1. Introduction
Many countries have recognized that relying on one or two kinds of energy sources is not conducive to sustainable development [
1,
2,
3]. A hybrid energy system (HES), which is a system that can accommodate a variety of energy input and has multiple output functions and transport forms [
4], is gaining more attention from all over the world. In 2001, the United States put forward an HES development plan to promote the application of distributed energy and combined heating and power (CHP) technologies and to increase the proportion of clean energy use [
5]. Canada regards the HES as an important supporting technology for achieving its emission reduction targets by 2050 [
6]. Japan has become the first Asian country to carry out HES research because of its heavy dependence on imports of energy; it hopes to ease the pressure of its energy supply through technological innovation in this field [
7]. The Chinese government focuses on the implementation of the HES during the “13th Five-Year” period [
8]. Several demonstration HES projects have begun to operate and have achieved significant social and economic benefits, as shown in
Table 1.
According to the scope of supply areas, the HES can be divided into national-level, regional-level or building-level [
10]. This paper focuses on the regional-level HES. A diagram of a regional-level HES—which consists of wind power, solar photovoltaic (PV) power, natural gas, combined cooling and heating, power storage, and energy storage—is presented in
Figure 1. Its main objective is to achieve complementary and cascade utilization of multiple energy sources. Moreover, implementing the HES is an effective way to handle the serious problems of power curtailment which has occurred in North China [
11].
The performance of an HES is the most concerning problem for local governments as well as potential investors. Understanding the performance of an HES can help the investors select the most promising project and help the government to take measures to improve project performance and encourage more private capital to enter.
Nowadays, research on the performance evaluation of an HES has been quite fruitful. Khosravi et al. [
12] assessed the energy, exergy, and economic performance of an off-grid hybrid renewable energy system integrated with solar PV, wind energy, hydrogen production unit, and fuel cells. Li et al. [
13] carried out an exergy and energy performance evaluation of photovoltaic-thermoelectric (PV-TE) hybrid systems. Sahoo et al. [
14] performed an energy, exergy, and economic performance evaluation for a hybrid solar and biomass system. Yildirim and Bilir [
15] evaluated a hybrid energy system with photovoltaics and a ground source heat pump from the economic and environmental perspective. Kalinci et al. [
16] evaluated the energy and exergy performance of a hybrid hydrogen energy system which consists of a PV array, wind turbines, an electrolyzer, a polymer electrolyte membrane fuel cell, a hydrogen tank, and a converter. Ma, Xue, and Liu [
10] presented a techno-economic performance evaluation of the hybrid renewable energy system at specific spatial scales based on computer software and arithmetic models. Their research helps decision makers at different renewable energy planning levels to choose suitable approaches among a significant number of existing methods.
From above reviews, it can be concluded that the existing research on HES project performance evaluation, which enriches and develops the relative theories, mainly focuses on energy, exergy, technological, or economic aspect. However, there is a lack of literature on performance evaluation of HES projects from a sustainability perspective. The term “sustainability” refers to the long-term development which includes economic, environmental, and social dimensions [
17]. This concept has been emphasized in the development of energy projects [
18,
19,
20]. As a matter of fact, the HES has remarkable environmental benefits, such as carbon emission reduction, and social benefits, such as employment creation. Moreover, as awareness of sustainable developments has enhanced, more and more energy corporations have started to pay attention to the harmonious development of the environment, society, and economy. Therefore, this paper aims to evaluate the performance of the HES from a sustainability perspective.
The decision-making environment of HES sustainability performance evaluation is fraught with uncertainty. The reasons are as follows: On the one hand, HES performance evaluation is conducted in the early stages based on previous estimates about what its future values will be. Actually, the future values are difficult to predict precisely due to the rapid change of decision-making environments. On the other hand, some judgments involved in HES performance evaluations rely on experts’ experiences heavily; however, ambiguity always exists in the thinking of experts [
21]. Thus, HES sustainability performance evaluation is a tough process in an uncertain environment.
The fuzzy set theory, as proposed by Zadeh [
22], has emerged as a powerful way to represent such uncertain phenomena. By combining the fuzzy set theory and traditional evaluation methods, some fuzzy evaluation methods have been developed by scholars; these include fuzzy synthetic evaluation (FSE) [
23] and fuzzy multi-criteria decision-making (MCDM) approaches, such as fuzzy Order Preference by Similarity to Ideal Solution (TOPSIS) [
24], fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) [
25], fuzzy PROMETHEE [
26] and fuzzy TODIM [
27]. The FSE method is a comprehensive evaluation approach based on fuzzy mathematics. The advantage of the FSE method is that it can evaluate a single object; meanwhile, fuzzy MCDM approaches depend on the comparison between multiple projects, which leads to the phenomenon of selective reversal. Moreover, the FSE method has clear results, strong systematics, and is suitable for solving various uncertain problems. For these reasons, the FSE method has been widely applied in many fields [
23,
28,
29,
30].
The concept of uncertainty consists of concepts such as fuzziness, randomness, incompleteness, and instability. Among them, fuzziness and randomness are the most important and fundamental issues [
31]. There is a strong relationship between randomness and fuzziness. Though the FES method has unique advantages in dealing with multi-factor and multi-level complex evaluation problems, it has a shortcoming in that it ignores the inherent randomness of information. Fortunately, the cloud model first put forward by Deyi et al. [
32] can depict the relationship of randomness and fuzziness. Therefore, due to its numerous advantages, the cloud model has been successful applied in energy management fields. Wu et al. [
33] proposed a cloud-based decision framework for waste-to-energy plant site selection. In their study, a cloud choquet integral (CCI) operator is constructed to evaluate the alternatives. Wu, Xu, Li, Wang, Chen, and Xu [
31] assessed the risk level of public–private partnership waste-to-energy incineration projects in China by using a model for converting the two-dimensional linguistic variables into clouds. Zhang et al. [
34] evaluated renewable energy project performance using a hybrid approach mixing two-dimensional uncertain linguistic variables, cloud models, and an extended TODIM.
In view of this, this study proposes a cloud-based fuzzy synthetic evaluation method to evaluate the sustainability performance of the HES by studying the complementary advantages of these two methods. A three-stage framework is put forward in this paper: (1) Establishment of a comprehensive criteria system based on literature review; (2) determination of the important weights of each criterion by using the group analytic hierarchy process (GAHP) technique; and (3) evaluation of HES alternatives by employing the cloud-based fuzzy synthetic evaluation approach.
The main novelties of this paper are two-folds: (1) From the literature review, we can learn that current researchers evaluate the performance of HES projects by using a limited number of energy, exergy, technological, or economic criteria. This is the first study to establish a comprehensive evaluation criteria system for HES project performance evaluation from the four aspects of economy, technology, society, and environment; (2) the shortcomings relative to traditional FSE can be identified in the previous research (e.g., ignoring randomness), which are addressed in this paper by developing an improved fuzzy synthetic evaluation approach based on the cloud model. The traditional membership function of the FSE approach has been replaced by the membership function of the cloud model.
The other parts of this paper are organized as follows: in
Section 2, an evaluation criteria system for HES sustainability performance evaluation is established; in
Section 3, the basic theories of the GAHP, fuzzy synthetic evaluation approach, cloud model, and cloud-based fuzzy synthetic evaluation approach are elaborated upon; in
Section 4, an empirical study in Zhejiang Province, China is provided; in
Section 5, the discussion includes sensitivity analysis and comparative analysis is conducted; and the last section concludes this paper.
3. Methodology
This section presents the methodological background of the research. The research methodology introduces GAHP, fuzzy synthetic evaluation approach, cloud model, and cloud-based fuzzy synthetic evaluation approach.
3.1. GAHP Technique
GAHP was first proposed by Saaty [
44] in the mid-1970s. It is a systematic and hierarchical analysis method which combines qualitative and quantitative information. Because of its practicality and effectiveness in dealing with complex decision-making issues, it has gained worldwide attention.
Let be the set of criteria, be the th decision-makers.
Step 1: Construct individual pairwise comparison matrix
.
where
and
;
is the relative importance of
to
; and the values of
are based on a regular comparison scale of nine levels, as shown in
Table 2.
Step 2: Examine the consistency of each comparison matrix.
The consistency index
can be calculated as follows:
where
is the largest eigenvalue of its adjacency matrix, and
is the dimension of this matrix. This formula reflects that the consistency of a comparison matrix can be measured by the value of
.
Then the random consistency ratio
can be obtained, as indicated in Equation (9):
where
is the mean random consistency index whose values are shown in
Table 3. If
, the comparison matrix is considered to pass the consistency test. Otherwise, the corresponding decision-maker need to adjust their judgement matrix again until it passes the consistency test.
Step 3: Aggregate each weight.
The GAHP allows a group of individuals to join in the decision-making process [
45]. In the GAHP, every member fills up their own comparisons and records them in an individual pairwise comparison matrix. In the individual pairwise comparison matrices, each entry of the group pairwise comparison matrix is then determined as the geometric mean of the respective entries. The formula of geometric mean is as follows:
3.2. Fuzzy Synthetic Evaluation Approach
Fuzzy comprehensive evaluation is a method of comprehensive evaluation of many objects affected by various factors. The fuzzy comprehensive evaluation method is divided into single-layer and multi-layer. The use of fuzzy comprehensive evaluation can effectively deal with people’s subjectivity in the evaluation process and the objective phenomenon of ambiguity. The principle of this method is to first determine the multiple evaluation indicators by Equation (11).
where
is the evaluation factor, and it is the number of individual factors at the same level.
Next, it is divided into multiple levels according to the affiliation of each indicator, as shown in Equation (12).
where
is the evaluation levels, and
is the number of factors.
Next, an evaluation matrix is established as Equation (13) based on the membership function:
where
is the number of the factors,
is the number of the evaluation rating, and
represents the degree of
belongs to
.
Finally, turning up until the final level, the evaluation results are shown as Equation (14).
where
is the membership degree of a objective belongs to
when all factors are taken into account, and
is the criteria of factor
. The symbol
represents the weighted averaging operator in which
.
3.3. Cloud Model
In general, the uncertainty is mainly represented by two different aspects: Randomness and fuzziness. Randomness is caused by the causality of events, which is the probability in the probability theory. Besides, ambiguity means the boundaries are not clear. In general, the cloud model can better solve the problem in an uncertain situation. The cloud model can effectively integrate the randomness and fuzziness of concepts and describe the overall quantitative property of a concept by three numerical characteristics—expectation (
), entropy (
), and hyper entropy (
). Expectation
represents the mean value of the domain; Entropy
represents the fuzziness measurement of a qualitative concept; Hyper entropy
is the entropy of entropy
, which reflects the dispersion of the cloud drops. If A is a cloud with three numerical characteristics
,
, and
, then cloud A can be described as A (
).
Figure 3 shows a cloud of (20,1,0.1).
Definition 1 [
46].
Let be the universe of discourse and a qualitative concept in if is a random instantiation of concept , which satisfies , and the certainty degree of belonging to concept satisfies Equation (15).where in the universe is called a normal cloud. is the membership function of belonging to concept . It measures the property that the certainty degree of belonging to a qualitative concept . The larger the value , the more subordinate it is to the qualitative concept . Definition 2 [
47].
Assume that there are two clouds: and . Some operations between cloud and can be defined as follows: 3.4. Cloud-Based Fuzzy Synthetic Evaluation Approach
The FSE approach utilizes the membership function to transform uncertainty into certainty so that the traditional mathematical methods can be used for analysis and processing. However, the juncture function of membership function is questioned in literature [
32]. In this section, we use the cloud model instead of the membership function to propose the cloud-based fuzzy synthetic evaluation approach, as shown in
Figure 4. The process of this approach is presented as follows:
Step 1. Define the linguistic variable scale for evaluation .
For example, the linguistic assessment set can be established as .
Step 2. Determine the discourse universe of each linguistic variable for each criterion.
This process is undertaken by experts. For example, a expert may assign the interval [0%,20%] to the criterion “energy utilization rate” regarding the linguistic variable “Very Bad”.
Step 3. Calculate the membership degree of each criterion regarding every linguistic variable by using the Equation (15).
Step 4. Aggregate all the criteria and obtain the final result by using the Equation (14).
Compared with FSE, the cloud-based FSE has the following advantages: On the one hand, the randomness of uncertainty is taken into account; on the other hand, it is easy to determine the universe of discourse rather than the membership degree. The pseudocodes of the cloud-based fuzzy synthetic approach are given in
Appendix A2.
4. An Empirical Study
With the technology upgrading and cost decreasing of an HES, a large state-owned energy corporation is planning to invest an HES in Zhejiang Province, China. An industrial park is identified as a promising alternative, as shown in
Figure 5. It is planned to be built in Jinhua city. The energy sources of this HES include solar PV, wind power, and natural gas. The daily load curve of the HES in a typical summer day is shown in
Figure 6. In order to decide whether to invest or not, this corporation plans to conduct a sustainability performance evaluation on this system. A decision-making committee was established for this task, which consisted of an internal senior project manager and three external experts in the background of the HES. Their profile details are presented in
Table 4. The roles of these experts are to: (1) assess the performances of the HES on these qualitative sub-criteria; (2) provide the weight judgement matrices of each criteria and sub-criterion; and (3) determine the universe of each linguistic variable rate on each criterion.
In this region, the annual average radiation intensity is 1200 W/m
2, and the average wind speed is 4.7 m/s. This park covers an area of about 64,000 m
2. The designed electric load is about 2.0 MW, the heat load is about 1.2 MW, and the cold load is about 1.0 MW; annual power consumption of this park is about 2120 MWh. The days of heating and cooling are 120 in every year. The relative parameters are set in
Table 5.
Based on these parameters, the performances of the HES on the quantitative sub-criteria could be calculated, as shown in
Table 6.
Then, the experts were asked to express their opinion on the performances of the HES on the qualitative sub-criteria, as presented in
Table 7.
In order to determine the criteria weight, the judgement matrices were given by the three experts in the decision-making committee according to the form of Equatoin (7), which are as follows:
All of these judgement matrices passed the consistency test according to the Equations (8,9). Then, the criteria and sub-criteria weights were determined by using the Equations (10,11). The results are presented in
Figure 7 vividly. The numbers in the outer ring of this graph are the absolute weights of the four aspects, while the numbers in the inner ring are the absolute weights of the associated eight criteria.
After that, the universe of each linguistic variable rate on these criteria was provided by these experts, as shown in
Table 8.
According to the information in
Table 8, the membership degree of each criterion on each linguistic variable can be calculated by using Equation (15). The results are given in
Table 9.
For example, the calculation process of the membership degree of C11 for “VG” can be expressed as follows:
Ex = (0.5 + 0.7)/2 = 0.6; En = (0.7 − 0.5)/6 = 0.033; He = 0.1 (given by expert); En’– N (En, He2) = 0.624; .
The numbers in this table mean the membership degree of each criterion on each linguistic variable.
Finally, on the basis of previous criteria weights (
Figure 6), the overall membership degree of this HES project on each linguistic variable can be obtained as (0.849, 0.948, 0.983, 0.957, 0.889) by Equation (14). For example, the first element “0.849: can be obtained by the average weight operation: 0.877 × 0.253 + 0.992 × 0.139 + 0.996 × 0.140 + 0.000 × 0.111 + 0.987 × 0.070 + 0.948 × 0.102 + 0.999 × 0.137 + 1.000 × 0.048. According to the principle of maximum membership degree, the overall performance of the HES is “Moderate” and shows bias towards to “Bad”.
As can be observed from the
Table 9, the performance of the HES on criterion C11 “LCOE” and C22 “power supply reliability” are not good because the membership degrees of VG on C11 and C22 are relatively low. Therefore, to improve the overall performance of the HES, some measurements of these two aspects should be taken.
6. Conclusions
The hybrid energy system has become one a research hot spot because it can not only achieve multi-energy supply but can realize a cascade utilization of energy resources. However, a performance evaluation of an HES from the sustainability perspective are rarely studied. Therefore, this paper evaluates the performance of an HES from the sustainability perspective by using an integrated approach consisting of a GAHP and cloud-based FSE. The merit of the GAHP is that it cannot only take the preferences of experts into consideration but also avoid the prejudice of individual experts. The advantages of the cloud-based FSE over traditional FES lie in that it considers randomness of uncertainty and alleviates the decision-making stress of experts.
The integrated approach was applied to a real regional-level HES case study in Zhejiang province, China. The results showed that the criteria “levelized cost of energy” is the most important criterion with a weight of 0.253, followed by “energy utilization rate” with a weight of 0.140. Moreover, the synthesis result of the sustainability performance of this HES was calculated as (0.840, 0.948, 0.983, 0.957, 0.889). According to the principle of maximum membership degree, the overall qualitative performance of the HES was “Moderate”, and showed a trend towards to “Bad”. The followed sensitivity analysis showed that the proposed approach is robust, and the comparative analysis with the traditional FSE indicated that the proposed approach is superior. This paper can provide a reference for the investor to choose a high quality project and, at the same time, facilitate the government in taking effective measures to enhance the performance of an HES so as to attract more investors.
Although the contributions of this work are significant, some limitations still exist. First, it cannot take the criteria interaction into account. Second, the calculation process will become complex if the number of evaluation objects increases. Therefore, in the future work, it will be meaningful to introduce the group analytic network process to simulate the interaction relationship and some intelligent algorithms, such as deep learning, to undertake the calculations.