**1. Introduction**

Against the background of rising environmental concerns and the depletion of fossil energy reserves, renewable energy resources are expected to be an important part of the world's future energy supply [1–3]. Marine energy, as a type of renewable energy with wide distribution, abundant reserves, and broad development prospects, has received considerable attention in many coastal countries around the world [4,5]. Wave energy is one of the major forms of marine energy, with strong predictability, high stability, and significantly higher density than other marine-energy sources [6,7]. Research has shown that the world's available wave energy could reach 2 billion kW, equivalent to twice the current total power generation [8]. At present, the harnessing and exploitation of wave energy in China is still in the research and development stage. To promote wave energy development, there has been an urgent push for research on selecting satisfactory wave power plant sites. Appropriate site selection is the prerequisite for wave energy industrialization, and it directly affects electricity-generation capacity and future socioeconomic benefits [9,10].

At present, the harnessing and exploitation of wave energy is often applied on islands that are far from the shore [11,12]. Because of the limitations of power-grid access, a large number of inhabited islands currently face power-shortage problems, which has placed great constraints on local economic development and population growth [13,14]. With island development becoming more and more important, the construction of reliable and affordable island power systems has become an urgent task [15,16]. The local development

**Citation:** Shao, M.; Zhang, S.; Sun, J.; Han, Z.; Shao, Z.; Yi, C. GIS-MCDM-Based Approach to Site Selection of Wave Power Plants for Islands in China. *Energies* **2022**, *15*, 4118. https://doi.org/10.3390/en15114118

Academic Editors: Eugen Rusu, Kostas Belibassakis and George Lavidas

Received: 21 April 2022 Accepted: 1 June 2022 Published: 3 June 2022

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and utilization of wave energy resources around inhabited islands will be a promising path [17]. Considering the high construction costs of wave power plants, it is necessary to select feasible islands and determine their prioritization for the deployment of wave power plants.

Selecting a wave power plant site involves multiple factors related to resources, technology, economy, society, and environment; it is usually regarded as a complex multi-criteria decision-making (MCDM) problem. Applying MCDM to site-selection decision-making can support dealing with multiple, often conflicting criteria in a structured way, allowing different preferences to be considered. Another excellent tool, geographic information systems (GIS), can help decision-makers carry out the collection, storage, management, calculation, analysis, and visualization of geo-referenced data [18]. In previous studies of renewable-energy site selection, GIS has been frequently combined with MCDM to form decision-support tools to exclude unsuitable sites based on restrictions or to calculate site-suitability indexes based on the established criteria system [19–22].

Moreover, some scholars try to carry out research from multi-objective planning, and the most widely used method is data envelopment analysis (DEA) [23]. DEA is a methodology based on linear programming to measure the relative efficiency of homogenous decision-making units (DMUs) with multiple inputs and multiple outputs [24,25]. Wang et al. (2022) proposed a combined method based on DEA, Grey Analytic Hierarchy Process (G-AHP), and Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) for solar PV power plants site selection, in which DEA was used in the first phase to select high-efficiency locations based on various measurable criteria [26]. Pambudi et al. (2019) presented a hierarchical fuzzy data envelopment analysis model for identifying suitable locations for the construction of wind farms in the Indonesian archipelago [27]. However, in the evaluation process, DEA focuses on economic cost and power generation efficiency, and can only perform quantitative analysis [28,29]. On the other hand, each DMU obtains the weights from the most favorable aspect, and it will cause these weights to be different with different DMUs so that the characteristics of each DMU lack comparability, and the results obtained in this way may be not reliable.

In terms of energy sources, previous studies of site selection have mainly focused on solar, onshore wind, and offshore wind power; few, however, have investigated wave power. To the best knowledge of the authors, there has been no research on site selection for wave power plants for islands in the existing literature. This is likely because the harnessing and exploitation of solar and wind energy have entered the development stage in terms of industrialization and practical use, and wave energy is still in the research and development stage, or the early stage of industrialization. Therefore, research on site selection for wave energy is of great significance for making progress in the industrialization of renewable energy.

A few studies investigating wave power plant site selection have been conducted in various areas. Ghosh et al. (2016) employed analytic hierarchy process (AHP) to obtain evaluation criteria weights and then used an artificial neural network to determine a suitability index for wave-energy-conversion device site selection in the UK and Jamaica [30]. Abaei et al. (2017) developed a new site-selection decision method to estimate the expected utility of different sites for wave power plants in Tasmania; the approach was based on a Bayesian network model and could be extended to influence diagrams [31]. Vasileiou et al. (2017) used GIS and AHP to obtain evaluation criteria weights and employed weighted linear combination (WLC) to determine suitable areas for hybrid offshore wind and wave energy systems in Greece [32]. Gradden et al. (2016) proposed a GIS-based approach for the site selection of hybrid wind and wave energy platforms along the Atlantic-facing coasts of Europe [33]. Shao et al. (2020) employed GIS, AHP, and WLC methods to calculate a suitability index and drew a suitability map for constructing wave energy power stations in Qingdao, China [34]. Nobre et al. (2009) proposed a framework based on a combination of reclassification and weighting procedures in a GIS environment. In that framework, expert experience and WLC were applied to determine suitability for wave farm deployment in

an area off the southwest coast of Portugal [35]. Flocard et al. (2016) determined criteria weights based on expert experience and obtained a suitability index for wave energy converter site selection using WLC [36].

The literature review reveals that a few studies have undertaken large-scale site selection for wave power plants based on a combination of GIS and MCDM methods. Those studies have primarily been limited to obtaining regional suitability indexes or classes for wave development. It appears, however, that no studies have considered small-scale site selection to determine the priority order of feasible site alternatives. Yet, large-scale and small-scale site selection are both essential components of research on site-selection decision-making. Moreover, in previous studies, criteria weighting and alternatives evaluation have been regarded as two core and troublesome stages that affect the decision results. Some researchers have done effective work on these two stages; nevertheless, certain problems remain to be solved, as outlined below.

(1) In published studies, AHP is the most common weighting method [37]. As a subjective weighting method, AHP relies on experts' subjective judgment to give a comparative matrix and determine criteria weights. However, the process has difficulty on avoiding subjective deviations caused by factors such as insufficient expert knowledge or experience, which affect the reliability of the weighting results.

To reduce subjective deviations and improve reliability, some researchers have attempted to improve traditional AHP by integrating it with other approaches. Integrating fuzzy theory with AHP can determine criteria weights by considering fuzzy linguistic variables from decision-makers. Sánchez-Lozano et al. expressed expert-group opinions using triangular fuzzy numbers (TFNs) and then used them in the AHP method [38]. Ayodele et al. proposed interval type-2 fuzzy AHP, which reduces uncertainty in decision-making processes [39,40]. A few researchers have combined subjective and objective weighting methods to determine criteria weights; this approach can not only consider subjective expert judgment but also reflect information in the data itself [41].

(2) Currently, the alternative evaluation methods in wave energy site selection research are mainly limited to WLC. WLC is a classical, simple, straightforward MCDM method. In recent years, it has been popularized and applied to many decision-making problems. However, it has also been criticized because its mathematical model is not sufficiently clear. Accordingly, many MCDM methods have been proposed and employed. Among them, TOPSIS (technique for order preference by similarity to an ideal solution), which has a clear mathematical model, is generally considered to be the most scientific and convenient one. Currently, this method has been used in several studies of solar energy site selection and wind energy site selection, but never been used in the field of wave energy site selection.

Sánchez-Lozano et al., used traditional TOPSIS to assess alternatives for solar power plants [42]. Several researchers used fuzzy TOPSIS to evaluate alternatives [43–47]. Fang et al. proposed an extended TOPSIS method to rank the order of photovoltaic power plant sites [43]. Sánchez-Lozano employed two different MCDM methods, TOPSIS and Elimination and Choice Expressing the Reality TRI (ELECTRE-TRI), to evaluate and classify suitable locations for solar farms; that study also examined the differences and similarities between the two methods [44].

Despite the popularity and application of TOPSIS, it still has some limitations and needs to be improved. In alternative evaluation, the classical TOPSIS method only considers the distances to the best and worst ideals while ignoring other dimensions.

This study develops a two-stage decision framework based on GIS and MCDM for wave power plant site selection for islands, and applies it in Shandong, China. The framework solves the aforementioned problems and its innovation lies in the following aspects:

(1) It includes both large-scale site selection and small-scale site selection. The first stage aims to exclude unfeasible marine areas and identity island alternatives for constructing wave power plants. The second stage aims to evaluate island alternatives to determine priority order.


The rest of this paper is organized as follows. Section 2 establishes a criteria system for site selection, including exclusion and evaluation criteria. Section 3 presents the decision framework for site selection and introduces GIS, the combined weighting method, and TOPSIS-GRA. Section 4 presents a case study of Shandong Province to identify the suitable islands for the siting of wave power plants; sensitivity analysis is performed as well. Finally, the conclusions and outlook are presented in Section 5.
