Elsevier

Renewable Energy

Volume 160, November 2020, Pages 1349-1363
Renewable Energy

A high-resolution wave energy resource assessment of Indonesia

https://doi.org/10.1016/j.renene.2020.06.017Get rights and content

Highlights

  • Wave energy assessment for Indonesian archipelago with spatial resolution of 0.05°.

  • Based on a dynamically downscaled wave model with observation-based physics (ST6).

  • Mean wave power in excess of 30 kW/m was found for season June to August.

  • 10% of mean wave power exceeds 40 kW/m with the top-one percent up to 60 kW/m.

Abstract

Indonesia is an archipelago country with great potential for marine renewable energy, particularly for wave energy. This study will provide a wave energy assessment of Indonesia over a 6.5-year period (2011–2017) with resolution about 5.5 km. This assessment is based on data generated with a two-way nested high-resolution wave model WAVEWATCH III with observation-based physics (ST6). Three grids have been generated, namely a ‘high resolution’ of 3 arc-minute (0.05°) grid is nested inside a 12 arc-minute (0.2°) grid which is nested within a 0.5° global grid. Validations against altimeters and buoys show good agreement with the model. Mean wave energy has been classified based on meteorological seasons and it is found that the most energetic months are June, July, August for all areas of south, southwest and west of Indonesia where it can exceed 30 kW/m. In some locations wave energy is available throughout the entire year, that is in the south of Jawa island, Bali island and West Nusa Tenggara while in the region of west Sumatera, promising wave energy is available during the time from March to November. In addition, within the Indonesian Archipelago the monthly variations are about 5 kW/m and relatively small in terms of absolute values but this region is large relative to the mean wave power energy.

Introduction

The Indonesian government has formally considered renewable energy as an alternative source of energy and legislated it in the presidential decree no. 5 in 2006. One of the drivers for this consideration is to become self-sustaining by reducing the countries dependency on oil imports and dependency on oil price and its fluctuations. The national energy council of Indonesia reported in 2015 that the volume of oil imported to the country has increased about 9% in eight years, from 35% in 2007 to 44% in 2015. The presidential decree no. 5 is targeting for 17% of renewable energy contributions of the total primary energy mix by 2025. However, this figure has been revised in 2014 through Indonesian government regulation no. 79, which saw an increase to 23% by 2025. The Indonesian government set a second target aiming for 31% contribution of renewable energy by 2050. In order to reach such objectives, the assessments on renewable energy have to be carried out specifically for marine renewable energy. Guidelines and methodologies to study the feasibility for ocean renewable energy have been set out by the European Marine Energy Centre (EMEC) [1] and the International Electrotechnical Commission (IEC) [2,3]. Moreover, as WEC design parameters are uncertain [4] we will perform a theoretical resource assessment.

Indonesia is an archipelago country that consists of more than 17,000 islands and about 65% ocean (see Fig. 1). This means Indonesia has more ocean than land and hence it has great potential to harness wave energy as well as tidal energy [[5], [6], [7]]. The Indonesian Agency for Assessment and Application of Technology and Ribal and Zieger [5] found that west of Sumatera, south of Jawa, Bali, West Nusa Tenggara and East Nusa Tenggara are very promising for wave energy generation although a hard estimate for wave energy production has not been established. We note that, this study will only assess wave energy.

Wave energy around Indonesia has been previously studied by Ribal and Zieger [5] and was based on two years of satellite data (i.e. ENVISAT altimeter). An extension to this study was carried out by Amiruddin et al. [8], which included 10 years (2002–2012) of satellite data from ENVISAT altimeter. Altimeters estimate the sea state (i.e. significant wave height) over a small footprint (typically 8–16 km). In order to get continuous spatial coverage, altimeter data was averaged in 1° × 1° bins [5,[9], [10], [11]]. In transition from sea to land and in between islands, altimeter wave forms contain spikes and lead to spurious estimates for significant wave height. A filter was applied to eliminate erroneous observations from the database [12].

Hemer et al. [13] assessed the wave power resource around Australia based on a long-term wave hindcast [14]. This assessment covered a small part of the Indonesian archipelago. High-resolution wave model, time-variable boundaries and accurate wind conditions are seen as essential to properly resolve wave energy resource for reconnaissance [15]. Morim et al. [15] concluded that improper calibration and verification of the wave model can lead to discrepancies of 20–30% in wave power between wave model and buoy records in the Australian region. Similarly, Hemer et al. [13] revisited their initial estimate [16] of wave resource 10% up and attributed the differences to bulk wave parameter prescribed at the boundary.

In this study a high-resolution dynamically downscaled wave hindcast was created using a recent version of the 3rd generation spectral wave model WAVEWATCH III (v5.16) for 79 months over the period from 01 January 2011 until 31 July 2017. The highest resolution in the global wave model is three arc-minutes (∼5.5 km) or 0.05°. The results will be validated against buoys’ observation data and the altimeter data from seven different satellites, namely JASON-1, ENVISAT, JASON-2, CRYOSAT-2, SARAL, JASON-3 and SENTINEL-3A (expressed in the order of launch).

This paper is organised as follows. Section 2 describes data and models used in this study. Results from model verification and wave energy resource assessment are presented in Section 3. A discussion in Section 4 is followed by conclusions in Section 5.

Section snippets

Buoy data

Indonesia’s archipelago is lacking in-situ wave observations as well as dedicated observation programs for long observation data. The closest three buoys are close to the area of interest are owned by Australian state government, more precisely the Queensland Department of Science, Information Technology and Innovation (QLD DSITI). One buoy is located in the Gulf of Carpentaria near Weipa (station number 52121) and contains ongoing records since 25th November 2008. This buoy records mean wave

Model verification

Verification is focussed on significant wave height and wave power energy from Table 4. To assess the skill of the model (M) against observation (O), four verification metrics were selected, including bias (B), root-mean square error (RMSE), Pierson’s correlation coefficient (R) and scatter index (SI) as defined in equation (1), (2), (3), (4) [29]. The correlation coefficient R given in equation (3) is a function of covariances (cov) between the model and observations.B=1Ni=1N(MiOi)RMSE=1Ni=1

Discussion

The Indonesian government’s target is to increase the contribution of renewable energy (including ocean renewable energy) to about 23% by 2025. A high-resolution wave power energy assessment around Indonesia archipelago has been carried out using the recent version of WAVEWATCH III v5.16 with observation-based physics (ST6). As shown in all previous references such as Ribal and Zieger [5] and Amiruddin et al. [8], wave power energy is very promising around southern part of Jawa, Bali and West

Conclusion

High-resolution wave energy assessments around Indonesian archipelago have been carried out in this present work. The spatial resolution in this assessment is 0.05° or about 5.5 km. Wave height and wave power have been validated against altimeter data obtained from AODN and three in-situ buoy locations. The assessment showed that wave power energy in excess of 30 kW/m is available all year around in locations south of Jawa, Bali and West Nusa Tenggara. Seas west of Sumatera are a potential wave

CRediT authorship contribution statement

Agustinus Ribal: Conceptualization, Methodology, Data curation, Validation, Formal analysis, Writing - original draft, Writing - review & editing. Alexander V. Babanin: Supervision, Writing - original draft, Writing - review & editing. Stefan Zieger: Methodology, Formal analysis, Validation, Writing - original draft, Writing - review & editing. Qingxiang Liu: Methodology, Validation, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The lead author would like to thank Australian Government for their support through Endeavour Research Fellowship program. This work was carried out during my first visit to the University of Melbourne, Victoria, Australia, under the fellowship program. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. We would also like to acknowledge the use of altimeter data as part

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