Multi-Scenario Spatial Modeling of PLTAL Distribution in Indonesia to Support Clean and Affordable Energy

Increasing consumption of non-renewable energy has become a topic of global problems today due to its limited availability with a high potential for environmental damage. Seeing these problems, it is necessary to develop renewable energy that is more environmentally friendly and has great potential in Indonesia. One of Indonesia’s potentials is an archipelago country with the second-largest coastline in the world with a coastline of 108,000 km. This causes Indonesia to have quite a large potential for ocean currents, namely around 1.5 m / s and reaching 2.5-3.4 m / s for the Nusa Tenggara region. The energy of ocean currents has the potential to be developed because of its relatively stable and predictable nature. Good planning by observing the potential resources and energy needs spatially is needed in the process of managing and utilizing energy in a maximum and sustainable manner. This study aims to analyze areas that have the potential to generate ocean current energy in Indonesia and utilize spatial and socioeconomic elements to determine the effective and efficient distribution of Ocean Current Power Plants (PLTAL), especially for coastal areas and small islands. This study will integrate multi-dynamic spatial data, such as ocean current data, electrification needs of each region, economic and transportation data, as well as a model of potential land availability based on remote sensing data. The result of this research is in the form of priority analysis of PLTAL development in Indonesia in terms of energy output, spatial aspects, and socioeconomic aspects. It is hoped that the results of this study can be used as considerations and solutions for policymakers in making decisions regarding the development of PLTAL in Indonesia, especially to meet the energy needs of coastal areas and small islands.


INTRODUCTION
As time goes by, concerns over environmental damage caused by greenhouse emissions from fossil fuels , the use of volatile energy and waste around the production of fossil fuels emakin increased (Nicholas & James, 2012). In 2019 the world's energy commisumi reached 158,839 TWh. The most commercialized energy produced from nonrenewable energy such as oil contributed 53,620 TWh,coal amounted to 43,849 TWh and ga amounted to 39,292 TWh (Smil,2016). In the midst of such issues renewable energy appears and is projected as the fastest growing source of energy (Energy, 2010). In Indonesia energy consumption is used in several fields, among others in the industry, households, transportation, agriculture and other sectors and the provision of energy is mostly non-renewable natural resources such as coal, crude oil, naturalgas, and biomass (Elinur,D, Mangara,& Muhama,2010). The increase in the consumption of un renewable energy is a topic of global problems today due to its limited willingness and high potential for environmental damage. Therefore, renewable energy development is needed to meet the growing consumption of energy. Renewable energy is energy obtained from the flow of energy that repeats naturally and continuously occurs in the local tingkagan (Jhon & Tony, 2015) As an indonesian archipelago has the second longest coastline in the world, which is along 99,181 kilometers with an area of sea waters reaching 6.8 million square,which is 71% of the entire territory of Indonesia (KKP & Indonesia, 2019). This causes Indonesia to have a considerable sea current potential of 1.5 m/s and reach 2.5-3.4 m/s for the Nusa Tenggara region. Marine current energy has a high potential to be developed because of its relatively stable and predictable nature. Good planning by looking at the potential of resources and energy needs spatially required in the process of development and management of energy to the maximum and sustainable. The utilization of spatial elements and sosioekomomi data is expected to determine the potential area to determine the distribution of Sea Current Power Plants (PLTAL) that are effective and efficient, especially for coastal areas and small islands.
The purpose of this study isto determine the distribution of pltal in Indonesia effectively by using a multi-scenario spatial model,menganalisis any factor that is a consideration in the determination of effective and efficient nuclear power plant,and determinethe level of trust of the distribution of pltal in coastal areas in Indonesia. With this research, it is expected that meng optimizethe poteni of marine aru power in the pesisisr region in Indonesia efficiently,memhelp the government in determining the construction site of nuclear power plants in Indonesia effectively and efficiently,and mememnuhi access to electricity in coastal areas and small islands that have not been supplied with electricity.

Data
The data used in this study can be seen in table Table 1

Method
In general, the process of data processing to determine the effective distribution of plts development in Indonesia using multi-scenario spatial model is shown by the picture below. Data processing is divided into three stages, namely modeling based on spatial aspects, modeling based on socioeconomic aspects and modeling based on effective power aspects. The data processing process in this study can be seen in Figure1.

ModelingBased on Spatial Aspects
In performing modeling based on spatial aspects, the steps performed are as follows: 1. DEM Data Processing Processing is done using ArcMap 10.6 software, where DEM data throughout Indonesia is analyzed to get the slope of each region. The slope data is grouped by class and scored. In addition, because dem data is basically altitude data, it can also be grouped by height, after which it is scored for each class as shown by Figure 2.  Table 2. After the buffer analysis process is done, the next is the scoring for each criterion.

Scoring
The scoring of each spatial parameter can be seen in Table 2

Modeling based on socio-economic aspects
The modeling process based on socioeconomic aspects is divided into the following stages: 1. Electrification and Regional 3T Data Processing Based on the electrification data used, it is divided into two classes, namely the class that is supplied with electricity and not. Scoring is done to convert the data into quantitative data, where a score of 3 is given for areas that have not been supplied with electricity, and a score of 1 is given for areas that have been supplied with electricity. Then for 3T area data, classification is done into 3T and non-3T areas. Referring to what is the target of the government, where the 3T area becomes the priority location of plts development, so that the 3T area is given a higher score than non-3T areas, as shown by Figure3.
Figure 3 Electrification and Regional 3T Data Scoring

HdI Classification and Population Density
HdI classification refers to the standard used by the Central Bureau of Statistics, after which a score is performed for each criterion as shown by Figure 4. The same is also done for population density data where classification and scoring of the data (Statistics, 2010).

Scoring
The scoring of each spatial parameter can be seen in table Table  3 below Table 3 Parameters and Criteria for Socioeconomic Aspects

Effective Power Modeling of PLTAL
In modeling the effective power of PLTAL, it must first know the speed of current in each region. The current speed in the data has speed on vector you and vector v, so it needs to be calculated as a vector resultant to know the current speed. In calculating the resultant current speed can be used Where you is the current speed in the direction of vectors you (m/s) and V is the current speed in vector v (m/s). Obtained results as in Figure 5.    (Noir, P, et.al., 2015). The equation used is as follows.
P=1/2×r×v^3× Where P (Watt) is the Effective Energy of a power plant, ρ is the period of seawater type (1025 kg/m3), v is the current speed (m/s), A is the area of turbines used with an area of 1.2 m2. η is a coeficient generator (%) with a value of 79.07%. Furthermore, it is scored by means of Geometrical Interval classification because it is considered most suitable for current models.

Incorporation of 3 Conformity models
The three models above, combined into one. However, in merging has constraints that are different study areas. In spatial and socio-economic areas are more reviewed on land areas, on the contrary in areas of potential effective current power, more reviewed in areas of sea waters. Therefore, to combine the two, a buffer is carried out on a land area of 20 km (approach of 12 miles or the length of territorial sea in Indonesia).  Based on the results of spatial and socio-economic modeling, the area of conformity in Indonesia is dominated by appropriate and very high classes. Spatial modeling is done to see the effectiveness of electricity distribution to land areas through slope data and so that the construction of pltal is not in protected areas. Socio-economic modeling, carried out so that the distribution of pltal can be precisely targeted based on needs.

Effective Power Potential Modeling
Flow speed data after processing produces effective power potential. The potential data is then grouped into 5 conformity classes based on geometrical intervals. The results can be seen in Figure   Based on the results obtained that the area of pltal is very suitable found in the strait area (sea between two islands). This means that the speed of sea currents in the strait area has a higher speed when compared to other areas. From the results, paling areais found in the sea area of Indonesia is an area that is not suitable for pltal built.

Multi Scenario Modeling
Spatial, socio-economic, and effective power model data are further combined resulting in multiskenario modeling which can be seen in Figure Kesalahan! Sumber referensi tidak ditemukan. below.

Figure 4 Multi-scenario modeling
Based on the results of multiskenario modeling, it was obtained that as a large coastal area in Indonesia has a class of conformity is not appropriate to be built pltal.

Multi Scenario Modeling
Data validation is done with EBTKE data in accordance with Figure Kesalahan! Sumber referensi tidak ditemukan. and Figure Kesalahan! Sumber referensi tidak ditemukan. From the picture, it can be seen that the PLTASL area has a very suitable current speed but, the laying of pltal is in a protected area that should not be built. However, this data sufficiently validates the resulting current data.

Figure 8
Pltal validation in Indonesia based on current data Figure 9 Validation of PLTAL position based on socioeconomic and spatial data

CONCLUSION
Pltal distribution was successfully determined, from the results can be seen that only about 8.5% of areas in Indonesia have a high conformity to be established PLTAL. The determining factor of effective and efficient pltal conformity location should be reviewed from three aspects, namely spatial, socio-economic and effective power aspects of current. This research can be developed again by including other spatial factors that affect in determining the suitability of pltal location. In addition, in the use of ocean currents can be developed to use sea current data at an even higher resolution. The results of this study can also be developed by validation to the field to the area of conformity.