DOI: 10.3303/CET25120078 Paper Received: 15 May 2025 ; Revised: 30 August 2025; Accepted: 1 November 2025 Please cite this article as: Nizamuddin A.D., Ho W.S., Hashim H., Zubir M.A., Muis Z.A., Wong K.Y., 2025, Modelling of Dual-Battery Energy Storage System in a Renewable Energy Power System, Chemical Engineering Transactions, 120, 463-468 DOI:10.3303/CET25120078 CHEMICAL ENGINEERING TRANSACTIONS VOL. 120, 2025 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Bing Shen How, Viknesh Andiappan, Denny K.S. Ng, Hon Loong Lam, Petar S. Varbanov Copyright © 2025, AIDIC Servizi S.r.l. ISBN 979-12-81206-21-2; ISSN 2283-9216 Modelling of Dual-Battery Energy Storage System in a Renewable Energy Power System Arfah Diyanah Nizamuddina, Wai Shin Hoa,*, Haslenda Hashima, Muhammad Afiq Zubira, Zarina Ab Muisa, Keng Yinn Wongb aProcess Systems Engineering Centre (PROSPECT), Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia bFaculty of Mechanical Engineering, Universiti Teknologi Malaysia Johor Bahru, 81310, Skudai, Johor, Malaysia hwshin@utm.my As the global energy transition accelerates, integrating renewable energy into off-grid and hybrid systems poses challenges due to intermittency and storage limitations. Battery energy storage systems (BESS) are essential for ensuring stability and reliability. Conventional single-battery configurations, however, suffer from high costs and reduced lifespan, especially in lead-acid batteries, due to frequent incomplete charge/discharge cycles. To address this, a dual-battery energy storage system (DBESS) is proposed, in which two batteries operate in parallel: the main battery performs complete cycles to maintain health, while the secondary battery buffers partial loads. This approach improves system longevity and energy management. A techno-economic optimization model was developed using hourly load and solar irradiation data to minimize the 20 y system cost. The optimized configuration selected lead-acid batteries for both units, with 611.21 kW solar PV, 171.83 kW biomass generator, and storage capacities of 388.23 kWh and 118.59 kWh for the main and secondary batteries, respectively. The total system cost was minimized to $ 1,468,729. These results highlight the effectiveness of DBESS in enhancing performance and reducing long-term costs in renewable hybrid systems. 1. Introduction The utilization of battery energy storage systems (BESS) has been deemed of utmost importance as the world transitions towards increased implementation of renewable energy. Although renewable energy is abundantly available in nature, its intermittency necessitates the use of BESS. Excess energy can be stored during the availability of solar power during the day, while the stored energy can be supplied to meet demand at night. Biomass, on the other hand, has emerged as a sustainable fuel for power generation. However, its inconsistent supply and high cost compared to fossil fuels make it difficult to rely solely on biomass for an off-grid power system (Ogunrewo and Nwulu, 2024). Therefore, the operation of an off-grid power system that integrates a biomass generator, solar PV, and battery storage can ensure the delivery of consistent and stable electricity to meet variable demand (Come Zebra et al., 2021). Over the past decades, the efficiency and cost of solar PV have improved tremendously (IRENA, 2021). However, energy storage remains relatively expensive and constitutes the largest cost component of a power system (Balducci et al., 2021). As a result, many researchers have attempted to optimize both the cost and operation of BESS. For instance, Khlifi et al. (2021) proposed a techno-economic optimization of BESS sizing in microgrids. Sharma et al. (2025) developed an advanced control strategy to enhance BESS lifespan. Köppchen et al. (2025) examined battery scheduling for demand-side management. Chen et al. (2023) integrated time-of-use tariffs with BESS operation. Symeonidou et al. (2021) focused on lifecycle cost analysis for different battery chemistries in renewable energy systems. Despite these advances, conventional single-battery systems often face limitations in terms of flexibility, cycling efficiency, and system degradation. A promising configuration that has shown improved performance is the dual-battery energy storage system (DBESS) (Neto et al., 2018). In DBESS, two types of batteries—main and secondary—work in a complementary manner, each serving different purposes within the power system. The 463 main battery typically handles bulk energy shifting, while the secondary battery manages short duration loads or transient events (Atawi et al., 2022). This arrangement can enhance overall system performance, reduce battery stress, and extend operational life. Nevertheless, the application of DBESS in hybrid off-grid systems remains underexplored, particularly in configurations involving both renewable and biomass-based generation. Given the complexity of managing energy flows between multiple generation sources and two energy storage systems, modelling serves as a vital tool to understand system interactions and improve operational strategies (Tezer, 2025). While single-battery systems have been widely modelled, research on the dynamic modelling and simulation of DBESS remains scarce, particularly in hybrid configurations combining solar PV and biomass generation. This paper presents a comprehensive model that simulates the energy flow, battery charge-discharge cycles, and generator utilization within such a hybrid system. The objective is to evaluate the performance, energy balance, and cost implications of DBESS integration, offering guidance for designing efficient off-grid renewable power systems. 2. Methodology This section presents the methodology used in this paper. The first part outlines the General Algebraic Modeling System (GAMS) mathematical model formulation for the renewable energy power system with DBESS, while the second part describes the data inputs for the model. 2.1 Mathematical Formulation A GAMS-based mathematical model is developed with the objective of minimizing the cost of an off-grid power system comprising a biomass generator, solar PV, and the main battery and secondary battery. The model minimize the total system cost over 20 y, which align with the typical lifespan of solar PV systems (approximately 20–25 y) and to provide a sufficient time horizon for assessing the cost-effectiveness and replacement cycles of the energy storage systems, as defined in Eq(1). 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝐶𝑜𝑠𝑡 = 𝑏𝑖𝑜𝑐𝑜𝑠𝑡 + 𝑠𝑜𝑙𝑎𝑟𝑐𝑜𝑠𝑡 + 𝐸𝑆𝑐𝑜𝑠𝑡1 + 𝐸𝑆𝑐𝑜𝑠𝑡2 (1) Eq(2) calculates the total solar PV cost ( 𝑠𝑜𝑙𝑎𝑟𝑐𝑜𝑠𝑡 ) using its installed capacity ( 𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝 ), capital cost (𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝𝑐𝑜𝑠𝑡), and fixed O&M cost (𝑠𝑜𝑙𝑎𝑟𝑓𝑖𝑥𝑐𝑜𝑠𝑡) over 20 y. Eq(3) defines the biomass cost (𝑏𝑖𝑜𝑐𝑜𝑠𝑡 ), combining generation (𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡)) with its variable cost (𝑏𝑖𝑜𝑣𝑎𝑟𝑐𝑜𝑠𝑡), fuel use (𝑏𝑖𝑜𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛) with cost (𝑏𝑖𝑜𝑓𝑢𝑒𝑙𝑐𝑜𝑠𝑡), and capacity (𝑏𝑖𝑜𝑐𝑎𝑝) with capital (𝑏𝑖𝑜𝑐𝑎𝑝𝑐𝑜𝑠𝑡) and fixed O&M costs (𝑏𝑖𝑜𝑓𝑖𝑥𝑐𝑜𝑠𝑡). Eq(4) and Eq(5) compute costs for the main and secondary batteries, respectively, based on energy (𝐴𝑐𝑎𝑝𝐸𝑆𝐸1/2) and power capacities (𝑐𝑎𝑝𝐸𝑆𝑃1/2), their capital (𝐸𝑆𝐸𝑐𝑜𝑠𝑡1/2, 𝐸𝑆𝑃𝑐𝑜𝑠𝑡1/2) and fixed O&M (𝐸𝑆𝑃𝑓𝑖𝑥𝑐𝑜𝑠𝑡1/2) costs. Battery replacements are included: once for the main (10 y lifespan) and three times for the secondary (5 y), using multipliers of 2 and 4. No replacements are needed for solar or biomass (25 y lifespan). 𝑠𝑜𝑙𝑎𝑟𝑐𝑜𝑠𝑡 = 𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝 ∙ 𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝𝑐𝑜𝑠𝑡 + (𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝 ∙ 𝑠𝑜𝑙𝑎𝑟𝑓𝑖𝑥𝑐𝑜𝑠𝑡) ∙ 20 (2) 𝑏𝑖𝑜𝑐𝑜𝑠𝑡 = 𝛴𝑡𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) · 𝑏𝑖𝑜𝑣𝑎𝑟𝑐𝑜𝑠𝑡 + 𝑏𝑖𝑜𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 · 𝑏𝑖𝑜𝑓𝑢𝑒𝑙𝑐𝑜𝑠𝑡 + 𝑏𝑖𝑜𝑐𝑎𝑝 · 𝑏𝑖𝑜𝑐𝑎𝑝𝑐𝑜𝑠𝑡 + (𝑏𝑖𝑜𝑐𝑎𝑝 · 𝑏𝑖𝑜𝑓𝑖𝑥𝑐𝑜𝑠𝑡) · 20 (3) 𝐸𝑆𝑐𝑜𝑠𝑡1 = (𝐴𝑐𝑎𝑝𝐸𝑆𝐸1 · 𝐸𝑆𝐸𝑐𝑜𝑠𝑡1 + 𝑐𝑎𝑝𝐸𝑆𝑃1 · 𝐸𝑆𝑃𝑐𝑜𝑠𝑡1) · 2 + 𝑐𝑎𝑝𝐸𝑆𝑃1 · 𝐸𝑆𝑃𝑓𝑖𝑥𝑐𝑜𝑠𝑡1 · 20 (4) 𝐸𝑆𝑐𝑜𝑠𝑡2 = (𝐴𝑐𝑎𝑝𝐸𝑆𝐸2 · 𝐸𝑆𝐸𝑐𝑜𝑠𝑡2 + 𝑐𝑎𝑝𝐸𝑆𝑃2 · 𝐸𝑆𝑃𝑐𝑜𝑠𝑡2) · 4 + 𝑐𝑎𝑝𝐸𝑆𝑃2 · 𝐸𝑆𝑃𝑓𝑖𝑥𝑐𝑜𝑠𝑡2 · 20 (5) Energy balance is maintained by matching demand with supply from solar, biomass, and storage in Eq(6), where 𝐼𝑛𝑣𝐸𝐹𝐹 and 𝐸𝑆𝐸𝐹𝐹 represent the inverter efficiency and the round-trip efficiency of the batteries, respectively. The energy balance for biomass and solar is shown in Eq(7) and Eq(8), respectively, where excess energy from both sources is stored in the main and secondary batteries. Biomass generation (𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡)) must be less than or equal to the biomass capacity (𝑏𝑖𝑜𝑐𝑎𝑝), as constrained in Eq(9), and is modeled using heat rate (𝑏𝑖𝑜𝐻𝑅), fuel utilization (𝑏𝑖𝑜𝑢𝑡𝑙𝑖𝑧𝑖𝑎𝑡𝑖𝑜𝑛), and biomass heating value (𝑏𝑖𝑜𝐸𝑃) in Eq(10). Bioutilization must not exceed the annual biomass availability (BioAnnualAvailability) in Eq(11), while solar generation depends on installed capacity (𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝) and irradiation (𝑆𝑜𝑙𝑎𝑟𝑅𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛(𝑡)) in Eq(12). 𝐹𝑖𝑥𝐷𝑒𝑚𝑎𝑛𝑑(𝑡) = 𝑏𝑖𝑜𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑(𝑡) + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑(𝑡) ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 + 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑1(𝑡) ∙ 𝐸𝑆𝐸𝐹𝐹 ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 + 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑2(𝑡) ∙ 𝐸𝑆𝐸𝐹𝐹 ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 (6) 𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) = 𝑏𝑖𝑜𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑(𝑡) + 𝑏𝑖𝑜𝑡𝑜𝐸𝑆1(𝑡) + 𝑏𝑖𝑜𝑡𝑜𝐸𝑆2(𝑡) (7) 464 𝑠𝑜𝑙𝑎𝑟𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) = 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑(𝑡) + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆1(𝑡) + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆2(𝑡) (8) 𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) ≤ 𝑏𝑖𝑜𝑐𝑎𝑝 (9) Σ𝑡𝑏𝑖𝑜𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) ∙ 𝑏𝑖𝑜𝐻𝑅 = 𝑏𝑖𝑜𝑢𝑡𝑙𝑖𝑧𝑖𝑎𝑡𝑖𝑜𝑛 ∙ 𝑏𝑖𝑜𝐸𝑃 (10) 𝑏𝑖𝑜𝑢𝑡𝑙𝑖𝑧𝑖𝑎𝑡𝑖𝑜𝑛 ∙ 365 < BioAnnualAvailability (11) 𝑠𝑜𝑙𝑎𝑟𝑝𝑜𝑤𝑒𝑟𝑔𝑒𝑛(𝑡) = 𝑠𝑜𝑙𝑎𝑟𝑐𝑎𝑝 ∙ 𝑆𝑜𝑙𝑎𝑟𝑅𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛(𝑡) (12) Eq(12) to Eq(36) model the operation and sizing of the main and secondary batteries in an off-grid power system. The energy balance in Eq(12) and Eq(13) ensures that the DBESS stored energy updates hourly based on charging from biomass/solar and discharging to meet demand. The cumulative stored energy at the next hour (𝐶𝑢𝑚𝑢𝐸𝑆(𝑡 + 1)) is equal to the energy stored at current hour t (𝐶𝑢𝑚𝑢𝐸𝑆(𝑡)), plus the net charging input, minus the energy discharged to supply the load during the same hour. 𝐶𝑢𝑚𝑢𝐸𝑆(𝑡 + 1) = 𝐶𝑢𝑚𝑢𝐸𝑆(𝑡) + 𝑏𝑖𝑜𝐸𝑆1(𝑡) ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 ∙ 𝐸𝑆𝐸𝐹𝐹 + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆1(𝑡) ∙ 𝐸𝑆𝐸𝐹𝐹 − 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑1(𝑡) (12) 𝐶𝑢𝑚𝑢𝐸𝑆(𝑡 + 1) = 𝐶𝑢𝑚𝑢𝐸𝑆(𝑡) + 𝑏𝑖𝑜𝐸𝑆2(𝑡) ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 ∙ 𝐸𝑆𝐸𝐹𝐹 + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆2(𝑡) ∙ 𝐸𝑆𝐸𝐹𝐹 − 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑2(𝑡) (13) A single daily Σ𝑝𝐶ℎ𝑎𝑟𝑔𝑒𝑃𝑎𝑡𝑡𝑒𝑟𝑛(𝑝, 𝑡) and Σ𝑝𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒𝑃𝑎𝑡𝑡𝑒𝑟𝑛(𝑝, 𝑡) as tabulated in Table 3 and 4 for the main battery is enforced with a binary, 𝑥(𝑝) by Eq(14) to Eq(17), where 𝐿 is a very large number. This ensures the main battery restricted to one cycle per day to preserve its lifespan. The model selects patterns in that result in the lowest overall cost while meeting system constraints. Σ𝑝𝑥(𝑝) = 1 (14) 𝑏𝑖𝑜𝑡𝑜𝐸𝑆1(𝑡) ≤ Σ𝑝𝐶ℎ𝑎𝑟𝑔𝑒𝑃𝑎𝑡𝑡𝑒𝑟𝑛(𝑝, 𝑡) ∙ 𝑥(𝑝) ∙ 𝐿 (15) 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆1(𝑡) ≤ Σ𝑝𝐶ℎ𝑎𝑟𝑔𝑒𝑃𝑎𝑡𝑡𝑒𝑟𝑛(𝑝, 𝑡) ∙ 𝑥(𝑝) ∙ 𝐿 (16) 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑1(𝑡) ≤ Σ𝑝𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒𝑃𝑎𝑡𝑡𝑒𝑟𝑛(𝑝, 𝑡) ∙ 𝑥(𝑝) ∙ 𝐿 (17) To avoid simultaneous charging and discharging, binary logic 𝑦𝑙(𝑡) for charging and 𝑧𝑙(𝑡) for discharging in Eq(26) is applied to the secondary battery in Eq(27) and Eq(28). 𝑦𝑙(𝑡) + 𝑧𝑙(𝑡) ≤ 1 (18) 𝑏𝑖𝑜𝑡𝑜𝐸𝑆2(𝑡) + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆2(𝑡) ≤ 𝐿 ∙ 𝑦𝑙(𝑡) (19) 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑2(𝑡) ≤ 𝐿 ∙ 𝑧𝑙(𝑡) (20) Finally, Eq(29) to Eq(32) for the main battery and Eq(33) to Eq(36) for the secondary battery ensure that energy and power flows remain within capacity limits, factoring in depth of discharge (𝐷𝑂𝐷𝐸𝑆1/2) for proper battery sizing. 𝐶𝑢𝑚𝑢𝐸𝑆1(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝐸1 (29) 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑1(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝑃1 (30) 𝑏𝑖𝑜𝑡𝑜𝐸𝑆1(𝑡) ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆1(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝑃1 (31) 𝐴𝑐𝑎𝑝𝐸𝑆𝐸1 ∙ 𝐷𝑂𝐷𝐸𝑆1 = 𝑐𝑎𝑝𝐸𝑆𝐸1 (32) 𝐶𝑢𝑚𝑢𝐸𝑆2(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝐸2 (33) 𝐸𝑆𝑡𝑜𝑑𝑒𝑚𝑎𝑛𝑑2(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝑃2 (34) 465 𝑏𝑖𝑜𝑡𝑜𝐸𝑆2(𝑡) ∙ 𝐼𝑛𝑣𝐸𝐹𝐹 + 𝑠𝑜𝑙𝑎𝑟𝑡𝑜𝐸𝑆2(𝑡) ≤ 𝑐𝑎𝑝𝐸𝑆𝑃2 (35) 𝐴𝑐𝑎𝑝𝐸𝑆𝐸2 ∙ 𝐷𝑂𝐷𝐸𝑆2 = 𝑐𝑎𝑝𝐸𝑆𝐸2 (36) 2.2 Data Inputs Figure 1a illustrates the hourly solar irradiation profile in kW/m² for a typical day in Malaysia, showing peak irradiance around noon. Figure 1b presents the corresponding hourly electricity demand profile in commercial area, with the highest demand observed between 11:00 and 14:00. Table 1 and Table 2 provides technical and economic parameters for the power generators (solar PV and biomass) and battery storage systems. Figure 1: a) Solar Irradiation b) Hourly Demand Profile Table 1: Parameters for power generators (IRENA, 2019; Iqbal et al., 2024) Type of Generator Capital Cost ($/kW) O&M Cost ($/kW) Biomass Cost ($/t) Heat Rate (GJ/kWh) Biomass Lower Heating Value (GJ/t) Annual Availability of Biomass (t/y) Inverter Efficiency (%) Solar PV 400 20 - - - - 95 Biomass 2,100 10 16.9 0.01235 4.3 2600 95 Table 2: Parameters for lead-acid battery (Iqbal et al., 2024). Type of Battery Energy Capacity Cost ($/kWh) Power Capacity Cost ($/kW) O&M Cost ($/kW) Charging and Discharging Efficiency (%) Lead Acid 400 125 20 80 To model the behaviour of the main battery in response to the variability of demand profile and power generation, a predefined charging and discharging pattern is introduced, as shown in Table 3 and Table 4. A total of 58 charging and discharging patterns were formulated. Each pattern is defined over 24-time steps, representing hourly intervals in a day. These patterns were developed to represent all feasible combinations that allow the main battery to complete one full charging and discharging cycle per day by using the binary parameters ChargePattern(p,t) and DischargePattern(p,t), which permit (1) or prohibit (0) the charging/discharging of the battery during specific time windows. During each period, the charging and discharging patterns correspond to each other. Table 3: Charging pattern for the main battery p t1 t2 t3 t4 ... t22 t23 t24 p1 0 1 1 1 ... 1 1 1 p2 0 0 1 1 ... 1 1 1 ... p58 0 1 1 1 1 1 1 0 466 Table 4: Discharging pattern for the main battery p t1 t2 t3 t4 ... t22 t23 t24 p1 1 0 0 0 ... 0 0 0 p2 1 1 0 0 ... 0 0 0 ... p58 1 0 0 0 0 0 0 1 3. Result and Discussion The optimization model yields a minimized cost of $ 1,468,729 for 20 y. The capacity and cost for each component are summarized in Table 4. In terms of cost, solar PV represents the largest total cost at $ 488,968, followed by the main battery, biomass generator and secondary battery. This is justified as solar PV contributes the most during peak demand, as shown in Figure 2a. Table 4: Optimal Modelling Result for Renewable Energy Power System with DBESS During the peak demand period from 11:00 to 14:00, when the demand reaches 420 kW, most of the load is satisfied by solar PV generation, as illustrated in Figure 2a. The solar system with a capacity of 611.21 kW, produces excess energy that charges both batteries, ensuring adequate stored energy for periods with low or no solar input. Additionally, the biomass generator with a capacity of 171.83 kW is used to supplement energy production when solar availability is insufficient, especially during early morning and late evening hours. This complementary operation between solar PV and biomass reduces the dependency on large battery storage and contributes to the overall cost-effectiveness of the system. Overall, the model presents a well-balanced hybrid renewable energy system that maximizes the use of solar energy while relying on biomass and battery storage as flexible support. The results emphasize the importance of optimal sizing and operational coordination of generation and storage to achieve both reliability and cost minimization in renewable-powered microgrids. Figure 2: a) Power Generation with Solar PV and Biomass Generator b) Daily Energy Content of DBESS According to Figure 2b, which illustrates the daily energy content of the DBESS, the main battery has a significantly higher energy capacity of 388.23 kWh compared to the secondary battery's 118.59 kWh. The data shows that the main battery charges from 10:00 to 17:00, when solar energy is abundant, and discharges from 17:00 to 22:00 to meet residual demand, reducing reliance on the biomass generator. The secondary battery acts as a buffer, supplying power when the main battery is unavailable, typically between 05:00 and 08:00. It also operates in coordination with the main battery, charging and discharging between 12:00 and 19:00. The secondary battery handles rapid, minor energy fluctuations and smooths short-term variations in supply, especially during transitional periods such as sunrise and sunset. This behaviour demonstrates that the model Component Capacity Cost ($) Solar PV 611.21 kW 488,968 Biomass Generator 171.83 kW 395,392 Main Battery 388.23 kWh 412,954 Secondary Battery 118.59 kWh 171,415 467 effectively optimizes DBESS sizing by leveraging the secondary battery’s support function, avoiding the need to oversize the main battery 4. Conclusion The optimization model successfully identified the most cost-effective configuration for a hybrid renewable energy system incorporating solar PV, biomass generation, and DBESS over 20 y with minimized total system cost of $ 1,468,729. The model effectively balanced generation and storage capacities, ensuring a reliable power supply during peak demand and periods of solar unavailability. The system's design leverages the strengths of each component, where solar PV supplies most of the power during midday peak hours, excess solar energy is stored in both batteries, and the biomass generator supplements the supply during low solar periods. The DBESS configuration enhances system reliability and flexibility. The main battery handles bulk energy storage with a single daily charge-discharge cycle. In contrast, the secondary battery provides critical support during short-term fluctuations and transitional periods, reducing stress on the main battery and improving overall efficiency. The results confirm that coordinated operation between generation sources and dual batteries allows for a balanced, cost-effective, and reliable power supply. Acknowledgement Financial supports are acknowledged by the Ministry of Higher Education (MOHE), Malaysia, and Universiti Teknologi Malaysia via UTM Fundamental Research Grant (Q.J130000.3846.23H42). References Atawi I.E., Al-Shetwi A.Q., Magableh A.M., Albalawi O.H., 2022, Recent Advances in Hybrid Energy Storage System Integrated Renewable Power Generation: Configuration, Control, Applications, and Future Directions. Batteries, 9(1), 29. Balducci P., Mongird K., Weimar M., 2021, Understanding the Value of Energy Storage for Power System Reliability and Resilience Applications. Current Sustainable/Renewable Energy Reports, 8(3), 131–137. 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International Journal of Hydrogen Energy, 142, 1249–1271. 468 0228.pdf Modelling of Dual-Battery Energy Storage System in a Renewable Energy Power System