Elsevier

Energy

Volume 165, Part A, 15 December 2018, Pages 1089-1101
Energy

Risk management of public-private partnership charging infrastructure projects in China based on a three-dimension framework

https://doi.org/10.1016/j.energy.2018.09.092Get rights and content

Highlights

  • A comprehensive risk index system concerning the PPP charging infrastructure is established.

  • A three-dimension framework consisting of possibility, uncontrollability and losses is employed for risk management.

  • ANP-grey fuzzy comprehensive assessment method is utilized for risk assessment.

  • Risks are allocated to different participants and risk countermeasures are proposed.

Abstract

As electric vehicles present booming development in China, insufficient supply of charging infrastructure has become a serious issue which hinders the development of electric vehicles. Under this circumstance, public-private-partnership mode is introduced to attract private capital so as to promote the construction of charging infrastructure. Nevertheless, the public-private-partnership charging infrastructure project, with characteristics of high investment, many participants and long payback period, possesses high risks including political risks, economic risks, technical risks and participants risks for all the project participants. To better implement the project, this paper proposes a risk management framework to control risks. First, a comprehensive risk index system is established using Delphi method. Second, a three-dimension model including probability, losses and uncontrollability are employed for risk assessment in which analytic hierarchy process method is used for weight determination and grey fuzzy method is employed for assessment. Finally, risks are allocated to different participants and corresponding countermeasures are put forward. This paper presents a comprehensive study of risk assessment, risk sharing scheme and risk response measures for the public-private partnership charging infrastructure. And furthermore, the result is helpful for both the concessioner and the grantor to develop equitable concession agreements, take measures to manage risks and further achieve win-win goal.

Introduction

As energy and environment have become serious issues in China, both consumers and producers of energy begin to invest in the required shift to a low carbon economy [1]. And in the field of transportation, electric vehicles (EVs) have become the developing tendency of the energy transformation on account of the characteristic of clean, economy and noiseless [2]. Thus, the government issued a series of policies to promote EV industry with certain results obtained in the past few years. And in 2016, China surpassed the United States as the biggest EV market with the sales reached 648 thousand.

In the process of extending the EV industry, the charging infrastructure, mainly including charging stations and charging points, plays a significant role. There have been numerous researches in charging infrastructure, such as site selection [3], smart charging strategy [4], planning of the usage and power requirements [5] and so on. These researches have laid the technical foundation for the development of charging infrastructure. Nevertheless, the quantity of charging infrastructure cannot meet the demand of customers. As is shown in Fig. 1, the quantity of charging infrastructure has been much less than that of EV since 2015. Insufficient number of charging infrastructure brings inconvenience to the utilization of EV, which tends to be a serious obstacle to the promotion of EV.

In September of 2015, China's State Council announced to ease market access and absorb the private capital through application of the Public-Private Partnership (PPP) mode in order to broaden the sources of funding to build charging infrastructure. And in the planning, the ratio of EV and charging infrastructure should be close to 1:1 in 2020 [6]. Stakeholders such as government, petroleum, grid companies and so on are active in PPP charging infrastructure projects and current mode in detail is presented in Fig. 2 [7].

Compared with other PPP infrastructure projects, the PPP charging infrastructure project possesses its own characteristics. First, the site location is significant in the whole life cycle of the project in that the bad location may directly result in the low rate of use, which may cause the failure of this project. Second, technical problems especially the battery and charging technology are to be settled. And these problems may become the big obstacle of the project. Third, the project still lacks of clear profit pattern. And there are four main reasons applying the PPP mode in the construction of charging infrastructure. Firstly, it saves government resources in that it transfers financial responsibility to the private sector [8]. Secondly, since the contract of PPP has the long-term characteristic, it forces private sector pays more attention to project life cycle cost [9]. Thirdly, PPP mode is able to effectively allocate the risks so as to achieve better risk control effect [10]. Last but not least, this mode can encourage the private sector to develop innovative proposals for that they attach great importance on the output specification [11].

There exist risk factors that may result in the failure of the PPP charging infrastructure project. For sake of avoiding and controlling these risks, it is necessary for us to implement effective risk management to deal with them. For instance, political risks including the unstable policy may directly determine whether the project can be approval of commencement or not [12]. Besides, financial risks, such as inflation and changes in interest rates [13] may bring unexpected costs increasing and reduces incomes. Economic risks such as high costs of operation and maintenance can block the continuation of the project. What's more, technical risks, especially charging technology, inadequate interconnection and technology payment risks may bring about the decreasing frequency of the charging infrastructure, which directly results in the losses of the PPP project. Apart from these, the risks brought by the project and the project participant itself such as the PPP experience cannot be ignored [14].

The PPP project involves higher degree of risks than conventional projects on account of the many participants, thus risk management is essential for it. Risk management consisting of risk identification, risk assessment and risk countermeasures is a scientific process to analyze, assess and manage the identified risks of projects [15].

For risk identification, Pellegrino et al. [16] divides previous researches concerning PPP risk identification into two groups: mainly nature of risks and risks in the phase of project. Song et al. [17] identify the risks of WTE projects making up of ten key risk factors. Furthermore, Wu et al. [18] divides risks into 4 groups respectively construction and operation risk, micro-economic risk, legal and social-political risk and government risk. On the basis of that, Liu and Wei [19] researches the risks of charging infrastructure, but the technology and economic parts are lacking of pertinence.

As for risk assessment, the method of risk assessment plays a significant role in the integrate risk management. Quantities of researches deal with this process from two dimensions and then obtain the overall risk level. For example, Xu et al. [14] take probability and severity into consideration to rank the risks, while likelihood of occurrence and magnitude of impact are two factors in the study of Wu et al. [20]. In spite of the usefulness and simplicity, the two dimensions fail to take the characteristics of PPP charging infrastructure into consideration for that the many risks which mainly resulted from the participants themselves, they can be controlled to some extent. Thus, in this paper, we employ a three-dimension (3D) framework including probability of occurrence (P), uncontrollability of risks (U) and losses resulted from the risks (L). Before the assessment, we first need to determine the weight of each factor. Analytic Network Process (ANP) developed from analytic hierarchy process (AHP) [21] which can reflect the interaction between the elements of the system is widely used [22,23]. Fuzzy comprehensive method (FCM) has presented great performance in risk assessment of PPP straw-based power generation [20], technology method selection of municipal solid waste treatment [24] and multiple flood vulnerability evaluation [25]. However, the single fuzzy method may result in information loss. We usually combine fuzzy method with grey theory applying to the assessment of risks with characteristics of fuzziness and grey in order to take advantage of both methods. For instance, Li et al. [26], Chen and Ren [27] employed this method for evaluation. And with this model, Jiang et al. [28] prejudge the warning line a hydropower program risk is on, so that they can take risk control measures.

Risks include losses and uncertainty. In practical projects, the project managements not only focus on the probability of occurrence and the losses resulted from the risks, but also focus on the controllability. That is to say, if a certain risk can be easily controlled, it should not be taken too much concentration on though it may result in a great loss and has a great probability to happen. Thus, taking losses, probability and uncontrollability into consideration, this framework is applicable for general projects. Risk assessment plays an important role in the process of risk management. This paper employs ANP-grey fuzzy comprehensive method for assessment. First, for the risk index system, the relevance between the risk factors cannot be ignored. As ANP can take full consideration into the relevance, it is employed for determination of weights. Second, the risk index system is a complex system that is related to risk factors and each risk factor has the characteristic of randomness, which is accordance with grey characteristics. Besides, the risk factors cannot be quantitatively described with an exact word, which indicates that the fuzziness exists in the risk factors. As relevance, complexity, uncertainty and fuzziness generally exist in risk assessment. ANP-grey fuzzy comprehensive method is adopted for settling these problems. Combined the framework and ANP-grey fuzzy comprehensive method, it can also be used for supplier selection, site selection and other projects risk analysis.

As for risk countermeasures, risk allocation in PPP projects is responsible for the risk response mechanism. With Delphi method, Ke et al. [29] determines the risk allocation of China's PPP projects. Sastoque et al. [30] allocates risks of social infrastructure in Colombia to participants according to different categories of risks. Jin and Zhang [31] adopts artificial neural networks to optimum risk allocation.

Up to now, only Liu and Wei [19] has investigated the risks of PPP charging infrastructure project. The study evaluates the risks from two dimensions of the impact and the probability. It contributes for participants to make decisions, but shortcoming in the study cannot be ignored. First, the risk factor index lacks of comprehensiveness as economic risks and technical risks such as interconnection risk are not sufficiently taken into account. Second, the relationship between risks and the dimension of uncontrollability receive scant attention. Third, the risk countermeasures lack of pertinent.

The originality of this paper is as follows: 1) a comprehensive risk index system is established through literature review, case study and questionnaire survey as unique risks of the project such as charging technology, operation income and layout were incorporated into the new index system. 2) A 3D framework taken the uncontrollability into consideration is employed for more comprehensive assessment of the risk. 3) ANP-grey comprehensive assessment method is employed for the relevance between the risk factors. 4) Each risk is allocated to a certain participant and response measures are proposed in consideration of current status in China.

The remainder of this study is organized as follows. Section 2 describes the risk index system. Detailed framework of the risk management is presented in section 3. In section 4, sensitivity analysis is conducted. Risk countermeasures are proposed on the basis of the result obtained in section 5. And finally in Section 6, conclusion is presented.

Section snippets

Establishment of risk index system

The establishment of the risk index system plays a significant role in the risk management in that it is the foundation of the whole process, which directly affects the accuracy and efficiency of the result. Therefore, in the process of establishment, we should take all the risk into account from multiple perspectives as much as possible. To achieve this goal, we invite relating experts from the field of EV, PPP mode, and risk management to identify the risk factors using Delphi method. Through

Methodology

To better achieve the goal of win-win status, effective risk management is of great significance. The detailed process is presented in Fig. 4. The process consists of four parts, respectively risk identification, risk analysis, risk assessment and risk response. As the risk index system is presented in section 3, the remainder parts are shown in this section and detailed steps are as follows.

Sensitivity analysis

The risk score calculated by this framework is easy influenced by the number of the experts and their different opinions to the importance of these risks. In the real practice, the experts' attitudes may vary due to different conditions. Therefore, it is necessary to perform a sensitivity analysis for the proposed framework. In this section, two different ways of testing the stability of this framework are conducted.

Risk response

As is shown in Section 3, the PPP EV charging infrastructure project in China faces great challenges because of the relatively high risks. Thus, efficient risk response plays a significant role in the whole life cycle project. Before taking measures to response the risks, it is necessary for us to determine the risk sharing party. After literature researches [[44], [45], [46], [47]] and experts consultant, the suggestions of risk sharing are presented in Table 6.

According to the Table 6,

Conclusion

The past few years has witnessed the great development of EV infrastructure in China. And PPP mode was introduced to support the EV charging infrastructure project in 2015. In this paper, we develop a risk management framework to help better implement the PPP charging infrastructure project. First, a comprehensive risk index system is established which consists of 4 first level index and 19 s level risk factors. Second, a 3D framework including probability, uncontrollability and losses is

Acknowledgements

Project supported by the 2017 Special Project of Cultivation and Development of Innovation Base (NO. Z171100002217024), the Fundamental Research Funds for the Central Universities (NO.2018ZD14) and the National Natural Science Foundation of China ( No. 71803046).

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