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All Journal JESCEE- Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Eduvest - Journal of Universal Studies
Ghusaebi
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Social Sciences

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The Role Of Lng Regasification In Reducing Fuel Consumption And Improving The Reliability Of The Tarakan Isolated System: A Monte Carlo Simulation Approach Ghusaebi Ghusaebi; Tri Wahyu Adi
Eduvest - Journal of Universal Studies Vol. 6 No. 7 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i7.53391

Abstract

The Tarakan Electricity System is an isolated electricity system that relies on local power plants and local primary energy supply. Reliance on oil-fired (BBM) plants poses challenges to operational efficiency, while gas supply fluctuations, peak load variations, and plant readiness affect system power adequacy. This study aims to evaluate the role of Liquefied Natural Gas (LNG) regasification in reducing fuel consumption and improving the reliability of the Tarakan Electricity System. The Monte Carlo simulation approach is used because average-based deterministic analysis has not been able to represent operational variability, extreme conditions, and power deficit risk in isolated systems affected by uncertainty of gas supply, peak load, and plant readiness. This study uses Monte Carlo simulations based on actual operational data by comparing two scenarios, namely the baseline scenario without LNG regasification and the intervention scenario with LNG regasification. Stochastic input variables include pipeline gas supply, peak load, PLTD readiness, and PLTMG readiness. The input distribution is based on historical data, while the scenario comparison is carried out using the Common Random Numbers (CRN) principle so that the impact of LNG interventions can be evaluated more objectively. The simulation was carried out as many as 10,000 iterations. Fuel consumption models and power models are able to be validated before being used in scenario analysis.
Short-Term Electric Load Forecasting Study Using Linier Regression and Time Series Models at PT. PLN (Persero) Tarakan Kartika Putri Wardani; Ismit Mado; Achmad Budiman; Sugeng Riyanto; Ghusaebi; Rustam Effendy
Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Vol 4 No 2 (2025): Journal of Emerging Supply Chain, Clean Energy and Process Engineering
Publisher : Universitas Pertamina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57102/jescee.v4i2.101

Abstract

In the electricity operating system, it is very necessary to balance the electrical power transferred by the power plant with the electricity load consumption at the customer. Fluctuations in the electrical load cause the operation of the electrical power system to become unreliable. Electrical load forecasting studies are very necessary to ensure optimal electrical power system conditions. PT. PLN (Persero) Tarakan as an electrical energy service provider really needs electrical load forecasting studies. This study applies the time series analysis method and as a comparison uses the linear regression method. This method is used to forecast short-term needs in the electrical power operating system. The results of the study showed that the mean absolute error percentage (MAPE) with the linear regression method was 7.87 percent. With the ARIMA time series method, the MAPE was 14.68 percent. However, by looking at the seasonal plot in the time series or SARIMA method, the MAPE was 6.13 percent. The results of this study indicate that the SARIMA model is the best forecasting method. Kata Kunci: Regresi Linier, Beban Listrik, Time Series, PT. PLN (Persero) Tarakan.
Co-Authors Achmad Budiman Ismit Mado Kartika Putri Wardani Rustam Effendy Sugeng Riyanto Tri Wahyu Adi