**1. Introduction**

Oceans cover over 70% of the Earth's surface and represent a wealth of renewable energy resources [1]. Many potential candidates for ocean energy sources exist, including wave energy, tide energy, tidal current energy, thermal gradients and salinity gradients [2,3]. Of these, wave power is currently the most researched area; it represents the largest ocean energy resource and is the most widely distributed marine renewable energy around the world. Many methods of wave energy conversion have been developed to extract wave power from oceans. The overtopping/terminator type of wave energy converter (WEC) consists of two wave reflectors that collect overtopping water and convert the pressure head into power [4]. The raft-type WEC uses relative rotation around a hinge to drive an electrical generation system to convert wave energy to electricity [5]. Another approach involves a point-oscillating absorber type WEC, which utilizes relative translational motion in which the oscillating motion of the floater is converted into electricity by a power take off (PTO) system for the oscillating-body WEC. This type of WEC is widely used for offshore deployments [6]. The two-floater WEC also uses a PTO system to convert ocean wave-induced motion into electricity [7]. The attenuator type of WEC captures energy from the relative motions of two arms as waves pass.

The use of numerical models to assess ocean wave power has been widely adopted on both regional [8–11] and global scales [12–15] and has been implemented for several islands, including the Canary Islands, Madeira, and the Azores in the Atlantic Ocean [16–19], Hawaii and Taiwan in the Pacific Ocean [20–23], Fuerte Island in the Caribbean Sea [24], and Sardinia and Menorca in the Mediterranean Sea [25–27].

Hashemi and Neill [28] used a wave-tide coupled model called the Simulating WAves Nearshore-Regional Ocean Modeling System (SWAN-ROMS) to investigate wave energy resources in the seas along the northwest European shelf. Their results suggested that tidal impact is significant and that the contributions from the effects of tidal currents on wave power resources are greater than the contribution from variations in tide levels. The maximum current speeds of the Kuroshio in the eastern offshore sea of Taiwan range between 0.6 and 1.2 m/s [29]. Therefore, using a tide-surge-wave coupled model is essential for accurately assessing the distribution of wave power in Taiwanese waters.

There are substantial ocean energy resources around the Island of Taiwan. However, the statistical data issued from the Ministry of Economic Affairs, Taiwan, revealed that the annual total electricity generating capacity of Taiwan was 264.1 billion kWh in 2016, of which fuel-fired power accounted for 82% (216.56 billion kWh), nuclear power accounted for 12% (31.69 billion kWh), hydroelectric power accounted for 1.2% (3.17 billion kWh), and renewable power accounted for 4.8% (12.68 billion kWh). Regarding renewable energy, only wind power and solar power are currently utilized in Taiwan. However, exploitation of energy resources in the ocean is urgently needed in Taiwan because they are renewable and do not contribute to atmospheric pollution.

Assessments of optimal locations or hotspots for deploying wave energy converters in previous studies have usually been conducted by reporting the location of selected "points". For instance, Morim et al. [30] evaluated the wave energy resources and optimal locations along the southeast coast of Australia, and Su et al. [23] assessed the distribution of wave power and hotspots for the surrounding waters of Taiwan. Although this method is quite straightforward, the possible energetic "sea area" where wave energy converters can be deployed is not definite. This is an important issue; wave energy converters should be placed in an "area" rather than at a "point". In the present study, numerical simulations based on unstructured grids were converted to structured (square) grids to identify energetic "sea areas" using the gridding method available through ArcGIS software. The annual mean wave energy yields of the final optimal areas for WEC deployments were acquired by spatial averaging over 5 km square regions. The approach proposed in the present study is both innovative and helpful for assessing the most appropriate offshore sea areas in which to deploy wave energy converters in Taiwanese waters.

The primary objectives of the present study were as follows: (1) implement a tide-surge-wave fully coupled high-resolution model for Taiwanese waters; (2) validate the fully coupled model with available observations; (3) create spatial distribution maps for annual and 12-year-average wave power; (4) using the gridding method, identify the most energetic and optimal areas in the offshore seas of Taiwan for deploying wave energy converters; and (5) assess the annual total wave energy density and the dominant wave direction in each optimal area.

#### **2. Methods and Data**
