population, reduce the probability of mutation and retain the excellent chromosome pattern;

[0054] The crossover probability is set as follows:

[0055]

[0056] Where: P m1 , P m2 is a constant; F is the fitness value of the chromosome after crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population;

[0057] If the active output of the generator set corresponding to the chromosome after mutation does not satisfy the minimum output corresponding to the minimum water head in the stable operation area and less than or equal to the rated output of the generator set, the chromosome mutation operation is performed again.

[0058] Furthermore, the S8 further includes:

[0059] Evaluate the fitness of the newly generated offspring chromosomes, and update the population of the parent and offspring chromosomes according to the fitness;

[0060] Determine whether the preset iteration evolution generation is reached. If the preset iteration generation is reached, the operation is terminated, otherwise, the next generation operation is continued.

[0061] Furthermore, the method further comprises:

[0062] The active output corresponding to the chromosome with the highest fitness in the population is the active output of the front-end generator sets of each power plant when the power generation flow of the hydropower station is the minimum, and the active output of the last generator set is the difference between the power generation plan of the plant and the active output of the front-end generator set.

[0063] The present application relates to the field of hydropower generation technology, and in particular to a method for optimizing the active output allocation of generator sets in a hydropower station based on a genetic algorithm, obtaining basic data of different types of generator sets in a hydropower station, determining different startup combinations of power plants according to a power generation plan, solving the minimum power generation flow of the hydropower station under comprehensive operation constraints, and constructing a mathematical model for optimizing the active output allocation of generator sets in a hydropower station according to the operation constraints and the power generation flow calculation formula, calculating the fitness of each chromosome in the population, determining the chromosomes of the population to cross and perform mutation operations, eliminating chromosomes with low fitness and retaining chromosomes with high fitness as a new generation population. The present invention minimizes the overall power generation flow of the hydropower station by reasonably arranging the active output of each hydro-turbine generator set, coordinating the mutual influence between the unit output, gross head, and power generation flow, improving the power generation efficiency of the water volume, and maximizing the benefits of water resource power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A working flow chart of a method for optimizing active output allocation of generating units of a hydropower station based on a genetic algorithm as claimed in an embodiment of the present invention;

[0065] Figure 2 A method for optimizing active power output allocation of generating units of a hydropower station based on a genetic algorithm as claimed in the embodiments of the present invention is provided. Embodiment 1 of the invention is a diagram of the minimum power generation flow of successive populations of a hydropower station;

[0066] Figure 3 Embodiment 1 of a method for optimizing active output allocation of generating units of a hydropower station based on a genetic algorithm as claimed in the embodiments of the present invention is a diagram of an optimal active output allocation method of a hydropower station;

[0067] Figure 4 Embodiment 2 of a method for optimizing active output allocation of generator sets of a hydropower station based on a genetic algorithm as claimed in the embodiments of the present invention is a diagram of the minimum power generation flow of successive populations of a hydropower station;

[0068] Figure 5 Embodiment 2 of a method for optimizing active output distribution of generating units in a hydropower station based on a genetic algorithm as claimed in the embodiments of the present invention is a diagram of the optimal active output distribution method of the generating units in the hydropower station. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] According to a first embodiment of the present invention, the present invention claims a method for optimizing active output allocation of a hydropower station generator set based on a genetic algorithm, comprising:

[0073] S1, obtain the basic data of the power generation flow characteristic curve, head loss curve, stable operation curve, and downstream water level flow relationship curve of different types of generating units in the hydropower station.

[0074] S2, determining different startup combinations of power plants according to a given power generation plan of a hydropower station power plant;

[0075] S3, under different power plant startup combinations, taking the power generation plan of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, taking the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the limit of the generator set output by the power grid safety and stability control system as the operation constraint condition, arranging the active output of each hydro-generator set, and performing objective function optimization to solve the minimum power generation flow of the hydropower station;

[0076] S4, randomly assigning the active output of the first N-1 generator sets of each power plant of the hydropower station, the active output of the Nth generator set is determined by the power generation plan of the power plant and the active output of the first N-1 generator sets, and the binary codes corresponding to the active output of the first N-1 generator sets of each power plant are used as the initial population;

[0077] S5, constructing a mathematical model for optimizing the active output allocation of the hydropower station generator set according to the operation constraint conditions and the power generation flow calculation formula in combination with the penalty function, and calculating the fitness of each chromosome in the population;

[0078] S6, performing crossover on the population chromosomes according to a roulette wheel algorithm, and performing a mutation operation on offspring of the crossover chromosomes;

[0079] S7, based on the principle of survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation, and retain chromosomes with high fitness as the new generation population;

[0080] S8, repeat S3-S7 until the group iteration reaches the specified number of times.

[0081] Among them, in this embodiment, the water level of the hydropower station reservoir, the water level of the lower power station reservoir, and the power generation plan of each branch are used as input conditions, and the number of generator sets that can be started in each branch is preliminarily determined through the lower limit of the stable operation area under the minimum gross head condition of the generator set of each branch, the rated output of the unit, the number of installed units, etc.; under different branch startup combinations, the operating constraints such as the stable operation limit of the generator set output and the limit of the generator set output by the power grid safety and stability control system are comprehensively considered, and the genetic algorithm is used to find the optimal active output of each generator set to minimize the power generation flow of the hydropower station; by comparing the minimum power generation flow under different startup combinations of each branch, the optimal generator set output distribution method of the final hydropower station can be determined.

[0082] Objective function:

[0083]

[0084] Where: Q is the power generation flow of the hydropower station; m is the number of power plants of the hydropower station; n is the total number of operating generator sets in the kth power plant of the hydropower station; Q k，i is the power generation flow of the i-th generator unit in the k-th power plant of the hydropower station; P k，i is the active power output of the i-th generator set in the k-th power plant of the hydropower station; H is the gross water head upstream and downstream of the hydropower station.

[0085] Furthermore, the S2 further includes:

[0086] According to the power generation plan of each power plant of the hydropower station, the rated output of the generator set is taken as the upper limit of the output of a single generator set, and the minimum number of units to be started in the power plant is preliminarily determined using formula (1):

[0087]

[0088] Where: N k，min is the minimum number of units in operation for the kth power plant of a hydropower station; CP k The power generation plan for the power plant; dP k，max is the rated output of the generator set;

[0089] Taking the minimum output corresponding to the minimum water head in the stable operation area as the lower limit of the output of a single generator set, the maximum number of units in operation of the branch is preliminarily determined using formula (2):

[0090]

[0091] Where: N k，max The maximum number of units in operation in the power plant; dP k，min B is the minimum output corresponding to the minimum water head in the stable operation area of ​​the unit; k The number of installed capacity of the power plant.

[0092] In this embodiment, the operating constraints are set as follows:

[0093] (1) Power balance constraints of power plant branches:

[0094]

[0095] Where: CP k It is the power generation plan of the kth power plant of the hydropower station.

[0096] (2) Constraints on stable operation of generator output:

[0097] P k,，i,min ≤P k，i ≤P k，i,max ;

[0098] Where: P k，iThe output of the i-th generator set in the k-th power plant of the hydropower station; P k，i，min is the lower limit of stable operation of the i-th generator set; P k，i，max is the upper limit of stable operation of the i-th generator set.

[0099] P k，i ≥P′ k，i，min ;

[0100] Where: P k，i is the output of the i-th generator set; P′ k，i，min It is the output requirement of the grid safety and stability control system for the i-th generator set.

[0101] (3) The gross head of the unit is the water level difference between the upstream and downstream of the hydropower station. The downstream water level depends on the power generation flow of the hydropower station. If the downstream hydropower station returns water to the tail water of the power station, the water level downstream of the power station will also be affected by the water level support of the downstream hydropower station.

[0102] H = H1 - f(H2, Q);

[0103] Where: H1 is the reservoir water level of the hydropower station; H2 is the reservoir water level of the downstream hydropower station.

[0104] (4) The power generation flow of the hydropower station depends on the active output of the generator set and the gross head. The power generation flow of the power station and the downstream water level affect each other.

[0105] The stable operating range of a hydro-turbine generator set is also affected by the gross water head. Generally speaking, the higher the gross water head, the higher the upper and lower limits of the stable operating range.

[0106] The objective function is solved by using a genetic algorithm, which is a global adaptive search algorithm developed by drawing on the principle of natural selection and natural genetic evolution in the biological world. The basic idea is to represent each possible solution in the problem space as an "individual", and a group of such individual strings is formed into a "population". The population is placed in a specific environment of the problem, and each individual is evaluated according to the predetermined objective function to give the individual fitness. In the process of genetic operation, the fitness size is used as the basis of "natural selection" to determine its survival and elimination. Initially, individuals are randomly generated. Due to the principle of "survival of the fittest" in the evolution process, the offspring individuals are significantly better than the previous generation, which ensures that the population gradually evolves towards a better solution.

[0107] Before solving, the genetic algorithm needs to map the solution space into the genetic algorithm's genotype string (i.e., chromosome) structure data. Different combinations of these string structure data constitute different points. Common mapping methods include binary coding, Gray code coding, floating point coding, parameter cascade coding, multi-parameter cross coding, etc. Among them, binary coding has the characteristics of simple and easy encoding and decoding, and easy crossover and mutation program implementation.

[0108] Furthermore, the S4 further includes:

[0109] The active output of the first (n-1) generator sets is calculated according to σ k Discrete, then each generator has an active output P k，i Total Z k =(dP k，max -dP k，min ) / σ k +1 solution:

[0110] P k，i ∈(dP k，min , dP k,min +σ k , dP k,min +2σ k ,…,dP k,max ) (3).

[0111] Furthermore, the S4 further includes:

[0112] For each of the first (n-1) generators, select a value less than or equal to Z k The integer I k，i , then the active output of the i-th generator set is:

[0113] P k，i =dP k,min +I k，i ×σ k (4)

[0114] The last generator set has an active output P k，n The difference between the power generation plan of the power plant and the active output of the first (n-1) generator sets is:

[0115]

[0116] If the active output of the last generator set meets the maximum and minimum output constraints, then the generator set sequence (I1, I2, ..., I n-1 ) can be used as the chromosome of the power plant of the hydropower station.

[0117] Furthermore, the S4 further includes:

[0118] The chromosomes of all power plants are combined to form a complete chromosome. The total number of genes contained in each chromosome is:

[0119]

[0120] Where: j k Z k The corresponding binary encoding length.

[0121] In this embodiment, the optimization solution is performed using binary coding.

[0122] Taking the kth power plant of a hydropower station as an example, the encoding method is as follows:

[0123] The minimum output corresponding to the minimum water head in the stable operation area and the rated output of the generator set are respectively used as the lower and upper limits of the output of a single generator set. The range of the active output of the generator set of the power plant is:

[0124] dP k，min ≤P k，i ≤dP k,max , i∈(1,2,…,n);

[0125] Where: n is the number of operating units in the kth power plant of the hydropower station.

[0126] Furthermore, the S5 further includes:

[0127] The fitness F is used to measure the degree to which each chromosome in the population finds the optimal solution in the optimization calculation;

[0128] Chromosomes with high fitness have a high probability of being inherited to the next generation, while chromosomes with low fitness have a low probability of being inherited to the next generation;

[0129] In order to find the minimum power generation flow of the hydropower station, the objective function is linearly transformed accordingly, and the fitness function is:

[0130]

[0131] Where: const1 and const2 are constants; c1 is the output penalty factor required for stable operation of the hydropower station generator set; f1(P k，i ) is the slack variable of the stable operation output requirement of the hydropower station generator set; c2 is the output penalty factor required by the power grid security and stability control system; f2(P k，i ) is the required output relaxation variable of the power grid security and stability control system; c3 is the maximum and minimum required output penalty factor of the hydropower station generator set; f3(P k，i ) is the maximum and minimum required output relaxation variable of the hydropower station generator set.

[0132] Furthermore, the S6 further includes:

[0133] Pair the chromosomes generated by the selection operation in pairs and perform gene crossover operation according to the uniform crossover principle:

[0134] Randomly generate a floating point number greater than or equal to 0 and less than 1. If it is less than the crossover probability P cThe genes are exchanged, the good genes are passed on to the next generation, and new excellent chromosomes are produced.

[0135] Crossover probability P c The larger the setting, the faster new chromosomes are generated, and the greater the possibility that the genetic pattern will be disrupted, destroying excellent gene combinations;

[0136] Crossover probability P c If the setting is too small, the search process becomes slow;

[0137] Adaptive crossover probability is used during chromosome crossover to increase the crossover probability of chromosomes with low fitness in the population and speed up the convergence;

[0138] For chromosomes with high fitness in the population, the crossover probability is reduced to protect the excellent chromosomes in the population from being destroyed. The crossover probability is set as follows:

[0139]

[0140] Where: P c1 , P c2 is a constant; F′ is the larger fitness value of the two chromosomes selected for crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population.

[0141] If the active output of the generator set corresponding to the next generation chromosome after crossover does not meet the minimum output requirements of less than or equal to the rated output of the generator set and greater than or equal to the minimum water head in the stable operation area, the crossover operation is performed again until the maximum and minimum output requirements are met.

[0142] Furthermore, the S6 further includes:

[0143] In order to prevent the genetic algorithm from falling into a local optimal solution during the optimization process, the chromosome is mutated during the search process, 0 in the binary code is changed to 1, 1 is changed to 0, and a floating point number greater than or equal to 0 and less than 1 is randomly generated. If it is less than the mutation probability, the gene is mutated;

[0144] Adaptive mutation probability is used during chromosome mutation. For offspring chromosomes generated after chromosome crossover, if the fitness is low, the mutation probability is increased to increase population diversity and jump out of the local solution.

[0145] For chromosomes with high fitness in the population, reduce the probability of mutation and retain the excellent chromosome pattern;

[0146] The crossover probability is set as follows:

[0147]

[0148] Where: P m1 , P m2 is a constant; F is the fitness value of the chromosome after crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population;

[0149] If the active output of the generator set corresponding to the chromosome after mutation does not satisfy the minimum output corresponding to the minimum water head in the stable operation area and less than or equal to the rated output of the generator set, the chromosome mutation operation is performed again.

[0150] Among them, in this embodiment, chromosomes with good performance in the population are selected and chromosomes with poor performance are eliminated. The selected chromosomes participate in the crossover operation and pass their own good performance genes to the offspring chromosomes. Roulette selection is the earliest and most commonly used selection method. It is named because its principle is similar to the roulette wheel on the gambling table. It is a selection method based on the proportion of individual fitness. The higher the fitness, the greater the probability of the chromosome being selected.

[0151] The chromosomes generated by the selection operation are paired and subjected to gene crossover operation according to the uniform crossover principle: a floating point number greater than or equal to 0 and less than 1 is randomly generated. If it is less than the crossover probability P c The genes are exchanged, thus passing the good genes to the next generation and producing new excellent chromosomes.

[0152] Crossover probability P c The larger the setting, the faster the new chromosomes are generated, and the greater the possibility that the genetic pattern will be destroyed, causing some excellent gene combinations to be destroyed; the crossover probability P c If the value is set too small, the search process will be slow.

[0153] Adaptive crossover probability is used during chromosome crossover. For chromosomes with lower fitness in the population, the crossover probability is increased to speed up the convergence; for chromosomes with higher fitness in the population, the crossover probability is appropriately reduced to protect the excellent chromosomes in the population from being easily destroyed.

[0154] If the active output of the generator set corresponding to the next generation chromosome after crossover does not meet the minimum output requirements of less than or equal to the rated output of the generator set and greater than or equal to the minimum water head in the stable operation area, the crossover operation is performed again until the maximum and minimum output requirements are met.

[0155] In order to prevent the genetic algorithm from falling into a local optimal solution during the optimization process, the chromosome needs to be mutated during the search process. Taking binary coding as an example, 0 becomes 1, and 1 becomes 0. A floating point number greater than or equal to 0 and less than 1 is randomly generated. If it is less than the mutation probability, the gene is mutated.

[0156] If the mutation probability is set too small, it is difficult to generate a new chromosome structure and it is easy to fall into the local optimum; if the mutation probability is set too large, the algorithm becomes a pure random search.

[0157] Furthermore, the S8 further includes:

[0158] Evaluate the fitness of the newly generated offspring chromosomes, and update the population of the parent and offspring chromosomes according to the fitness;

[0159] Determine whether the preset iteration evolution generation is reached. If the preset iteration generation is reached, the operation is terminated, otherwise, the next generation operation is continued.

[0160] Furthermore, the method further comprises:

[0161] The active output corresponding to the chromosome with the highest fitness in the population is the active output of the front-end generator sets of each power plant when the power generation flow of the hydropower station is the minimum, and the active output of the last generator set is the difference between the power generation plan of the plant and the active output of the front-end generator set.

[0162] The following is an explanation using a specific embodiment as an example:

[0163] Referring to Example 1, Figure 2 and 3 , taking the actual operation process of the hydropower station on June 17, 2021 as an example.

[0164] The power generation plan of the left bank power plant from 15:30 to 20:15 is 2.9 million kW, and the power generation plan of the right bank power plant from 13:45 to 23:45 is 3.55 million kW. The average upstream water level from 18:00 to 20:00 is 959.07m. The actual average output of the left bank power plant and the right bank power plant is 2.8925 million kW and 3.544 million kW respectively, and the power flow is 5230m 3 / s, and the water consumption rate is 8.12.

[0165] According to the above method, the minimum power generation flow of the hydropower station in two periods is solved.

[0166] The objective function is constructed as follows:

[0167]

[0168] Where: Q is the power generation flow of the hydropower station; Q k，i is the power generation flow of the i-th generator unit in the k-th power plant of the hydropower station; P k，i is the active power output of the i-th generator set in the k-th power plant of the hydropower station; H is the gross water head upstream and downstream of the hydropower station.

[0169] Constraints:

[0170] Power balance constraints of power plant branches:

[0171]

[0172] Where: CP k It is the power generation plan of the kth power plant of the hydropower station.

[0173] Constraints on stable operation of generator output:

[0174] P k，i，min ≤P k，i ≤P k，i，max ;

[0175] Where: P k，i is the output of the i-th generator set; P k，i，min is the lower limit of stable operation of the i-th generator set; P k，i，max is the upper limit of stable operation of the i-th generator set.

[0176] P k，i ≥P′ k，i，min ;

[0177] Where: P k，i is the output of the i-th generator set; P′ k，i，min It is the output requirement of the grid safety and stability control system for the i-th generator set.

[0178] Taking the rated output of the generator set as the upper limit of the output of a single generator set and the minimum output corresponding to the minimum head in the stable operation area as the lower limit of the output of a single generator set, it is preliminarily determined that the minimum and maximum number of units in operation of the Left Bank Power Plant are 4 and 6 respectively.

[0179] The minimum and maximum number of operating units of the right bank power plant are 5 and 6 respectively.

[0180] According to different start-up combinations of branch plants, the objective function is solved by genetic algorithm, taking into account the stable operation limit of generator set output and the limit of generator set output imposed by power grid safety and stability control system.

[0181] When the four generator sets of the left bank power plant and the five generator sets of the right bank power plant are in operation, the minimum power generation flow is 5181.29, and the corresponding water consumption rate is 8.03.

[0182] When the five generator sets of the left bank power plant and the five generator sets of the right bank power plant are in operation, the minimum power generation flow is 5210.26, and the corresponding water consumption rate is 8.08.

[0183] When the 6 generator sets of the left bank power plant and the 5 generator sets of the right bank power plant are in operation, the minimum power generation flow is 5304.01, and the corresponding water consumption rate is 8.22.

[0184] When the four generator sets of the left bank power plant and the six generator sets of the right bank power plant are in operation, the minimum power generation flow is 5209.75, and the corresponding water consumption rate is 8.08.

[0185] When the 5 generator sets of the left bank power plant and the 6 generator sets of the right bank power plant are in operation, the minimum power generation flow is 5234.88, and the corresponding water consumption rate is 8.12.

[0186] When the six generating units of the left bank power plant and the six generating units of the right bank power plant are in operation, the minimum power generation flow is 5331.95, and the corresponding water consumption rate is 8.27.

[0187] In summary, the theoretical minimum water consumption rate of the hydropower station is 8.03, and compared with the actual operating conditions, the amount of water used for power generation is saved by 1.11%.

[0188] Referring to Example 2, Figure 4 and 5 , taking the actual operation process of the hydropower station on August 13, 2023 as an example.

[0189] From 04:00 to 06:00, the power generation plan of the left bank power plant and the right bank power plant was 3.2 million kW. The average water level upstream was 959.31m. The actual average output of the left bank power plant and the right bank power plant was 3.2038 million kW and 3.2055 million kW respectively. The power flow was 5230m 3 / s, and the water consumption rate is 8.16.

[0190] According to the above method, the theoretical minimum power generation flow of the hydropower station during the period of 04:00-06:00 is solved.

[0191] The theoretical minimum current under different startup modes is 5145.16m 3 / s, the water consumption rate is 8.04, which is 1.48% lower than the actual water consumption rate.

[0192] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0193] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0194] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for optimizing active output distribution of hydropower station generator sets based on genetic algorithm, characterized in that: include: S1, obtain basic data of power generation flow characteristic curves, head loss curves, stable operation curves, and downstream water level flow relationship curves of different types of generating units in the hydropower station; S2, determining different startup combinations of power plants according to a given power generation plan of a hydropower station power plant; S3, under different power plant startup combinations, taking the power generation plan of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, taking the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the limit of the generator set output by the power grid safety and stability control system as the operation constraint condition, arranging the active output of each hydro-generator set, and performing objective function optimization to solve the minimum power generation flow of the hydropower station; S4, randomly assigning the active output of the first N-1 generator sets of each power plant of the hydropower station, the active output of the Nth generator set is determined by the power generation plan of the power plant and the active output of the first N-1 generator sets, and the binary codes corresponding to the active output of the first N-1 generator sets of each power plant are used as the initial population; S5, constructing a mathematical model for optimizing the active output allocation of the hydropower station generator set according to the operation constraint conditions and the power generation flow calculation formula in combination with the penalty function, and calculating the fitness of each chromosome in the population; S6, performing crossover on the population chromosomes according to a roulette wheel algorithm, and performing a mutation operation on offspring of the crossover chromosomes; S7, based on the principle of survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation, and retain chromosomes with high fitness as the new generation population; S8, repeat S3-S7 until the group iteration reaches the specified number of times.

2. The method for optimizing active output distribution of hydropower station generator sets based on genetic algorithm according to claim 1 is characterized in that: The S2 further includes: According to the power generation plan of each power plant of the hydropower station, the rated output of the generator set is taken as the upper limit of the output of a single generator set, and the minimum number of units to be started in the power plant is preliminarily determined using formula (1): Where: N k，min is the minimum number of units in operation for the kth power plant of a hydropower station; CP k The power generation plan for the power plant; dP k，max is the rated output of the generator set; Taking the minimum output corresponding to the minimum water head in the stable operation area as the lower limit of the output of a single generator set, the maximum number of units in operation of the branch is preliminarily determined using formula (2): Where: N k，max The maximum number of units in operation in the power plant; dP k，min B is the minimum output corresponding to the minimum water head in the stable operation area of ​​the unit; k The number of installed capacity of the power plant.

3. The method for optimizing active output distribution of hydropower station generator sets based on genetic algorithm according to claim 2 is characterized in that: The S4 further includes: The active output of the first (n-1) generator sets is calculated according to σ k Discrete, then each generator has an active output P k，i Total Z k =(dP k，max -dP k，min ) / σ k +1 solution: P k，i ∈(dP k，min ,dP k，min +σ k ,dP k，min +2σ k ,…,dP k，max ) (3)。 4. The method for optimizing active output distribution of hydropower station generator sets based on genetic algorithm according to claim 3 is characterized in that: The S4 further includes: For each of the first (n-1) generators, select a value less than or equal to Z k The integer I k，i , then the active output of the i-th generator set is: P k，i =dP k，min +I k，i ×σ k (4) The last generator set has an active output P k，n The difference between the power generation plan of the power plant and the active output of the first (n-1) generator sets is: If the active output of the last generator set meets the maximum and minimum output constraints, then the generator set sequence (I1, I2, ..., I n-1 ) can be used as the chromosome of the power plant of the hydropower station.

5. The method for optimizing active output distribution of hydropower station generator sets based on genetic algorithm according to claim 4 is characterized in that: The S4 further includes: The chromosomes of all power plants are combined to form a complete chromosome. The total number of genes contained in each chromosome is: Where: j k Z k The corresponding binary encoding length.

6. The method for optimizing active output distribution of generating units of a hydropower station based on genetic algorithm according to claim 5 is characterized in that: The S5 further includes: The fitness F is used to measure the degree to which each chromosome in the population finds the optimal solution in the optimization calculation; Chromosomes with high fitness have a high probability of being inherited to the next generation, while chromosomes with low fitness have a low probability of being inherited to the next generation. In order to find the minimum power generation flow of the hydropower station, the objective function is linearly transformed accordingly, and the fitness function is: Where: const1 and const2 are constants; c1 is the output penalty factor required for stable operation of the hydropower station generator set; f1(P k，i ) is the slack variable of the stable operation output requirement of the hydropower station generator set; c2 is the output penalty factor required by the power grid security and stability control system; f2(P k，i ) is the required output relaxation variable of the power grid security and stability control system; c3 is the maximum and minimum required output penalty factor of the hydropower station generator set; f3(P k，i ) is the maximum and minimum required output relaxation variable of the hydropower station generator set.

7. The method for optimizing active output distribution of generating units of a hydropower station based on genetic algorithm according to claim 6 is characterized in that: The S6 further includes: Pair the chromosomes generated by the selection operation in pairs and perform gene crossover operation according to the uniform crossover principle: Randomly generate a floating point number greater than or equal to 0 and less than 1. If it is less than the crossover probability P c The genes are exchanged, the good genes are passed on to the next generation, and new excellent chromosomes are produced. Crossover probability P c The larger the setting, the faster new chromosomes are generated, and the greater the possibility that the genetic pattern will be disrupted, destroying excellent gene combinations; Crossover probability P c If the setting is too small, the search process becomes slow; Adaptive crossover probability is used during chromosome crossover to increase the crossover probability of chromosomes with low fitness in the population and speed up the convergence; For chromosomes with high fitness in the population, the crossover probability is reduced to protect the excellent chromosomes in the population from being destroyed. The crossover probability is set as follows: Where: P c1 , P c2 is a constant; F′ is the larger fitness value of the two chromosomes selected for crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population. If the active output of the generator set corresponding to the next generation chromosome after crossover does not meet the minimum output requirements of less than or equal to the rated output of the generator set and greater than or equal to the minimum water head in the stable operation area, the crossover operation is performed again until the maximum and minimum output requirements are met.

8. The method for optimizing active output distribution of generating units of a hydropower station based on genetic algorithm according to claim 7 is characterized in that: The S6 further includes: In order to prevent the genetic algorithm from falling into a local optimal solution during the optimization process, the chromosome is mutated during the search process, 0 in the binary code is changed to 1, 1 is changed to 0, and a floating point number greater than or equal to 0 and less than 1 is randomly generated. If it is less than the mutation probability, the gene is mutated; Adaptive mutation probability is used during chromosome mutation. For offspring chromosomes generated after chromosome crossover, if the fitness is low, the mutation probability is increased to increase population diversity and jump out of the local solution. For chromosomes with high fitness in the population, reduce the probability of mutation and retain the excellent chromosome pattern; The crossover probability is set as follows: Where: P m1 , P m2 is a constant; F is the fitness value of the chromosome after crossover; F avg is the average fitness of all chromosomes in the population; F max is the maximum fitness of all chromosomes in the population; If the active output of the generator set corresponding to the chromosome after mutation does not satisfy the minimum output corresponding to the minimum water head in the stable operation area and less than or equal to the rated output of the generator set, the chromosome mutation operation is performed again.

9. The method for optimizing active output distribution of generating units of a hydropower station based on genetic algorithm according to claim 1, characterized in that: The S8 further includes: Evaluate the fitness of the newly generated offspring chromosomes, and update the population of the parent and offspring chromosomes according to the fitness; Determine whether the preset iteration evolution generation is reached. If the preset iteration generation is reached, the operation is terminated, otherwise, the next generation operation is continued.

10. The method for optimizing active output distribution of generating units of a hydropower station based on genetic algorithm according to claim 1, characterized in that: Also includes: The active output corresponding to the chromosome with the highest fitness in the population is the active output of the front-end generator sets of each power plant when the power generation flow of the hydropower station is the minimum, and the active output of the last generator set is the difference between the power generation plan of the plant and the active output of the front-end generator set.

Images