Analysis of Power Marketing and Comprehensive Energy

Collaborative Development Mode by Using Particle Swarm

Optimization Algorithm

Quan Sun, Wenjun Qi, Shuai Yuan, Tingting Zhang and Yan Pan

Jiangsu Frontier Electric Power Technology Co., Ltd., Jiangsu, 211102, China

Keywords: Particle Swarm Optimization Algorithm, Power Marketing, Integrated Energy, Collaborative Development,

Model.

Abstract: Nowadays, the power marketing using particle swarm optimization algorithm is the key trend in the

coordinated development mode of comprehensive energy. The application of power marketing platform using

particle swarm optimization algorithm helps to promote the efficient development of the coordinated

development mode of comprehensive energy, and has a direct impact in the application of the coordinated

development mode of comprehensive energy. Nowadays, using the particle swarm optimization algorithm of

electric power marketing platform running integrated energy coordinated development mode application

research mainly focused on theory, the electric power marketing content, comprehensive energy coordinated

development of content, lack of practical case analysis, etc., also not clear using the particle swarm

optimization algorithm of the importance of electric power marketing platform operation. In order to study

the particle swarm optimization algorithm of electric power marketing platform operation of the actual impact

of comprehensive energy coordinated development mode, this paper with comprehensive energy coordinated

development mode analysis method, to use the particle swarm optimization algorithm of electric power

marketing platform operation content and indicators, discussion analysis, on the basis of the particle swarm

optimization algorithm in the power marketing platform running integrated energy coordinated development

mode application regression analysis, and combined with the problems existing in the operation of electric

power marketing platform analysis, thus targeted feasibility solution strategy. The research results show that

the coordinated development mode of comprehensive energy is helpful to the operation and development of

the power marketing platform using the particle swarm optimization algorithm, and the application results of

the coordinated development mode of comprehensive energy are more effective.

1 INTRODUCTION

Today, the power market is gradually treating the

power marketing using particle swarm optimization

algorithms and the operation degree of the market as

the focus of its lasting progress, which also

constitutes the main competitive advantage of its

high-level power marketing (De, and Wang, et al.

2022). This paper first studies the application index

and quantitative application means of scientific

comprehensive energy collaborative development

mode of power marketing association using particle

swarm optimization algorithm (Geng, and Zheng, et

al. 2022). By applying the application framework and

strategy of this comprehensive energy collaborative

development mode, and referring to the operation of

the power marketing platform using the particle

swarm optimization algorithm, this research carries

out the application of the comprehensive energy

coordinated development mode of the operation of

the power marketing platform using the particle

swarm optimization algorithm (Huang, and Ren, et al.

2024). The research goal is to promote the

comprehensive energy coordinated development

mode of particle swarm optimization algorithm of

power marketing analysis and problems, enhance the

use of particle swarm optimization algorithm of

power marketing analysis and problems associated

scientific management ability, and promote the

particle swarm optimization algorithm of power

marketing analysis and the problem of integrated

energy coordinated development mode application

Sun, Q., Qi, W., Yuan, S., Zhang, T. and Pan, Y.

Analysis of Power Marketing and Comprehensive Energy Collaborative Development Mode by Using Particle Swarm Optimization Algorithm.

DOI: 10.5220/0013538600004664

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 221-226

ISBN: 978-989-758-763-4

221

and the progress of research technology (Li, and Han,

et al. 2024).

2 RELATED WORKS

Based on this background, the construction and

progress of the power marketing industry using

particle swarm optimization algorithm need to

continuously adjust to adapt to the transformation of

various power environment, all of which are closely

related to the special power marketing environment

using particle swarm optimization algorithm (Li, and

Hu, et al. 2024). With the progress of power

marketing using particle swarm optimization

algorithm, its industry and the surrounding natural

and power environment are interwoven and limited

(Liu, and Sun, et al. 2023). With the continuous

progress of power marketing using particle swarm

optimization algorithm, especially driven by the

current power marketing market, the disorder and

lack of planned expansion of the power marketing

industry using particle swarm optimization algorithm

has caused serious damage to the power environment,

causing many power marketing problems (Liu, and

Xu, et al. 2023). The power marketing analysis and

problem correlation using particle swarm

optimization algorithm are similar to other fields, and

the scientific power environment problems caused are

gradually increasing (Stecyk, and Miciula, 2023).

Therefore, it is inevitable to promote the development

of power marketing using particle swarm

optimization algorithm and choose the path to

sustainable development (Yin, and Zhou, et al. 2024).

This research should take the reasonable

operation of the power marketing system, the stability

of the power marketing system and the maximum

utilization of the power resources as the goal of the

power marketing by using the particle swarm

optimization algorithm, so as to promote the

sustainable development of the power marketing

industry (Zhang, and Jiang, et al. 2023). For using

particle swarm optimization algorithm of power

marketing in comprehensive energy coordinated

development mode application index system, the core

is to seek a standardized method, to measure and

integrated coordinated development of energy mode

application of electric power marketing resources, use

and management of each link of "reduce, recycling,

reuse" level. This study aims to compensate for the

defects between indicators, such as lack of

connection, subjectivity and randomness, through the

principle and method of power marketing index

generation.

Comprehensive energy coordinated development

mode application using particle swarm optimization

algorithm of electric power marketing index system

belongs to a new category, so, in the process of

formulating the execution index system, the study

requires clear the meaning of particle swarm

optimization algorithm of electric power marketing

and the goal of comprehensive energy coordinated

development model. For the application of the

integrated energy collaborative development mode of

the power marketing platform using the particle

swarm optimization algorithm, it is a variable and

dynamic open system, which must have a set of

representative standards covering all related topics.

Therefore, this research needs to clarify which

problems are corresponding, and also needs to build

an application framework of integrated energy

collaborative development model. In view of the

hierarchical relationship between the various indexes,

this study needs to be accurately interpreted. The

advanced index can not conflict or intersect with the

lower indexes derived from them. After the

application index of the comprehensive energy

coordinated development model of power marketing

using the particle swarm optimization algorithm is

determined, this study needs to create the application

model of the comprehensive energy coordinated

development model and implement the

corresponding calculation according to its

measurable characteristics.

3 METHODS

3.1 Power Marketing Using the

Particle Swarm Optimization

Algorithm

The power marketing function using the particle

swarm optimization algorithm is a way to evaluate the

operational efficiency, which mainly means by

setting a power marketing vector using the particle

swarm optimization algorithm to improve the

expected output, and also reduces the input and

unexpected output accordingly. However, there are

some deficiencies in this approach. For example, if

there are non-zero relaxation variables in power

marketing using particle swarm optimization

algorithms, then the efficiency estimate may be

overstated. Based on the previous steps, we calculate

the relaxation variables of power marketing using the

particle swarm optimization algorithm, and also

introduce the theory of power marketing function that

INCOFT 2025 - International Conference on Futuristic Technology

222

does not use the particle swarm optimization

algorithm:see Eq. (1).

()

[]

{

)(,,,,:F

,,,,

LKJYTREWGD

YTREWQ

∈×+

=）（

(1)

In formula (1), Q (W, E, R, T, Y) represents a

preset power marketing vector using the particle

swarm optimization algorithm, D represents the

changing trend of input-output, and FG represents the

proportional factor vector, which can be used to

measure the increase or decrease of input-output. J

represents the standardized weight vector of each

input-output index, and K (L) is used to indicate the

key nature of each input-output index. Thus the model

represents the total weight of the increase or decrease

of all the input-output variables. It can be seen that

the power marketing function using the particle

swarm optimization algorithm can adjust the input

and output involuntarily. The power marketing vector

using the particle swarm optimization algorithm can

be obtained by solving the following models: see Eq.

(2)

HJKLASDFYTREWQ +=）,,,,(

(2)

Formula (2), ASDF represents using particle

swarm optimization algorithm of electric power

marketing system in a series of overlapping window

efficiency evolution, and to measure the particle

swarm optimization algorithm of power marketing

optimization the application of integrated energy

coordinated development model, in order to more

accurately reflect the integrated energy power

marketing optimization of the application of dynamic

changes, see Eq. (3)

kkn

NBVVCXZ −≤



)

In formula (3), Z and X representatives

respectively calculate the average value of different

technologies and CV representatives, which can

obtain the final annual application of the integrated

energy collaborative development mode of power

marketing optimization using particle swarm

optimization algorithm, see Eq. (4)

LLn

FDSSA -

≤



)

In formula (4), AS represents the sample size

requirement. When the explanatory variables D and F

account for a large number, they may face "dimension

decline", so as to reduce the accuracy of power

marketing.

3.2 Coordinated Development Model of

Integrated Energy Sources

A number of studies show that there may be a linear

relationship between the application and regulation of

the coordinated development model of

comprehensive energy. Some scholars have proved

that the application of the comprehensive energy

coordinated development model will directly reduce

the regulation and improve the quality of power

environment, while some scholars have found that

there is no significant causal relationship between the

two, see Eq. (5)

EEn

VCXXZ −≤



)

In formula (5), ZX represents the robustness test

result, and CV represents obtains the regional

heterogeneity of the average impact of power

marketing resources on the application of integrated

energy optimization collaborative development mode

by particle swarm optimization algorithm, see Eq. (6)

yyn

GFDDS +≥



)

In formula (6), SD represents the equilibrium

degree of the coordinated development of

comprehensive energy, D represents the controllable

factor of the equilibrium degree, and FG represents

the heterogeneity test factor of the coordinated

development of comprehensive energy, see Eq. (7)

ccn

KJHHG −=



)

In formula (7), GH represents that the traditional

parametric model needs to assume the functional

relationship between variables, H represents that it

needs to pay attention to the average effect between

the explained variables and the explained variables,

JK represents that there are many non-linear

relationships between variables in real life, and the

traditional parameter estimation has certain

limitations, see Eq. (8)

Analysis of Power Marketing and Comprehensive Energy Collaborative Development Mode by Using Particle Swarm Optimization

Algorithm

223

NnTPTOTITUTY ,,2,10,,,, =≥ ；

(8)

Based on the formula (8), T represents the

constant factor of integrated energy coordinated

development, Y, U, I, O, P on behalf of electric power

marketing resources using the particle swarm

optimization algorithm of power marketing

optimization of integrated energy coordinated

development model of net effect, N represents

depends on "regulation effect" and "relative

marketing effect" size, which is likely to have a

nonlinear relationship.

3.3 The Application of Power

Marketing Using Particle Swarm

Optimization Algorithm in the Co-

Development Mode of Integrated

Energy

The nonparametric method does not need to assume

the functional form between the variables in advance,

and can mine the nonlinear relationship completely

based on the numerical characteristics of the

variables, so as to avoid the problem of model setting

bias. Based on the preliminary calculation method,

the application of the power marketing optimization

comprehensive energy collaborative development

mode using the particle swarm optimization

algorithm is the ratio of the potential target power

intensity to the actual power intensity: see Eq. (9)

HGF

DSA

ASDF

−

























×−

(9)

In formula (9), D represents the optimal GDP

added value and the reduction value of power

marketing resources. ASDF represents the fixed-

effect model, A, S, D, F, G, H represent the linear

relationship between the application of integrated

energy collaborative development model and the

optimization of power marketing using particle

swarm optimization algorithm:see Eq. (10)

titi

ititi

MNBVCZX

,1,

++=



(10)

In the formula, ZX is a semi-parametric model

between the traditional parametric model and the non-

parametric model, CV represents the restriction factor

of the integrated energy collaborative development

model, BN represents the sample size coefficient, and

M represents the efficiency and accuracy of the model

estimation.

4 RESULTS AND DISCUSSION

4.1 Application of Index System

Framework of Integrated Energy

Collaborative Development Mode

of Electric Power Marketing by

Using Particle Swarm Optimization

Algorithm

After many studies and discussions, the study has

defined the stratified target index, and has been used

in the application index system of comprehensive

energy collaborative development model, as shown in

Table 1. According to Table 1, it can be clearly

observed in this study that the whole application

index system of integrated energy collaborative

development mode covers power supply, power

marketing materials using particle group optimization

algorithm, power marketing, planning, power

environment, quality, power data monitoring and

efficient use of power personnel. The purpose of these

standards is to measure the use efficiency of power

resources and the degree of reduction, which

constitutes the core of the application environment of

Table 1: Application index system of the integrated energy

collaborative development mode of electric power

marketing by using the particle swarm optimization

algorithm

Electric power

marketing integrated

energy coordinated

development mode

Particle

Swarm

Optimization

Control

algorithm

Power

marketing

index W

Power

marketing

index E

Power

marketing

index R

Power

marketing

index T

Power

marketing

index Y

152.2

663

18.23

140.2

219

17.21

10.26

27.1056

25.1086

290.111

27.1925

236.107

INCOFT 2025 - International Conference on Futuristic Technology

224

the integrated energy collaborative development

model. Therefore, the goal of implementing power

marketing using particle swarm optimization

algorithm is a multifaceted benefit sharing, and each

element is developed under the restriction and

promotion of each other, as shown in Table 1.

4.2 Application Method and

Calculation of Power Marketing in

Comprehensive Energy

Collaborative Development Mode

by Using Particle Swarm

Optimization Algorithm

In fact, the application of a scientific comprehensive

energy coordinated development mode of power

marketing or environment using particle swarm

optimization algorithm is an application process of

diversified comprehensive energy coordinated

development mode, including standardized treatment

indicators, the determination of index weight and the

comprehensive analysis of indicators. In view of the

unique characteristics of all the parameters in the

execution system, in order to reduce the difference

between the parameters, this study needs to adopt

appropriate conversion, so that the parameter values

of the measured power marketing system can be

normalized without parameters. This article

standardizes the application index of the

comprehensive energy coordinated development

Table 2: Calculated values of power marketing using the

particle swarm optimization algorithm

Particle

swarm

timization

Estimate

error

Electric

power

marketin

Estimate

error

Power

marketing

index W

19.233×8-6 Power

marketing

index W

13.464×10-

Power

marketing

index E

15.643×8-6 Power

marketing

index E

12.344×10-

Power

marketing

index R

13.132×8-6 Power

marketing

index R

13.437×10-

Power

marketing

index T

11.435×8-6 Power

marketing

index T

13.560×10-

Power

marketing

index Y

13.765×10-

Power

marketing

index Y

13.782×10-

Power

marketing

index W

14.433×10-

Power

marketing

index W

13.911×10-

mode of electric power marketing through the index

method. All the true values (i. e., current values) in

the index system are compared with the

corresponding reference values (i. e., standard values)

to reveal the achievement of each indicator, as shown

in Table 2.

In order to make the integrated energy

coordinated development mode application more fair

and has substantial meaning, the study of the

integrated energy coordinated development mode of

various aspects of the importance of the quantitative

distribution, and adopted the linear weighted

comprehensive way, so that each application

influence of coordinated development mode of

integrated energy elements to make differentiation

processing. The strategy of power marketing using

particle swarm optimization algorithm is used to

provide the most basic optimal implementation

scheme for the implementation of sustainable

development strategy. Therefore, by observing the

overseas power marketing guide using particle swarm

optimization algorithm, the planning and

implementation are very detailed and complex, and

indeed the integrated into the whole process of power

marketing using particle swarm optimization

algorithm, to ensure that each stage can be fully

implemented, as shown in Figure 1.

Figure 1: Comparison of power marketing using the particle

swarm optimization algorithm

According to the current research results, this

research is very important to the application standard

system of comprehensive energy coordinated

development mode of power marketing using particle

swarm optimization algorithm. The research from the

establishment of the particle swarm optimization

algorithm of electric power marketing platform

running comprehensive energy coordinated

development mode of application index system on the

Analysis of Power Marketing and Comprehensive Energy Collaborative Development Mode by Using Particle Swarm Optimization

Algorithm

225

basis of work, scientifically build the execution

system, and using the particle swarm optimization

algorithm of power marketing platform operation

implement the diversified application, to strengthen

the proof, as shown in figure 2.

Figure 2: Comparison of power marketing using particle

swarm optimization algorithm in the integrated energy

collaborative development mode

The study chose the particle swarm optimization

algorithm of power marketing area basic standard,

and the model unit, as a large power marketing area,

in order to enrich and perfect the particle swarm

optimization algorithm of power marketing research

content, and based on this to use the development of

particle swarm optimization algorithm of electric

power marketing, and clear the deviation value of

different power environment, as shown in figure 3.

Figure 3: Calculation and analysis of the deviation value

5 CONCLUSIONS

In general, the score of power marketing operation

using particle swarm optimization algorithm reaches

the standard, which indicates that the platform is a

high-quality power marketing market, and its power

marketing effect, operation concept and status quo are

very consistent. The implementation of

comprehensive energy coordinated development

mode application system has the ability to implement,

and it is reasonable. However, the power marketing

implementation index framework still needs to be

optimized, and many related values are difficult to

collect. This study makes an in-depth analysis of the

application of power marketing using particle swarm

optimization algorithm in the integrated energy

collaborative development mode, and makes a more

accurate definition of relevant indicators, in order to

improve the efficiency and quality of power

marketing.

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