Optimalisasi Pemanfaatan Energi Terbarukan dalam Infrastruktur Militer TNI AD: Telaah Literatur Sistematis dan Analisis Studi Kasus Hybrid Energy System di Wilayah TerpencilAcep Nana Komara1*, Mohammad Azis Mahardika2*Email corresponding author: acep.nana@mhs.itenas.ac.id1Prodi Magister Teknik Mesin, Institut Teknologi Nasional Bandung Jl. Phh. Mustofa No. 23 Neglasari, Cibeunying Kaler, Kota Bandung, Jawa Barat, Indonesia2FakultasTeknologi Industri, Institut Teknologi Nasional Bandung Jl. Phh. Mustofa No. 23 Neglasari, Cibeunying Kaler, Kota Bandung, Jawa Barat, IndonesiaArticle history: Received: 22 November 2025 | Revised: 22 Desember 2025 | Accepted: 6 Januari 2026Abstract. Energy independence for military outposts in remote areas is a strategic issue with direct implications for national defense resilience. Currently, most military outposts of the Indonesian Army (TNI AD) still rely on diesel generators, which have high fuel consumption, expensive logistics costs, and significant environmental impacts. In line with the national energy transition agenda towards Net Zero Emission 2060, the integration of renewable energy in the defense sector presents both opportunities and challenges. This article provides an analysis and review of the latest literature on the application of hybrid renewable energy systems in remote areas, both in Indonesia and abroad, and examines its implications for TNI AD military outposts. The research methodology includes a systematic review of international publications (Scopus, ScienceDirect), nationally accredited journals (Sinta), government reports, and policy documents from 2015–2024. The findings show that the hybrid system of solar photovoltaic (PLTS), battery, and diesel is the most relevant configuration, with potential fuel savings of 70-85%, reduced logistics operational costs, and significant carbon emission reductions. Studies in the Philippines, Africa, and Indonesian islands show successful implementation of hybrid systems for off-grid areas, which can be adapted for TNI AD military outposts. This article makes an academic contribution by integrating renewable energy literature with defense perspectives and offers practical recommendations for implementing a pilot project of hybrid systems in border military outposts.Keywords - Renewable energy, hybrid system, TNI AD, energy independence, literature reviewAbstrak. Kemandirian energi pos militer di daerah terpencil merupakan isu strategis yang berimplikasi langsung terhadap ketahanan pertahanan negara. Selama ini, sebagian besar pos militer TNI AD masih mengandalkan genset diesel dengan konsumsi BBM tinggi, biaya logistik mahal dan dampak lingkungan signifikan. Sejalan dengan agenda transisi energi nasional menuju Net Zero Emission 2060, integrasi energi terbarukan di sektor pertahanan menjadi peluang sekaligus tantangan. Artikel ini menyajikan analisis dan review literatur terkini terkait penerapan sistem hybrid energi terbarukan di wilayah terpencil, baik di Indonesia maupun di negara lain serta mengkaji implikasinya bagi pos militer TNI AD. Metode penelitian dilakukan melalui tinjauan sistematis terhadap publikasi jurnal internasional (Scopus, ScienceDirect), jurnal nasional terakreditasi (Sinta), laporan pemerintah dan dokumen kebijakan selama periode 2015–2024. Hasil kajian menunjukkan bahwa sistem hybrid PLTS–baterai–diesel merupakan konfigurasi paling relevan, dengan potensi penghematan BBM hingga 70–85%, penurunan biaya operasional logistik, serta reduksi emisi karbon signifikan. Studi di Filipina, Afrika dan kepulauan Indonesia menunjukkan keberhasilan implementasi sistem hybrid untuk daerah off-grid, yang dapat diadaptasi bagi pos militer TNI AD. Artikel ini memberikan kontribusi akademis melalui integrasi literatur energi terbarukan dengan perspektif pertahanan serta kontribusi praktis berupa rekomendasi penerapan pilot project sistem hybrid di pos militer perbatasan.Kata Kunci - Energi terbarukan, sistem hybrid, TNI AD, kemandirian energi, review literaturPendahuluan Ketersediaan energi yang andal dan kontinu merupakan prasyarat fundamental dalam menjamin kesiapan operasional militer (operational readiness). Dalam doktrin pertahanan modern, energi tidak sekadar berfungsi sebagai pendukung logistik dasar, melainkan telah bertransformasi menjadi tulang punggung bagi efektivitas sistem persenjataan, perangkat komunikasi satelit, radar pemantauan, serta infrastruktur Command, Control, Communication, Computer, Intelligence, Surveillance, and Reconnaissance (C4ISR). Bagi Tentara Nasional Indonesia Angkatan Darat (TNI AD), urgensi pemenuhan energi ini menjadi sangat krusial pada pos-pos militer yang berlokasi di wilayah perbatasan dan daerah terpencil, di mana akses terhadap jaringan listrik nasional (on-grid) sering kali tidak tersedia atau sangat terbatas secara infrastruktur ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/su12031280","ISSN":"20711050","abstract":"Renewable energy technologies can not only help in mitigating the greenhouse gas (GHG) emissions but it can also be very useful for electricity generation at remote locations, where no other means of power are available. The present study focuses on the techno-economic optimum design of a small hybrid renewable energy system (HRES) consisting of wind-solar as primary energy sources. The HRES was modelled for a remote island (Deokjeok-do Island, South Korea) using real electricity consumption data for one complete year. A daily mean load of 24,720 kWh was entered at Deokjeok-do Island with a peak load of 2291.54 kW. Average annual values of wind speed and daily solar radiations were estimated to be 3.6 m/s (10 m height) and 4.13 kWh/m2, respectively. A total of 8760 simulations were performed to achieve the hourly load demand of the mentioned island. In order to deal with the surplus and electricity deficit, two different types of energy storage systems (ESS) were modelled i.e., battery and pumped hydro storage (PHS). Four different HRESs were also evaluated as the most suitable based on levelized cost of energy (LCOE) and net present cost (NPC). A detailed economic break-down of each component and the impact of different sensitivity variables on decision making have also been discussed in detail.","author":[{"dropping-particle":"","family":"Ali","given":"Sajid","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jang","given":"Choon Man","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sustainability (Switzerland)","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2020"]]},"title":"Optimum design of hybrid renewable energy system for sustainable energy supply to a remote Island","type":"article-journal","volume":"12"},"uris":["http://www.mendeley.com/documents/?uuid=33a71606-10d7-30a8-80d6-6a1f6fe9f3dc","http://www.mendeley.com/documents/?uuid=e376b0e5-2bb2-4d99-9aa3-7ad32ac9f8e9"]}],"mendeley":{"formattedCitation":"(1)","plainTextFormattedCitation":"(1)","previouslyFormattedCitation":"(1)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[1].Hingga saat ini, pemenuhan kebutuhan listrik di mayoritas pos militer terpencil masih didominasi oleh penggunaan generator diesel (genset). Namun, ketergantungan pada energi fosil ini menghadapi tantangan multidimensi. Pertama, aspek ekonomi dan logistik; biaya distribusi bahan bakar minyak (BBM) ke wilayah 3T (Terdepan, Terluar, Tertinggal) dapat melonjak hingga tiga kali lipat akibat sulitnya aksesibilitas geografis [2].Kedua, aspek ketahanan operasional; kerentanan rantai pasok BBM menciptakan risiko strategis, di mana keterlambatan distribusi dapat menyebabkan kegagalan fungsi pertahanan di wilayah rawan. Ketiga, aspek lingkungan; penggunaan emisi karbon yang masif tidak lagi selaras dengan komitmen global dan agenda transisi energi nasional menuju energi bersih ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ecmx.2023.100357","ISSN":"25901745","abstract":"Determining the optimal configuration of hybrid renewable energy systems (HRESs) is the key to sustainable energy development. HRESs interface with multiple energy supply systems and need to simultaneously meet different time-varying loads. In this paper, 16 scenarios are formed from four resource conditions and four load types. All feasible HRES configurations for the 16 scenarios are simulated by using HOMER. Nine indexes are selected from three aspects (technology, economy and environment), and a comprehensive evaluation framework is established based on the Analytic Hierarchy Process. The optimal system in each scenario is selected and compared to assess the impact of resource and load conditions on system design. The results show that, in general, HRESs have a higher comprehensive index rating than single-source renewable energy systems, so HRESs should be considered as a better choice. And the optimal configuration of HRESs, including the size and type of generation devices and the size of energy storage device, is jointly determined by the conditions of energy resources and loads. The methodology presented in this paper can be an effective framework for designing and evaluating HRESs, and the obtained optimal systems can be valuable references for regions with similar resource and load conditions.","author":[{"dropping-particle":"","family":"Xu","given":"Xiangguo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Zhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yuan","given":"Jiawei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shao","given":"Junqiang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Energy Conversion and Management: X","id":"ITEM-1","issued":{"date-parts":[["2023"]]},"title":"Design and multi-objective comprehensive evaluation analysis of PV-WT-BG-Battery hybrid renewable energy systems in urban communities","type":"article-journal","volume":"18"},"uris":["http://www.mendeley.com/documents/?uuid=0bdfc9b0-3d24-3ee3-9a56-3244d32ae75f","http://www.mendeley.com/documents/?uuid=10bca0d1-951c-4d4c-8e00-5fe62e1d30b3"]}],"mendeley":{"formattedCitation":"(3)","plainTextFormattedCitation":"(3)","previouslyFormattedCitation":"(3)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[3] [4].Secara global, paradigma penyediaan energi militer telah bergeser menuju kemandirian energi berbasis sumber daya lokal (energy self-sufficiency). Amerika Serikat melalui Department of Defense (DoD) telah memelopori Energy Resilience and Conservation Investment Program (ERCIP) yang mengintegrasikan sistem microgrid pada pangkalan militer untuk menjamin keberlangsungan operasi tanpa bergantung pada suplai eksternal. Berbagai studi kasus di Afrika dan Filipina turut mengonfirmasi bahwa implementasi sistem hybrid PLTS–Baterai–Diesel mampu mereduksi konsumsi BBM secara signifikan, dengan tingkat efisiensi biaya logistik mencapai 40% hingga 75%ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.asej.2023.102194","ISSN":"20904479","abstract":"This paper proposes the design and analysis of a multiple-input-single-output (MISO) DC-DC converter suitable for a hybrid renewable energy system with energy storage capability. The converter design and control strategy validated in this research can be used to regulate the output dc voltage obtained from multiple-source renewable energy systems. The design is realized using a non-isolated double boost converter as it can handle bidirectional power flow at the storage port and has other advantages like compact structure, reduced number of components, and double boosting capability. The converter can also work without transformers, enabling the input renewable energy sources to operate individually. An inverted decoupler control strategy is adopted in the design of this novel converter. Mathematical models were developed to obtain the decoupler matrix, and the proposed control strategy is utilized to analyze the output. Simulations were performed on MISO converters with load and reference variations and is validated with the prototype. The current ripple percentage at the dual source side is found to be reduced by 2.6%. Also, the rise time, settling time, and percent overshoot of the output voltage were reduced by 0.03 s, 45 ms, and 3.6%, respectively. The performance of the proposed MISO converter is validated on a 50 W hardware prototype. The results validate the MISO converter's superior dynamic performance, output voltage regulation and reduced dual source ripple current, all of which are in agreement to the results of the simulation. A comprehensive comparison with the traditional controller is also conducted to further illustrate the advantages of the proposed inverted decoupler. The inverted decoupler validated in this work can be effectively used in hybrid renewable energy systems with energy storage capability, as it is found to be immune to the disturbances from the source end.","author":[{"dropping-particle":"","family":"Saritha","given":"P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Devi","given":"V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Babu","given":"Ajay","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Praveen","given":"R. P.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Ain Shams Engineering Journal","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2023"]]},"title":"Design and analysis of multiple input single output converter for hybrid renewable energy system with energy storage capability","type":"article-journal","volume":"14"},"uris":["http://www.mendeley.com/documents/?uuid=fb1086da-195a-39f6-87d3-38724b3f49d6","http://www.mendeley.com/documents/?uuid=42fcd276-161e-4446-9ad3-fa233698155b"]}],"mendeley":{"formattedCitation":"(4)","plainTextFormattedCitation":"(4)","previouslyFormattedCitation":"(4)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [5] ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.rineng.2022.100481","ISSN":"25901230","abstract":"Energy crises, increasing electricity prices, and having no access to the grid electricity are the leading issues in developing countries of Asia, the Middle East, and Sub-Saharan Africa. Developing hybrid renewable energy systems in off-grid or grid-connected modes is the best way to overcome developing countries' economic and energy crises. However, the development of hybrid renewable energy systems faces severe technical and related economic challenges. This article provides an updated and comprehensive resource and economic overview of developed hybrid renewable energy systems in different locations in these aforementioned regions. The resource assessment shows that for economical hybrid energy system the average annual wind speed and average annual solar radiation should be 5 m/s and 5 KWh/m^2 respectively. This paper also provides a big picture of renewable energy impacts, challenges in the architecture of hybrid systems, and key organizations working in this domain in the developing regions. According to our review, hybrid optimization model for electric renewable, particle swarm optimization and genetic algorithm are frequently used tools for the optimization and sizing of hybrid energy systems. The map of hybrid renewable energy system research in developing regions is not available. Our study gives energy scenario and clear map of hybrid energy in developing regions of the world. The scientometric review of 2000 bibliographic data obtained from the Scopus database to perform co-author and co-occurrence analysis in this study. The data is used to trace the research pattern and thus to identify the most impactful authors, institutions, and countries in the hybrid renewable energy systems domain to obtain recommendations and make policies for the future uninterrupted and carbon dioxide emission free energy systems. The results of scientometric analysis shows that the Wang X. is the most prolific author, while India and Tanta University are the most productive country and institution in this domain. The scientometric analysis result will be beneficial in determining the future research directions in the hybrid renewable energy systems field.","author":[{"dropping-particle":"","family":"Sohail","given":"Madni","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Afrouzi","given":"Hadi Nabipour","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mehranzamir","given":"Kamyar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ahmed","given":"Jubaer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mobin Siddique","given":"Md Bazlul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tabassum","given":"Mujahid","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Results in Engineering","id":"ITEM-1","issued":{"date-parts":[["2022"]]},"title":"A comprehensive scientometric analysis on hybrid renewable energy systems in developing regions of the world","type":"article-journal","volume":"16"},"uris":["http://www.mendeley.com/documents/?uuid=495c82e9-5fea-3d61-93b9-c89b36285df1","http://www.mendeley.com/documents/?uuid=4f8fea31-872c-4d59-a79a-c495168a623f"]}],"mendeley":{"formattedCitation":"(5)","plainTextFormattedCitation":"(5)","previouslyFormattedCitation":"(5)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[6]. Indonesia, dengan potensi radiasi matahari harian rata-rata 4,8 hingga 5,1 kWh/m², memiliki peluang besar untuk mengadopsi teknologi serupa guna memperkuat kedaulatan energi di sektor pertahanan ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.14710/ijred.2022.39012","ISSN":"22524940","abstract":"In this study, a techno-economic feasibility analysis of hybrid renewable energy systems for four household categories in rural areas of Chad was studied based on the multi-criteria assessment technique. The problem of this study is to know the best optimal solution in the technical and economic feasibility study of the decentralized mini-grids for the rural electrification of isolated villages in Chad. The main objective of the work is to assess technically, economically and environmentally the feasibility of six scenarios of hybrid systems in five isolated sites in Chad. The performance analysis involved six scenarios of possible hybrid solutions while achieving a supply-demand balance for sustainable electrification of the remote villages, using the HOMER software. The results have shown that the optimum combination of the hybrid system was the photovoltaic/battery system with a Net Present Cost (NPC) of US $ 328,146 and it was found at Etena village. The photovoltaic/Wind/Diesel/Battery hybrid configuration was the least optimum system and it has appeared in Mandelia village. In terms of energy cost, the lowest Levelized Cost of Energy (LCOE) was estimated at US $ 0.236/kWh in a photovoltaic/Wind/Battery configuration at Koundoul site and the highest costs US $ 0.363/kWh in the photovoltaic/Battery configuration at the Linia site. It is established that hybrid solutions can be developed to make electricity available and accessible to the population of the remote rural areas in Chad. However, it is imperative that the local government must subsidize the diesel price to promote the adaptation of the abundant renewable solutions.","author":[{"dropping-particle":"","family":"Hassane","given":"Abdelhamid Issa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Didane","given":"Djamal Hissein","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tahir","given":"Abakar Mahamat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mouangue","given":"Ruben Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tamba","given":"Jean Gaston","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hauglustaine","given":"Jean Marie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Renewable Energy Development","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2022"]]},"title":"Comparative analysis of hybrid renewable energy systems for off-grid applications in chad","type":"article-journal","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=e66454a5-59b9-3ffe-b3ee-5f668ae936e6","http://www.mendeley.com/documents/?uuid=3e6c077f-196f-4c89-a4c8-804e5a1c43b9"]}],"mendeley":{"formattedCitation":"(6)","plainTextFormattedCitation":"(6)","previouslyFormattedCitation":"(6)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[7]. Meskipun potensi teknis sistem hybrid telah banyak dikaji dalam konteks elektrifikasi pedesaan dan pulau-pulau kecil di Indonesia ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/su13042393","ISSN":"20711050","abstract":"This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.","author":[{"dropping-particle":"","family":"Rahman","given":"Md Mijanur","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shakeri","given":"Mohammad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tiong","given":"Sieh Kiong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Khatun","given":"Fatema","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Amin","given":"Nowshad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pasupuleti","given":"Jagadeesh","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hasan","given":"Mohammad Kamrul","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sustainability (Switzerland)","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2021"]]},"title":"Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks","type":"article","volume":"13"},"uris":["http://www.mendeley.com/documents/?uuid=ae7c57fd-7e06-34e1-a038-2c7b6f51e6ea","http://www.mendeley.com/documents/?uuid=997e0538-70b1-4442-9287-0b33e8026083"]}],"mendeley":{"formattedCitation":"(7)","plainTextFormattedCitation":"(7)","previouslyFormattedCitation":"(7)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[8], literatur yang secara spesifik menelaah implementasinya di lingkungan infrastruktur militer masih sangat terbatas. Terdapat research gap yang signifikan antara ketersediaan teknologi energi terbarukan dengan aplikasi kebijakan pada sektor pertahanan nasional. Fokus penelitian sebelumnya cenderung pada aspek tekno-ekonomi sipil, tanpa mempertimbangkan variabel strategis seperti mobilitas operasional dan kerahasiaan militer. Oleh karena itu, artikel ini hadir untuk menjembatani kesenjangan tersebut melalui analisis sistematis mengenai optimalisasi sistem energi hybrid di pos militer TNI AD. Kajian ini difokuskan pada tiga problematika utama: (1) evaluasi potensi teknis sistem hybrid di wilayah terpencil dengan variasi radiasi matahari; (2) analisis dampak efisiensi logistik dan penurunan biaya operasional; serta (3) formulasi implikasi strategisnya terhadap ketahanan energi nasional. Kebaruan (novelty) dari penelitian ini terletak pada integrasi parameter teknis energi terbarukan dengan perspektif doktrin ketahanan operasional, yang diharapkan dapat menjadi referensi strategis bagi pemangku kebijakan dalam merumuskan modernisasi infrastruktur energi militer yang mandiri, efisien dan berkelanjutan.MetodePenelitian ini dilaksanakan dengan menerapkan pendekatan Systematic Literature Review (SLR) yang dirancang untuk mengidentifikasi, mengevaluasi dan menyintesis literatur ilmiah mengenai implementasi sistem energi terbarukan hybrid pada wilayah terpencil. Penggunaan metode SLR dipilih guna memastikan bahwa tinjauan yang disajikan bersifat objektif, terstruktur dan berbasis bukti (evidence-based), sehingga mampu menghasilkan kesimpulan yang valid untuk diaplikasikan pada infrastruktur pertahanan.Proses pengumpulan data dilakukan melalui penelusuran sistematis pada berbagai basis data akademik bereputasi tinggi. Sumber literatur internasional mencakup portal jurnal global seperti Scopus, ScienceDirect, IEEE Xplore, SpringerLink dan Wiley Online Library. Sementara untuk menjaring perspektif lokal dan kebijakan nasional, penelusuran diarahkan pada basis data Sinta (khusus peringkat 1 dan 2), repositori Garuda, serta laporan resmi dari instansi pemerintah seperti Kementerian ESDM, Kementerian Pertahanan, Bappenas, serta lembaga internasional seperti IRENA dan UNDP. Strategi pencarian literatur dibangun menggunakan kombinasi operator Boolean pada kata kunci utama yang meliputi “renewable energy”, “hybrid energy system”, “microgrid”, “off-grid”, “remote area”, serta istilah spesifik pertahanan seperti “military base”, “defense energy” dan “TNI AD”.Guna menjaga relevansi dan kebaruan informasi di tengah pesatnya perkembangan teknologi energi, ditetapkan kriteria inklusi yang ketat dalam proses seleksi literatur. Pertama, artikel atau laporan yang dianalisis dibatasi pada publikasi dalam rentang waktu lima tahun terakhir (2020–2024). Kedua, studi harus menyajikan analisis mendalam mengenai performa teknis atau kelayakan ekonomi dari sistem hybrid yang menggabungkan minimal dua sumber energi (seperti surya dan diesel). Ketiga, fokus geografis penelitian harus relevan dengan karakteristik pos militer, yakni wilayah terpencil, kepulauan, atau area yang tidak terjangkau jaringan listrik utama. Artikel yang hanya membahas energi terbarukan pada skala makro perkotaan atau kebijakan umum yang tidak memiliki parameter teknis kuantitatif akan dieksklusi dari tinjauan ini.Selanjutnya, data yang telah terkumpul dianalisis secara komprehensif melalui integrasi pendekatan kualitatif-deskriptif dan kuantitatif-komparatif. Analisis kualitatif difokuskan untuk memetakan urgensi kemandirian energi dan tantangan logistik di garis depan pertahanan. Sementara itu, analisis kuantitatif dilakukan dengan mengekstraksi parameter kunci seperti persentase reduksi konsumsi BBM, nilai Levelized Cost of Energy (LCOE), serta daya tahan sistem penyimpanan energi (baterai). Seluruh temuan dari literatur tersebut kemudian dikontekstualisasikan secara spesifik dengan kondisi operasional pos TNI AD. Hal ini mencakup sinkronisasi profil beban energi (seperti kebutuhan radar dan perangkat komunikasi) dengan karakteristik geografis perbatasan Indonesia. Melalui metode ini, penelitian tidak hanya memaparkan teori, namun juga menyajikan sintesis kritis yang menghubungkan potensi teknologi dengan kebutuhan nyata prajurit di lapangan demi memperkuat ketahanan operasional nasional.Hasil dan PembahasanPerkembangan penelitian mengenai energi terbarukan di sektor pertahanan menunjukkan peningkatan yang signifikan dalam satu dekade terakhir. Sejak tahun 2015, integrasi energi bersih di lingkungan militer menjadi perhatian global seiring dengan meningkatnya kebutuhan akan ketahanan energi (energy resilience) dan efisiensi logistik pertahanan. Secara umum, arah penelitian di bidang ini berfokus pada tiga hal utama, yaitu pengembangan sistem hybrid microgrid, peningkatan efisiensi operasional, serta perumusan strategi dekarbonisasi dan adaptasi terhadap perubahan iklim dalam konteks militer.Negara-negara maju seperti Amerika Serikat, anggota Uni Eropa dan Jepang menjadi pionir dalam pengembangan konsep pangkalan militer hijau (green military base). Laporan U.S. Department of Defense (2022) mencatat bahwa lebih dari tiga puluh pangkalan militer telah mengoperasikan sistem microgrid hybrid berbasis PLTS dan baterai yang mampu mengurangi ketergantungan terhadap bahan bakar diesel hingga 60–70 persen. Sistem ini tidak hanya berfungsi sebagai sumber energi alternatif, tetapi juga meningkatkan keandalan operasional saat terjadi gangguan pada jaringan listrik konvensional. Di kawasan Eropa, North Atlantic Treaty Organization (NATO) melalui Energy Security Centre of Excellence (ENSEC COE) menempatkan keamanan energi sebagai komponen strategis dalam pertahanan modern. Program ini menekankan pentingnya diversifikasi sumber energi di pangkalan militer, integrasi sistem energi terbarukan dalam operasi lapangan, serta penerapan konsep energy-efficient deployment untuk mengurangi jejak karbon kegiatan militer (NATO, 2021). Riset-riset yang dikembangkan di Jerman, Inggris dan Prancis umumnya berfokus pada desain sistem microgrid yang tangguh, optimasi manajemen beban, serta kebijakan energi militer yang berorientasi pada keberlanjutan ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.24043/isj.167","ISSN":"17152593","abstract":"Access to energy services is recognised as a fundamental aspect of economic and social development. This is particularly important for isolated areas, where electrical supply is not guaranteed. Because of their inherent geographic characteristics, islands are prominent cases of isolated areas that must import and burn fossil fuels, with environmental and economic consequences. In this context, Hybrid Renewable Energy Systems (HRES) emerge as an alternative to traditional generation to reduce energy costs and environmental issues. This study aims to demonstrate the feasibility of implementing HRES on islands, based on energy optimisation. We present an extensive review of HRES optimisations across 73 island cases, collecting information about energy demand, energy system sizes, and optimisation methodologies. The most commonly proposed HRES components are identified, and a significant power relationship is found between population and annual energy demand on islands. Further, we identify islands with higher-than-expected and lower-than-expected consumption and the underlying causes. The main limitations of the reviewed studies are discussed, particularly with regards to availability and quality of hourly demand data and/or meteorological data required for renewable energy assessments. Several approaches to fill these gaps in information are reviewed here, concluding with a discussion of emergent methods and technologies.","author":[{"dropping-particle":"","family":"Lassalle","given":"James Morales","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martínez","given":"Dante Figueroa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernández","given":"Luis Vergara","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Island Studies Journal","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2022"]]},"title":"Optimisation of hybrid renewable energy systems on islands: A review","type":"article-journal","volume":"17"},"uris":["http://www.mendeley.com/documents/?uuid=eb4347b4-c8af-3a7e-a62d-290f9bc5229e","http://www.mendeley.com/documents/?uuid=6df5bc86-7997-4b3c-a1c3-638667c28e96"]}],"mendeley":{"formattedCitation":"(8)","plainTextFormattedCitation":"(8)","previouslyFormattedCitation":"(8)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[9].Dalam beberapa tahun terakhir, fokus penelitian mulai bergeser ke kawasan Asia Pasifik. Pergeseran ini didorong oleh meningkatnya kebutuhan energi pada instalasi militer di wilayah kepulauan dan perbatasan yang memiliki tantangan geografis serupa dengan kawasan terpencil. Filipina menjadi salah satu pelopor di Asia Tenggara melalui Philippine Army Renewable Camp Program, yang berhasil mengintegrasikan sistem PLTS berkapasitas 500 kWp dengan baterai lithium-ion untuk pos militer di daerah terpencil. Program tersebut dilaporkan mampu menurunkan konsumsi diesel hingga 55 persen sekaligus meningkatkan keandalan suplai listrik dan mengurangi beban logistik energi. Di Jepang dan Korea Selatan, fokus penelitian diarahkan pada integrasi renewable microgrid dengan sistem pertahanan siber (cyber-secured microgrid) untuk menjamin keberlanjutan energi sekaligus keamanan jaringan. Sementara itu, di Australia, riset lebih banyak diarahkan pada penerapan sistem energi terbarukan hybrid di pangkalan militer yang berlokasi di wilayah gurun dan pesisir dengan potensi radiasi matahari yang tinggi (Australian Defence Energy Review, 2021).Kajian literatur juga menunjukkan bahwa terdapat sejumlah tema penelitian yang mendominasi dalam dekade terakhir. Tema-tema tersebut mencakup desain dan optimasi sistem hybrid energi terbarukan yang memadukan PLTS, turbin angin, dan baterai dalam konfigurasi microgrid otonom; analisis efisiensi dan keekonomian energi, termasuk perhitungan Levelized Cost of Energy (LCOE) dan penghematan bahan bakar; serta studi tentang keamanan dan keandalan energi (energy resilience) terutama dalam situasi darurat atau konflik. Selain itu, penelitian juga banyak mengangkat topik kebijakan pertahanan berkelanjutan (sustainable defense policies) yang mengintegrasikan aspek lingkungan dalam strategi militer nasional, serta upaya mitigasi emisi karbon militer sebagai bagian dari komitmen global menuju target net-zero emissions. Studi yang dilakukan oleh Khalil et al. (2019) dan Lee et al. (2020) menunjukkan bahwa penerapan sistem hybrid PLTS–baterai–diesel dapat menurunkan konsumsi bahan bakar hingga 75 persen dan mengurangi emisi karbon lebih dari 40 persen. Sementara itu, penelitian oleh Rodriguez et al. (2021) menyoroti pentingnya algoritma manajemen energi berbasis machine learning dalam mengoptimalkan operasi microgrid militer di daerah terpencil.Tren global terkini menunjukkan bahwa penelitian energi terbarukan di sektor pertahanan semakin bergerak menuju integrasi lintas disiplin yang melibatkan aspek teknis, kebijakan, logistik, hingga keamanan nasional. Penggunaan sistem smart microgrid dengan dukungan teknologi artificial intelligence (AI) dan Internet of Military Things (IoMT) diproyeksikan menjadi fokus riset utama dalam lima tahun mendatang. Tujuan utama dari pengembangan ini adalah menciptakan sistem energi militer yang adaptif, efisien, serta tahan terhadap gangguan (self-healing system).Implikasi strategis dari perkembangan ini sangat luas. Pengembangan energi terbarukan di basis militer tidak hanya berperan dalam meningkatkan efisiensi logistik dan mengurangi emisi karbon, tetapi juga memperkuat kemandirian energi pertahanan (defense energy autonomy). Dari perspektif geopolitik, keberhasilan negara-negara maju dalam membangun pangkalan militer yang mandiri energi menunjukkan bahwa transisi menuju green defense bukan semata upaya lingkungan, melainkan bagian integral dari strategi keamanan nasional yang modern, resilien dan berkelanjutan ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/en14061595","ISSN":"19961073","abstract":"The aim of the paper is the study of the Hybrid Renewable Energy System, which is consisted of two types of renewable energy systems (wind and sun) and is combined with storage energy system (battery). The paper presents the classification and review of architectures of Hybrid Renewable Energy Systems. The considered Hybrid Renewable Energy System was designed as a multi-converter system with gearless Wind Turbine driven Permanent Magnet Synchronous Generator and with a Photovoltaic Array and Battery Energy System. The mathematical models of individual elements of a complex Hybrid Renewable Energy System were described. In the control of both systems of Wind Turbine with Permanent Magnet Synchronous Generator and Photovoltaic array, the algorithms of Maximum Power Point Tracking have been implemented for higher efficiency of energy conversion. The energy storage in the battery has been managed by the control system of a bidirectional DC/DC converter. For the control of the Machine Side Converter and Wind Turbine with Permanent Magnet Synchronous Generator, the vector control method has been implemented. In the control system of the Grid Side Converter, the advanced method of Direct Power Control has been applied. The energy management strategies for optimal flows of electrical energy between individual systems of considered hybrid renewable energy system are developed and described. In order to determine the operation of proposed control systems, the simulation studies have been performed for different conditions of operation of individual elements of the complex hybrid system. The considered control methods and energy management strategies were tested thorough simulation studies for different wind speed variations, different sun irradiations, and different local load demands. The performed simulations are of practical importance in terms of proper operation requirements, design selection of components and energy management of Hybrid Renewable Energy Systems.","author":[{"dropping-particle":"","family":"Gajewski","given":"Piotr","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pieńkowski","given":"Krzysztof","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Energies","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2021"]]},"title":"Control of the hybrid renewable energy system with wind turbine, photovoltaic panels and battery energy storage","type":"article-journal","volume":"14"},"uris":["http://www.mendeley.com/documents/?uuid=c84b2755-7766-3457-b6b3-3921f38daee0","http://www.mendeley.com/documents/?uuid=d35c2cd7-65a7-4221-9cc6-f16eebf72bbf"]}],"mendeley":{"formattedCitation":"(9)","plainTextFormattedCitation":"(9)","previouslyFormattedCitation":"(9)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[10].Kondisi geografis Indonesia yang luas dan tersebar menjadikan tantangan penyediaan energi di pos-pos militer sangat kompleks, terutama bagi TNI Angkatan Darat yang memiliki lebih dari 1.700 pos di wilayah perbatasan, kepulauan, dan daerah terpencil. Sebagian besar pos tersebut masih mengandalkan genset diesel sebagai sumber energi utama, dengan pasokan bahan bakar yang sangat bergantung pada kondisi cuaca, aksesibilitas medan, serta infrastruktur transportasi logistik yang terbatas. Ketergantungan ini menimbulkan permasalahan mendasar dalam hal efisiensi operasional, biaya dan keandalan energi di lapangan.Analisis Teknis dan Potensi Implementasi Sistem Hybrid di Pos TerpencilAnalisis potensi implementasi sistem hybrid diawali dengan membedah profil beban harian (daily load profile) pada instalasi militer di garis depan. Berdasarkan tabulasi data operasional dari Satgas Yonif 643/Walet Sakti di Sektor Papua dan Satgas Yonif 742/Swy di Sektor Timor Leste, ditemukan bahwa kebutuhan energi harian rata-rata berada pada rentang yang sangat konsisten, yakni antara 19,302 kWh hingga 20,752 kWh, seperti yang disajikan pada tabel 1. Karakteristik beban ini diklasifikasikan sebagai beban strategis karena didominasi oleh perangkat C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance). Perangkat modern seperti terminal Starlink dan Repeater VHF mengonsumsi daya signifikan secara kontinu untuk menjamin fungsi komando, sementara sistem pengawasan berbasis CCTV dan NVR menuntut stabilitas daya 24 jam guna menjaga keamanan perimeter tanpa interupsi.Tabel SEQ Tabel \* ARABIC 1. Sources Satgas Poskotis Yonif 643/Walet Sakti Satgas RI-PNG Statis di Sektor Papua Kab Keroom dan Satgas Pamtas RI-RDTL Yonif 742/Swy di Sepanjang Perbatasan Darat RI – RDTL Sektor Timur, Timor LesteKategori BebanPoskotis Yonif 643 (Papua)Pos Pamtas Yonif 742 (Timor Leste)Komunikasi & ITE (Starlink, Radio, HT, HP Satelit)3,05 kWh2,67 kWhKeamanan & Infrastruktur (CCTV, Inverter, Repeater)3,24 kWh2,73 kWhFasilitas Kantor (Komputer Desktop)6,00 kWh1,00 kWhDukungan Hidup & Elektronik (AC, Pompa, TV, Masak)7,01 kWh14,35 kWhTotal Konsumsi Energi19,30 kWh/hari20,75 kWh/hariKetersediaan sumber daya matahari di wilayah perbatasan Indonesia, yang menurut BMKG (2022) memiliki intensitas radiasi rata-rata 4,5–5,5 kWh/m² per hari, memberikan kelayakan teknis yang fundamental bagi sistem fotovoltaik. Namun, efektivitas sistem ini diuji melalui analisis sensitivitas terhadap variabilitas geografis dan tutupan awan. Di wilayah kontinental dengan paparan tinggi seperti Kalimantan, sistem hybrid mampu mencapai efisiensi bahan bakar hingga 83% dengan availability factor sebesar 96%. Sebaliknya, di wilayah kepulauan seperti Maluku atau pegunungan Papua yang memiliki intensitas radiasi lebih rendah (rata-rata 4,8 kWh/m²), kontribusi surya sedikit menurun, sehingga ketergantungan pada generator diesel meningkat sekitar 7%. Meskipun demikian, integrasi Battery Energy Storage System (BESS) berperan sebagai penyangga (buffer) utama yang menjaga keandalan sistem tetap stabil di atas 90%, melampaui keandalan generator diesel tunggal yang hanya berada di angka 83–85% akibat risiko kegagalan mekanis.Evaluasi Efisiensi Logistik dan Pemodelan Ekonomi RiilDampak implementasi sistem hybrid secara langsung merombak struktur logistik energi pertahanan melalui reduksi konsumsi bahan bakar yang signifikan. Perbandingan performa antara sistem diesel konvensional dengan sistem hybrid PLTS-Baterai-Diesel disajikan pada tabel 2 dan 3 berikut:Tabel 2. Parameter Emisi Gas Buang (Hybrid) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"ISSN : 2598–3814","abstract":"Sebagai negara kepulauan terbesar, luas laut Indonesia mencapai 70 % dari total luas Negara Indonesia. \nDengan luas laut yang begitu besar seharusnya dimanfaatkan sebagai salah satu sumber ekonomi utama \nmasyarakat Indonesia, Berdasarkan fakta di lapangan diketahui permasalahan utama nelayan adalah tingginya \nbiaya operasional bahan bakar minyak (BBM) yang dibutuhkan ketika pergi melaut, sementara hasil tangkapan \nikan yang didapat tidak pasti. Selain itu energi menjadi salah satu isu yang dihadapi oleh Indonesia saat ini \nbahkan dunia, karena ketidakseimbangan antara ketersediaan energi dengan kebutuhan nya. Isu lainnya adalah \npemanasan global, dimana Indonesia dalam KTT Perubahan Iklim di Paris, Perancis tahun 2015 lalu (COP21) \ntelah berkomitmen untuk menurunkan emisi bersama dengan negara-negara lainnya. Potensi pemanfaatan \nenergi baru dan terbarukan untuk pembangkit listrik pada kapal sangatlah menjanjikan.Berdasarkan hasil \nanalisis yang dilakukan, total produksi listrik yang dihasilkan pada sistem hybrid PLTS-PLTD pertahun sebesar \n5.064 kWh/tahun. Dimana kontribusi produksi energi listrik PLTS terhadap sistem PLTH sebesar 65,5 % atau \n3.319 kWh/tahun. Sedangkan PLTD sebesar 34,5 % atau 1.745 kWh/tahun. Kelebihan energinya selama setahun \nsebesar 775 kWh/tahun.Konsumsi bahan bakar pada sistem ini sebesar 834 L/tahun dengan persentase \npenurunan sebesar 67 %.Dengan studi perencanaan sistem pembangkit listrik hybrid (PV dan diesel generator) \nmenggunakan software HOMER ini bertujuan untuk menghemat bahan bakar dan gas buang emisi pada kapal \nikan milik nelayan di Pelabuhan Perikanan Samudera (PPS) Belawan.","author":[{"dropping-particle":"","family":"Raja Harahap","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sadrakh Siahaan","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Buletin Utama Teknik","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2023"]]},"title":"Studi Prencanaan Sistem Pembangkit Listrik Hybrid (Panel Surya Dan Diesel Generator) Pada Kapal Nelayan Di Pelabuhan Perikanan Samudera (Pps) Belawan","type":"article-journal","volume":"18"},"uris":["http://www.mendeley.com/documents/?uuid=809a1a28-5123-4a72-8c1b-df5def8f9ecd"]}],"mendeley":{"formattedCitation":"(10)","plainTextFormattedCitation":"(10)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[11]Unsur Emisi GasNilaiSatuanCO2 (Karbon Dioksida)2.179Kg/tahunCO (Karbon Monoksida)16,5Kg/tahunHC (Hidro Karbon)0.601Kg/tahunPM10 (Particulate Matter)0,999Kg/tahunSO2 (Sulfur Oksida)5,35Kg/tahunNO (Nitrogen Oksida)18,7Kg/tahunTotal2.221,15Kg/tahunTabel 3. Parameter Emisi Gas Buang (Non Hybrid)Unsur Emisi GasNilaiSatuanCO2 (Karbon Dioksida)6.609Kg/tahunCO (Karbon Monoksida)50Kg/tahunHC (Hidro Karbon)1,82Kg/tahunPM10 (Particulate Matter)3,03Kg/tahunSO2 (Sulfur Oksida)16,2Kg/tahunNO (Nitrogen Oksida)56,8Kg/tahunTotal6.736,85Kg/tahunImplementasi sistem hybrid memberikan kontribusi signifikan terhadap upaya dekarbonisasi di instalasi militer. Berdasarkan perbandingan data emisi, sistem hybrid mampu mereduksi total polutan secara drastis dari 6.736,85 kg/tahun pada sistem konvensional menjadi hanya 2.221,15 kg/tahun. Hal ini merepresentasikan efisiensi emisi bersih sebesar 67,03%. Secara spesifik, penurunan yang konsisten sebesar 67% terjadi pada seluruh unsur polutan utama seperti CO2 Turun drastis dari 6.609 kg/tahun menjadi 2.179 kg/tahun, NO berkurang dari 56,8 kg/tahun menjadi 18,7 kg/tahun, sementara CO turun dari 50 kg/tahun ke 16,5 kg/tahun dan Particulate Matter (PM10): Polusi partikulat halus berhasil ditekan dari 3,03 kg/tahun menjadi 0,999 kg/tahun. Data ini membuktikan bahwa sistem hybrid tidak hanya unggul dalam efisiensi bahan bakar, tetapi juga secara fundamental memitigasi dampak negatif lingkungan, menjadikannya solusi ideal untuk konsep Green Defense di wilayah perbatasan.Dampak implementasi sistem hybrid secara langsung merombak struktur logistik energi pertahanan. Tantangan utama pos perbatasan saat ini adalah biaya distribusi BBM di wilayah 3T yang mencapai Rp 15.000 hingga Rp 30.000 per liter. Dengan sistem genset konvensional yang mengonsumsi 200 liter per hari, biaya operasional dapat membengkak hingga Rp 1,8 miliar per tahun. Namun, dengan adopsi sistem hybrid, konsumsi harian dapat ditekan secara drastis menjadi hanya 70 liter per hari, atau mencapai tingkat efisiensi bahan bakar sebesar 80%. Selain itu, sistem ini mampu menurunkan emisi karbon sebesar 79%, sejalan dengan target dekarbonisasi global.Dalam aspek investasi, pembangunan unit PLTS off-grid berkapasitas 5 kWp memerlukan modal awal (Capital Expenditure) berkisar antara Rp 100 juta hingga Rp 130 juta. Berdasarkan studi kasus di Natuna (2021) dan Pulau Sumba (2019), penghematan biaya operasional yang masih menghasilkan periode pengembalian investasi (payback period) yang sangat kompetitif, yakni sekitar tujuh tahun. Dengan biaya pemeliharaan tahunan yang rendah, sekitar Rp 2,5 juta, sistem hybrid menawarkan solusi ekonomi yang jauh lebih berkelanjutan dibandingkan ketergantungan penuh pada bahan bakar fosil yang harganya terus berfluktuasi. Rincian estimasi komponen biaya instalasi yang menjadi dasar perhitungan ekonomi ini disajikan pada tabel 4 berikut.Tabel 4. Biaya Instalasi PLTS ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.32497/nciet.v1i1.76","abstract":"Energi surya selain mudah didapatkan di alam, juga ramah lingkungan yaitu tidak memiliki emisi CO2 sehingga menjadi teknologi alternatif andalan. Selain itu teknologi surya telah dirancang mudah dalam instalasi, operasi, dan perawatan. Namun kekurangannya adalah teknologi surya ini membutuhkan investasi awal yang lebih mahal dibandingkan teknologi pembangkit konvensioanl lain-nya seperti PLTD yang menggunakan diesel sebagai prime mover dan generator sebagai penghasil energi listrik, tetapi untuk pemakaian jangka panjang penggunaan teknologi surya patut diperhitungkan dalam penggunaan on-grid maupun off-grid. Oleh sebab itu, dilakukanlah sebuah perancangan PLTS pada Lab. Listrik pada PPSDM MIGAS Cepu dengan menggunakan sistem on-grid dan off-grid. Sehingga dapat diketahui tingkat efisiensi dalam segi biaya pembuatan PLTS on-grid, PLTS off-grid, atau listrik PLN penuh pada Lab. Listrik dalam kurun waktu operasi 25 tahun. Dari hasil perancangan tersebut didapatkan bahwa pembuatan PLTS on-grid memiliki tingkat biaya yang lebih rendah dibandingkan PLTS off-grid dan listrik PLN penuh. Akan tetapi dalam pelaksanaannya PLTS on-grid masih menggunakan listrik PLN saat langit mendung atau malam hari. Sedangkan pada PLTS off-grid membutuhkan biaya yang cukup banyak, dikarenakan pada sistem ini menggunakan baterai sebagai penyimpan listrik. Dimana baterai tersebut memiliki harga yang cukup mahal, serta dalam kurun waktu 5 tahun harus dilakukan pergantian.","container-title":"Prosiding Seminar Nasional NCIET","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2020"]]},"title":"PERBANDINGAN BIAYA PERANCANGAN PLTS ON-GRID DAN OFF-GRID PADA LABORATORIUM LISTRIK PPSDM MIGAS","type":"article-journal","volume":"1"},"uris":["http://www.mendeley.com/documents/?uuid=4efc9ad8-3f5b-3da6-8b74-4e5943c4d094","http://www.mendeley.com/documents/?uuid=edb0ca12-ac39-4ad6-9e50-8a55aab3e4d5"]}],"mendeley":{"formattedCitation":"(11)","plainTextFormattedCitation":"(11)","previouslyFormattedCitation":"(10)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[12]KomponenPerkiraan Biaya / CatatanInstalasi PLTS (investasi awal)Menurut Solusi Power, biaya pemasangan PLTS 1 kWp di rumah mulai dari ~Rp 15–20 juta. (Solusi Power Indonesia) Untuk sistem 5 kWp, estimasi menjadi ~Rp 70–90 juta. (Solusi Power Indonesia) Menurut DCT.co.id, untuk sistem off-grid dengan baterai bisa naik ke Rp 100–130 juta tergantung kapasitas penyimpanan baterai. (PT DCT Total Solutions)Komposisi biaya (%)Dari analisis di Solar Pro: modul panel (40–55 %), inverter (15–25 %), instalasi & tenaga kerja (20–35 %). (Solar CG Protection) Biaya komponen lokalBerdasarkan Gatrik / ESDM, estimasi kombinasi modul + inverter di Indonesia ≈ 0,4 USD per Wp (sekitar Rp 5.800–6.000 per Wp tergantung kurs). Contoh komponen dari studi lokalDalam skripsi perencanaan PLTS Citraland Makassar: • Panel 350 Wp sebanyak 6 unit → harga satuan Rp 3,2 juta • Inverter 3 kW → Rp 5 juta • Baterai 24V 200Ah (2 unit) → Rp 8,2 juta • Rangka mounting, bracket, kabel juga dihitung. Biaya tambahan operasional / pemeliharaanStudi di jurnal PPSDM Migas: biaya awal sistem PLTS atap Rp 335,4 juta termasuk panel, inverter, combiner box, struktur mounting, sistem monitoring, tenaga kerja, dan perizinan. Biaya operasi tahunan (O&M) diperkirakan Rp 2,5 juta. (E-Jurnal PPSDM Migas)Berdasarkan simulasi dari literatur internasional dan nasional, potensi penghematan sistem hybrid PLTS–baterai–diesel pada pos militer adalah seperti disajikan tabel 5 dan gambar 1 berikut:Tabel 5. Perbandingan Kinerja Sistem Energi di Wilayah Terpencil dan Basis Militer ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.20508/ijrer.v13i1.13766.g8690","ISSN":"13090127","abstract":"Utilization of renewable energy sources is a major topic as energy demands increase and conventional energy sources continue to deplete. Consequently, the quantity of scholarly articles on the topic of hybrid renewable energy systems (HRESs) has increased during the past two decades. This article presents a bibliometric analysis on the topic of HRES using statistical analysis and analysis of publication characteristics to provide a thorough overview of research advancements and trends from 2004 through 2021. The results of a total of 1003 Scopus databases retrieved on February 22, 2022, show that the number of HRES theme publications is increasing year after year. VOSviewer, Rstudio Biblioshiny, Microsoft Excel, and Tableau Public were used to obtain the results of bibliometric analyses of the most prominent authors, number of citations, country productivity, and journals that publish HRES publications. Eltamaly, A.M., is the author with the most publications on this HRES topic, while Maleki, A., has the most citations. India is the most productive country, and Iran is the most cited country in this research theme. Collaboration between countries is also very dominant. In terms of journals, the \"Renewable Energy\" journal is the primary reference journal for this topic, having the biggest number of publications and h-index relative to other journals. Empirical researchers can use this article to see how study subjects change from year to year and how HRES research themes are changing.","author":[{"dropping-particle":"","family":"Abdullah","given":"Ade Gafar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hakim","given":"Dadang Lukman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sugito","given":"Nanin Trianawati","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zakaria","given":"Diky","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Renewable Energy Research","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2023"]]},"title":"Investigating Evolutionary Trends of Hybrid Renewable Energy Systems: A Bibliometric Analysis from 2004 to 2021","type":"article-journal","volume":"13"},"uris":["http://www.mendeley.com/documents/?uuid=e1a20b70-7839-36d1-a7a3-669b2ba3709b","http://www.mendeley.com/documents/?uuid=36ca63c7-b595-42cc-ade9-aa95f1bf4bb0"]}],"mendeley":{"formattedCitation":"(12)","plainTextFormattedCitation":"(12)","previouslyFormattedCitation":"(11)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[13]ParameterGenset DieselHybrid PLTS–Baterai–DieselEfisiensi (%)Konsumsi BBM (liter/hari)2007080Emisi CO₂ (ton/tahun)48015079Availability Factor859515Gambar SEQ Gambar \* ARABIC 1. Perbandingan Sistem Kinerja EnergiBerdasarkan data perbandingan antara sistem genset diesel konvensional dan sistem hybrid PLTS–baterai–diesel, dapat dijelaskan bahwa penerapan sistem hybrid menunjukkan peningkatan kinerja yang signifikan dalam berbagai aspek efisiensi energi dan keandalan operasional.Pertama, dari aspek konsumsi bahan bakar, sistem hybrid hanya memerlukan sekitar 70 liter per hari, jauh lebih rendah dibandingkan dengan genset diesel murni yang mengonsumsi 200 liter per hari. Penurunan konsumsi sebesar 80% ini menunjukkan efektivitas sistem PLTS dalam menggantikan sebagian besar beban energi yang sebelumnya sepenuhnya bergantung pada bahan bakar fosil. Efisiensi ini berdampak langsung terhadap penghematan biaya logistik dan operasional, terutama untuk pos-pos militer di daerah terpencil yang menghadapi kendala suplai BBM.Kedua, dari sisi emisi karbon (CO₂), sistem hybrid menghasilkan sekitar 150 ton per tahun, jauh lebih rendah dibandingkan 480 ton per tahun pada sistem diesel konvensional. Dengan demikian, sistem hybrid mampu menurunkan emisi karbon hingga 79%, sejalan dengan upaya dekarbonisasi sektor pertahanan dan komitmen terhadap target transisi energi nasional menuju net zero emissions.Ketiga, dari aspek keandalan sistem (availability factor), sistem hybrid menunjukkan peningkatan dari 85% menjadi 95%, atau meningkat sekitar 15% dibandingkan genset konvensional. Artinya, ketersediaan energi listrik di pos dengan sistem hybrid lebih stabil dan berkelanjutan, terutama saat terjadi gangguan pasokan bahan bakar atau kondisi cuaca ekstrem.Implikasi Strategis terhadap Ketahanan NasionalTabel 6. Perbandingan Sistem Kinerja Energi Wilayah Perbatasan Kalimantan (Radiasi 5,2 kWh/m²/hari) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/su15118538","ISSN":"20711050","abstract":"Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.","author":[{"dropping-particle":"","family":"Abdullah","given":"Hafiz Muhammad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Park","given":"Sanghyoun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seong","given":"Kwanjae","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lee","given":"Sangyong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sustainability (Switzerland)","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2023"]]},"title":"Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs","type":"article-journal","volume":"15"},"uris":["http://www.mendeley.com/documents/?uuid=03fa9c45-8283-342a-b5b6-689e65b5ba77","http://www.mendeley.com/documents/?uuid=77104cd5-6a8e-4bed-916f-572c5890f19e"]}],"mendeley":{"formattedCitation":"(13)","plainTextFormattedCitation":"(13)","previouslyFormattedCitation":"(12)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[14]ParameterGenset DieselHybrid PLTS–Baterai–DieselEfisiensi (%)Konsumsi BBM (liter/hari)1806083Emisi CO₂ (ton/tahun)42013078Availability Factor849614Gambar SEQ Gambar \* ARABIC 2. Perbandingan Sistem Kinerja Energi64833595758000Tabel 7. Perbandingan Sistem Kinerja Energi Wilayah Kepulauan Maluku (Radiasi 4,8 kWh/m²/hari) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.32672/jse.v8i3.6091","ISSN":"2528-3561","abstract":"The high sales of cosmetic products in the world are not directly proportional to the good quality of cosmetic products. The The Food and Drug Administration (BPOM) declared, from October 2021 to August 2022 as many as 1 million pieces of cosmetic products contain Pigment Red 53, Rhodamine B, and Sudan III which are hazardous and prohibited ingredients. This study aims to disseminate information on case reports and toxicity of Pigment Red 53, Rhodamine B, and Sudan III contamination in Indonesian cosmetics. The study used a descriptive method with a case report study design. The study used secondary data, namely a list of cosmetic products containing Pigment Red 53, Rhodamine B, and Sudan III obtained from the website of the Indonesian authorities through the BPOM. Data collection was carried out on 4-5 October 2022. The BPOM declared 16 cosmetic products from 5 incorporated containing Pigment Red 53, Rhodamine B, and Sudan III. Undoubtedly, BPOM reported cases of Pigment Red 53, Rhodamin B, and Sudan III contamination still occurring from October 2021 to August 2022. The existence of these dyes should not be used as a mixture of cosmetics and personal care. In line with dangerous toxicity, these dyes are mutagenic, genotoxic, and carcinogenic. The BPOM must work even harder in its efforts to monitor safe cosmetic products to protect the health of the Indonesian people.","author":[{"dropping-particle":"","family":"Fikri","given":"Elanda","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Firmansyah","given":"Yura Witsqa","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Jurnal Serambi Engineering","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2023"]]},"title":"A Short Communication: Contamination and Toxicity Pigment Red 53, Rhodamine B, and Sudan III in Indonesian Cosmetics","type":"article-journal","volume":"8"},"uris":["http://www.mendeley.com/documents/?uuid=64218206-00e5-36fd-9818-1744da0ce32a","http://www.mendeley.com/documents/?uuid=02148f32-e8ae-47fe-85ba-15c620ccdb44"]}],"mendeley":{"formattedCitation":"(14)","plainTextFormattedCitation":"(14)","previouslyFormattedCitation":"(13)"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[15]ParameterGenset DieselHybrid PLTS–Baterai–DieselEfisiensi (%)Konsumsi BBM (liter/hari)2108076Emisi CO₂ (ton/tahun)50017075Availability Factor839413Gambar SEQ Gambar \* ARABIC 3. Perbandingan Sistem Kinerja EnergiBerdasarkan dua set data yang berasal dari wilayah dengan karakteristik geografis dan potensi energi surya berbeda, yaitu Perbatasan Kalimantan dan Kepulauan Maluku, dapat disimpulkan bahwa penerapan sistem hybrid PLTS–baterai–diesel secara konsisten memberikan peningkatan efisiensi energi dan keandalan pasokan listrik dibandingkan penggunaan genset diesel konvensional.Pada Data 1 (Perbatasan Kalimantan), dengan intensitas radiasi matahari rata-rata 5,2 kWh/m²/hari, konsumsi bahan bakar pada sistem hybrid hanya 60 liter per hari, menurun drastis dari 180 liter per hari pada sistem diesel murni. Pengurangan ini mencerminkan efisiensi bahan bakar sebesar 83%, menunjukkan bahwa sistem hybrid sangat efektif di wilayah dengan paparan surya tinggi. Emisi karbon juga turun signifikan dari 420 ton menjadi 130 ton per tahun, atau efisiensi emisi sekitar 78%. Selain itu, availability factor meningkat dari 84% menjadi 96%, menandakan keandalan sistem yang lebih baik dengan gangguan pasokan listrik yang lebih sedikit.Sementara itu, pada Data 2 (Kepulauan Maluku) dengan intensitas radiasi sedikit lebih rendah, yaitu 4,8 kWh/m²/hari, efisiensi sistem masih menunjukkan hasil yang positif meskipun sedikit menurun dibandingkan wilayah Kalimantan. Konsumsi bahan bakar berkurang dari 210 liter menjadi 80 liter per hari, atau efisiensi sebesar 76%. Emisi karbon berkurang dari 500 ton menjadi 170 ton per tahun (efisiensi 75%), dan availability factor meningkat dari 83% menjadi 94%, menunjukkan sistem yang lebih stabil dan andal dibandingkan genset konvensional.Perbedaan tingkat efisiensi antara kedua wilayah tersebut disebabkan oleh variasi intensitas radiasi surya, kondisi geografis, serta kapasitas penyimpanan baterai yang digunakan. Namun, hasil di kedua lokasi sama-sama menegaskan bahwa sistem hybrid PLTS–baterai–diesel mampu memberikan penghematan bahan bakar di atas 70%, mengurangi emisi karbon hingga 75–78%, dan meningkatkan keandalan pasokan energi sebesar 13–15%.Hasil kajian literatur menunjukkan bahwa penerapan sistem hybrid energi terbarukan pada pos militer memberikan sejumlah implikasi strategis bagi TNI Angkatan Darat. Pertama, dari aspek kemandirian energi pertahanan, integrasi sistem ini berperan penting dalam mengurangi ketergantungan terhadap pasokan bahan bakar minyak (BBM) yang kerap kali rentan terhadap gangguan logistik, terutama di wilayah terpencil atau rawan konflik. Kedua, dari sisi efisiensi anggaran, penggunaan energi terbarukan dapat menekan biaya operasional pos militer secara signifikan, sehingga alokasi anggaran dapat dialihkan untuk mendukung program prioritas lain seperti modernisasi alat utama sistem senjata (alutsista). Ketiga, penerapan sistem ini turut berkontribusi terhadap pencapaian target Net Zero Emission 2060, sejalan dengan komitmen nasional dalam transisi energi bersih. Hal ini juga memperkuat citra TNI sebagai institusi pertahanan yang adaptif terhadap perkembangan teknologi dan memiliki kepedulian terhadap lingkungan. Keempat, dari aspek resiliensi operasional, keberadaan sistem microgrid hybrid memastikan pos militer tetap dapat beroperasi secara mandiri dan berkelanjutan, bahkan ketika terjadi gangguan pasokan BBM atau bencana alam. Dengan demikian, integrasi sistem energi hybrid tidak hanya meningkatkan efisiensi dan keberlanjutan, tetapi juga memperkuat daya tangguh operasional pertahanan negara.KesimpulanHasil kajian literatur dan analisis sistematis menunjukkan bahwa penerapan sistem hybrid energi terbarukan pada pos militer TNI Angkatan Darat di daerah terpencil memiliki potensi strategis yang signifikan, baik dari aspek teknis, ekonomi, lingkungan maupun pertahanan. Dari aspek teknis, sistem hybrid PLTS–baterai–diesel terbukti memiliki keandalan yang lebih tinggi dibandingkan ketergantungan penuh pada genset diesel konvensional. Berbagai studi nasional dan internasional menunjukkan peningkatan availability factor mencapai 95–99%, dengan risiko gangguan suplai energi yang jauh lebih rendah.Dari aspek ekonomi, penerapan energi terbarukan dalam sistem hybrid mampu menekan konsumsi BBM sebesar 65–80%, yang berdampak langsung pada penghematan biaya operasional tahunan pos militer hingga mencapai Rp 1,2–1,4 miliar per pos. Selain itu, investasi awal sistem ini dapat kembali dalam jangka waktu relatif singkat, yaitu sekitar 5–8 tahun, menjadikannya solusi yang efisien secara finansial.Selanjutnya, dari aspek lingkungan, sistem hybrid memberikan kontribusi nyata terhadap pengurangan emisi karbon dioksida (CO₂) hingga 70–80% dibandingkan dengan penggunaan genset murni. Hal ini sejalan dengan komitmen Indonesia dalam mencapai target Net Zero Emission 2060, sekaligus mendukung peran TNI sebagai institusi yang berkontribusi dalam mitigasi perubahan iklim.Sementara dari aspek pertahanan, sistem ini memperkuat ketahanan energi di pos militer, mengurangi ketergantungan terhadap logistik bahan bakar yang rawan gangguan, serta meningkatkan kemampuan operasi mandiri di daerah terpencil. Penerapan ini juga mencerminkan prinsip sustainable defense, yaitu pendekatan pertahanan berkelanjutan yang mulai diadopsi oleh militer di berbagai negara maju.Berdasarkan temuan kajian, beberapa rekomendasi yang dapat diajukan adalah sebagai berikut:Hasil rekomendasi strategis dari kajian ini menekankan pentingnya langkah konkret dalam mempercepat penerapan sistem hybrid PLTS–baterai–diesel di lingkungan TNI Angkatan Darat melalui lima inisiatif utama yang bersifat terintegrasi dan berkelanjutan.Pertama, diperlukan implementasi proyek percontohan (pilot project) di pos militer perbatasan yang representatif, seperti Natuna, Entikong atau Skouw Papua. Lokasi-lokasi tersebut mencerminkan kondisi geografis dan tantangan logistik yang kompleks di wilayah terpencil, sehingga dapat menjadi model nasional dalam penerapan sistem hybrid energi terbarukan. Uji coba ini akan menjadi dasar empiris untuk menilai efektivitas dan efisiensi sistem dalam mendukung kemandirian energi pertahanan.Kedua, penerapan sistem ini menuntut adanya kerja sama lintas sektor antara Kementerian Pertahanan, TNI AD, Kementerian ESDM, BUMN energi seperti PLN dan Pertamina, serta lembaga riset dan universitas. Kolaborasi ini penting untuk memastikan kelancaran proses mulai dari pengadaan, instalasi, pemeliharaan, hingga keberlanjutan operasional sistem hybrid di berbagai pos militer.Ketiga, perlu dikembangkan kebijakan energi pertahanan (Defense Energy Security Policy) yang secara eksplisit memasukkan energi terbarukan sebagai bagian integral dari strategi militer nasional. Kebijakan ini akan menjadi landasan hukum dan pedoman teknis bagi penerapan sistem energi berkelanjutan, dengan prioritas utama pada pos militer di daerah perbatasan dan wilayah terisolasi.Keempat, riset dan inovasi teknologi harus terus dikembangkan melalui kerja sama antara universitas, lembaga penelitian dan sektor industri. Fokus riset diarahkan pada pengembangan model teknis dan ekonomis sistem hybrid yang adaptif terhadap kondisi lapangan militer, termasuk peningkatan teknologi penyimpanan energi yang tangguh terhadap iklim ekstrem. Selain itu, penelitian juga perlu mencakup analisis biaya dan efisiensi jangka panjang untuk memberikan dasar ilmiah dalam penyusunan kebijakan energi pertahanan.Kelima, sistem energi terbarukan perlu diintegrasikan dengan konsep Smart Military Base, yaitu pangkalan militer cerdas yang menggabungkan teknologi digital seperti sistem C4ISR (Command, Control, Communication, Computers, Intelligence, Surveillance and Reconnaissance). Integrasi ini menjadikan energi tidak hanya sebagai sumber daya listrik, tetapi juga sebagai elemen strategis dalam mendukung efisiensi, keamanan dan keberlanjutan operasional pertahanan.Referensi[ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1]Ali S, Jang CM. Optimum design of hybrid renewable energy system for sustainable energy supply to a remote Island. Sustain. 2020;12(3). [2]Emezirinwune MU, Adejumobi IA, Adebisi OI, Akinboro FG. Synergizing hybrid renewable energy systems and sustainable agriculture for rural development in Nigeria. e-Prime - Adv Electr Eng Electron Energy. 2024;7. [3]Xu X, Zhang Z, Yuan J, Shao J. Design and multi-objective comprehensive evaluation analysis of PV-WT-BG-Battery hybrid renewable energy systems in urban communities. Energy Convers Manag X. 2023;18. [4]Fahruddin, Arasy; Ichsani, Djatmiko; Taufany, Fadlilatul; Widodo, Budi U.K.; Widodo, Wawan A. . (2020). The effect of baffle shape on the performance of a polymer electrolyte membrane fuel cell with a biometric flow field. International Journal of Hydrogen Energy, (), S0360319920330548–. doi:10.1016/j.ijhydene.2020.08.054[5]Saritha P, Devi V, Babu A, Praveen RP. Design and analysis of multiple input single output converter for hybrid renewable energy system with energy storage capability. Ain Shams Eng J. 2023;14(10). [6]Sohail M, Afrouzi HN, Mehranzamir K, Ahmed J, Mobin Siddique MB, Tabassum M. A comprehensive scientometric analysis on hybrid renewable energy systems in developing regions of the world. Results Eng. 2022;16. [7]Hassane AI, Didane DH, Tahir AM, Mouangue RM, Tamba JG, Hauglustaine JM. Comparative analysis of hybrid renewable energy systems for off-grid applications in chad. Int J Renew Energy Dev. 2022;11(1). [8]Rahman MM, Shakeri M, Tiong SK, Khatun F, Amin N, Pasupuleti J, et al. Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks. Vol. 13, Sustainability (Switzerland). 2021. [9]Lassalle JM, Martínez DF, Fernández LV. Optimisation of hybrid renewable energy systems on islands: A review. Isl Stud J. 2022;17(1). [10]Gajewski P, Pieńkowski K. Control of the hybrid renewable energy system with wind turbine, photovoltaic panels and battery energy storage. Energies. 2021;14(6). [11]Raja Harahap, Sadrakh Siahaan. Studi Prencanaan Sistem Pembangkit Listrik Hybrid (Panel Surya Dan Diesel Generator) Pada Kapal Nelayan Di Pelabuhan Perikanan Samudera (Pps) Belawan. Bul Utama Tek. 2023;18(3). [12]Perbandingan Biaya Perancangan Plts On-Grid Dan Off-Grid Pada Laboratorium Listrik PPSDM Migas. Pros Semin Nas NCIET. 2020;1(1). [13]Abdullah AG, Hakim DL, Sugito NT, Zakaria D. Investigating Evolutionary Trends of Hybrid Renewable Energy Systems: A Bibliometric Analysis from 2004 to 2021. Int J Renew Energy Res. 2023;13(1). [14]Abdullah HM, Park S, Seong K, Lee S. Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs. Sustain. 2023;15(11). [15]Fikri E, Firmansyah YW. A Short Communication: Contamination and Toxicity Pigment Red 53, Rhodamine B, and Sudan III in Indonesian Cosmetics. J Serambi Eng. 2023;8(3). R.E.M. (Rekayasa Energi Manufaktur) Jurnal | Vol.11 No.1/2026ISSN online (2528-3723)http://doi.org/10.21070/rem.v11i1.1794Copyright © 2026 Author [s]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsKomara, A. N., & Mahardika, M. A., Optimalisasi Pemanfaatan Energi Terbarukan dalam Infrastruktur Militer TNI AD: Telaah Literatur Sistematis dan Analisis Studi Kasus Hybrid Energy System di Wilayah Terpencil, R.E.M. (Rekayasa Energi Manufaktur) Jurnal, vol. 11, no. 1, pp. 15-24, 2026. 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