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Journal : International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)

Transmission Outage Cost Analysis Using Value of Loss Load Approach Based on Macro Economic Data Suwargono, Son; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 2 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i2.48

Abstract

The West Nusa Tenggara (NTB) electricity system, which consists of the Lombok System and the Sumbawa-Bima System, has an important role in supporting the country's economy, especially in the tourism segment. Quantitatively, there has been no measurement of the impact of electricity disruption on the macro economy. Value of Loss Load (VoLL) is a useful quantity parameter in the economic evaluation of electric power systems. It can be represented as the value of losses borne by customers in case of electricity service interruptions. For policy makers and electricity management, the size of the VoLL would affect decisions regarding investment. A low VoLL requires for a low reliability level and a high VoLL for a high reliability level. This research will calculate Transmission outage costs using the Value of Loss Load approach based on macro economic data and predicting VoLL 2024 - 2030. The outcome of the research shows that The Lombok System VoLL is lower than Bima – Sumbawa System. Outage costs due to disruptions on the Transmission System side affect GDP by 0.001% / year. The trend of VoLL 2024 – 2030 is estimated to decrease by an average of 2.29% / year which is indicate it is is inline with Rencana Usaha Penyediaan Tenaga Listrik 2021 - 2030.
Techno-Economic Optimization Study of Renewable Energy Planning in Buru Island Electricity System Z Day, Faizatul Hasanah; Samual, Muhammad Gillfran; Garniwa, Iwa; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.73

Abstract

One of the strategies to achieve Indonesia's NDC target in 2030 is through the development of renewable energy power plants, and the transition from fossil fuels to renewable energy. The use of diesel power plants, especially with the case on Buru Island as the only electricity supply, contributes to the production of emissions, and increases the Cost of Energy (CoE) of the utility system. On the other hand, Buru Island is rich in renewable energy potential, such as geothermal, hydropower, bioenergy, and solar energy. This study aims to design an optimal power generation system on Buru Island by considering the renewable energy mix, financial feasibility, reduction in the CoE of local electricity system, reduction in CO2 emissions, and the potential load growth of the local industry, i.e. fisheries industry sector. This study utilizes HOMER software to obtain a power generation scenario that can supply the load with the most optimal renewable energy penetration, the lowest Levelized CoE (LCOE), and the lowest CO2 emissions. Seven electrical systems on Buru Island were implemented to form 4 systems, namely an integrated system of 4 previously distributed systems, and 3 other distributed systems. The result of this research gives out the most optimum configuration of hybrid or complete renewable energy-based power plant configuration for each system. The configurations can reduce the CoE up to 20.17 cUSD/kWh, and up to zero CO2 emission.
Optimizing Generation Costs in Electricity Supply Business Plan for Electricity Companies in Indonesia: A Reliability-Based Approach for the Sumatra Power System Sikumbang, Supriyanto; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.111

Abstract

Ensuring a stable and sustainable electricity supply requires effective planning that balances cost efficiency and system reliability. This study explores the optimization of Basic Generation Cost (BPP) in PT PLN (Persero)'s Electricity Supply Business Plan (RUPTL) 2025-2034 while considering the reliability of the Sumatra power system. Using Digsilent PowerFactory, simulations incorporating Unit Commitment and Economic Dispatch methodologies were conducted to achieve cost reductions without compromising system stability. The significant result of this theses is optimization with economic dispatch reduces BPP up to 41,4% compared to conventional methods, enhancing power system cost efficiency. Increasing voltage reliability from 0.99 p.u. in 2025 to 1.01 p.u. in 2034. Higher renewable energy integration in 2034 reduces fuel costs but increases challenges in maintaining frequency and voltage stability. Strategic recommendations include increasing transmission capacity, implementing energy storage systems, and optimizing unit commitment to balance cost and reliability. This research offers valuable insights for power system planning, addressing energy transition challenges and facilitating the integration of renewable energy sources in Sumatra. Keywords: Basic Generation Cost, System Reliability, Economic Dispatch, Digsilent PowerFactory
Analysis of Additional Generation Planning in the Batam-Bintan Power System to Improve Reliability Purba, Kevin Pangestu; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.133

Abstract

The Batam-Bintan electrical system encounters operational challenges due to inadequate new power plants being commissioned to meet the increasing demand. Bintan Island's supply dependency on Batam Island through the undersea cables and 150 kV SUTT places operational stress systemically and adds vulnerability to disruption. The focus of the research is to optimize the system reliability through peak load forecasting up to 2030 and refining the strategic locations and sizes for the new power plants. The calculation forecast employs a second-order polynomial regression method, whereas the load flow analysis is performed with DIgSILENT PowerFactory 2022 software. Based on the research, the peak load is expected to grow from 675.2 MW in 2024 to 1,322.1 MW by 2030. To attain reliability, 940 MW of additional generation capacity is required, which is made up of 580 MW of DG (distributed generation) and 360 MW of central generation. The placement of DG is focused on substations that are overloaded or approaching overload, while centralized generation is positioned where power loss is lowest. The evaluation results indicate the additional generation makes it possible to maintain voltage stability, reduce dependence on PLTU XYZ and meet the reserve power requirement of a 35% power margin.
Development of Disturbance Type Detection Using Convolution Neural Network for Fault Signature Analysis Putra, Kharisma Darmawan; Garniwa, Iwa; Jufri, Fauzan Hanif; Oh, Seongmun
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.136

Abstract

The development of technology in electrical systems is growing rapidly, increasing power system complexity, which causes the operation and maintenance of the power system networks to become more complicated, especially when a disturbance occurs in the networks. To overcome the issue, there is a need to utilize the tools available as much as possible to manage the power system networks. Nowadays, the power system network is equipped with protection relays and controls that provide various data about the systems, such as the Disturbance Fault Recorder (DFR), which monitors and records the system’s characteristics during network disturbance events. DFR holds information on the system’s parameters during a fault, but it cannot recognize the type or cause of the disturbance. Hence, this paper proposes a method based on the Convolution Neural Network (CNN) model to analyze the DFR’s data and determine the type/cause of disturbance so it can be used to manage the follow-up actions properly. Based on the research results, CNN, with six types of disturbance classification, has an accuracy of 93,87%. Based on the results obtained, the accuracy of CNN using the VGG19 type in handling disturbance analysis in graphical patterns is satisfactory.
Risk Assessment of Solar Power Plant Development in Indonesia Using The Analytic Hierarchy Process Method Ginting, Frederick Sakaja; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.110

Abstract

The Ministry of Energy and Mineral Resources is committed to building Solar Power Plant in Indonesia with the aim of increasing the national electrification ratio and ensuring equitable energy access, especially for people who have not enjoyed electricity. This study aims to determine what risk factors exist in the construction of Solar Power Plant and conduct a risk assessment using the Analytic Hierarchy Process (AHP) method. The results of the analysis show that there are 7 criteria and 38 risk sub-criteria. The project risk criterion has the highest weight with an expert value of 25.2%, self-assessment 36.9% and employees 2.2%. In the sub-criteria, the provider is late in completing the work, it has the highest weight with an expert value of 6.7%, self-assessment 10.6% and employees 0.1%.
Optimization of Preventive Maintenance Planning for the Motor Cooling System at PLTGU Using Differential Evolution Putranugraha, Derry; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.132

Abstract

Determination of the optimal preventive maintenance time of the three-phase induction motor (88WC) during operation at 380V in the cooling system of the Semarang Gas and Steam Power Plant (PLTGU) is done by combining the Power-Law Non-Homogeneous Poisson Process (NHPP) model and the Differential Evolution (DE) Algorithm to achieve minimum total maintenance cost. The parameters of NHPP, β = 1.75 and η = 7,198.99 hours, are estimated using the least squares method from the historical failure data for the 2020–2024 period, recording failures beyond 20,000 operating hours. The DE optimization results provide the optimum PM time of 371.60 hours to reduce the total cost from IDR 28,198,935 (for the 500-hour interval) to IDR 20,299,822, achieving a cost savings of 38%. Validation is performed using Monte Carlo simulations with 1,000,000 iterations that yield a pre-optimization failure probability of 0.56%. Sensitivity analysis using a ±20% parameter variation also proves the model's robustness. This data-driven framework is thus anticipated to increase the reliability and cost-effectiveness of the PLTGU cooling system and is scalable to other power-generating facilities
Reliability Improvement of Defense Scheme Implementation Using Adaptive Load Shedding Based On System Strength Index Widyantara, Dwitiya Bagus; Garniwa, Iwa; Jufri, Fauzan Hanif
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.150

Abstract

One of the defense schemes in power systems is Under Frequency Load Shedding (UFLS), designed to mitigate cascading blackouts caused by frequency disturbances. UFLS operates based on predetermined frequency thresholds and time delays, which inherently characterizes it as a static protection mechanism and may cause unnecessary excessive or insufficient load shedding. Therefore, an Adaptive Load Shedding (ALS) approach started to gain popularity, which enables load shedding based on real-time conditions, particularly during generator outages. In this research, a comparative analysis is conducted between the conventional UFLS method and a newly developed ALS scheme that integrates the System Strength Index (SSI) to improve the system's reliability, as evaluated by Energy Not Served (ENS). The proposed ALS algorithm processes real-time feeder load data, ranks the feeders by load magnitude in descending order, and optimizes the load shedding setpoints by incorporating the SSI. The proposed method is simulated in the Flores power system model using actual historical data for two load conditions: the highest and the lowest. The results show that the proposed method outperforms the conventional UFLS by 7.31% in terms of improved ENS.