Smart grid service evaluation system

Smart grid service evaluation system

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect ScienceDirect Available online atonline www.science...

631KB Sizes 0 Downloads 17 Views

Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect ScienceDirect Available online atonline www.sciencedirect.com Available at www.sciencedirect.com Procedia CIRP 00 (2019) 000–000 Procedia CIRP 00 (2019) 000–000

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia CIRP 00 (2017) 000–000 Procedia CIRP 83 (2019) 440–444 www.elsevier.com/locate/procedia

11th CIRP Conference on Industrial Product-Service Systems 11th CIRP Conference on Industrial Product-Service Systems

Smart grid service evaluation system

Smart grid service evaluation system 28th CIRP Design Conference, May 2018, Nantes, France Lin Xiqiao*, Liu Yukun, Bai Xianhong

A new methodology to analyze theLiufunctional physical architecture of Lin Xiqiao*, Yukun, Bai and Xianhong center, 6# Minzhu road, Nanning Guangxi 530000, China existing productsGuangxi for power an planning assembly oriented product family identification Guangxi power planning center, 6# Minzhu road, Nanning Guangxi 530000, China * Corresponding author. Tel.: +86-0771-2552165. E-mail address: [email protected] * Corresponding author. Tel.: +86-0771-2552165. E-mail address: [email protected]

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

Abstract *Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

With the development of smart grids and advanced technologies, the application of industrial product-service system (IPS2) in power systems become Theofpower has changed from athe pure power supplier to a comprehensive energy service With thepossible. development smart grid gridscompany and advanced technologies, application of industrial product-service system (IPS2)provider in powerproviding systems energy electric rentalcompany and electric heating. IPS2 an integrated system,toinvolving quantification, service, flexibility becomestorage, possible. The vehicle power grid has changed from is a pure power supplier a comprehensive energyquality serviceofprovider providing Abstract and other processes. how and to establish the evaluation systemsystem, of smart grid is of great significance to of theservice, construction and energy storage, electricTherefore, vehicle rental electric heating. IPS2 isindicator an integrated involving quantification, quality flexibility operation smart grid.Therefore, This paperhow proposes an indicator-based grid service system aspects oftosafety, serviceability, and other of processes. to establish the evaluationsmart indicator system evaluation of smart grid is offrom greatfour significance the construction and Insustainability today’s business environment, the trend towards more product variety customization issystem unbroken. this development, the need of and intelligence. In proposes addition, a comprehensive evaluation model isevaluation established by using analytic hierarchy process and fuzzy operation of smart grid. This paper an indicator-based smart gridand service fromDue fourtoaspects of safety, serviceability, agile and reconfigurable production emerged to cope with variousbut products and product To design and optimize production evaluation method. It not only provides different calculation processes, alsoisprovides different case analytic studies from smart grids in different sustainability and intelligence. In systems addition, a comprehensive evaluation model established byfamilies. using hierarchy process and fuzzy systems as well as tois choose theand optimal product analysis are Indeed, most offrom the known methods aim to cities. The model described can beproduct used asmatches, acalculation decision support system for gridneeded. planning. evaluation method. It not only provides different processes, butmethods alsosmart provides different case studies smart grids in different analyze product or is one product and family physical level. Different productfor families, however, may differ largely in terms of the number and cities. aThe model described canon bethe used as a decision support system smart grid planning. nature of components. This fact by impedes anB.V. efficient comparison and choice of appropriate product family combinations for the production © Authors. Published Published © 2019 2019 The The Authors. by Elsevier Elsevier B.V. system. A new methodology is proposed to analyze existing in CIRP view of their functional andProduct-Service physical architecture. The aim is to cluster Peer-review under responsibility of the scientific committee of 11th Conference on Industrial Product-Service Systems. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee ofproducts thethe 11th CIRP Conference on Industrial Systems these productsunder in new assembly oriented product families for the optimization existing assembly lines and the creation of future reconfigurable Peer-review responsibility of the scientific committee of the 11th CIRPofConference on Industrial Product-Service Systems. Keywords: Power planning; grid; Fuzzy; Analytic process assembly systems. Based onProduct Datumservice Flowsystem; Chain,Smart the physical structure of hierarchy the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, hybrid functional and physical graph (HyFPAG) is the output which depicts the Keywords: Power planning; Product service system;a Smart grid; Fuzzy; Analytic hierarchyarchitecture process similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of 1.Introduction Grid Corporation have different definitions for smart grids [9thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. ©1.Introduction 2017 The Authors. Published by Elsevier B.V. 10]. For instance, have the core of thedefinitions smart gridfor defined China Grid Corporation different smart by grids [9Peer-review responsibility of scientific of the 28th Conference With under the development of the smart gridcommittee construction, the CIRP Southern Power Grid Corporation is “5 + 4bysupport 10].Design For instance, the2018. core of the smart gridlinks defined China

additional service attribute ofofpower more and more With the development smartbecomes grid construction, the

Keywords: Assembly; Design method; Family identification important. Nowadays, theofemergence of energy storage and additional service attribute power becomes more and more

other technologies has also led to the ofemergence of power important. Nowadays, the emergence energy storage and value-added services power of industry. other technologies has in alsothe led traditional to the emergence power levels of advanced grids provide different value-added services in the smart traditional power industry. 1.Different Introduction services. However, the industrial system Different levels of advanced smart product-service grids provide different services. However, the industrial product-service system realizes and processes the dynamic changes of products and Due to the fast development in the domain of services in production [1], and many scholars have studied this realizes and processes the dynamic changes of products and communication and an ongoing trend of digitization and process In addition, the production cycle of electric services [2-5]. in production [1], andenterprises many scholars have studied this digitalization, manufacturing are facing important power is also longer than that of ordinary industrial products process [2-5]. In addition, the production cycle of electric challenges in today’s market environments: a continuing [6]. example, the construction ofdevelopment a thermal power plant powerFor is towards also longer than that of ordinary industrial products tendency reduction of product times and takes 2 to 3 years [7], which determines that the power system [6]. For example, the construction of a thermal power plant shortened product lifecycles. In addition, there is an increasing is a highly planned production system takes 2 to years [7], which determines that the power demand of 3customization, being at the[8]. same time in a system global The smart grid is a high-level form of the grid, is a highly planned production system [8]. competition with competitors all over the world. Thisdifferent trend, entities have different definitions.form China's two major grid Theissmart grid isthea high-level of the grid, which inducing development from macro todifferent micro operators, State Grid Corporation and China Southern Power entities have different definitions. China's two major grid markets, results in diminished lot sizes due to augmenting operators, State Grid Corporation and China Southern Power product varieties (high-volume to low-volume production) [1].

systems” 32 key tasks[9]. Since paper Southern framework Power Gridand Corporation is “5 links + this 4 support mainly discusses the evaluation of smart grid, is systems” framework and 32 keysystem tasks[9]. Since thisthere paper little introduction to the composition of smart grid. should mainly discusses the evaluation system of smart grid,Itthere is be in composition smart grid systems, many littleemphasized introductionthat to the of smart there grid. are It should service-related indicators, most grid of which are interrelated. be the emphasized that and in smart systems, there are and/or many of product range characteristics manufactured Therefore, the establishment of an evaluation system to guide service-related indicators, most of which are interrelated. assembled in this system. In this context, the main challenge in Therefore, the establishment of an evaluation system to guide the planning and construction of smart grid has become the key modelling and analysis is now not only to cope with single the planning and construction of smart grid becomefamilies, the key work of agrid construction [11-12]. The has evaluation system products, limited product range or existing product work of grid construction [11-12]. The evaluation system framework includes power generation, transmission and but also to be able to analyze and to compare products to define framework includes power generation, transmission and distribution, intelligent energy, etc. However, smart grid new product families. It can be observed that classical existing distribution, intelligent energy, etc. However, smart grid construction is a complex system. It is impossible to effectively product families are regrouped in function of clients or features. construction is a complex system. It is impossible to effectively judge a assembly single indicator system, and aaremulti-objective However, oriented product families hardly to find. judge a single indicator system, and a multi-objective evaluation system must be established [13]. In the in multiOn the product family level, products differ mainly two evaluation system must be established [13]. In the multiobjective evaluation system, each indicator has different main characteristics: (i) the number of components and (ii) the objective evaluation system, eachindicators indicatorand has different functions. How to(e.g. combine these finally get a type of components mechanical, electrical, electronical). functions. How to combine these indicators finally get a Classical methodologies considering mainlyand single products

or solitary, already existing product families analyze the 2212-8271 © 2019 Theaugmenting Authors. Published by Elsevier To cope with this variety as wellB.V. as to be able to product structure on a physical level (components level) which Peer-review the scientific committee the 11th CIRP Conference Product-Service 2212-8271 possible ©under 2019responsibility The optimization Authors. of Published by Elsevier B.V. identify potentials in ofthe existing causeson Industrial difficulties regardingSystems. an efficient definition and doi:10.1016/j.procir.2017.04.009 Peer-review under responsibility of the scientific committee of the 11th CIRP Conference on Industrial Product-Service Systems.families. Addressing this production system, it is important to have a precise knowledge comparison of different product doi:10.1016/j.procir.2017.04.009

2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-reviewunder underresponsibility responsibility scientific committee of the CIRP Conference on 2018. Industrial Product-Service Systems. Peer-review of of thethe scientific committee of the 28th11th CIRP Design Conference 10.1016/j.procir.2019.04.138



Lin Xiqiao et al. / Procedia CIRP 83 (2019) 440–444 Lin Xiqiao et al. / Procedia CIRP 00 (2019) 000–000

comprehensive and operational indicator system has become the focus of this paper. This paper establishes a set of smart grid indicator evaluation model based on AHP-Fuzzy evaluation methods. AHP (Analytic Hierarchy Process) is a practical multi-objective decision-making method proposed by American researcher Professor TL Saaty in the 1970s. It is a combination of qualitative and quantitative decision analysis methods [14-15]. This method is suitable for decision-making problems with complex structure [16]. Fuzzy is a comprehensive evaluation method based on fuzzy mathematics [17]. The core for Fuzzy method is to convert qualitative evaluation into quantitative evaluation according to the membership degree theory of fuzzy mathematics. A general assessment of the things or objects that are constrained. It has the characteristics of clear results and strong system, which can solve the problems which is difficult to quantify. An evaluation method was proposed that can evaluate smart grids service quality considering the development of these areas and flexibly adjust the evaluation weights between the indicator. The results show that under this indicator system, the method can well evaluate the smart grid construction in a region. 2. Managerial implications in Smart Grid

441 2

○ ○ ○

Score calculation for each level Convert score to a comment Consistency test First of all, AHP is used to scientifically determine the weight of evaluation elements. Establish the hierarchical structure of the problem, i.e. analyze the evaluation object hierarchically, establish a clear hierarchical indicator system, and give the factor set and subfactor set of the judgment object. First-level factor set: A = {𝐵𝐵1 , 𝐵𝐵2 , ⋯ 𝐵𝐵𝑚𝑚 },Second-level factor set: B = {𝐶𝐶1 , 𝐶𝐶2 , ⋯ 𝐶𝐶𝑚𝑚 }. 2.2. AHP Process

The experts firstly used the 1-9 scale method to qualitatively describe the relative importance of each level of evaluation indicator, and quantified them with accurate figures (shown in Table 1 to determine the pair wise comparison judgment matrix. Table 1. i factor than j factor

Relative importance scale the same

a little

obvious

strong

extreme

important

important

important

important

important

1

3

5

7

9

Evaluation value

The factors in the factor set A = {𝐵𝐵1 , 𝐵𝐵2 , ⋯ 𝐵𝐵𝑚𝑚 } are related Many scholars have focused on the contribution of to the total evaluation target, and the judgment matrix of the technological advances to smart grids. However, this paper pair wise importance comparison is as follows: believes that management progress is equally important to smart grids. The development of the power grid is a highly B1 B2 Bm regular system. In the past construction and management b1m   B1  1 b12 process, many key performance indicators (KPI) were created    B b b 1  2m  to ensure that the final grid can be built as planning. In = the A  2  21 = (b= (bij 1/ b ji ) (1) ij ) mm  construction process of smart grid, management type indicators      Bm bm1 bm 2 1  are the most difficult and most need to be quantified. For  example, the sense of innovation of managers affects the innovation of a regional grid, which is difficult to measure with Similarly, the element of the second-level factor set B = an accurate number. Therefore, we use the expert scoring {𝐶𝐶1 , 𝐶𝐶2 , ⋯ 𝐶𝐶𝑚𝑚 } can be obtained relative to the first-order prime, method for this process, The weights obtained by this method and the judgment matrix of the pair wise importance can guide the smart grid builders to focus on the comparison is as follows: implementation of the project. 2.Methodology 2.1. AHP-Fuzzy evaluation model

Ci1

Ci 2

Cik

i   1 f 12 f 1k   Ci1  i    i 1 Ci 2  f 21 f 2k  f i Bi = = k k      Cik  i i  process. The 1   f    k1 f k 2

This model is a combination of two different main steps include: (1) using the analytic hierarchy process to determine the indicator weights (2) calculating the indicator to be evaluated, designing the questionnaire (3) send the background and indicator of the objects to experts, using fuzzy comprehensive evaluation combined with expert opinions. In order to facilitate the better understanding, we will list the key step in each process and explain it separately. AHP-FUZZY evaluation model • AHP ○ Sort out the importance of the factors in each section ○ Sort out the importance of each evaluation section • FUZZY

i

(i 1,= 2, m) ( f 1/ = i

ij

f

i ji

(2)

)

2.3. Fuzzy Process The calculation methods for judging matrix eigenvectors include root method, sum method, eigen root method, logarithm least squares method, least square method, gradient method, etc. The root method is the simplest and easiest to use. and the accuracy requirement is not very high. The precision requirement of the eigenvector of the judgment matrix involved in this research is not very strict. In order to simplify the calculation, the root method is used to calculate the feature

Lin Xiqiao et al. / Procedia CIRP 83 (2019) 440–444 Lin Xiqiao et al. / Procedia CIRP 00 (2019) 000–000

442

vector and the maximum feature. The resulting weight vector is:

wi

n

n

n

= n bij /  ( n bij ) i 1, 2, (3)

n

i =1 j 1 =j 1 =

Where n is the order of the decision matrix, n = m for the decision matrix A, and n = k for the decision matrix . In order to check the consistency, the maximum characteristic root l max of the judgment matrix needs to be calculated.

= max

n ( Awi )i = , i 1, 2,  nwi i =1

n (4)

Consistency test, calculate consistency ratio C.R:

C= .R

C.I (max − n) / ( n − 1) (5) = R.I R.I

Where R.I is the average random consistency indicator, the specific values are shown in the table below. When C.R<0.1, it is generally considered that the consistency of the judgment matrix is acceptable. The smaller the value of C.R is, the smaller the value of the judgment matrix deviates from the actual situation, and the closer it is to the reality. Among them, R.I is the average random consistency indicator, the specific values are shown in the table below. When C.R<0.1, it is generally considered that the consistency of the judgment matrix is acceptable. The smaller the value of C.R is, the smaller the value of the judgment matrix deviates from the actual situation, and the closer it is to the reality. Table 2. The value of random consistency indicator R.I Order

1

2

3

4

5

R.I

0.00

0.00

0.58

0.90

1.12

3.Case Study 3.1. Guangxi Smart Grid service evaluation system The example provided in this paper is the smart grid indicator system from Guangxi, China. Guangxi Smart Grid service evaluation system focuses on building a ‘safety, serviceability, sustainability and intelligence’ smart grid. Basically, the system needs to meet the needs of consumers and the actual situation of power grid construction on the basis

3

of smart grid professional theory. The establishment of the indicator system should fully evaluate the smart grid and strive to scientifically and objectively reflect the foundation and comprehensive situation of smart grid construction. It is required that the indicator should have sufficient comparability, clear meaning, easy quantification and scientific calculation, and meet the needs of horizontal and vertical comparison of the indicator system. 3.2. Evaluation process A to B (Primary evaluation system) 1/5 1/2 1/3 2 ] 1 5 1/5 1

𝐵𝐵1 1 1/3 𝐵𝐵2 3 1 A={ [ 𝐵𝐵3 5 3 𝐵𝐵4 2 1/2

B to C(secondary evaluation system) C11 C12

C13

C14

C11  1 1/ 3 1/ 8 1/ 4     C 3 1 1/ 5 1/ 3  B1 =  12  1 5  C13  8 5 C14  4 3 1/ 5 1 

C21 C21  C B2 =  22  C23  C24

 1 1/ 5  1/ 4  1/ 3

C22

C23 C24

5

4

3  1 1/ 3 1/ 5  3 1 1/ 3  5 3 1 

C41 C42

C31 C32

C 1 1/ 3  C 1 1 / 3  B4 =  41  B3 =  31    C32 3 1  C42 3 1  The root method is used to solve each judgment matrix, and the relative weights of the compared elements under the single criterion are obtained. Weight vector: H = (B1 , B2 , B3 , B4 )T = (0.0823,0.225,0.5644,0.126)T

Maximum characteristic root: (𝐴𝐴𝑤𝑤 )𝑖𝑖 𝜆𝜆𝑀𝑀𝑀𝑀𝑀𝑀 = ∑𝑛𝑛𝑖𝑖=1 𝑖𝑖 =4.0593 Consistency test:

C.R=

𝑛𝑛𝑤𝑤𝑖𝑖

(4.0593−4)/(4−1) 0.9

=0.0198<0.1

(6)

(7)

According to formula (5), the evaluation result satisfies the consistency test.



Lin Xiqiao et al. / Procedia CIRP 83 (2019) 440–444 Lin Xiqiao et al. / Procedia CIRP 00 (2019) 000–000

443 4

Table 3. Smart grid service evaluation system weights Primary indicator

Weights

Safety

0.0823

B1

Serviceability B2

Sustainability B3

Intelligent B4

0.2257

0.5644

0.1276

Secondary indicator

Weights

Number of grid risk accidents C11

0.75

——

Anti-accident measures implementation rate C12

0.25

Number of implemented initiatives / Number of all initiatives×100%

Power supply reliability C21

0.5226

User average power outage time /User power supply time

Voltage pass rate C22

0.0638

----

Third-party evaluation score C23

0.1328

----

customer satisfaction C24

0.2808

----

Clean energy ratio C31

0.75

Total energy used by clean energy / Total energy generation

Demand side response power ratio C32

0.25

Demand side response adjustment capability / Maximum load×100%

Smart meter coverage C41

0.0533

Install smart meter quantity / Total number of meters× 100%

Low voltage centralized meter coverage C42

0.1116

Number of low-voltage collectors / Total number of meters ×100%

Smart device application coverage C43

0.6274

(Power supply - Electricity sales)/ Power supply× 100%

The proportion of integrated energy services C44

0.2077

Grid integrated energy service business profit / Total revenue×100%

3.3. Fuzzy evaluation process

Calculation formula

First-level fuzzy comprehensive evaluation to determine fuzzy relation matrix:

After calculating the indicator data according to table 3 T above, the expert survey method is used to determine the = R (R = 1 , R2 , R3 , R4 ) membership grade, and 10 experts are invited to evaluate the indicator. The evaluation grades are classified as ‘Great’, ‘Good’, ‘General’ ,‘Poor’, ‘very bad’ , then obtaining the fuzzy matrix of the first-level indicator as follows: 5 /10 6 /10 S1 =  7 /10  7 /10

4 /10 1/10 0 /10 0 /10  4 /10 0 /10 0 /10 0 /10  3 /10 0 /10 0 /10 0 /10  3 /10 0 /10 0 /10 0 /10 

 4 /10  2 /10 S2 =   3 /10   4 /10

3 /10 2 /10 1/10

0 /10  4 /10 2 /10 2 /10 0 /10  4 /10 2 /10 1/10 0 /10   4 /10 2 /10 0 /10 0 /10 

3 /10 3 /10 3 /10 1/10 0 /10  S3 =    2 /10 3 /10 3 /10 2 /10 0 /10  0 /10 0 /10 0 /10 0 /10 10 /10  S4 =   0 /10 0 /10 0 /10 0 /10 10 /10 

0 0  0.0562 0.02622 0.0004 0.2482 0.2308 0.1327 0.0519  0   0.0572 0.0572 0.0572 0.0191 0    0 0 0 0.063  0

Considering the weights obtained by the AHP scoring method. The final evaluation vector is: H

R=(0.16,0.166, 0.099, 0.038, 0.008)

The final ratio obtained after normalization is: Table 4. Comment results Comment

Great

Good

General

Poor

Very bad

Ratio

33%

34%

20%

8%

3%

Therefore, under this evaluation system, the level of smart grid construction in the region is between ‘Great’ and ‘Good’.

Lin Xiqiao et al. / Procedia CIRP 00 (2019) 000–000 Lin Xiqiao et al. / Procedia CIRP 83 (2019) 440–444

444

3.4. Comprehensive evaluation case Five network areas with distribution networks were randomly selected for evaluation ( V1, V2, V3, V4, V5 ). According to the above-mentioned methods, the final evaluation vectors of multiple users were obtained and normalized respectively. The obtained results are shown in the following table. Table 5. Evaluation Results of Municipal Smart Grid Construction

E

Cities

Type of cities

Final evaluation vector, normalized result

V1

Second-tier city

(0.3698,0.3409,0.2031,0.0782,0.0081)

V2

Fourth-tier city

(0.1362,0.273,0.3493,0.2097,0.031)

V3

Fourth-tier city

(0.4441,0.3502,0.1030,0.02175,0.08095)

V4

Third-tier city

(0.231,0.3602,0.2227,0.1304,0.05567)

V5

Fourth-tier city

(0.05766,0.2012,0.277,0.3319,0.1325)

Evaluation Results 1.5 1 0.5 0

V1 Great

Figure 1:

V2 Good

V3 General

V4 Poor

V5 Very bad

Evaluation Results of Municipal Smart Grid Construction

According to the principle of maximum membership (figure 1), the smart grid construction level in the above network area is ‘ great’ for cities V1 and V3. The best city is V4, which is better than ordinary or poor cities V2 and V5. Therefore, we believe that this method can comprehensively quantify the level of smart grid construction and evaluate the score. This method has the following advantages: 1) It can effectively avoid the problem that some indicators are too close to be evaluated. For example, the " comprehensive voltage pass rate" indicator ranges from 99.9% to 99.999%, and the real level cannot be effectively evaluated by numerical value or ranking alone. 2) Ability to evaluate difficult - to - quantify indicators, such as the overall quality of employees.3) Different evaluation objects can be adjusted adaptively. 4.Conclusion This paper proposes an indicator system for evaluating the construction of smart grids from three levels: provincial level,

5

city level, and park level. This system combines the development plan of China Southern Power Grid Corporation's smart grid to address the characteristics and development needs of Guangxi Power Grid Corporation. Reliable, clean and efficient four aspects to evaluate the construction of smart grid. A comprehensive evaluation method for the level of smart grid construction based on AHP-Fuzzy model is established. The results of the example show that the evaluation method can be well adapted to the smart grid indicator system of the South Network, which can clearly distinguish the excellent areas of smart grid construction and provide strong support for the development planning of the smart grid. References [1] Meier H, Roy R, Seliger G . Industrial product-service systems—IPS2. CIRP annals, 2010; 59:607-627. [2] Song WY. A rough set approach for evaluating vague customer requirement of industrial product-service system. International Journal of Production Research , 2013;22:6681-6701. [3] Pezzotta G, Pirola F, Rondini A, et al. Towards a methodology to engineer industrial product-service system – Evidence from power and automation industry. Journal of Manufacturing Science & Technology, 2016; 15:19-32. [4] Rese M, Strotmann W, Karger M. Which industrial product service system fits best. Journal of Manufacturing Technology Management, 2009; 20(5):640-653. [5] Choi J, Shin Y, Cho S. Study on information security sharing system among the industrial IoT service and product provider. International Conference on Information Networking,2018. [6] Di X, Nie Z, Yuan B, et al. Life cycle inventory for electricity generation in China. International Journal of Life Cycle Assessment, 2007; 12:217224. [7] Bin X , Chenxia S , Xiaofei Y . Comparing Chinese Clean Coal Power Generation Technologies with Life Cycle Inventory. 2010 international conference on energy, environment and development, 2011; 2195-2200. [8] El-Khattam W, Bhattacharya K, Hegazy Y, et al. Optimal Investment Planning for Distributed Generation in a Competitive Electricity Market. IEEE Transactions on Power Systems, 2004;19:1674-1684. [9] Chen YP, Huang XL, Du ZM. Energy Transformation and Smart Grid . China Electric Power Press, 2017. [10] Chen SY, Song SF, Li LX, et al. Review of Smart Grid Technology . Power System Technology, 2009; 33:1-7. [11] Zhang WL, Liu ZZ, Wang MJ, et al. Research progress and development trend of smart grid. Power System Technology, 2009;33:1-11. [12] Wang J M, Shi T. Construction of Evaluation indicator System for Smart Grid. East China Electric Power, 2012. [13] Su WH. Research on the theory and method of multi-indicator comprehensive evaluation. Xiamen University, 2000. [14] Wang LF, Xu SB. Introduction to Analytic Hierarchy Process . Renmin University of China Press, 1990. [15] Saaty T L. How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research, 1994;48:9-26. [16] Zhou RJ, Wan TL, Yang Y, et al. Research on Comprehensive Evaluation System of Power Grid Company Based on AHP, China Electric Power, 2002;35(9):39-43. [17] Zadeh L A. Fuzzy sets . Information & Control, 1965; 8:338-353.