Keywords

1 Introduction

In the electricity market, grid companies are service-oriented and always operating power supply products. Scientific and rational evaluation of electric power supply service becomes critical to improve service quality [1]. Currently, the fuzzy mode is widely used to evaluate the service quality.

2 Build System of External Evaluation Index

2.1 Dimensions of Index

At present, dimension of service quality evaluation that is widely accepted and used internationally is proposed by Parasuraman, Zeithaml, and Berry based on the user perception [2]. The five dimensions of service quality evaluation system include type, reliability, assurance, impact resistance, and empathy [3, 4]. Because of the characteristics of the power industry, we increase two dimensions: the security and stability. This chapter establishes an external evaluation system based on seven dimensions:

  1. 1.

    Type: Service can be observed in the process.

  2. 2.

    Reliability: Reliability refers to the completion of service commitment accurate and reliable, such as the restoration of electricity.

  3. 3.

    Assurance: Assurance is the ability to increase customer confidence in the quality of service and security.

  4. 4.

    Response: It is the ability to make accurate response of the requirements from customers, such as blackout notification timeliness.

  5. 5.

    Empathy: Empathy refers that the power companies according to the customer needs provide differentiated services.

  6. 6.

    Security: Guarantee security of electricity supply companies is important. The power supply enterprise monitors and guides the safe use of electricity.

  7. 7.

    Stability: Stability is the impact of voltage stability and frequency stability.

2.2 Index Building

According to the power industry’s seven dimensions of service quality evaluation above, based on the feature of power supply enterprise services, combined with supply services at regional power companies in Guangdong Province, we determine the index system for this external evaluation. External evaluation index system in this chapter has set up dozens of secondary indexes. Below the secondary indexes, the third indexes include 40 evaluation components. The names of the secondary and the third indexes are shown in Fig. 17.1.

Fig. 17.1
figure 1

Index system of external evaluation

Full size image

3 Power Supply Service External Evaluation Method

3.1 G1 Method Determines Weights

AHP is one of the methods which widely used for weighing purposes. When a large number of elements exist, it is computational intensive and difficult to meet the accurate requirements. G1 method to determine the weights is an improved method of AHP, without judgment matrix, and there is no restriction on the number of elements on the same level [5]. G1 method to determine the weights is as follows [6]:

  1. 1.

    Indexes sorted by importance. Select the most important indicator from the index set and mark I i , and then select the most important indicator from the remaining indicators and mark I j . Followed the rule, suppose that a total of m indicators in index set, the indicator sequence relationship is I i  > I j  > … > I k (1 < i, j, k < m).

  2. 2.

    Determine the relative importance between adjacent indicators. The importance ratio between adjacent indicators is r k  = ω k-1 /ω k .ω k is the weight of the k indicators [7]. According to the sequence relationship in the first step, determine the relative importance r k of each indicator. The relative importance r k is firstly determined by the experts and then averaged. Relative importance’s valuing method is as shown in Table 17.1.

    Table 17.1 The value method of r k
    Full size table
  1. 3.

    Calculate the index weight. The formula is

$$ {{\omega }_{m}}={{\left[1+\sum_{k=2}^{m}{\prod_{i=k}^{m}{{{r}_{i}}}}\right]}^{-1}}$$
(17.1)
$$ {{\omega }_{k-1}}={{r}_{k}}{{\omega }_{k}}$$
(17.2)
$$ k=m,m-1,\cdots,3,2 $$

3.2 Evaluation Method Based on Zadeh Operator

Fuzzy evaluation method based on Zadeh operator is as follows:

  1. 1.

    Determine the evaluation factor set \(U=\left\{{{u}_{1}},{{u}_{2}},\cdots,{{u}_{m}}\right\}\), and determine the evaluation factor system.

  2. 2.

    Determine the component set \({{Q}^{i}}=\left\{q_{j}^{i},(j=1,2,\cdots,n ) \right\}\) for each u i , and n is the number of components.

  3. 3.

    Determine the comment set \(V_{j}^{i}=\left\{{{v}_{1}},{{v}_{2}},\cdots,{{v}_{s}}\right\}\) for each q i j , and s is the number of comments.

  4. 4.

    Evaluation of the individual indicators. r ij of each component v i composite an evaluation vector \(\alpha_{j}^{i}=\left\{{{r}_{i1}},{{r}_{i2}},\cdots,{{r}_{is}}\right\}\).

  5. 5.

    Establish a fuzzy relationship matrix of centralized component factors:

$$ {{R}_{i}}=\left[\begin{matrix} \alpha_{1}^{i}\\ \alpha_{2}^{i}\\ \vdots \\ \alpha_{n}^{i}\end{matrix} \right]=\left[\begin{matrix} {{r}_{11}}& {{r}_{12}}& \cdots& {{r}_{1s}}\\ {{r}_{21}}& {{r}_{22}}& \cdots& {{r}_{2s}}\\ \vdots& \vdots& \ddots& \vdots \\ {{r}_{n1}}& {{r}_{n2}}& \cdots& {{r}_{ns}}\end{matrix} \right] $$
(17.3)
  1. 6.

    Determine the weight set \({\omega ^i} = \left\{ {\omega _1^i,\,\omega _2^i,\, \ldots \omega _n^i} \right\}\) of fuzzy evaluation index Q i, which \(\omega_{j}^{i}(j=1,2,\cdots,n )\) is a fuzzy index weight of \(q_{j}^{i}(j=1,2,\cdots,n )\). Weight set of fuzzy evaluation of external power company is determined by G1 method from the above.

  2. 7.

    Use Zadeh operator and fuzzy synthesis. Fuzzy synthesis process is as follows:

$${B_i} = {\omega ^i} \circ {R_i} = \left\{ {b_j^i\left( {1 \le j \le s,j \in {\rm N}} \right)} \right\}$$
(17.4)
  1. 8.

    Using 7-point Likert scale to evaluate the quality of services. Let the service quality level be variable j, and then the 7-point Likert scale evaluation method is as shown in Table 17.2.

    Table 17.2 The value of 7-point Likert scale
    Full size table

The evaluation of power quality services is A. The formula is as follows:

$$ A={\sum_{j=1}^{s}{b_{j}^{i}}\cdot j}/{\sum_{j=1}^{s}{b_{j}^{i}}}\; $$
(17.5)

4 Case study

4.1 Data Statistics

This chapter assesses an area of Guangdong Province. We design the questionnaire based on the customer’s perception of power services. Comment uses 7-point Likert scale. During the calculation, the numbers of each comment questionnaire are divided by the total number of valid questionnaires and obtain the proportion.

This survey selects users in the area including 200 residents and 100 non-residents as a sample. We have given 300 questionnaires and received back 292 questionnaires, in which 268 questionnaires were valid. The effective rate was 89.3 %.

Take the third indexes B2 bills issued timeliness as an example, and the result of questionnaires is shown in Table 17.3.

Table 17.3 Index B2 questionnaire results
Full size table

Copies of each comment of B2 are divided by the total number of valid questionnaires’ copies. The rating of B2 is shown in the second line of the matrix B.

Statistical analysis gets 10 membership matrixes of 10 secondary indexes. A and B membership matrixes are listed below, C~J abbreviate.

$$ {{R}_{1}}=\left[\begin{matrix} 0.135 & 0.108 & 0.245 & 0.209 & 0.177 & 0.061 & 0.065\\ 0.097 & 0.232 & 0.144 & 0.307 & 0.152 & 0.022 & 0.046\\ 0.056 & 0.281 & 0.189 & 0.291 & 0.083 & 0.088 & 0.012\\ 0.173 & 0.198 & 0.204 & 0.368 & 0.028 & 0.021 & 0.008\\ 0.164 & 0.243 & 0.209 & 0.257 & 0.083 & 0.01 & 0.034\end{matrix} \right] $$
(17.6)
$$ {{R}_{2}}=\left[\begin{matrix} 0.152 & 0.183 & 0.21 & 0.184 & 0.097 & 0.086 & 0.088\\ 0.13\text{1} & 0.18\text{3} & 0.299 & 0.264 & 0.082 & 0.026 & 0.01\text{5}\\ 0.021 & 0.102 & 0.081 & 0.368 & 0.151 & 0.205 & 0.072\\ 0.082 & 0.106 & 0.403 & 0.235 & 0.076 & 0.059 & 0.039\\ 0.119 & 0.135 & 0.374 & 0.211 & 0.049 & 0.108 & 0.004\\ 0.067 & 0.158 & 0.105 & 0.423 & 0.108 & 0.112 & 0.027\\ 0.041 & 0.085 & 0.245 & 0.481 & 0.061 & 0.032 & 0.055\end{matrix} \right] $$
(17.7)

4.2 Weights’ Calculation

Fuzzy weights are determined by G1 method. In this assessment, the experts’ group determines that the sequence relationship is J > F > D > I > C > E > B > A > G > H

Each weight is marked as follows: \({{\omega }_{1}}>{{\omega }_{2}}>{{\omega }_{3}}>{{\omega }_{4}}>{{\omega }_{5}}>{{\omega }_{6}}>{{\omega }_{7}}>{{\omega }_{8}}>{{\omega }_{9}}>{{\omega }_{10}}\)

According to the weight principles in Table 17.1, the result is as follows: \({r_2}\) r 2  = 1.2, r 3  = 1.4, r 4  = 1.4, r 5  = 1.2, r 6  = 1.6, r 7  = 1.4, r 8  = 1.4, r 9  = 1.2, r 10  = 1.2 According to the formula (17.1),

$$ {{\omega }_{10}}={{\left[1+\sum_{k=2}^{10}{\prod_{i=k}^{10}{{{r}_{i}}}}\right]}^{-1}}=0.2 $$
(17.8)

The rest of the weight is determined by the formula (17.2).

The way of calculating the third indexes’ weight is the same as below. The weight of each index is shown in Table 17.4.

Table 17.4 Fuzzy weights of the external evaluation index
Full size table

4.3 Evaluation Calculates

According to the formula (17.3), the membership matrix and the three indexes’ weight were synthesized. Take the secondary index A as an example.

$$ {{B}_{1}}={{\omega }_{A}}\circ {{R}_{1}}=\left[\begin{matrix} 0.274 & 0.119 & 0.228 & 0.19 & 0.19\end{matrix} \right]\circ \left[\begin{matrix} 0.135 & 0.108 & 0.245 & 0.209 & 0.177 & 0.061 & 0.065\\ 0.097 & 0.232 & 0.144 & 0.307 & 0.152 & 0.022 & 0.046\\ 0.056 & 0.281 & 0.189 & 0.291 & 0.083 & 0.088 & 0.012\\ 0.173 & 0.198 & 0.204 & 0.368 & 0.028 & 0.021 & 0.008\\ 0.164 & 0.243 & 0.209 & 0.257 & 0.083 & 0.01 & 0.034\end{matrix} \right]={{\left[\begin{matrix} 0.135\\ 0.228\\ 0.245\\ 0.228\\ 0.177\\ 0.088\\ 0.065\end{matrix} \right]}^{T}}$$
(17.9)

The rest is similar. The results above constitute the secondary index membership matrix \({{R}_{10\times 7}}\), the secondary index weight and \({{R}_{10\times 7}}\) synthesis, the result is

$$ B=\omega \circ {{R}_{10\times 7}}=\left[\begin{matrix} 0.126 & 0.212 & 0.255 & 0.255 & 0.106 & 0.09 & 0.09\end{matrix} \right] $$
(17.10)

According to the formula (17.4), we obtained the external evaluation of power supply service in this area:

$$ A=\frac{\sum_{j=1}^{7}{b_{j}^{i}}\cdot j}{\sum_{j=1}^{7}{b_{j}^{i}}}=\frac{0.126\times 7+0.212\times 6+0.255\times 5+0.255\times 4+0.106\times 3+0.09\times 2+0.09\times 1}{0.126+0.212+0.255+0.255+0.106+0.09+0.09}=4.442 $$
(17.11)

External evaluation results of the secondary indexes are shown in Table 17.5.

Table 17.5 Results of the external evaluation
Full size table

4.4 Analyze Evaluation Results

External evaluation result in the area is 4.442 points, which is between the normal and the good. Studies show that the power supply service in this area meets the eligibility criteria (≧ 4.2 points). However, there still exists a gap in customers’ expectations, so the power company must improve its service in the future. From the results, we can see that on traditional services, such as power transmission services, fault repair, and electrical inspection, the points tend to be better, but in the C and I indexes, the service quality is inadequate. In times of power market, the power company in this region should further improve service levels in the aspect of direct contacting customers and strengthen the demand-side management.

5 Conclusion

In this chapter, the external quality evaluation system for power supply service fully reflects the status of enterprise services. Through the case study, the feasibility of this method is verified. This evaluation method focuses on the role of the main factors, reduces subjectivity, and shows good operability and accuracy. At present, China’s electricity market reforms have been carried out. The model in this paper provides a valid reference for the power companies to assess the quality of services under electricity market environment and helps power companies to increase market competitiveness.