ITB Graduate School Conference

The Impact of Local Temperature Variations on the Efficiency and Power Output of Solar Power Plants in Lombok Island

2025-05-30T17:08:40+08:00

Indonesia, located in the equatorial region, possesses vast solar energy potential reaching up to 200,000 MW. However, its utilization remains significantly low at only 0.08% of the total potential. In remote regions like Lombok Island, solar power plants serve as a strategic solution for clean and sustainable electricity, especially where access to fossil fuels is limited and costly. This study investigates the impact of local temperature variation on the efficiency and power output of PLTS in Lombok, a tropical region with high solar irradiance (4–6 kWh/m²/day) and elevated daytime temperatures (26–33°C). Using temperature and solar radiation data from 2019 to 2024, along with electricity output records from three major Solar Power Plant facilities (Pringgabaya, Selong, Sengkol, each 7 MWp), the research applies statistical correlation and regression modeling to quantify the relationships among temperature, irradiance, and energy output. The findings are expected to reveal a negative correlation between increased ambient temperature and photovoltaic efficiency due to rising electrical resistance in solar cells. Conversely, higher irradiance generally enhances power output, although its benefits may be offset by excessive heat. This study also incorporates Dipole Mode Index (DMI) analysis to understand the regional climatic influence on local temperature trends. DMI is selected due to its direct representation of the Indian Ocean Dipole (IOD) phenomenon, which significantly affects weather patterns, sea surface temperature, and consequently, regional thermal variations in the Indonesian maritime continent. By focusing on DMI, this research captures a dominant mode of interannual climate variability that is particularly relevant to temperature fluctuations in Lombok. Furthermore, the study provides quantitative results for the Lombok region, including the percentage impact of each variable on power production. It also analyzes the seasonal and intraseasonal variations of temperature and solar radiation to identify periods of optimal and suboptimal solar plant performance. These findings are expected to support predictive energy output modeling and inform technical recommendations such as thermal mitigation strategies and material optimization for improving solar power performance in tropical environments. These insights are crucial for supporting Indonesia’s energy transition and achieving greater integration of renewable sources in its national energy mix.

System Dynamics for Policy Simulation of FABA (Fly Ash and Bottom Ash) Waste Utilization

2025-05-20T00:11:03+08:00

Fly Ash and Bottom Ash (FABA) are by-products of coal combustion in power plants that pose environmental risks if not properly utilized. This study develops a System Dynamics model to evaluate policy scenarios for FABA utilization at the Nagan Raya coal-fired power plant, which produces an average of 2,295 tons of FABA per month. The model is based on historical data from 2016 to 2024 and simulates policy interventions for the 2025–2030 period, including biomass co-firing, public awareness campaigns, and subsidies for industrial users.

The simulation results for 2030 show that the combined policy scenario successfully reduces stored FABA from 98,352 tons to 37,011 tons and increases FABA utilization from 1,050 tons/month to 1,850 tons/month. CO₂ emission reductions also increase significantly, from 1,570 tons/month to 10,220 tons/month. The policy requires industrial subsidies of IDR 130 million/month and socialization costs of IDR 30 million/month. This study demonstrates that simulation-based waste policy modeling can effectively support the transition strategy toward achieving Indonesia’s Net Zero Emission 2060 target.

System Design for a Sustainable Returned Materials Management System at PT PLN UID East and North Kalimantan

2025-05-20T00:46:02+08:00

The Indonesian government through Presidential Regulation No. 111 of 2022 encourages the implementation of the Sustainable Development Goals (SDGs), including the efficient and responsible management of material resources. PT PLN (Persero), as a major energy provider, has responded through various sustainable transformation initiatives, including waste management based on the 3R principle. However, challenges are still faced in the management of returned materials, especially demolished materials (ATTB) at the distribution unit level such as PLN UID East Kalimantan and North Kalimantan. Problems such as the absence of standard classification procedures and the accumulation of unutilized materials indicate the need for a more effective system. Based on this, the problem formulation in this study is How is the design of a return material management business process that accommodates the 6R principles (Reduce, Reuse, Recycle, Repair, Refurbish, Remanufacture)? This research adopts the 6R-based closed-loop sustainable manufacturing approach to design a new digital-based business process for managing returned materials. The main focus is on designing a systematic classification mechanism into four categories based on technical, life, warranty, and economic aspects. This approach is expected to improve operational efficiency, support digital transformation, and strengthen PLN's contribution to the national sustainability agenda.

Development of Vendor Selection Criteria and Its Relationship using Delphi Method, Best Worst Method (BWM) and Structural Equation Modeling-Partial Least Squares (SEM-PLS) in PT PLN (Persero) UPT Durikosambi

2025-05-30T16:59:31+08:00

An accurate vendor selection process is a critical factor in ensuring the successful execution of construction projects and asset maintenance within PT PLN UPT Durikosambi. This study aims to develop a more objective vendor selection framework using a Multi-Criteria Decision Making (MCDM) approach combined with statistical validation. The research process includes a preliminary study, development of conceptual and operational models, data collection, and analysis and interpretation. Criteria and subcriteria were identified based on existing conditions and relevant literature, then validated through the Delphi method to achieve expert consensus. The weighting of criteria was determined using the Best-Worst Method (BWM), recognized for its high consistency and efficiency in comparisons. The relationships among criteria were further analyzed using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach to identify significant influences among variables. The findings of this research are expected to provide a strategic and adaptive foundation for decision-making in the procurement of construction services within PLN units or similar public infrastructure agencies.

The Projections of Solar Energy Potential in Indonesia under Climate Change Scenarios : A CMIP6 Multi-Model Ensemble Analysis

2025-05-30T17:08:01+08:00

Indonesia holds significant solar energy potential, making it a key resource in the country’s pathway toward net-zero carbon emissions by 2060. However, climate variability introduces uncertainties that may impact long-term solar energy production and investment planning. This study investigates the projected impacts of climate change on Indonesia’s solar photovoltaic (PV) potential for the period 2030–2060, utilizing outputs from five global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Two emission scenarios were considered: SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions). Key climate variables influencing PV performance surface downwelling shortwave radiation, near-surface air temperature, and wind speed were analyzed. Model outputs were regridded to a 0.25° spatial resolution and bias-corrected through statistical downscaling. Results reveal spatially variable responses of PV potential to future climate change, with a projected decrease during the rainy season and an increase during the dry season in specific regions. These findings highlight the necessity of integrating climate projection uncertainties into national renewable energy strategies, ensuring adaptive and resilient solar energy development in Indonesia under future climate conditions.

Optimization of the Biomass Supply Chain for Co-Firing at Adipala Power Plant to Improve Cost Efficiency

2025-05-30T16:57:57+08:00

The utilization of biomass as a renewable energy source is becoming increasingly important to reduce dependence on fossil fuels and decrease carbon emissions. However, the efficient distribution of biomass in the co-firing system at the Adipala Steam Power Plant (PLTU) faces significant challenges related to high logistics costs. This study aims to optimize the distribution costs of biomass in the co-firing system at PLTU Adipala using Linear Programming (LP) methods. The primary objective of this research is to determine the optimal amount of biomass needed to meet the energy requirements of the power plant with efficient distribution costs, as well as to identify supply chain strategies that can enhance cost efficiency. The method employed is a Linear Programming optimization model that considers factors such as transportation costs, supply capacity, and energy needs. The results indicate that the application of LP can reduce logistics costs by up to 15% and improve the efficiency of biomass distribution. These findings make a significant contribution to enhancing the efficiency of biomass supply chain management at PLTU Adipala and can serve as a reference for the development of renewable energy policies in Indonesia. In conclusion, the application of LP in biomass supply chain management can provide efficient and sustainable solutions while promoting the reduction of carbon emissions in the energy sector.

The Impact of El Niño on the incoming flow rate of the Saguling Hydroelectric Power Plant (PLTA) for Operational Sustainability

2025-05-19T15:57:09+08:00

This study investigates the impact of El Niño on the inflow dynamics of the Saguling Hydroelectric Power Plant (PLTA), located in West Java, Indonesia. Using 39 years of hydrometeorological data (1986–2024), including rainfall, reservoir inflow, and Oceanic Niño Index (ONI), the research identifies the climatic influence of El Niño–Southern Oscillation (ENSO) events on water availability and power generation sustainability. Results show a significant inverse correlation between ONI and both rainfall (r = –0.41) and inflow (r = –0.45), with El Niño years experiencing a 50–60% reduction in water inputs during dry months. La Niña events, in contrast, enhance inflow and precipitation, thereby supporting reservoir operations. Wavelet transform analysis reveals dominant periodicities in the 2–4 year range, consistent with ENSO cycles. These findings highlight the vulnerability of hydropower generation to climate variability and underscore the importance of adaptive reservoir management strategies based on ENSO projections. The study contributes to strategic planning in energy and water sectors, emphasizing the integration of climate forecasts in maintaining operational continuity of hydropower infrastructure.

Inventory Management Efficiency Strategy at UID Banten: A Study of Vendor Managed Inventory (VMI)

2025-05-30T17:16:38+08:00

Inventory management is critical to guaranteeing the efficiency and reliability of power distribution at PT PLN (Persero), notably at the Unit Induk Distribusi (UID) Banten. Challenges such as restricted storage space, installation delays owing to permit limits, and the buildup of heavy items such as medium-voltage cables have all had a substantial influence on operational performance. The purpose of this research is to investigate the feasibility of using Vendor Managed Inventory (VMI) as a strategic strategy to improve inventory efficiency at UID Banten. VMI enables suppliers to manage inventory levels based on real-time consumption data and demand projections, resulting in lower holding costs, increased stock availability, and reduced inventory buildup. Using literature review, the success of VMI implementations suggests that adopting VMI can provide substantial benefits, including streamlined warehouse operations, improved supplier collaboration, and greater responsiveness to demand fluctuations. However, several prerequisites, such as digital infrastructure readiness, data transparency, and mutual trust between stakeholders, must be met for successful VMI adoption. This study provides strategic recommendations for UID Banten to assess and prepare for VMI implementation as a means of achieving long-term inventory and distribution efficiency.

Analysis of MetOcean Changes in the Coastal Waters Surrounding Paiton Coal-Fired Power Plant, East Java

2025-05-30T17:00:34+08:00

The Paiton Coal-Fired Power Plant (CFPP), located on the northern coast of East Java, Indonesia, has been operational since 1994 and is suspected of influencing the surrounding coastal environment. This study analyzes changes in MetOcean parameters, including sea surface temperature (SST), wind patterns, and wave characteristics, in the Paiton coastal area between 1993 and 2024. Results indicate a clear seasonal variation in SST following the Indonesian monsoon cycle, with warmer temperatures during the rainy season and lower temperatures during the dry season due to coastal upwelling. A warming trend of approximately 0.0178°C per year has been identified, with additional increases possibly linked to thermal discharges from the plant's cooling system. Wind analysis shows a dominant flow from the east to southeast during the dry season, with speeds of 2–3 m/s, influencing evaporation rates and corrosion risks for infrastructure. Wave data reveal that most waves are locally generated, with heights between 0.2 and 0.4 meters, and only rare occurrences of waves exceeding 0.4 meters. These oceanographic dynamics collectively affect the operational efficiency and cooling performance of the CFPP. Continuous oceanographic monitoring and integrated environmental management are essential to support the sustainable operation and long-term resilience of coastal power generation facilities.

Projected Rainfall Trends and Variability in the Mrica Catchment under the SSP5-8.5 Scenario

2025-05-30T16:51:00+08:00

This study analyzes changes in rainfall, inflow discharge, and electricity production at PLTA Mrica using historical data (1985–2014) from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and six global climate models (GCMs) under CMIP6 (Coupled Model Intercomparison Project Phase 6). Future projections are based on the SSP5-8.5 (Shared Socioeconomic Pathway 5 – fossil-fueled development) scenario to represent a worst-case pathway, enabling assessment of maximum potential climate impacts on hydropower reliability [2] scenario for the period 2021–2100. A delta-based statistical downscaling method is applied to produce high-resolution rainfall projections. This method is deterministic in nature; it applies a fixed anomaly (delta) between future and historical climatologies onto observed datasets, without simulating transient atmospheric processes as done in prognostic models. It is computationally efficient and widely used for impact studies where capturing long-term mean changes is prioritized over day-to-day weather variability [2][3]. The results indicate an increase in rainfall during the wet season and a significant decline during the dry season, particularly from January to March, with projected rises of up to +2.5 mm/day, or approximately +30–40%, compared to the historical baseline. Conversely, the dry season (June–September) is projected to experience a decline of up to 1.5 mm/day, equivalent to a reduction of 25–40%, depending on the month and future time slice. Historical data indicate that monthly rainfall of at least 100–120 mm is generally required to sustain sufficient reservoir inflow for optimal electricity generation at PLTA Mrica, especially during the dry season. Variability in rainfall, particularly prolonged dry spells or delayed wet season onset, can lead to inflow shortages, reducing turbine operation hours and ultimately affecting annual energy output. This study highlights the importance of using climate data such as projected rainfall thresholds and variability to guide reservoir operations, optimize electricity production, and reduce risks during dry periods. Integrating such information supports more adaptive and resilient hydropower planning under future climate uncertainty.

Spatial Planning for Transmission Line Routing using Multi-Criteria Decision Analysis

2025-05-30T17:16:02+08:00

Through the Electricity Supply Business Plan (RUPTL) 2021–2023, PT PLN (Persero) outlined plans for the development of transmission systems across Indonesia, projecting an expansion of 76,662 MVA in substation capacity and 47,723 kilometres of transmission lines by 2030. Transmission infrastructure development is further detailed for each major island system in the country. In particular, the transmission network in Sulawesi remains divided into two systems: Southern Sulawesi (SULBAGSEL) and Northern Sulawesi (SULBAGUT). The RUPTL document also highlights several strategic projects in the Sulawesi transmission system, including the 150 kV Tambu–Bangkir Transmission Line. Following an instruction from the Directorate General of Electricity to accelerate the Commercial Operation Date (COD) for the Tambu–Bangkir segment to 2024, the project timeline has been adjusted accordingly and designated as a priority assignment. Current transmission line construction planning still relies on manual methods, which have several weaknesses, including route inefficiency, potential cost overruns, challenges in accessing planning locations, and low planning accuracy that often does not reflect actual field conditions. This approach also inadequately considers disaster factors and the aspects of environmental, social, and corporate governance. This research aims to develop a more effective and efficient method for transmission line planning that reduces cost overruns while improving accessibility and planning accuracy. Additionally, this study will explore ways to incorporate land use, road access, disaster factors, and environmental, social, and corporate governance considerations. Spatial modeling using the Multi-Criteria Decision Analysis (MCDA) method can offer a more comprehensive approach, making transmission line planning more optimal and sustainable.

Prediction of Hydropower Plant Electricity Production Dependence on Weather Conditions Using Machine Learning Approach

2025-06-03T10:16:32+08:00

To optimize the hydropower plant operations in the Sulawesi Generation Unit of PLN, this study proposes a data-driven approach to analyze electricity production by incorporating weather data. Utilizing historical data from January 2014 to December 2023, relevant indicators were extracted using machine learning algorithms. The integration of hydropower generation data, dam operational data, temperature, and rainfall enabled the prediction of electricity output through various models, including SARIMAX, Random Forest Regressor, Support Vector Regression, and Extreme Gradient Boosting. The dataset, consisting of 120 rows and 18 variables, demonstrated that combining diverse yet correlated data sources improve prediction accuracy. The best-performing model was validated and applied to forecast on new, unseen data. The findings indicate that machine learning offers a strategic advantage for PLN's decision-making in managing interconnected hydropower operations within the national power grid.

Supplier Selection and Order Allocation Based on Multi-Criteria Decision-Making Using AHP, Fuzzy TOPSIS, and Multi-Choice Goal Programming to Improve Local Content (TKDN)

2025-05-30T17:05:05+08:00

PT PLN Pusharlis menghadapi tantangan dalam pemilihan dan alokasi pemasok untuk komponen Control Board yang digunakan di Stasiun Pengisian Kendaraan Listrik Umum (SPKLU). Pemilihan pemasok yang tidak tepat dan alokasi pesanan yang tidak efisien dapat mengakibatkan keterlambatan pengadaan dan peningkatan biaya operasional. Penelitian ini bertujuan untuk mengembangkan model pengambilan keputusan terintegrasi yang memfasilitasi pemilihan pemasok yang paling sesuai dan alokasi pesanan yang optimal berdasarkan permintaan yang diproyeksikan. Tahap awal melibatkan peramalan permintaan Control Board menggunakan metode ARIMA. Proses pemilihan pemasok dan alokasi pesanan kemudian mengadopsi kerangka kerja pengambilan keputusan multikriteria. Analytical Hierarchy Process (AHP) diterapkan untuk menentukan bobot setiap kriteria, pemasok diperingkat menggunakan Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS), dan alokasi pesanan akhir dioptimalkan melalui Multi-Choice Goal Programming (MCGP). Kriteria evaluasi meliputi kinerja ekonomi, kepatuhan etika, dampak lingkungan, dan Persyaratan Kandungan Dalam Negeri (TKDN). Berbeda dengan pendekatan konvensional, kriteria TKDN dalam kajian ini dikembangkan sebagai indeks multiatribut, yang tidak hanya mencakup persentase bahan baku dalam negeri tetapi juga proses produksi lokal, pemanfaatan tenaga kerja, dan adopsi teknologi. Model yang diusulkan diharapkan dapat mendukung operasi SPKLU yang lebih akurat dan berkelanjutan.

Kata Kunci: ARIMA, AHP, Fuzzy TOPSIS, Multi-Choice Goal Programming, SPKLU, peramalan

Development of a Warehouse Management System for Reusable Materials in Power Plant Maintenance, Repair, and Overhaul Activities

2025-05-20T00:38:32+08:00

Ineffective management of reusable material warehousing at the Indralaya power generation unit has caused inefficiencies in material service time, primarily due to ineffective systems for receiving, storing, and issuing materials. These deficiencies have resulted in delays in the power plant's Maintenance, Repair, and Overhaul (MRO) processes. This study aims to develop an efficient and systematic Warehouse Management System (WMS) for managing reusable materials used in MRO activities, with the objective of improving warehouse operational efficiency and reducing material service lead time. The research methodology involved field observations, literature review, data collection, material classification, and service time analysis using Bizagi Modeler. The findings indicate that the development of a WMS—supported by redesigned business processes, improved warehouse layout, structured recording systems, and process digitalization—significantly enhances the efficiency of receiving, storage, and dispatch activities. These improvements contribute to faster material distribution and reduce search time, ultimately shortening the overall warehouse service time.

The Development of Vendor Performance Evaluation and Strategies Design Systems Using Fuzzy Best Worst Method (FBWM) and TODIM in PT PLN (Persero)

2025-05-19T16:08:08+08:00

In realizing its vision and mission, as well as in supporting electricity national fulfillment, PT PLN (Persero) is necessary to be supported by supply chain management, ranging from procurement to delivery activities. In the procurement process, one of the important aspects is the management of vendors, including the evaluation of their performance, to enable PT PLN (Persero) in selecting good performance vendors and determining appropriate development strategies for its vendor improvement. This paper discusses the development of vendor performance evaluation system by setting the criteria, sub criteria, and by a fuzzy multi-criteria decision making (MCDM). In the other hand, fuzzy best-worst method (BWM) is to determine importance weights of the evaluation criteria, while TODIM to asses vendor performance and subsequently to determine vendor development strategies based on its performance. Finally, this study is to develop framework in vendor performance evaluation system and strategies design.

A Conceptual Framework for the Selection of Biomass Alternatives for Coal Co-firing using Multi-Criteria Decision Making (MCDM) Approach

2025-05-30T17:03:55+08:00

The increasing demand for sustainable energy and the need to reduce carbon emissions have driven the implementation of biomass co-firing in coal-fired power plants. Selecting appropriate biomass alternatives for co-firing is a complex decision involving multiple technical, economic, environmental, social, and regulatory considerations. This study aims to develop a conceptual framework for selecting biomass alternatives for co-firing in coal power plants. The framework integrates Analytic Hierarchy Process (AHP) and Structural Equation Modeling-Partial Least Squares (SEM-PLS) to identify and prioritize key selection criteria. The model consists of five main criteria technical, economic, environmental, social, and policy/regulation with a total of 14 validated sub-criteria. The dependent variable, Selection of Biomass Alternative, is measured by four biomass types: Rice Husk, Wood Pellet, Palm Kernel Shell, and Sawdust. The proposed framework provides a comprehensive tool to support decision-making and ensure the sustainable implementation of biomass co-firing.

Thermal Discomfort and Its Impact on Urban Residential Electricity Use: A Case Study in Bandung, Indonesia

2025-05-30T17:01:06+08:00

Urban energy demand in tropical highland settings is increasingly sensitive to climatic stressors, particularly the combined effects of temperature and humidity on thermal comfort. This study quantifies the influence of the Thermal Humidity Index (THI) on monthly electricity consumption in Bandung, Indonesia, between January 2012 and May 2024. Daily THI values were derived from 2-meter air temperature and relative humidity observations, aggregated into monthly means, and paired with utility records of residential electricity use. Spearman rank correlation analysis indicates a moderate positive association (ρ = 0.35, p < 0.001) between elevated heat-stress conditions and increased consumption. However, this relationship is partly obscured by concurrent growth in household electrification, appliance ownership, and urbanization.

Despite these confounding trends, the findings highlight a growing reliance on mechanical cooling in Bandung, a city historically known for its naturally temperate climate. The results underscore the importance of incorporating dynamic thermal stress and demographic shifts into future energy planning. While Spearman's correlation offers a valuable starting point, future research should adopt multivariate or time-series models to better isolate causal factors. Furthermore, the behavioral assumption linking THI to air conditioning use remains unvalidated in this study; incorporating user-level data or surveys would enhance the robustness of the conclusions. Overall, this study contributes to the broader understanding of climate-sensitive energy behavior and offers a transferable framework for urban energy forecasting in tropical regions.

Harnessing the Indonesia’s Banda Sea Thermal Resources Through Comprehensive OTEC Potential Study

2025-05-19T16:05:24+08:00

The Banda Sea, located in Indonesia's equatorial region, exhibits significant potential for Ocean Thermal Energy Conversion (OTEC) due to its deep-water layers and consistent surface temperatures. This study evaluates the OTEC feasibility in the Banda Sea by analyzing monthly and seasonal sea surface temperature (SST) data from 2015, obtained from Copernicus ERA5 reanalysis. Spatial and vertical temperature profiles were processed using Ocean Data View (ODV) to identify mixed-layer, thermocline, and deep-layer characteristics. Results indicate an average temperature gradient (ΔT) of 22.7°C between the surface (29–30°C) and 600–700 m depth (5–7°C), meeting the minimum ΔT requirement (20°C) for OTEC. The Carnot efficiency (η) reached 77%, surpassing the 70% benchmark for viable OTEC systems. However, long-term time-series analysis data from 2005 -2017 revealed intermittent periods where ΔT fell below 20°C, likely due to seasonal upwelling, El Niño-Southern Oscillation (ENSO) events, or anomalous ocean mixing. Those are posing reliability challenges for continuous power generation. While the Banda Sea’s thermal resources are promising, operational intermittency suggests the need for hybrid energy systems to ensure grid stability. This study provides critical insights for OTEC deployment in tropical regions, balancing renewable energy potential with technical constraints.