A genetic algorithm-based method for optimizing active power output distribution of hydropower station generator sets

Abstract

The present application relates to the field of hydropower generation technology, and in particular to a method for optimizing the active power output allocation of hydropower station generator sets based on a genetic algorithm. The method obtains basic data of different types of generator sets at a hydropower station, determines different startup combinations of power plants according to the power generation plan, solves the minimum power generation flow of the hydropower station based on comprehensive operating constraints, and constructs a mathematical model for optimizing the active power output allocation of hydropower station generator sets based on the operating constraints and the power generation flow calculation formula. The method calculates the fitness of each chromosome in the population, determines the chromosomes in the population to cross and mutate, eliminates chromosomes with low fitness, and retains chromosomes with high fitness as the new generation population. The present invention minimizes the overall power generation flow of the hydropower station by rationally arranging the active power output of each hydro-turbine generator set and coordinating the mutual influence between the unit output, gross head, and power generation flow, thereby improving the power generation efficiency of the water volume and maximizing the benefits of water resource power generation.

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 continued growth of global energy demand and the accelerated transformation of the energy structure, hydropower, as a clean, renewable energy source, is playing an increasingly important role in the energy supply system. As the primary conversion facility for hydropower energy, the operational efficiency and economic efficiency of hydropower stations are directly related to the stability and sustainable development of the entire energy system. Extensive domestic and international data indicate that implementing economic operation within a hydropower station can improve economic efficiency by 1% to 3%. Therefore, the proper distribution of system load among operating units is crucial for achieving optimal operation and maximizing economic benefits.

[0003] Currently, the solutions to the problem of optimally allocating hydropower unit output mainly fall into two categories: traditional optimization algorithms and intelligent algorithms. Traditional optimization algorithms have a relatively simple structure, but are unable to adapt to current nonlinear, high-dimensional optimization problems. Intelligent algorithms, on the other hand, rely on initial solutions and search strategies during 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 prematurely, thereby missing the global optimal solution. Furthermore, the performance of intelligent algorithms is often affected by their parameter settings. For example, the settings of parameters such as the crossover probability and mutation probability in genetic algorithms, and the inertia weight and learning factor in particle swarm algorithms, all directly affect the algorithm's search efficiency and solution quality. Inappropriate parameter settings may lead to reduced algorithm performance or even the inability 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 protection for a method for optimizing active power output distribution of generator sets in a hydropower station based on a genetic algorithm, comprising:

[0006] 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 generator sets in the hydropower station.

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

[0008] S3, under different power plant startup combinations, using the power generation plans of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, and the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the output limit of the generator set by the power grid security and stability control system as the operation constraint condition, arrange the active output of each hydro-generator set, and perform 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 units of each power plant of the hydropower station, where the active output of the Nth generator unit is determined by the power generation plan of the power plant and the active output of the first N-1 generator units, and the binary codes corresponding to the active output of the first N-1 generator units of each power plant are used as the initial population;

[0010] S5, based on the operating constraints and the power generation flow calculation formula, combined with the penalty function, constructing a mathematical model for optimizing the active power output distribution of the hydropower station generator sets, and calculating the fitness of each chromosome in the population;

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

[0012] S7, based on the survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation population, 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 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 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 plant 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 units in 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 set has 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] Take a value less than or equal to Z for the first (n-1) generator sets respectively 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 hydropower station power plant.

[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] Use fitness F 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 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 slack 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 slack 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, and the good genes are passed on to the next generation, producing new excellent chromosomes.

[0043] Crossover probability P c The larger the setting, the faster new chromosomes are produced, and the greater the possibility that the inheritance 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 head corresponding to the stable operation area, the crossover operation will be 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. In the binary code, 0 is changed to 1 and 1 is changed to 0. A floating point number greater than or equal to 0 and less than 1 is randomly generated. If the floating point number 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 escape from local solutions.

[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 meet the requirements of being less than or equal to the rated output of the generator set and greater than or equal to the minimum output corresponding to the minimum water head in the stable operation area, the chromosome mutation operation is performed again.

[0058] Furthermore, the step 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 operation continues to the next generation.

[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. The active output of the last generator set is the difference between the power generation plan of the branch 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. The method obtains basic data of different types of generator sets in a hydropower station, determines different startup combinations of power plants according to a power generation plan, solves the minimum power generation flow of the hydropower station based on comprehensive operating constraints, constructs a mathematical model for optimizing the active output allocation of generator sets in a hydropower station based on the operating constraints and the power generation flow calculation formula, calculates the fitness of each chromosome in a population, determines the chromosomes of the population to cross and perform mutation operations, eliminates chromosomes with low fitness, and retains 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-generator set and coordinating the mutual influence between the unit output, gross head, and power generation flow, thereby improving the power generation efficiency of water and maximizing the benefits of water resource power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of a method for optimizing active power output distribution of generator sets in 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 generator sets of a hydropower station based on a genetic algorithm as claimed in the embodiment of the present invention is provided. Example 1 of water is a minimum power generation flow diagram of successive populations of a power station;

[0066] Figure 3 The embodiment 1 of the method for optimizing the active power output distribution of the generating units of a hydropower station based on a genetic algorithm claimed in the embodiment of the present invention is a diagram showing the optimal active power output distribution mode of the generating units of the hydropower station;

[0067] Figure 4 The embodiment 2 of the method for optimizing the active power output distribution of the generator sets of a hydropower station based on a genetic algorithm as claimed in the embodiment of the present invention is a minimum power generation flow diagram of the hydropower station population over generations;

[0068] Figure 5 Embodiment 2 of the method for optimizing active power 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 power 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 this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] The terms "first", "second" and "third" in this application are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second" and "third" may explicitly or implicitly include at least one of such features. In the description of this application, "multiple" means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of this application (such as up, down, left, right, front, back...) are only used to explain the relative positional 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 will also change accordingly. In addition, the terms "including" and "having" and any variations thereof 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 may optionally also include steps or units that are not listed, or may optionally also include other steps or units inherent to these processes, methods, products or devices.

[0071] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, 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 protection for a method for optimizing active power output distribution of generator sets in a hydropower station based on a genetic algorithm, comprising:

[0073] 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 generator sets in the hydropower station.

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

[0075] S3, under different power plant startup combinations, using the power generation plans of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, and the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the output limit of the generator set by the power grid security and stability control system as the operation constraint condition, arrange the active output of each hydro-generator set, and perform 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 units of each power plant of the hydropower station, where the active output of the Nth generator unit is determined by the power generation plan of the power plant and the active output of the first N-1 generator units, and the binary codes corresponding to the active output of the first N-1 generator units of each power plant are used as the initial population;

[0077] S5, based on the operating constraints and the power generation flow calculation formula, combined with the penalty function, constructing a mathematical model for optimizing the active power output distribution of the hydropower station generator sets, and calculating the fitness of each chromosome in the population;

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

[0079] S7, based on the survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation population, 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 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. are preliminarily determined; 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 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 unit 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 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 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 plant 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 units in 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 The power generation plan of the k-th 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 unit 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 power grid security and stability control system for the i-th generator set.

[0101] (3) The gross head of the unit is the difference in water levels 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 water level of the hydropower station reservoir; H2 is the water level of the downstream hydropower station reservoir.

[0104] (4) The power generation flow of the hydropower station depends on the active power 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-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 using a genetic algorithm, a globally adaptive search algorithm developed based on the principles of natural selection and natural genetic evolution. The basic idea is to represent each possible solution in the problem space as an "individual." A string of these individuals forms a group, or "population." This population is placed in a specific problem environment. Each individual is evaluated according to a predetermined objective function, and its fitness is assigned. During the genetic manipulation process, fitness serves as the basis for "natural selection," determining survival and elimination. Initially, individuals are randomly generated. Due to the "survival of the fittest" principle of evolution, successive generations are significantly superior to their predecessors, ensuring that the population gradually evolves toward a more optimal solution.

[0107] Before solving a problem, a genetic algorithm must map the solution space into the genetic algorithm's genotype string (i.e., chromosome) structure data. Different combinations of these string structures constitute different points. Common mapping methods include binary encoding, Gray code, floating-point encoding, parameter concatenation encoding, and multi-parameter cross encoding. Binary encoding offers advantages such as simple encoding and decoding, and easy crossover and mutation programming.

[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 set has 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] Take a value less than or equal to Z for the first (n-1) generator sets respectively 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 hydropower station power plant.

[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 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] Use fitness F 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 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 slack 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 slack 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, and the good genes are passed on to the next generation, producing new excellent chromosomes.

[0135] Crossover probability P c The larger the setting, the faster new chromosomes are produced, and the greater the possibility that the inheritance 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 head corresponding to the stable operation area, the crossover operation will be 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. In the binary code, 0 is changed to 1 and 1 is changed to 0. A floating point number greater than or equal to 0 and less than 1 is randomly generated. If the floating point number 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 escape from local solutions.

[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 meet the requirements of being less than or equal to the rated output of the generator set and greater than or equal to the minimum output corresponding to the minimum water head in the stable operation area, the chromosome mutation operation is performed again.

[0150] In this example, high-performing chromosomes are selected from the population, while poor-performing chromosomes are eliminated. The selected chromosomes participate in a crossover operation, passing their high-performing genes to their offspring. Roulette wheel selection is the earliest and most commonly used selection method, named for its principle, which resembles a roulette wheel on a gambling table. It is a selection method based on the proportion of an individual's fitness. The higher the fitness, the greater the probability that the chromosome will be 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 setting is 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 head corresponding to the stable operation area, the crossover operation will be 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 the floating-point number 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 step 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 operation continues to the next generation.

[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. The active output of the last generator set is the difference between the power generation plan of the branch plant and the active output of the front-end generator set.

[0162] The following is an example of a specific embodiment:

[0163] With reference 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 generation flow is 5230m 3 / s, and the water consumption rate is 8.12.

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

[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 unit 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 The power generation plan of the k-th 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 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 power grid security 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 starting units 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 starting 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 five 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 5234.88, and the corresponding water consumption rate is 8.12.

[0186] When the six 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 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] With reference 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 generation 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 to 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 this 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 merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as 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, the functional units in the various embodiments 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-mentioned integrated units 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 description and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0194] The above detailed description of the specific embodiments of the invention is intended only as an example, and the present application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions of the invention are also within the scope of the present application. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present application should be included within the scope of the present application.

Claims

1. A method for optimizing the active power output distribution of hydropower station generator sets based on genetic algorithm, characterized in that: include: S1, obtaining basic data on the power generation flow characteristic curve, head loss curve, stable operation curve, and downstream water level flow relationship curve of different types of generator sets in the hydropower station; S2, determining different power plant startup combinations according to a given power generation plan of a hydropower station power plant; S3, under different power plant startup combinations, using the power generation plans of each power plant of the hydropower station and the upstream water level and the downstream power station reservoir water level as input conditions, and the active output of the generator set as the state variable, comprehensively considering the stable operation limit of the generator set output and the output limit of the generator set by the power grid security and stability control system as the operation constraint condition, arrange the active output of each hydro-generator set, and perform 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 units of each power plant of the hydropower station, where the active output of the Nth generator unit is determined by the power generation plan of the power plant and the active output of the first N-1 generator units, and the binary codes corresponding to the active output of the first N-1 generator units of each power plant are used as the initial population; S5, based on the operating constraints and the power generation flow calculation formula, combined with the penalty function, constructing a mathematical model for optimizing the active power output distribution of the hydropower station generator sets, and calculating the fitness of each chromosome in the population; S6, performing a crossover on the population chromosomes according to a roulette wheel algorithm, and performing a mutation operation on the offspring of the crossover chromosomes; S7, based on the survival of the fittest, eliminate chromosomes with low fitness in the new generation and the previous generation population, 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; Said S5 further includes: Use fitness 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 flow of the hydropower station, the objective function is linearly transformed accordingly, and the fitness function is: （7） Where: 、 is a constant; The output penalty factor required for stable operation of the hydropower station generator set; The output slack variable required for stable operation of the hydropower station generator set; Output penalty factor required for power grid security and stability control system; To control the power grid security and stability, the control system requires output slack variables; The maximum and minimum required output penalty factors of the hydropower station generator sets; It is the slack variable of the maximum and minimum required output of the generator set of the hydropower station.

2. The method for optimizing active power output distribution of hydropower station generator sets based on genetic algorithm according to claim 1, characterized in that: Said 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): （1） Where: is the minimum number of operating units of the kth power plant of the hydropower station; Power generation plan for the power generation branch; is the rated output of the generator set; Taking 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, the maximum number of units in operation in the branch plant is preliminarily determined using formula (2): （2） Where: The maximum number of units in operation in the power plant; The minimum output corresponding to the minimum water head in the stable operation area of ​​the unit; The number of installed units in the power plant.

3. The method for optimizing active power output distribution of hydropower station generator sets based on genetic algorithm according to claim 2, characterized in that: Said S4 further includes: forward The active power output of each generator set is based on Discrete, then each generator set has active output Total Solution: （3）。 4. The method for optimizing active power output distribution of hydropower station generator sets based on genetic algorithm according to claim 3, characterized in that: Said S4 further includes: Give to the front Generator sets, select one that is less than or equal to Integer ,but The active output of each generator set is: （4） Active output of the last generator set For the power plant power generation plan and The difference in active power output of the generator sets is: （5） If the active output of the last generator set meets the maximum and minimum output constraints, the generator set sequence The corresponding binary code can be used as the chromosome of the hydropower station power plant.

5. The method for optimizing active power output distribution of hydropower station generator sets based on genetic algorithm according to claim 4, characterized in that: Said 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: （6） Where: for The corresponding binary encoding length.

6. The method for optimizing active power output distribution of hydropower station generator sets based on genetic algorithm according to claim 1, characterized in that: Said 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 Then they exchange genes, pass on good genes to the next generation, and produce new good chromosomes; Crossover probability The larger the setting, the faster new chromosomes are produced, and the greater the possibility that the inheritance pattern will be disrupted, destroying excellent gene combinations; Crossover probability 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: （8） Where: 、 is a constant; is the larger fitness value of the two chromosomes selected for crossover; is the average fitness of all chromosomes in the population; 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 head corresponding to the stable operation area, the crossover operation will be performed again until the maximum and minimum output requirements are met.

7. The method for optimizing active power output distribution of generator sets in a hydropower station based on genetic algorithm according to claim 6, characterized in that: Said 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. In the binary code, 0 is changed to 1, and 1 is changed to 0. A floating-point number greater than or equal to 0 and less than 1 is randomly generated. If the floating-point number 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 escape from local solutions. 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: （9） Where: 、 is a constant; is the fitness value of the chromosome after crossover; is the average fitness of all chromosomes in the population; 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 meet the requirements of being less than or equal to the rated output of the generator set and greater than or equal to the minimum output corresponding to the minimum water head in the stable operation area, the chromosome mutation operation is performed again.

8. The method for optimizing active power output distribution of generator sets in a hydropower station based on genetic algorithm according to claim 1, characterized in that: Said 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 iterative evolution generation is reached. If so, end the operation; otherwise, continue with the next generation operation.

9. The method for optimizing active power output distribution of hydropower station generator sets 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. The active output of the last generator set is the difference between the power generation plan of the branch plant and the active output of the front-end generator set.

Images