Electricity and Climate Change : Seeking for the triple nexus of electrification, climage change mitigation, and climate change adaptation

Handayani, Kamia

doi:10.3990/1.9789036548908

Cited by 2 publications

(1 citation statement)

References 109 publications

(204 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The participation of academics in encouraging the development of renewable energy is urgently needed, where PT PLN (State Power Plant Limited Company) seeks to minimize global temperature rise by stopping steam power plants until 2056, and initial participation in 2030-2035 by stopping the operation of several power plants. Fossil fuel electricity and concentrate on renewable energy (Handayani, 2019).…”

Section: Introductionmentioning

confidence: 99%

Solar Prediction Strategy for Managing Virtual Power Stations

Cahyadi,

Suwarno,

Dewi

et al. 2023

IJEEP

View full text Add to dashboard Cite

The potential for solar power is available in Indonesia because it is located on the equator, with good sunshine all year round. The Indonesian government is currently actively developing a solar power plant while still looking at the consequences of development, especially on the surrounding environment. It is necessary to pay attention so that it does not disturb the surrounding environment, which can also cause climate change. The city of Medan is one of the largest cities in Indonesia, which has direct exposure to sunlight which is quite promising for predicting solar power plants in the future. Solar energy generation in the last decade has continued to improve and develop in solar power predictions in a short period. Integration of solar power sources without accurate power predictions can hinder network operations and the use of renewable generation sources. To solve this problem, virtual power plant modeling can solve as a solution that manages to minimize the prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be virtual power plant modeling can solve it as a management solution to be minimal in its prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be virtual power plant modeling can solve it as a management solution to be minimal in its prediction error. This research studies methods that can efficiently generate significant daily Photovoltaic (PV) predictions at the locations studied using available data from the Meteorology, Climatology, and Geophysics Agency (MCGA). The approach of the two models based on RMSE (root mean square error) and MAE (Mean Absolute Error), can be taking into account the uncertainty of predictions that can provide additional information from virtual power plants. Verified prediction strategy performance against PV module power output and a set based on geographic meteorological station data have been used to simulate Virtual Power Plants (VPP). The power forecasting prediction refers to the LSTM (Long Short-Term Memory) network and gives an error close to that of other learning methods, based on the RMS characteristic of 4.19 W/m2 under lead time with different launch times. The application of the VPP model can reduce the global error by about 12.37% with the model RMSE, and shows great potential.

show abstract

Section: Introductionmentioning

confidence: 99%

Solar Prediction Strategy for Managing Virtual Power Stations

Cahyadi,

Suwarno,

Dewi

et al. 2023

IJEEP

View full text Add to dashboard Cite

show abstract

Techno-Economic Analysis of Indonesia Power Generation Expansion to Achieve Economic Sustainability and Net Zero Carbon 2050

2022

View full text Add to dashboard Cite

Indonesia’s power generation roadmap aspires to achieve 23%, 28%, and 31% of power from renewable energy by 2025, 2038, and 2050, respectively. This study presents a technoeconomic analysis of Indonesia’s power generation development plans using the LEAP model in the post-COVID-19 period, with a focus on achieving the renewable target. In this study, four scenarios were modeled: business as usual (BAU), cost optimization (CO), national plan (NP), and zero-carbon (ZC). The BAU scenario is based on the PLN Electricity Business Plan 2019–2028, which does not include a target for renewable energy. The CO scenario aims to meet the renewable energy mandate at the lowest possible cost. The NP scenario aims to achieve renewable energy, with an additional natural gas target of 22% by 2025 and 25% by 2038. The ZC scenario aims to achieve 100% renewable energy by 2050 at the lowest possible cost. In comparison to the other scenarios, the BAU scenario has the highest total cost of power production, with a total of 180.51 billion USD by 2050. The CO scenario has the lowest total cost of production with a total of 89.21 billion USD; however, it may not be practical to implement.

show abstract

Electricity and Climate Change : Seeking for the triple nexus of electrification, climage change mitigation, and climate change adaptation

Cited by 2 publications

References 109 publications

Solar Prediction Strategy for Managing Virtual Power Stations

Solar Prediction Strategy for Managing Virtual Power Stations

Techno-Economic Analysis of Indonesia Power Generation Expansion to Achieve Economic Sustainability and Net Zero Carbon 2050

Contact Info

Product

Resources

About