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Wind Energy Feasibility Study of Seven Potential Locations in Indonesia

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@article{IJASEIT10389,
   author = {- Ismail and Asrul Harun Ismail and Gama Harta Nugraha Nur Rahayu},
   title = {Wind Energy Feasibility Study of Seven Potential Locations in Indonesia},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {1970--1978},
   keywords = {annual energy production; capacity factor; Monte Carlo simulation; economic uncertainties; wind energy},
   abstract = {This study evaluated seven potential locations for wind energy in Indonesia. This study applied economic analysis using static analysis and the Monte Carlo simulation. The later was used to measure the financial indicators and then to analyze the sensitivity of economic uncertainties. The result findings show that in term of annual energy production volume among the seven selected locations, Jeneponto location has the highest value of 4,339,003.2 MWh with a capacity factor of 25.22% and Bantul location has the lowest value of 526,476 MWh with a capacity factor of 16.51%. According to economic uncertainty analysis, the highest NPV and attractive IRR values with high deviation standard values will be interesting to the risk-taking investors. Otherwise, the lowest NPV and IRR values fit the risk of avoiding-type investors due to its low deviation standard values. Based on sensitivity analysis, the increase of average NPV and IRR values of all seven locations are influenced positively by first 5-year of the selling price, which contributes approximately 30% and 50% of NPV and IRR’s percentage change. By contrast, the decrease in average NPV values of all seven locations is influenced by the discount rate, which contributes approximately 15% of NPV’s percentage change. Meanwhile, the decrease in average IRR is influenced by capital investment cost, which contributes approximately 20% of IRR’s percentage change. Policymakers can further consider the parameters mentioned above.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10389},
   doi = {10.18517/ijaseit.10.5.10389}
}

EndNote

%A Ismail, -
%A Ismail, Asrul Harun
%A Nur Rahayu, Gama Harta Nugraha
%D 2020
%T Wind Energy Feasibility Study of Seven Potential Locations in Indonesia
%B 2020
%9 annual energy production; capacity factor; Monte Carlo simulation; economic uncertainties; wind energy
%! Wind Energy Feasibility Study of Seven Potential Locations in Indonesia
%K annual energy production; capacity factor; Monte Carlo simulation; economic uncertainties; wind energy
%X This study evaluated seven potential locations for wind energy in Indonesia. This study applied economic analysis using static analysis and the Monte Carlo simulation. The later was used to measure the financial indicators and then to analyze the sensitivity of economic uncertainties. The result findings show that in term of annual energy production volume among the seven selected locations, Jeneponto location has the highest value of 4,339,003.2 MWh with a capacity factor of 25.22% and Bantul location has the lowest value of 526,476 MWh with a capacity factor of 16.51%. According to economic uncertainty analysis, the highest NPV and attractive IRR values with high deviation standard values will be interesting to the risk-taking investors. Otherwise, the lowest NPV and IRR values fit the risk of avoiding-type investors due to its low deviation standard values. Based on sensitivity analysis, the increase of average NPV and IRR values of all seven locations are influenced positively by first 5-year of the selling price, which contributes approximately 30% and 50% of NPV and IRR’s percentage change. By contrast, the decrease in average NPV values of all seven locations is influenced by the discount rate, which contributes approximately 15% of NPV’s percentage change. Meanwhile, the decrease in average IRR is influenced by capital investment cost, which contributes approximately 20% of IRR’s percentage change. Policymakers can further consider the parameters mentioned above.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10389
%R doi:10.18517/ijaseit.10.5.10389
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 5
%@ 2088-5334

IEEE

- Ismail,Asrul Harun Ismail and Gama Harta Nugraha Nur Rahayu,"Wind Energy Feasibility Study of Seven Potential Locations in Indonesia," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 1970-1978, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.10389.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ismail, -
AU  - Ismail, Asrul Harun
AU  - Nur Rahayu, Gama Harta Nugraha
PY  - 2020
TI  - Wind Energy Feasibility Study of Seven Potential Locations in Indonesia
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 1970
EP  - 1978
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - annual energy production; capacity factor; Monte Carlo simulation; economic uncertainties; wind energy
N2  - This study evaluated seven potential locations for wind energy in Indonesia. This study applied economic analysis using static analysis and the Monte Carlo simulation. The later was used to measure the financial indicators and then to analyze the sensitivity of economic uncertainties. The result findings show that in term of annual energy production volume among the seven selected locations, Jeneponto location has the highest value of 4,339,003.2 MWh with a capacity factor of 25.22% and Bantul location has the lowest value of 526,476 MWh with a capacity factor of 16.51%. According to economic uncertainty analysis, the highest NPV and attractive IRR values with high deviation standard values will be interesting to the risk-taking investors. Otherwise, the lowest NPV and IRR values fit the risk of avoiding-type investors due to its low deviation standard values. Based on sensitivity analysis, the increase of average NPV and IRR values of all seven locations are influenced positively by first 5-year of the selling price, which contributes approximately 30% and 50% of NPV and IRR’s percentage change. By contrast, the decrease in average NPV values of all seven locations is influenced by the discount rate, which contributes approximately 15% of NPV’s percentage change. Meanwhile, the decrease in average IRR is influenced by capital investment cost, which contributes approximately 20% of IRR’s percentage change. Policymakers can further consider the parameters mentioned above.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10389
DO  - 10.18517/ijaseit.10.5.10389

RefWorks

RT Journal Article
ID 10389
A1 Ismail, -
A1 Ismail, Asrul Harun
A1 Nur Rahayu, Gama Harta Nugraha
T1 Wind Energy Feasibility Study of Seven Potential Locations in Indonesia
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 1970
OP 1978
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 annual energy production; capacity factor; Monte Carlo simulation; economic uncertainties; wind energy
AB This study evaluated seven potential locations for wind energy in Indonesia. This study applied economic analysis using static analysis and the Monte Carlo simulation. The later was used to measure the financial indicators and then to analyze the sensitivity of economic uncertainties. The result findings show that in term of annual energy production volume among the seven selected locations, Jeneponto location has the highest value of 4,339,003.2 MWh with a capacity factor of 25.22% and Bantul location has the lowest value of 526,476 MWh with a capacity factor of 16.51%. According to economic uncertainty analysis, the highest NPV and attractive IRR values with high deviation standard values will be interesting to the risk-taking investors. Otherwise, the lowest NPV and IRR values fit the risk of avoiding-type investors due to its low deviation standard values. Based on sensitivity analysis, the increase of average NPV and IRR values of all seven locations are influenced positively by first 5-year of the selling price, which contributes approximately 30% and 50% of NPV and IRR’s percentage change. By contrast, the decrease in average NPV values of all seven locations is influenced by the discount rate, which contributes approximately 15% of NPV’s percentage change. Meanwhile, the decrease in average IRR is influenced by capital investment cost, which contributes approximately 20% of IRR’s percentage change. Policymakers can further consider the parameters mentioned above.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10389
DO  - 10.18517/ijaseit.10.5.10389