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Energy Policy 34 (2006) 3359–3364 www.elsevier.com/locate/enpol
Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market Zhihong Lianga,, Kun Yangb, Yaowei Sunb, Jiahai Yuana, Hongwei Zhangc, Zhizheng Zhanga a
North China Electric Power University, Zhu Xinzhuang, De Shengmenwai, Beijing 102206, China b State Electricity Regulatory Commission of China, Beijing, China c North East Wales Institute of Higher Education, Wrexham LL11 2AW, UK Available online 25 August 2005
Abstract In 2002, China began to inspire restructuring of the electric power sector to improve its performance. Especially, with the rapid increase of electricity demand in China, there is a need for non-utility generation investment that cannot be met by government finance alone. However, a first prerequisite is that regulators and decision-makers (DMs) should carefully consider how to balance the need to attract private investment against the policy objectives of minimizing monopoly power and fostering competitive markets. So in the interim term of electricity market, a decentralized decision-making process should eventually replace the centralized generation capacity expansion planning. In this paper, firstly, on the basis of the current situation, a model for evaluating generation projects by comprehensive utilization of fuzzy appraisal and analytic hierarchy process (AHP) is developed. Secondly, a case study of generation project evaluation in China is presented to illustrate the effectiveness of the model in selecting optimal generation projects and attracting private investors. In the case study, with considerations of attracting adequate private investment and promoting energy conservation in China, five most promising policy instruments selected as evaluation factors include project duration, project costs, predicted on-grid price level, environmental protection, enterprise credit grading and performance. Finally, a comprehensive framework that enables the DM to have better concentration and to make more sound decisions by combining the model proposed with modern computer science is designed. r 2005 Elsevier Ltd. All rights reserved. Keywords: Fuzzy comprehensive evaluation; Power generation project; Energy policy guideline
1. Introduction It is well known that, in March 2002, the State Council of China authorized Electric Power Sector Reform Plan (Document no. 5) to launch an aggressive reform of the electric sector and to establish the State Electricity Regulatory Commission (SERC). The overall objectives of the electric power sector reform are to break up monopoly, introduce competition, improve efficiency, lower cost, perfect power pricing mechanism, Corresponding author. Tel.: +86 10 80793475; fax: +86 10 80798618. E-mail address:
[email protected] (Z. Liang).
0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.06.021
optimize resource allocation, promote electric power development, push ahead with nationwide grid construction, and establish a power market system. This reform is expected to lead to greater economic efficiency and lower prices, and yet maintain the very high security of electricity supply that China has enjoyed for many years. Being practically non-substitutable for many endusers, electricity forms an integral part of solutions for China’s sustainable development. And the penetration of electricity is also expected to grow. Document no. 5 has paved the way for establishing an excellent framework for fair competitions in a free electricity market. In today’s competitive market structure, investments should be triggered by market decisions in a competitive
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framework, while the security of electricity supply should be obtainable without any distorting interventions to market force. Furthermore, Document no. 5 provides many tools for market surveillance. Unlike legislations that govern any other industrial sector, Document no. 5 goes even further. It allows governments to organize direct tendering procedures, which are judged appropriate, to ensure security of supply and sustained development, to improve the performance of the electricity supply industry, and to better meet the increasing demand for electricity as a result of rapid growth of national economy and improvement of peoples’ living standards. As the first step toward market competition, the central government of China has eliminated Stateowned Power Cooperation (SPC) and reorganized its assets into five state-owned independent generation companies. Six regional electric power markets are also being established. From the end of 2004, the pilot power markets have begun to operate in Northeast and East China. The other four markets, including the South China power market, the Middle China power market, the North China power market, and the Northwest power market, will be started before 2006. However, since implementing Document no.5, a phenomenon that deserves attention is that of the unconstrained upsurge of generation construction. By the end of 2004, the generating capacity of China has amounted to 447 GW (SERC, 2004). According to the statistics of the National Development and Reform Commission (NDRC, 2004) and State Environmental Protection Administration (SEPA, 2004), there had been about 21.4 GW of new generating project capacity being authorized in 2001 23.37 GW in 2002 and 31.11 GW in 2003. In 2004, 60.0 GW had been authorized, and new generating capacity of 51.0 GW had been put into production. New install capacity of about 61.1GW has been authorized in 2005, and it is forecast that 51.0 GW of new capacity will be put into production until the end of this year. By then, China’s generating capacity will reach 500 GW. In the last 3 years, although the average annual growth rates of demand are all beyond 13%, the generating capacity is still nationally unable to meet it. But, it is forecast that the amount of available generating capacity will meet increasing energy demands by 2007. Moreover, it is said that currently undergoing construction of new generating capacity amounts to 280 GW (SERC, NDRC, 2004–2005; SEPA, 2001–2005). Unfortunately, except for that being authorized, about 150 GW of generating capacity belongs to illegal projects and has no legal certificate of authority (SERC, NDRC, 2004–2005; SEPA, 2001–2005). Though the illegal projects are also considered as beneficial to mitigating the tense situation
of the supply shortage, in fact they will damage sustained development and the performance of the electricity supply industry. Out-of-order installation capacity has also resulted in the shortage of coal supply, and at the same time, it has increased the burden of the traffic system and the cost of power generation equipment. On the other hand, surplus power generation capacity at present will directly lead to those related assets being left unused and add to bad debts of banks, as well as increase the risk of finance. It not only does damage to the electricity supply industry, but also brings negative effects on the overall national economy. Consequently, in the interim term of the electricity market, a decentralized decision-making process is needed to replace the centralized generation capacity expansion planning. Also, the power generation projects need to be conducted in accordance with environmental policy, energy policy guidelines and requirements of a competitive market. Therefore, there is urgency to study and establish a new decision-making support system to guide installations of new capacity (Fang and Wang, 2003). This paper aims to study and provide such a solution.
2. A comprehensive power generation evaluation model based on fuzzy evaluation and analytic hierarchy process As an international convention, the bidding of power generation projects is guided by market orientation in a competitive electricity market. The best power generation project can be found by competitive bidding. The evaluation of the best project, on the one hand, is influenced by the project itself and government policy guidelines, such as environmental policy and energy policy. On the other hand, however, the decision process needs the participation of experts. The asymmetry of the information held by experts and the subjectivity of the evaluation will also play an important role. Especially during the interim term of the reform, a scientific and fair bidding system for power generation projects is the foundation of the industry’s sustainable development and also the key to the success of marketoriented reformation. This paper presents a model that comprehensively uses methods of analytical hierarchy process (AHP), fuzzy evaluation and Delphi. Though the AHP method has been accepted by the international research community as a robust and flexible multicriteria decision-making tool for dealing with complex decision problems (Kablan, 2004), it is difficult to identify fuzzy or uncertain factors. The theory of fuzzy evaluation can better deal with fuzzy factors, and the method of Delphi can avoid the experts’ asymmetry of information and subjective conducts (Ma and Wang, 2003).
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2.1. General description of the model
Table 1 The values of elements in comparision matrix H[2,4]:
Fuzzy analytic hierarchy evaluation is the process of evaluating an objective utilizing the fuzzy set theory. When evaluating an objective, multiple related factors must be considered comprehensively in order to give an appropriate, non-contradicting and logically consistent judgment (Jorge et al., 2000; Chen and Wei, 2002). Assume that the objective being evaluated contains n factors, i.e., the index set is U ¼ {u1,u2,u3,y,un}. The appraisal set V ¼ {v1,v2,v3,y,vm} is the possible evaluation grading result. The appraisal of the ith single factor is Ri ¼ (ri1,ri2,y,rim), which can be considered as a fuzzy subset of V, where rik is the fuzzy membership degree of appraisal of factor i to grade k. (1) f : U ! f ðV Þ; ui ! ðri1 ; ri2 ; . . . ; rim Þ:
Numerical rating and verbal judgments of preferences between factor i and alternative factor j
The overall fuzzy appraisal matrix of all n factors is 2 3 r11 ; r12 ; . . . . . . ; r1m 6 r21 ; r22 ; . . . . . . ; r2m 7 6 7 (2) R¼6 7. 4... ... 5 rn1 ; rn2 ; . . . . . . ; rnm When making a comprehensive evaluation, the influence of each factor on the overall grading should be considered. The method to measure those influences can be formulated as a fuzzy subset A of V, which is referred to as the overall weight vector in this paper. A ¼ ða1 ; a2 ; a3 ; . . . ; an Þ,
(3)
where ai is the relative importance of factor i. The overall appraisal result is B. B ¼ ðb1 ; b2 ; b3 ; . . . ; bm Þ ¼ A R, where _ ^ bj ¼ rj aj ¼ max minða1 ; r1j Þ; minða2 ; r2j Þ; minðan ; rnj Þ .
(4)
1: 3: 5: 7: 9: 2,
factor i is equally important to factor j factor i is slightly more important than factor j factor i is clearly more important than j factor i is strongly more important than factor j factor i is extremely more important than factor j 4, 6, 8: Intermediate values
qualitative ones. AHP provides scientific ground and method for the determination of overall weight vector. The procedures of determining the overall weight vector are as follows: Firstly, the weight comparison matrix H that compares the relative importance of different factors is constructed. In this paper, the model is based on the combination of qualitative policy analyses from the regulation institution and quantitative opinions from experts committees, and the factors are arranged in order to embody policy guidelines. Secondly, the method of eigenvector is used to work out the weight vector A. Finally, in order to avoid artificial errors and the contradiction of different factors, a consistence test is conducted until a satisfactory condition is met. The value basis of element in comparison matrix H is shown in Table 1 (Zhou and Dai, 2001). 2 3 W 1 =W 1 ; W 1 =W 2 ; . . . . . . ; W 1 =W m 6 7 6 W 2 =W 1 ; W 2 =W 2 ; . . . . . . ; W 2 =W m 7 7, H¼6 6 7 ... ... 4 5 W m =W 1 ; W m =W 2 ; . . . . . . ; W m =W m (6)
ð5Þ
The physical meaning of (5) is to modify the original membership degree rij to Rij ¼ min(ai,rij) and the overall membership degree is to consider only the grade with the most important factors while ignoring the influences of the others. This is the classical Zadeh operation. If there is more than one competitive objective to be appraised, all the objectives should be evaluated separately and finally the objective with the maximum membership degree should be taken as the optimal decision. 2.2. Determination of A by AHP In this paper, the overall weight vector A is obtained by AHP. AHP is a systematic method that captures a human being’s mind process in mathematically hierarchical levels. It utilizes mathematics as the grounds of decisions and combines quantitative analyses with
where m is the number of factors. CI ¼
lmax m . m1
(7)
Generally, when CIo0.1 the consistence test is passed. The weight vector A ¼ ða1 ; a2 ; a3 ; . . . ; an Þ is then determined (Lu and Wei, 2002). 2.3. Determination of the fuzzy member function of fuzzy evaluation matrix R When determining the membership function for factors, the specific characteristic of all factors should be considered. For example, the duration of a project should not be too long, because the longer a project, the more likely it is likely to be delayed. On the other hand, the duration of a project should not be too short either, otherwise the quality of the project cannot be guaranteed. Thus an ‘incline to smaller’ function is selected for
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the fuzzy membership function of project duration. 8 xoa; > < 0; (8) AðxÞ ¼ ðb xÞ=ðb aÞ; apxob; > : 1; a4b:
Start Input projects and establish factor set Determine factor weight Construct fuzzy appraisal matrix for every project
The membership function for factors of project costs, predicted on-grid price level and environmental protection level is similar to that of the project duration. When considering the factor of enterprise credit grading, an ‘incline to bigger’ membership function is selected. 8 xoa > < 0; AðxÞ ¼ ðx aÞ=ðb aÞ; a xob ; (9) > : 1; x4b: In the implementation process the parameters a, b in the membership functions are determined by an expert panel. After determining the membership functions, the membership degree of factor i to grading j is evaluated and hence the fuzzy appraisal matrix R is obtained.
3. A model of the analytic evaluation process When evaluating several different power generation projects, firstly, a set of factors is determined according to government policy guidelines. Based on the current situation in China, factors from the perspective of economics, planning and sustainable development are selected to form a factors database. In this work, project duration, project costs, predicted on-grid price level, environmental protection level, enterprise credit grading and performance are selected as factors. Secondly, AHP that combines qualitative policy analyses from the regulation institution and quantitative opinions from the experts committee is utilized to determine the weight of these five factors. Then experts are invited to evaluate the entire project programs based on each factor and calculate the fuzzy appraisal matrix. Finally the comprehensive evaluation of every program is obtained. In particular, the economic and environmental factors interact and are constrained by each other; their relative weight exactly indicates the orientation effect of policy on environment and new power generation technology. Therefore, sensitivity analyses are conducted on project costs and environmental factors to make the final decision more objectively. The principle of fuzzy analytic hierarchy evaluation process is shown in Fig. 1. 3.1. Utilizing the delphi method to determine factor weight and fuzzy appraisal matrix In the fuzzy comprehensive evaluation model, the effectiveness of expert evaluation determines the effects of the method. So the process of expert appraisal must
Project 1, 2, 3,…. Factors database: Project cost Project construction time Projected sale price to grid Environment protection Credit of enterprise …….
Get appraisal results of all projects Preliminarily determine best project Policy sensitivity analysis
Output best project and load all the information to case database
Fig. 1. Fuzzy Comprehensive Evaluation Principle of Generation Construction Project.
be properly managed. When determining factor weight and fuzzy appraisal matrix, the Delphi method is utilized to collect the opinions of experts in order to get consistent results. The process of Delphi is as follows: (1) Firstly, project details are provided to all the selected N experts and each expert will make his/her own judgment on the fuzzy membership degree of factor i to evaluation grading j and give his/her first estimation m1n ðn ¼ 1; 2; 3; . . . ; N Þ; (2) The mean and standard deviation of first estimation m ¯ 1 and d1; (3) All the data of first estimations m11,m12,m13,y, m1N,m ¯ 1 d1 is returned to every expert anonymously and each expert is asked to give new estimations m21,m22,m23,y,m2N,m ¯ 1; (4) Steps (2) and (3) are repeated assuming k times, until the standard deviation is less than the predetermined value; (5) m ¯ k and dk are provided to all the experts and get their final estimations m1,m2,m3,y,mN are obtained; meanwhile, their credit degree to the estimations e1,e2,e3,y,eN are also obtained; N N P P mi , e¯ ¼ 1=N ei is calculated. If e¯ is ¯ ¼ 1=N (6) m i¼1
i¼1
big enough then the standard is met, otherwise, further appraisal should be sought. 3.2. Policy sensitivity analysis for the appraisal result When carryingout sensitivity analysis the relative factor weight is changed to judge the robustness of the final judgment to policy changes. If the orientation of government policy in the future should highlight environmental protection, its related factors will be given more weight and a comprehensive appraisal needs to be carried out again. Comparing the results of the two judgments, if the original program is still the best, then it is not sensitive to environmental protection. If the difference is great, then further adjustment should be incorporated in the final decision.
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4.1. Determination of weight vector A factor According to the method presented in Section 3, the weight vector A is determined and a consistence test is carried out according to Eq. (7). CIo0.10. Based on comprehensive evaluation theory, it is decided that the evaluation matrix has acceptable inconsistency. Thus, the matrix is rational. A ¼ ð0:510; 0:264; 0:130; 0:064; 0:033ÞT 4.2. Determination of the fuzzy appraisal matrix The Delphi method is used when inviting experts to evaluate the projects. According to the requirement of evaluation, the judgment set is follows: V ¼ good; common; bad . Following the process of the Delphi method, the fuzzy appraisal matrix for these three projects is obtained as follows: 3 2 0:65; 0:32; 0:03 6 0:7; 0:25; 0:05 7 7 6 7 6 7, 0:05; 0:35; 0:60 (10) R1 ¼ 6 7 6 6 0:22; 0:37; 0:41 7 5 4 0:6; 0:32; 0:08
3
0:31;
0:57;
0:12
6 0:19; 6 6 R2 ¼ 6 6 0:70; 6 0:62; 4 0:5;
0:25; 0:25;
0:56 7 7 7 0:05 7 7, 0:08 7 5 0:18
(11)
3 0:03 0:04 7 7 7 0:08 7 7. 0:04 7 5 0:20
(12)
4. Case studies In this section, a 600 MW thermal power plant project is used as an example to illustrate the application of fuzzy hierarchy evaluation model presented in this paper. In this example three generation companies compete for ownership of the project and the main information is listed in Table 2. The model and process introduced in previous sections is used to evaluate these three projects. The five factors listed in Table 2 are selected as the guidelines of evaluation. Firstly relevant experts are invited to decide the order of these five factors from the perspective of economics, planning and sustainable development. The evaluation matrix is constructed and the consistence test is passed.
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2
0:42; 6 0:61; 6 6 R3 ¼ 6 6 0:52; 6 0:79; 4 0:49;
0:30; 0:32; 0:57; 0:35; 0:40; 0:16; 0:30;
4.3. Analyses of final judgment According to Eq. (4) Bi ¼ A Ri , i ¼ 1,2,3 is calculated as follows: B1 ¼ ð0:510; 0:250; 0:130Þ,
(13)
B2 ¼ ð0:310; 0:510; 0:264Þ,
(14)
B3 ¼ ð0:420; 0:510; 0:080Þ.
(15)
From the results it is preliminarily decided that project no. 1 is better than 2 and 3 since project no. 1 has the lowest cost and shortest project construction time. But its membership degree to ‘‘bad’’ is also at a distinct level, 0.13 mainly from the perspective of environmental protection. Under the strategy of sustainable development, environmental protection and effective resource utilization is an important part in China’s electric industry reform. So project no. 1 is not necessarily the best project, because environment protection and project cost is inter-related. For the same type of generation technology, the better the environmental protection level of the project is, the higher its costs would inevitably be. So the order of project cost and environment protection in the weight vector A is exchanged to carry out sensitivity analysis. A0 ¼ ð0:510; 0:064; 0:130; 0:264; 0:033ÞT
(16)
The results of re-evaluation are: B01 ¼ ð0:510; 0:320; 0:130Þ,
(17)
B02 ¼ ð0:310; 0:510; 0:120Þ,
(18)
Table 2 the basic information of three bidding projects Project
Project duration (Year)
Project Costs ðf=KWÞ
On-grid price ðf=KWhÞ
Environment Protection
Credit grade& Performance
No.1 No.2 No.3
3.5 4 4.5
Domestic 3700(desulphurization 4200) Imported 4200(desulphurization 5100) Domestic 3950(desulphurization 4560)
0.241 0.260 0.323
None is capable of producing desulphurization establishment.
All have experience in construction and operation of 600 MW thermal unit
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B03 ¼ ð0:420; 0:510; 0:080Þ.
(19)
The final evaluation shows that project no. 1 is not sensitive to environment protection and is still the optimal project.
5. Design of the decision support system software for the model presented In the above sections, the details of the model and implementation process of fuzzy comprehensive evaluation for power generation projects have been presented. In this section, we explain how a software system for the model shown in Fig. 2 will be constructed. With the help of this software system as a decision support system, some subjective factors can be avoided and high efficiency can be gained. In the software system, not only can mathematics operation be realized but also two important functions, i.e., database management and display of the evaluation process. Database management includes generation companies’ classification, factor classification and typical case database management. The information of generation companies in the market is collected and classified according to the requirement of database management. With this information, the potential of every generation company offering the bidding can be analyzed. On the other hand, a template of the evaluation system can be constructed to offer guidance for factor classification and appraisal matrix. Factor classification database is a standard factor database based on generation planning, sustainable development of electric industry and the experience of generation companies, etc. Because the evaluation factors lead directly to the selection of project and development of the generation market, with the advancement of the market process, the factors in the database should be adjusted dynamically. Such a database should play a guiding role for China’s generation market. Case project database management collects cases of generation plant bidding domestically and abroad and evaluates them based on the model presented in this paper. The evaluation results and their actual implementation processes are then compared to test the
Bidding projects Projects 1, 2, 3 ...
Database of Classification Generation companies Input interface
Select experts from expert bases
Database of factor Case database Classification
Factor operation Comprehensive appraisal Process and result
Fig. 2. Software Frameworks for Fuzzy Comprehensive Evaluation of power generation Projects.
reliability of the model. By comparison, valuable information related to generation ownership bidding can be obtained. An evaluation of processing and results display function produces a full report of the evaluation process and loads it into the case database for future reference.
6. Conclusions Based on the fuzzy evaluation theory, a model for generation project evaluation is established in this paper. The comprehensive evaluation system is constructed according to characteristics of generation plant project and policy guidelines of the Chinese government. Then the selection process to find the best project is described; in particular, policy sensitivity analysis is used to avoid sub-optimization. Finally, a software system is designed as a decision support system for the evaluation process. The evaluation system can be used to guide the earlier stage of market process in China and to provide help to the regulation of the electricity market. In addition, the methodology presented might be beneficial to decision-making of power generation projects in other countries.
Acknowledgements The authors wish to thank an anonymous referee and the Energy Policy’s editor for their helpful advice. All remaining errors are ours. References Chen, J., Wei, Z., 2002. A model of multi-objective comprehensive evaluation for power plant projects. Proceedings of the CSEE 22 (12), 152–155. Fang, D., Wang, X., 2003. Intelligent bidding decision support system for generating companies under electricity market. Power System Technology 27 (11), 38–42. Jorge, H., Antunes, C.H., Martins, A.G., 2000. A multiple objective decision support model for the selection of remote load control strategies. IEEE Transaction on Power Systems 15 (2), 865–872. Kablan, M.M., 2004. Decision support for energy conservation promotion: an analytic hierarchy process approach. Energy Policy 32, 1151–1158. Lu, Z., Wei, Z., 2002. Application of multi-object multi-layer fuzzy comprehensive evaluation of economic operation situation of electric power enterprise. Power System Technology 26 (2), 54–57. Ma, Y., Wang, Z., 2003. Fuzzy Comprehensive Method for Gas Turbine Evaluation. Proceedings of the CSEE 23 (9), 218–220. SERC, NDRC, 2004–2005. The Analysis of National Electric Supply and Demand, http://www.serc.gov.cn. State Electricity Information Network, SEPA, 2001–2005. Annul Reports, http://www.sp.com.cn. Zhou, H., Dai, R., 2001. Level analysis method for comprehensive appraisal of power generation technique. Electric Power Construction 22 (4), 23–25.
