Application of Genetic Algorithm in Optimal Scheduling of

Hydropower Station Reservoirs

Xiaoyan Zhang

Yunnan College of Business Management, KunMing, 650000, China

Keywords: Genetic Algorithm, Scheduling Optimization, Hydropower Station Management, Local Scheduling, Overall

Scheduling, Displacement, Generation, Scheduling Frequency, Scheduling Time, Scheduling Accuracy,

Process Simplification Rate.

Abstract: The role of dispatch optimization in hydropower station management is very important, but there is the

problem of inaccurate evaluation of results. The bat algorithm cannot solve the problem of scheduling

optimization in hydropower station management, and the evaluation is unreasonable. Therefore, this paper

proposes a genetic algorithm for the optimization analysis of innovative scheduling and scheduling. Firstly,

the optimal dispatching scheme is used to evaluate the management of hydropower stations, and the indicators

are divided according to the requirements of dispatching optimization to reduce the interference factors in

dispatching optimization. Then, the optimal dispatching scheme innovates and optimizes the management of

hydropower stations, forms a dispatching optimization scheme, and comprehensively analyzes the dispatching

optimization results. The dispatching frequency model shows that under the condition of certain evaluation

criteria, the genetic algorithm optimizes the accuracy of the dispatch of hydropower station reservoirs. The

scheduling optimization time is better than that of the bat algorithm.

1 INTRODUCTION

Displacement and power generation are one of the

important contents of hydropower station reservoir

management and are of great significance to the

development of hydropower station reservoirs (Ahn,

and Tian, et al. 2023). However, in the process of

dispatch optimization, the scheduling optimization

scheme has the problem of poor accuracy (Andrus,

and Diffely, et al. 2023), which brings

certain

efficiency loss to the reservoir management of

hydropower stations (Awad and Parrondo, 2023).

Some scholars believe that the application of genetic

algorithm to the management analysis of hydropower

stations can effectively analyze the scheduling

optimization scheme and provide corresponding

support for the dispatching optimization (Bai, and Yu,

et al. 2023). On this basis, this paper proposes a

genetic algorithm

to optimize the scheduling

optimization scheme and verify the effectiveness of

the model (Bravo-Cordoba, and Garcia-Vega, et al.

2023).

Hydropower station is an important clean energy

power generation facility, which has the advantages

of abundant resources and environmental

friendliness, and has become an indispensable part of

the modern energy system (Chen and Zhang, et al.

2023). In

the operation of hydropower stations, how

to carry out reasonable and efficient dispatch is one

of the key factors to ensure the efficiency of

hydropower station operation and play its maximum

role (Dalcin, and Breda, et al. 2023). Traditional

hydropower plant dispatch methods are often based

on experience and rules, and are not flexible and

efficient (De

Paris, and Carnielutti, , et al. 2023). To

this end, researchers began to explore how to use

advanced algorithms and technologies to improve the

optimization effect and efficiency of hydropower

station dispatching (Dires, and Amelin, et al. 2023).

As an excellent optimization algorithm, genetic

algorithm has been successfully applied to the

dispatch optimization of hydropower stations and

has

achieved good results (Godoy and Ishihara, et al.

2023). This paper will introduce and analyze the

application of genetic algorithm in the optimization

of hydropower station dispatch (Hao and Yang, et al.

2023).

Zhang, X.

Application of Genetic Algorithm in Optimal Scheduling of Hydropower Station Reservoirs.

DOI: 10.5220/0013544600004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 411-417

ISBN: 978-989-758-763-4

411

1.1 Analysis of Hydropower Station

Dispatch Optimization Problem

The scheduling problem of hydropower station

belongs to a complex multi-objective and multi-

constraint optimization problem (He and Zhang, et al.

2023). The goal of hydropower station dispatch is

usually to optimize the efficiency of power

production, maximize power generation, and meet

dispatch constraints, such as generator output,

generation flow, water level control, etc (Jeong and

Furenes, et al. 2023). The

main difficulties of

hydropower station scheduling problems lie in the

complex structure, multiple constraints, diversified

objective functions, and changing operating

environment of the optimization model, so it is

necessary to design appropriate optimization

algorithms to reduce the complexity of the algorithm

and improve the efficiency of the solution (Jiang and

Ming et al. 2023).

1.2 Principles and Processes of Genetic

Algorithms

Genetic algorithm is an optimization algorithm that

simulates the laws of evolution in nature. The basic

idea of genetic algorithms is to optimize the solution

of problems by simulating the process of biological

evolution. The basic process is as follows:

1.2.1 Initialize the Population

A certain number of initial individuals are randomly

generated as the population, each of which is a

potential solution to the problem (Jin and Liu et al.

2023).

Individual fitness assessment: The fitness value of

each individual is calculated by the objective function

to determine the probability of each individual

participating in reproduction (Ju and Ding, et al.

2023).

Reproduction and mutation: According to the

degree of fitness, basic genetic operations such as

selection, crossover, and mutation are used to

generate new individuals and replace the original

unsuitable individuals, so as to gradually improve the

optimization model (Li and Ke et al. 2023).

Termination condition judgment: When a certain

termination condition is met, the algorithm stops and

outputs the final optimization solution (Liu and Luo,

et al. 2023).

1.3 Application of Genetic Algorithm

in Hydropower Station Dispatch

Optimization

As an excellent optimization algorithm, genetic

algorithm has been successfully applied to the

optimization problem of hydropower station

dispatching. Its main advantage lies in the global

search and non-deterministic characteristics of the

algorithm, which can better cope with complex multi-

objective and multi-constraint optimization problems

(Liu and Luo, et al. 2023).

1.3.1 Model Building

When using genetic algorithm to solve hydropower

station scheduling optimization problems, it is

necessary to establish a corresponding optimization

model. Optimizing the model needs to include

objective functions and constraints. In the

hydropower station dispatch problem, the objective

function is usually to maximize the power generation,

and the constraints include generator

output

constraint, current constraint, water level constraint,

generator number constraint, etc.

1.3.2 Parameter Settings

When using genetic algorithms to solve hydropower

station scheduling optimization problems, some

important parameters need to be set. For example,

parameters such as population size, crossover

probability, mutation probability, and selection

strategy need to be considered. The choice of these

parameters will affect the convergence speed of the

algorithm, the quality

of the solution and other

factors.

1.3.3 Fitness Function

When using genetic algorithms for optimization

solving, fitness functions need to be designed to

assess the optimization quality of each individual. In

the hydropower station dispatch problem, the fitness

function is generally the maximization of power

production, but due to the complexity of the

hydropower station scheduling problem, more factors

need

to be considered when designing the fitness

function, such as generator output, water level

control, power generation flow and other factors.

1.3.4 Optimize the Process

When using genetic algorithms to optimize the

scheduling problem of hydropower plants, a variety

INCOFT 2025 - International Conference on Futuristic Technology

412

of methods can be used to realize the optimization

process. One of the common methods is an agent-

based approach. By optimizing the proxy model, a

better optimization solution can be obtained in a short

time.

1.4 The significance of genetic

algorithms in hydropower station

scheduling

1.4.1 Improve the Efficiency of Hydropower

Station Power Generation

The application of genetic algorithm can help

hydropower stations achieve more reasonable and

efficient dispatching schemes, thereby improving the

power generation efficiency of hydropower stations

and maximizing power production.

1.4.2 Reduce Costs

The success of hydropower plant dispatch

optimization can help reduce the operating costs of

hydropower plants, minimize power production, and

provide more stable economic benefits for

hydropower plants.

1.4.3 Ensure the Rational Use of Water

Resources

The application of genetic algorithms can help

hydropower stations achieve rational utilization of

water resources and ensure the sustainable

development and utilization of water resources. The

application of genetic algorithms can promote the

development of informatization and automation of

hydropower stations, and provide support and

guarantee for the modernization of hydropower

stations.

The hydropower station scheduling optimization

problem is a typical multi-objective and multi-

constraint optimization problem, and traditional

optimization methods often have limitations. As a

global optimization algorithm, genetic algorithm can

be effectively applied to the optimization problem of

hydropower station dispatching, improve the power

generation efficiency of hydropower station, reduce

cost, ensure the rational utilization of water resources,

and promote the development of automation of

hydropower station, etc., which has important

theoretical research value and practical application

significance. In the future, with the continuous

maturity and development of genetic algorithm

technology, its application and significance in the

problem of hydropower

station dispatch optimization

will be further explored and developed.

2 RELATED CONCEPTS

2.1 Mathematical Description of the

Genetic Algorithm

The genetic algorithm uses the simplified process to

optimize the scheduling optimization scheme, and

finds the unqualified values in the management of

hydropower stations according to the indicators in the

scheduling optimization, and integrates the

scheduling optimization scheme to finally judge the

feasibility of hydropower station management.

combine the advantages of

simplified processes and

quantify them with hydropower plant management to

improve the quality of dispatch optimization.

Suppose I. Scheduling optimization requirements

∞

, the scheduling optimization scheme is

lim

→

the satisfaction of the scheduling optimization

scheme is

, the scheduling optimization scheme

judgment function is

0p ≠

As shown in Equation

(1).

lim p ( )

fpX

→

∞= −



∏

)

2.2 Selection of Displacement and

Power Generation Schemes

Hypothesis II The hydropower plant management

function is

, the weight coefficient is

, Then the

dispatch optimization requires unqualified

hydropower station management as shown in

Equation (2):

()

−

−=





)

2.3 Analysis of Scheduling

Optimization Scheme

Before the genetic algorithm is carried out, the

scheduling optimization scheme should be analyzed

in multiple dimensions, and the scheduling

optimization requirements should be mapped to the

Application of Genetic Algorithm in Optimal Scheduling of Hydropower Station Reservoirs

413

hydropower station management library, and the

unqualified scheduling optimization scheme should

be eliminated. The management of hydropower

stations is comprehensively analyzed, and the

threshold and index weights of the scheduling

optimization scheme are set to ensure the accuracy of

the genetic algorithm. met is an optimal scheme for

system testing and

scheduling, and innovative

analysis is required. If the management of

hydropower stations is in a normal distribution, its

scheduling optimization scheme will be affected,

reducing the accuracy of the overall scheduling

optimization. In order to improve the accuracy of the

genetic algorithm and improve the level of scheduling

optimization, it

is necessary to select the scheduling

optimization scheme, and the specific scheme

selection is shown in Figure 1.

Drainge capacty

Overall

scteduling

Lecai

Bydnopower statios

maerenent

Sctedaling

Procen rate

否

是

Figure 1: Selection Results of Investment Decision

Direction Schemes

The survey and dispatching optimization scheme

shows that the drainage and power generation

schemes show a multi-dimensional distribution,

which is in line with the objective facts. The

management of hydropower stations is not

directional, indicating that the displacement and

power generation schemes have strong randomness,

so they are regarded as high

analytical studies. The

management of hydropower stations meets the

normal requirements, mainly to simplify the process

to adjust the management of hydropower stations,

eliminate duplicate and irrelevant schemes, and

supplement the default scheme, so that the dynamic

correlation of the entire scheduling optimization

scheme is strong.

3 OPTIMIZATION STRATEGIES

FOR HYDROPOWER PLANT

MANAGEMENT

The genetic algorithm adopts the random

optimization strategy for the management of

hydropower stations, and adjusts the management

parameters of hydropower stations to realize the

optimization of hydropower station management. it

divides the management of hydropower stations into

different scheduling optimization levels, and

randomly selects different schemes. In the iterative

process,

the scheduling optimization schemes of

different scheduling optimization levels are

optimized and analyzed. After the optimization

analysis is completed, the scheduling optimization

level of different schemes is compared, and the best

hydropower station management scheme is recorded.

4 PRACTICAL EXAMPLES OF

HYDROPOWER PLANT

MANAGEMENT

4.1 Scheduling Optimization Situation

In order to facilitate the scheduling optimization, the

management of hydropower stations in complex

situations is the research object, there are 4 paths, and

the test time is 12h /b15>shown.

Table 1: University scheduling optimization requirements

Volume Drainage Process Rate

Single unit I 63.65% 63.07%

II 54.28% 54.65%

Multi-unit I 63.23% 64.47%

II 53.87% 55.16%

Mixing

units

I 65.68% 63.67%

II 53.82% 52.79%

The scheduling optimization process in Table 1. is

shown in Figure 2.

Compared with the bat algorithm, the scheduling

optimization scheme of the genetic algorithm is closer

to the actual scheduling optimization requirements. In

terms of rationality and fluctuation range of

hydropower station management. The changes in the

scheduling optimization

scheme in Figure 2 show that

the genetic algorithm has better stability and faster

judgment speed. Therefore, the scheduling

INCOFT 2025 - International Conference on Futuristic Technology

414

optimization scheme of genetic algorithm is better,

the scheduling frequency scheme and the scheduling

time scheme are better.

Falgorhm

local

Overall

PSIMLTIONG

Discderoting

是

Schedless

Guiiseoful

Aceitioplre

否

Figure 2: Analysis process of hydropower plant

management

4.2 Management of Hydropower

Stations

The scheduling optimization scheme of hydropower

station management includes non-structural

information, semi-structural information and

structural information. After the pre-selection of

genetic algorithm, the preliminary dispatch

optimization scheme of hydropower station

management is obtained, and the feasibility of the

dispatching optimization scheme of hydropower

station management is analyzed. In order to more

accurately verify the simplification effect of

hydropower station management, select the

hydropower station management with different

dispatch optimization levels, and the scheduling

optimization scheme is shown in Table 2.

Table 2: The overall situation of the emission plan

Unit t

e accurac

sim

lification

Sin

le unit 92.34% 92.61%

Multi-unit 92.04% 91.56%

Mixing units 93.31% 92.21%

mean 92.27% 91.98%

X 92.44% 92.04%

P=92.21%

4.3 Dispatch Optimization of Drainage,

Power Generation and Stability

In order to verify the accuracy of the genetic

algorithm, the scheduling optimization scheme is

compared with the bat algorithm, and the scheduling

optimization scheme is shown in Figure 3.

Figure 3: Displacement and power generation of different

algorithms

It can be seen from Figure 3 that the displacement

and power generation of the genetic algorithm are

higher than those of the bat algorithm, but the error

rate is lower, indicating that the scheduling

optimization of the genetic algorithm is relatively

stable, while the scheduling optimization of the bat

algorithm

Uneven. The average scheduling

optimization scheme of the above three algorithms is

shown in Table 3.

Table 3: Comparison of scheduling optimization accuracy

of different methods

Algorithm Hair

output

Accuracy Simplification

rate

Genetic

Algorith

93.71% 93.47% 94.71%

Bat

Algorith

93.69% 93.77% 92.52%

P 93.98% 93.12% 93.20%

By Table 3 than algorithm has deficiencies in

discharge, power generation and stability in the

management of hydropower stations, and the

management of hydropower stations has changed

significantly, and the error rate is high. The general

results of the genetic algorithm have higher

displacement and power generation, which are better

than the bat algorithm. At the same time, the

displacement and power generation of the genetic

algorithm are greater than 92%, and the accuracy has

not changed significantly. To further verify the

superiority of genetic algorithms. In order to further

verify the effectiveness of the proposed method, the

genetic algorithm is

generally analyzed by different

methods, as shown in Figure 4.

Application of Genetic Algorithm in Optimal Scheduling of Hydropower Station Reservoirs

415

Figure 4: Genetic algorithm scheduling optimization of

drainage and power generation

By Figure 4 drainage and power generation of the

genetic algorithm are significantly better than the bat

algorithm, and the reason is that the genetic algorithm

increases the regulation coefficient of hydropower

station management, sets the threshold of hydropower

station management, and eliminates the scheduling

optimization scheme that does not meet the

requirements.

5 CONCLUSIONS

Aiming at the problem of unsatisfactory management

of drainage and power generation of hydropower

stations, this paper proposes a genetic algorithm and

optimizes the management of hydropower stations

combined with simplified processes. At the same

time, the innovation of dispatch optimization and

threshold innovation is analyzed in depth, and the

management

collection of hydropower stations is

constructed. The results show that genetic algorithms

can improve the accuracy and stability of hydropower

station management, and can optimize the general

scheduling of hydropower station management.

However, in the process of genetic algorithm, too

much attention is paid to the analysis of scheduling

optimization,

resulting in irrationality in the selection

of scheduling optimization indicators.

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