Power grid user asset value evaluation method and application under the background of artificial intelligence and smart grid

Abstract

In the context of artificial intelligence and smart grids, the decision-making of power grid users’ asset acceptance by power grid companies is critical to their operation and development. The research adopts the method of modeling analysis, and aims to study the method and application of power grid user asset value evaluation in the context of smart grid. Based on the analysis of the characteristics of user assets, the economic impact of user asset value evaluation, the characteristics of power grid user asset value evaluation methods, and the fuzzy comprehensive evaluation method, combined with the principle of constructing power grid user assets evaluation index, a fuzzy comprehensive evaluation method construction model is constructed. Finally, this method is used to evaluate the value of power grid users’ assets. The evaluation results show that the total amount of power assets in the government park is the highest, followed by company assets. From another perspective, the impact of residential parks on the social value of user assets is more prominent, but its economic significance and strategic value are relatively weak. The development and in-depth research is expected to further enhance the customer analysis and accurate power supply capabilities of power companies.

Keywords

Artificial intelligence smart grid user assets value evaluation

1. Introduction

With the gradual reform of China’s electricity market, power supply companies have become competitive market players and need to operate independently and be responsible for their own profits and losses. This means that the increase in production decision-making, operation and service awareness of power companies will affect the future development of energy companies [1, 2]. With the gradual deepening of the power system reform, the four major links of power generation, transmission, distribution, and sales will be gradually released, and power supply companies will transform into independent entities. After these roles change, power product suppliers and customer service providers will be the two links of electricity sales and electricity sales have become more important, and the value evaluation of network users has become the basis and key issue of the marketing management of energy companies [3, 4].

Many foreign researchers have carried out a lot of research on the characteristics and value of user assets, and put forward different opinions. Some researchers have made a more detailed elaboration on the value of user assets, and made more systematic explanations and references to the concepts of user financial status, user relationship, user portfolio, user rights, and value user relationship, and put forward the value of user assets. It is the lifetime value of the user, minus the difference between user acquisition cost, user development cost, and user maintenance cost. The actual value of user assets and the long-term net cash flow generated by it, provide a customer equity value model that considers the value of individual user assets and the probability of purchasing the target group of users’ asset value [5, 6]. Some researchers pointed out that the long-term value of an enterprise depends to a large extent on the long-term value of the relationship with enterprise users. This is called business user assets [7, 8]. However, many domestic researchers have different views on this. They thought the expected net contribution is commensurate with the discount, and divided user assets into three main parts: value assets, brand assets, and maintenance assets [9, 10]. In addition, some Chinese scholars pointed out that the value of user assets is the expected benefits that users bring to the company during their life cycle. On this basis, the product cost and related service and marketing costs are deducted, and it is important to add the sum of the value brought by satisfactory user recommendations to other potential users. They believe that the users of the enterprise are regarded as the assets of the enterprise, and the value of the assets must first be evaluated and managed. Domestic research on user assets is relatively late [11, 12]. Some researchers began to use the concept of user assets to explain the importance of users and the relationship between users and enterprises. They also found that the company uses user assets to illustrate the value association between users and the company is more accurate, and believes that user assets are owned or managed by the company for a considerable period of time, and can be valued in currency to provide the company with expected benefits [13, 14]. The research results of predecessors provide a theoretical basis for the research of this article.

The development of this research provides a very valuable reference for the application of computer science and engineering computing in the field of user asset evaluation. On the basis of consulting a large number of references related to “user asset value evaluation”, this paper combines the characteristics of user assets, the economic impact of user asset value evaluation, the characteristics of power grid user asset value evaluation methods and the characteristics of fuzzy comprehensive evaluation method [15, 16]. Based on the principles of asset evaluation index construction, a fuzzy comprehensive evaluation method construction model is constructed, and finally it is analyzed by calculation examples and relevant suggestions are put forward.

Figure 1.

The research framework.

2. Power grid user asset value evaluation method and application under the background of artificial intelligence and smart grid

2.1 Features of user assets

(1)
Manage user assets and facilities, such as dedicated lines and equipment, at intervals between substations. This part of assets is stored in substations or user residential areas, and there is a clear boundary between this part of assets and the assets of public utilities. This part of the asset has a clear ownership. The utility company is solely responsible for the maintenance and management of this part of the asset, and the ownership of the asset belongs to the user. The asset management cost is borne by the power company, and the equipment cost is borne by the user [17, 18].
(2)
It is not clear whether the ownership of transmission line assets belongs to SME users, because a large number of assets are invested in distribution lines, and the city frequently replaces the distribution network during the construction process. At present, even if the power distribution program has not been completed, the power company will be responsible for all maintenance work to ensure the safe supply of power and the proper management of the power grid. The user does not need to pay for the maintenance of the line, and all costs are borne by the power company.
(3)
For the houses invested by the real estate development enterprise, the power grid enterprise will conduct the management inspection and acceptance after the allotment equipment is completed, and the power will be sent after the acceptance is passed [19, 20]. This time is also the beginning of the equipment maintenance work by the power supply company. There is no contract between power grid companies and real estate developers to transfer or receive fixed assets, but in fact the public utility sector needs to maintain and repair these fixed assets.
(4)
For the residential communities built by enterprises and institutions, these communities are the products of the old system, and the public utilities must follow the previous plan to manage the energy distribution facilities in the community. There is no agreement between the power company and the company or organization that raises the funds. In actual management, electricity companies are solely responsible for the maintenance of power supply components, and the material and labor costs incurred during the repair process must be borne by the company or institution.

2.2 Economic impact of user asset valuation

Valuation of user assets is a new concept to understand user value. The introduction of user asset theory has also had a certain impact on the company’s management and financing.

(1)
Assess the value of user assets, leading to a new understanding of users. Companies that implement value management of user assets need to distinguish them according to the value of user assets. Therefore, how the acquisition, retention, and transfer of users affect the company, the user cost of the company, the value that users bring to the company, etc., must be determined by how the company operates with a brand-new concept and treats it in a brand-new way.
(2)
Provide a new basis for user classification. Enterprises can define corresponding management strategies for different types of users. Classification of users based on asset value can help companies allocate resources more effectively. Companies can invest more resources to serve high-value users, while low-value users do not have to constantly invest in costs, which gives them profit and competitive advantages and maximizes the value of user assets. The evaluation results of the value of user assets can provide a reference for companies to acquire and retain customers more effectively and in a more targeted manner.
(3)
Influencing user assets to enter the corporate financial system. Users are regarded as important assets of their business and valued, and must be measured and disclosed like any other asset under management. User assets are also important intangible assets of an enterprise. At the same time, users are the most direct source of profits from products. Its scale may reflect the actual scale of the company and its growth potential to a certain extent. Therefore, it adds traditional financial reporting methods. The introduction of user assets can better reflect the company’s financial strength and growth.

2.3 Method for evaluating the value of power grid users’ assets

(1) Expert analysis method

Expert analysis methods are greatly influenced by subjective judgments. Experts carried out a qualitative analysis on the five major aspects of consumers’ electricity consumption, household electricity costs, business conditions, credit history, and economic growth. The main disadvantage of this method is that it cannot measure and standardize the market, it cannot compare the credit status of various users in all aspects, and it has no scientific method to make objective evaluation and effective supervision of expert results.

(2) AHP

The analytic hierarchy process is a combination of qualitative and quantitative decision analysis methods. This method pays close attention to the role of qualitative factors, incorporates them into expert empirical judgments, analyzes and judges from various angles, uses mathematical methods to obtain quantitative results through calculations, and allows expression and retrieval. This method is very popular with many people because it is scientific, reliable and simple. The evaluation process of the analytic hierarchy process is shown in Fig. 2.

Figure 2.

Judgment flowchart of analytic hierarchy process.

(3) Univariate model

In the univariate model, the various indicators of grid users are compared with standards, and the development of these variables is evaluated to predict that users will have great value. It is similar to a typical enterprise financial management technology. The difference lies in the comparison of indicators such as user profitability, asset liquidity, and solvency with industry standards.

(4) Fuzzy comprehensive evaluation method

Fuzzy comprehensive evaluation method is a quantitative and qualitative evaluation method based on fuzzy mathematics, which can effectively deal with fuzzy research objects that are difficult to quantify. There are several steps specifically. First, analyze the factors that affect the research topic, and create a complete evaluation system; secondly, compare the importance of the indicators in the system to compare the weights of the results, which can be combined with the AHP analytic method or directly calculated based on the expert experience method. In practice, the analytic hierarchy process is usually chosen to divide the complex problem into several sub-problems, and then after a comparison to obtain the decision data, and finally the index ranking weight; in the valuation, these valuation opinions are often given by the research experts. The score is finally calculated based on the fuzzy valuation table combined with the weight of the valuation set.

2.4 Features of fuzzy comprehensive evaluation method

(1) Fuzziness

The conclusion of the price is a synthesis, and the conclusion of the fuzzy comprehensive evaluation is a synthesis, not a single point value; it more accurately describes the fuzzy state of the event itself, so the fuzzy comprehensive evaluation conclusion has the quality of all information advantage.

(2) Quantitative

The quantitative indicators are evaluated by simulation or statistical methods, and the standard evaluation level is determined by traceability indicators, and then evaluated by specific evaluations. Finally, the results are summarized, that is, the average of each index is the degree of membership of the index, while the empirical evaluation index belongs to macro-evaluation indicators, which do not have special evaluable measurement tools. Therefore, the value of the index is measured according to the fuzzy principle of mathematics education subordination by evaluating the experience of related managers and experts.

(3) Hierarchical

An important issue in the evaluation is the comprehensive treatment of multiple indicators. There are many indicators for evaluating emergency response capabilities, and there is a level between them. In order to ensure the scientificity and feasibility of the evaluation model, it is necessary to classify many indicators and establish an indicator scoring system.

The basic principle of fuzzy comprehensive evaluation is to start with various factors that affect the problem, determine the overall evaluation from good to bad, and the weight of evaluation indicators, and make corresponding fuzzy evaluations for each indicator. Determine the participation function and create a fuzzy decision table, use the weight table for fuzzy calculation, and obtain a quantitative comprehensive evaluation result.

2.5 Principles for the construction of evaluation indicators

(1)
Comprehensive and systematic principles. Not missing the main indicators is the basic feature of the whole. Consistency, relevance and purpose are important aspects of system evaluation, and various indicators need to have a corresponding order in order to form a relatively complete system indicator.
(2)
The principle of relative independence. When configuring indicators, the duplication between indicators should be minimized or avoided, and the correlation should be minimized.
(3)
The principle of orientation. The business philosophy of treating users as key strategic resources should be reflected in common indicators. For business-related behaviors, the establishment of indicator-related systems should play a positive role in guiding to a certain extent.
(4)
The principles of predictability and comparability. It puts forward powerful functional requirements for the evaluation indicators, which are very simple, clear, and have a certain ease of use.
(5)
The basic principle of combining quantitative and qualitative. Based on qualitative variables, this is a very critical indicator that cannot be ignored, but it is difficult to measure in the process of overall evaluation of user value, so it is necessary to explain the qualitative indicators to a certain extent.
(6)
Highlight relevant key principles. Highlight the key points in the establishment and establishment of indicators. In other words, it is impossible to construct indicators comprehensively, and key factors need to be determined based on the completeness of the indicators.

3. Experiment

3.1 User asset value evaluation system

This paper constructs an index system for evaluating the value of power grid users’ assets in the context of smart grids, as shown in Table 1. The indicator system includes three levels: target level, criterion level and sub-rule level.

Table 1
User asset value evaluation index system

Target layer	Criterion layer	Sub-criteria layer
User asset value (A)	Social value ( $B_{1}$ )	Improve user energy quality ( $C_{11}$ )
		Reduce user energy costs ( $C_{12}$ )
	Economic value ( $B_{2}$ )	Net value of fixed assets ( $C_{21}$ )
		Electricity fee growth potential ( $C_{22}$ )
		Operation and maintenance cost reduction space ( $C_{23}$ )
	Strategic value ( $B_{3}$ )	Enhance the competitiveness of enterprises ( $C_{31}$ )
		Reduce the risk of user churn ( $C_{32}$ )

Among them, criterion layer can be divided to social value, economic value and strategic value. Sub-criteria layer can be divided to seven interm such as improving user energy quality, reducing user energy costs, and net value of fixed assets, etc.

3.2 Fuzzy comprehensive evaluation method construction model

The fuzzy comprehensive evaluation method transforms the original qualitative evaluation based on fuzzy mathematics theory into quantitative evaluation. It is to use the related principles of fuzzy mathematics to make a comprehensive evaluation of an object or an object restricted by various factors. The step table generally includes:

(1)
Establish the factor set U of fuzzy comprehensive evaluation. U is a collection of various evaluation indexes in the comprehensive evaluation. $U=\{{u_{1},u_{2},\ldots,u_{m}}\}$ . Among them, $u_{i}({i=1,2,3,\ldots,m})$ is the evaluation index factor.
(2)
Establish a comment set V of evaluation factors. The evaluation set refers to the collection of all the evaluation conclusions made by the evaluator on the evaluated object. $V=\{{v_{1},v_{2},\ldots,v_{n}}\}$ , where $v_{j}({j=1,2,3,\ldots,n})$ is the j-th evaluation result, and each level is equivalent to a fuzzy subset.

Figure 3.
Flow chart of power grid user asset value evaluation model.

(3)
Establish fuzzy relation matrix R. The score is based on only one factor, called the fuzzy score of the element, to determine the degree of membership of the score in the review set V. After constructing the hierarchical fuzzy subset, each element of the evaluated object needs to be quantified. That is, it is necessary to use a single element to determine the degree of membership of the evaluation objects at each level of the fuzzy subgroup, and obtain the fuzzy relationship matrix:

$\displaystyle R=\left[{{\begin{array}[]{*{20}c}{r_{11}}&{r_{12}}&\Lambda&{r_{1% n}}\\ {r_{21}}&{r_{22}}&\Lambda&{r_{2n}}\\ {\rm M}&{\rm M}&{\rm O}&{\rm M}\\ {r_{m1}}&{r_{m2}}&\Lambda&{r_{mn}}\\ \end{array}}}\right]$ (1)

Among them, $r_{ij}({i=1,2,\ldots,m;j=1,2,\ldots,n})$ represents the membership degree of a certain evaluated object to the level $v_{j}$ fuzzy subset from the factor $u_{i}$ point of view. The performance of an evaluated object in a certain factor $u_{i}$ is described by the fuzzy vector $r_{i}=({r_{i1},r_{i2},\ldots,r_{im}})$ .
(4)
Determine the fuzzy weight vector of the evaluation factor. In order to reflect the importance of each factor, each factor U should be assigned a weight that represents the i-th factor with an appropriate weight $b_{i}({i=1,2,3,\ldots,m})$ , which usually requires $b_{i}$ to satisfy $b_{i}\geqslant 0$ ; $\sum{b_{i}=1}$ , and then each weight constitutes a weight set B.
(5)
Fuzzy comprehensive evaluation. Combine B and the fuzzy relation table R with a suitable composition operator to obtain the vector of the fuzzy evaluation result C of each evaluated object. The basic model of fuzzy comprehensive evaluation is:

$\displaystyle B=({b_{1},b_{2},\ldots,b_{m}})=A\circ R$ (2)
(6)
Analyze the results of fuzzy comprehensive evaluation. The result of fuzzy comprehensive evaluation is the membership degree of the evaluation object at each level of the fuzzy subgroup. Common methods for processing fuzzy aggregate evaluation vectors include the principle of maximum participation and the principle of weighted average.

In summary, the process of this article’s construction of the value assessment model of grid users’ assets in the context of smart grids is shown in Fig. 3.
4. Discussion

4.1 Model measurement

Using the sequence relationship method, the index weight vectors of each layer can be obtained, as shown in Table 2.

Table 2
Index system weight vector

Index level	Weight vector
Target layer	$A=({0.40,0.33,0.24})$
Standard level (social value)	$B_{1}=({0.61,0.37})$
Standard level (economic value)	$B_{2}=({0.42,0.30,0.25})$
Standard level (strategic value)	$B_{3}=({0.49,0.49})$

The survey selected 20 evaluators and selected a series of index evaluations based on the actual value of three assets: residential community users, park government, and user-provided power plants. Taking the asset value evaluation process of residents and community users as an example, the analysis is carried out. According to the scores of the evaluators, the fuzzy comprehensive evaluation matrix of the user assets of the residential community is shown in Table 3.

Table 3

Fuzzy evaluation matrix of user assets appraisal value in residential district

Index	Excellent	Good	Middle	Bad	Very bad
$C_{11}$	0.32	0.49	0.16	0.00	0.00
$C_{12}$	0.26	0.52	0.12	0.06	0.00
$C_{21}$	0.06	0.12	0.32	0.39	0.06
$C_{22}$	0.00	0.00	0.02	0.56	0.39
$C_{23}$	0.02	0.06	0.26	0.42	0.19
$C_{31}$	0.00	0.06	0.12	0.62	0.16
$C_{32}$	0.00	0.00	0.06	0.42	0.49

Using the weighted average method, the fuzzy evaluation matrix of user assets in residential communities can be obtained as shown in Eq. (3).

$\displaystyle R_{1}=\left[{{\begin{array}[]{*{20}c}{B_{1}}\\ {B_{2}}\\ {B_{3}}\\ \end{array}}}\right]=\left[{{\begin{array}[]{*{20}c}{0.30}\\ {0.03}\\ {0.00}\\ \end{array}}{\begin{array}[]{*{20}c}{0.50}\\ {0.06}\\ {0.02}\\ \end{array}}{\begin{array}[]{*{20}c}{0.14}\\ {0.21}\\ {0.09}\\ \end{array}}{\begin{array}[]{*{20}c}{0.02}\\ {0.45}\\ {0.52}\\ \end{array}}{\begin{array}[]{*{20}c}{0.00}\\ {0.19}\\ {0.32}\\ \end{array}}}\right]$ (3)

By inputting the weighting factor vector, the comprehensive evaluation vector of the user asset value of the residential community can be obtained as shown in Eq. (4).

$\displaystyle F_{1}=A\circ R_{1}=({0.13,0.23,0.15,0.29,0.14})$ (4)

In the same way, the comprehensive evaluation vector for government parks and self-provided power plants can be obtained as shown in Eqs (5) and (6).

$\displaystyle F_{2}=A\circ R_{2}=({0.47,0.38,0.09,0.02,0.00})$ (5) $\displaystyle F_{3}=A\circ R_{3}=({0.31,0.29,0.24,0.11,0.01})$ (6)

From the comment set and the corresponding comprehensive evaluation scores, the comprehensive evaluation values of various user asset values can be obtained, as shown in Table 4.

Table 4

Comprehensive evaluation results of various user asset values

Asset value	Residential area	Government park	Enterprise self-provided
Social value	82.06	81.00	70.26
Economic value	45.58	87.87	76.99
Strategic value	36.66	92.32	84.66
Comprehensive value	58.31	86.58	76.15

Table 4 shows that among the three types of user assets, government parks have the highest total power assets, followed by company assets. From another perspective, the impact of residential parks on the social value of user assets is more prominent, but its economic significance and strategic value are relatively weak. The market prices of local government park assets and large enterprises’ self-provided assets are relatively stable in all aspects. The distribution of these two values is quite similar, which corresponds to the actual situation that the assets used in the government park can be equivalent to the collection of user-provided fixed assets.

4.2 Suggestions for improving user asset evaluation

(1) Standardize asset value verification methods

Standardize asset value verification methods to ensure the accuracy and legality of asset value information. Power supply companies usually judge the true value of an asset through the information provided to users, but such methods have obvious shortcomings in actual operation. Information about asset value is often irregular or lacking, and the entire process of verifying value is also very irregular. In this case, a third-party asset appraisal company can choose to confirm the entry price of the asset. After the power supply company signs the transfer with the user, it entrusts a trustworthy professional third party to evaluate the value of the fixed assets, and the net value after the valuation is recorded at the book value. Using these methods, the accuracy and fairness of the asset value can be guaranteed.

(2) Improve the evaluation system and improve the accuracy

Grid user asset assessment is to better cooperate with the development of power supply work. A perfect evaluation system has a direct relationship with the accuracy of evaluation and the degree of conversion, so great attention should be paid to the improvement of the evaluation system. It is not only necessary to actively explore rating standards that meet actual needs, but also to continuously improve the accuracy of evaluation. The reference data for evaluation is only the power consumption of users in the past time period, and it is also necessary to predict their power consumption potential to continuously improve the accuracy of assessment work. In this way, the dynamic update of the asset evaluation work can be realized.

5. Conclusions

With the gradual opening of the power market, power grid companies need to maintain market share and shift from managing consumer demand to responding to demand. In the face of severe challenges, it is the key to the development of power grid companies to investigate and evaluate the asset value of power grid users. The research builds a model of fuzzy comprehensive evaluation method through model analysis, and evaluates the value of power grid users’ assets through this method. The evaluation results show that the total amount of power assets in government parks is the highest, followed by enterprise assets. From another perspective, the impact of residential parks on the social value of user assets is relatively prominent, but its economic significance and strategic value are relatively weak. Research has confirmed that this model is helpful for a comprehensive understanding of electricity users, so as to provide users with perfect services. In the next research, the method of case analysis will be adopted to optimize the recommendation system through the comparative study on the optimization of user management in some power enterprises.

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