The Integration of Wind Power Plants to Southern Sulawesi Power System

Barus, Dhany Harmeidy; Dalimi, Rinaldy

doi:10.1109/icecce49384.2020.9179480

Cited by 2 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sidrap WPP is located at a higher altitude with hilly terrain, while the Jeneponto WPP is at a lower one with flat coastline terrain. In [17], the authors have described the detailed technical specification of both WPPs. Table II shows the composition of power plants (PPs) in the southern Sulawesi power system.…”

Section: A System Overviewmentioning

confidence: 99%

Determining Optimal Operating Reserves Toward Wind Power Penetration in Indonesia Based on Hybrid Artificial Intelligence

Barus¹,

Dalimi

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

The stability and economic level of the power system operation during the penetration of Wind Power Plants (WPPs) are much determined by the variability and uncertainty of the wind power output. The characteristics of seasonal wind power output can be used to define the optimal operating reserves of a stable and cost-effective power system operation. This paper proposes a comprehensive algorithm of hybrid Artificial Intelligence (AI) approach that combines the Seasonal Autoregressive Integrated Moving Average (SARIMA) and selected Neural Network Variants (NNVs) in Seasonal Daily Variability and Uncertainty (SDVU) scheme. Among all NNVs, Long Short-Term Memory (LSTM) shows the most consistent and accurate results. With the hybrid AI approach, this algorithm calculates the Dynamic Confidence Level (DCL) to determine hourly operating reserves on a daily basis. The proposed algorithm has been successfully tested using historical data of real-world WPPs that operated in Indonesia. Furthermore, the comparison toward non-seasonal with a Static Confidence Level (SCL) in several percentile scenarios is made to prove the cost-effectiveness advantages of this new algorithm that may save up to 4.2% of total daily energy consumption. An interface application is added so that the results of this research can be directly utilized by users both on the observed power system and generally in Indonesia.INDEX TERMS dynamic confidence level, neural network, operating reserve, wind power, SARIMA.

show abstract

Section: A System Overviewmentioning

confidence: 99%

Determining Optimal Operating Reserves Toward Wind Power Penetration in Indonesia Based on Hybrid Artificial Intelligence

Barus¹,

Dalimi

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In 2014, wind turbines' installed capacity was only 1.6 MW in Indonesia [3]. However, this number increased in 2018 with wind farm construction in two locations: Sidrap and Jeneponto, Sulawesi [4].…”

Section: Introductionmentioning

confidence: 99%

“…The Sidrap wind farm has 30 turbines with each turbine capacity of 2.5 MW, while the Jeneponto wind farm has 20 units of turbines with an individual capacity of 3.6 MW [4].…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Potential on A Highrise Building: A Preliminary Study

Firdaus

Prasetyo

Ab-Samat

et al. 2021

ARFMTS

View full text Add to dashboard Cite

Indonesia has an abundant renewable energy source. One of them is wind energy resources. Unfortunately, Indonesia's wind energy resource is not fully utilized, especially for application in high-rise buildings. The paper investigates the potential of energy production from the horizontal-axis wind turbine (HAWT) and the vertical-axis wind turbine (VAWT) on the rooftop of a university building in Indonesia. The wind speed data were measured on the rooftop of the building for seven months. The data was analyzed using Weibull distribution. Based on the probability density function of the Weibull distribution, the potential energy production was calculated using the power curves from the manufacturer. Comparing energy production between HAWTs and VAWTs has shown that VAWTs can produce more energy than HAWTs. Using six turbines, VAWTs can produce 48,476 kWh. On the other hand, with four turbines, HAWTs can produce 41,729 kWh. The reason is that VAWT requires shorter distance requirements for inter-turbine and between rows. Therefore, VAWT can use more turbines than HAWT in the limited area. In conclusion, VAWT for high-rise buildings is more preferred because VAWT can generate more energy. Further study should investigate the optimal configuration with varying the wind direction and quantifying the wake effect on power output.

show abstract

The Integration of Wind Power Plants to Southern Sulawesi Power System

Cited by 2 publications

References 5 publications

Determining Optimal Operating Reserves Toward Wind Power Penetration in Indonesia Based on Hybrid Artificial Intelligence

Determining Optimal Operating Reserves Toward Wind Power Penetration in Indonesia Based on Hybrid Artificial Intelligence

Wind Energy Potential on A Highrise Building: A Preliminary Study

Contact Info

Product

Resources

About