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The Design and Evaluation of Nanogrid-Based Solar Photovoltaic Light-Emitting Diode Street Lighting Systems: A Techno-Economic and Voltage Drop Analysis for Secondary Roads in Thailand
(2026-05-01)
Bunjongjit, Sulee
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Wang, Hongyan
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Huang, Yansheng
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Songsukthawan, Panapong
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Yoomak, Suntiti
Street lighting systems are essential for ensuring nighttime road safety and visibility. The integration of solar photovoltaic (PV) systems into street lighting infrastructure improves energy efficiency and sustainability; however, the mismatch between daytime energy generation and nighttime lighting demand requires effective energy management solutions. In addition, long-distance electrical connections introduce voltage drop constraints, which are often overlooked in conventional design approaches. This study addresses the integration of lighting design, electrical constraints, and techno-economic performance in nanogrid-based LED street lighting systems for secondary roads. A unified framework is developed to evaluate lighting performance, PV–battery sizing, voltage drop behavior, and lifecycle cost under different system architectures. Optimal pole spacing and luminaire ratings are determined using DIALux, while PV–battery configurations are optimized using HOMER Pro based on site-specific solar irradiance. The analysis focuses on voltage drop as the key electrical constraint and examines its impact under decentralized and centralized nanogrid configurations (25%, 50%, and 100%) in both stand-alone and grid-connected modes. The results show that increasing centralization reduces component redundancy but significantly increases cable length, conductor sizing, and infrastructure cost. A techno-economic assessment with lifecycle cost and sensitivity analysis indicates that a 25% centralized configuration reduces total system cost by approximately 23% compared to fully decentralized systems while avoiding excessive cabling costs. These findings demonstrate that voltage drop and electrical infrastructure constraints play a decisive role in determining optimal system design, highlighting the importance of system-level integration rather than isolated optimization of lighting or energy components.
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SOC-Dependent Soft Current Limiting for Second-Life Lithium-Ion Batteries in Off-Grid Photovoltaic Battery Energy Storage Systems
(2026-04-01)
Wang, Hongyan
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Chiradeja, Pathomthat
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Ngaopitakkul, Atthapol
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Yoomak, Suntiti
The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework.
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SOH- and Temperature-Aware Adaptive SOC Boundaries for Second-Life Li-Ion Batteries in Off-Grid PV–BESSs
(2026-02-01)
Wang, Hongyan
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Ngaopitakkul, Atthapol
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Yoomak, Suntiti
In this study, an adaptive state-of-charge (SOC) boundary strategy (ASBS) is proposed that dynamically adjusts the admissible upper and lower SOC limits of second-life lithium-ion batteries in off-grid photovoltaic battery energy storage systems (PV-BESSs) based on real-time state of health (SOH) and temperature feedback. The strategy is formulated using a unified electrical–thermal–aging model with an online state estimator and ensures both electrical safety and power feasibility while remaining fully compatible with standard energy management functions. Two representative simulations—a single-day operating profile and a continuous thirty-day sequence—demonstrate the effectiveness of the ASBS. In the twenty-four-hour case, the duration spent in high state-of-charge conditions is reduced by approximately 0.30–0.50 h, the abrupt end-of-charging transition is eliminated, and the temperature rise is slightly moderated, all without any loss of energy supply. Over thirty days, the difference between the ASBS and a fixed state-of-charge window remains effectively zero for almost all hours, with only a brief midday deviation of −4 to −5 percentage points and no cumulative drift. Indicators of electrical and thermal stress improve substantially, including an approximate 70% reduction in the root mean square charging current. These results confirm that the ASBS provides a practical and non-intrusive means of mitigating stress on second-life lithium-ion batteries while preserving full energy autonomy in off-grid photovoltaic systems.
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Evaluation of the Climate Influence on the Thermal Characteristics of Residential Buildings in Thailand
(2026-01-01)
Lertwanitrot, Praikanok
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Thongsuk, Surakit
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Jettanasen, Chaiyan
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Yoomak, Suntiti
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Ananwattanaporn, Santipont
An increasing population causes more energy demand, resulting in an increased rate of electricity production. Thus, increasing the electricity production rate wastes more fuel resources. These limited resources are depleted if not used properly. Therefore, energy conservation is an essential point that we should be aware of. In addition, weather and geography are significant factors that directly affect the energy consumption rate. Fundamentally, cold regions use more energy for heating, while tropical areas use more energy for air conditioning. Therefore, if the energy used for these fundamental needs can be saved, it will effectively reduce the overall rate of energy use. Consequently, environmental factors that affect energy consumption, such as wind flow direction, building orientation, and pressure, were observed. The observation was done by simulating in a Computational Fluid Dynamics (CFD) program. At the same time, an energy-saving method is also proposed in this paper. The results showed that the proposed method can reduce the energy consumption rate, which is beneficial for the development of energy management systems in the future.
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Transient Analysis to Distinguish Mechanically Switched Capacitors Using Discrete Wavelet Transform and Artificial Intelligence
(2026-01-01)
Thongsuk, Surakit
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Bunjongjit, Sulee
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Yoomak, Suntiti
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Ananwattanaporn, Santipont
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Jettanasen, Chaiyan
Capacitor banks are widely used in modern power systems for reactive power compensation and voltage regulation. However, switching operations of mechanically switched capacitors (MSCs) can generate transient phenomena, such as inrush currents, which may resemble fault currents and lead to misoperation of protection systems. Therefore, accurate detection and classification of transient events are essential for reliable system operation. This study proposes a hybrid approach for transient signal analysis and classification by integrating the discrete wavelet transform (DWT) with artificial intelligence (AI) techniques, including probabilistic neural networks and fuzzy inference systems (FIS). The DWT performs time–frequency analysis to extract multi-scale wavelet features from three-phase current signals. The proposed method enables both discrimination between inrush and fault currents and multi-class classification of transient events among six capacitor switching conditions, namely base case, pre-insertion resistor, pre-insertion inductor, current limiting reactor, 6% reactor, and synchronous closing. The methodology is validated using PSCAD/EMTDC simulations under isolated and back-to-back capacitor switching scenarios. The results demonstrate that the proposed DWT–AI approach achieves high classification accuracy exceeding 95%, outperforming conventional methods based on DWT alone and DWT combined with FIS. Furthermore, the proposed method improves protection system performance by reducing false tripping caused by transient inrush currents, while maintaining reliable fault detection capability. The findings confirm that integrating time–frequency signal processing with AI-based classification provides an effective and practical solution for transient event discrimination in MSC capacitor bank systems.
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Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network
(2025-12-01)
Huang, Yansheng
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Ngaopitakkul, Atthapol
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Yoomak, Suntiti
As the world’s energy structure is gradually changing, the automotive industry is shifting its focus to new energy vehicles in an effort to improve the performance and service life of the charging pile. To solve the problem that traditional models tend to fall into locally optimal solutions (i.e., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. By introducing the whale optimization mechanism to globally optimize the key parameters of the neural network, the method improved the model’s ability to model complex time series data. Moreover, the method also effectively avoided the problem of traditional methods falling into local optimal solutions, thus improving the training efficiency and generalization ability while maintaining the model accuracy. It took only 21 s to complete the training of 600 samples, and the prediction accuracy was as high as 91%. In the four classes of fault classification experiments, the proposed model performs well in classification accuracy in all classes, showing strong multi-class fault recognition capability. Therefore, the fault prediction model developed in this study can accurately and effectively identify and predict charging pile faults, and shows high performance. This not only provides a strong theoretical foundation for the application of deep learning in charging pile fault prediction, but is also of great significance in terms of reducing operation and maintenance costs, supporting energy structure transformation, and promoting green development.
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Design and techno-economic evaluation of a nanogrid system for a small-scale public railway station building in Thailand
(2025-12-01)
Ngaopitakkul, Atthapol
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Songsukthawan, Panapong
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Bunjongjit, Sulee
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Sreewirote, Bancha
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Yoomak, Suntiti
In Thailand, numerous small-scale public facilities continue to rely on the electrical grid. The integration of renewable energy sources such as photovoltaic (PV) and wind power offers a sustainable alternative; however, their inherent intermittency necessitates advanced solutions such as nanogrid systems, which enable localized energy generation, storage, and management to enhance reliability and autonomy. This study develops a nanogrid-based energy management system for Hua Takhe train station, Thailand, by integrating photovoltaic (PV) panels, wind turbines, and battery energy storage systems (BESS). Using HOMER Pro, multiple configurations were simulated and evaluated across key financial indicators, including Payback Period (PB), Internal Rate of Return (IRR), Return on Investment (ROI), Net Present Value (NPV), and Levelized Cost of Electricity (LCOE). The analysis framework further incorporates economic feasibility evaluation, sensitivity and scenario analysis, and long-term performance and life cycle evaluation, thereby ensuring that both short-term financial viability and long-term sustainability are comprehensively assessed. Results show that PV-dominant nanogrid systems, particularly at larger scales, represent the most practical and cost-effective pathway for sustainable electrification of public facilities in Thailand. The 30PV Nanogrid achieves the most favorable balance between cost and performance, with a NPV of 23,925 USD, a LCOE of 0.04 USD/kWh, a PB of 7 years, an IRR of 13 %, and a ROI of 215 % under long-term life-cycle evaluation. In comparison, the 15PV Nanogrid proves economically marginal, with NPV falling to –9117 USD, PB extending beyond 20 years, IRR turning negative (–5 %), and ROI declining to –40 %, confirming that small-scale nanogrids cannot offset the costs of ESS and hybrid inverters.
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Development and Analysis of a Fast-Charge EV-Charging Station Model for Power Quality Assessment in Distribution Systems
(2025-09-01)
Chiradeja, Pathomthat
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Yoomak, Suntiti
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Srisuksai, Panu
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto (Formula presented.) current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks.
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Analysis of Investment Feasibility for EV Charging Stations in Residential Buildings
(2025-09-01)
Chiradeja, Pathomthat
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Yoomak, Suntiti
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Sottiyaphai, Chayanut
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Ngaopitakkul, Atthapol
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Klomjit, Jittiphong
This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors.
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Investigation and Study of High Voltage Capacitor Bank Techniques for Voltage Drop Improvement in 22-kV Distribution Line
(2025-01-01)
Thongsuk, Surakit
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Lertwanitrot, Praikanok
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Songsukthawan, Panapong
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Ananwattanaporn, Santipont
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Ngaopitakkul, Atthapol
Voltage drop in distribution lines contributes to power losses, leading to a decrease in the overall efficiency of the electrical power system. In this paper, we investigate methods to improve voltage drop through capacitor bank allocation techniques, specifically installation at the end of the distribution line, the 1/2-kvar rule, and the 2/3-kvar rule. In the case study, a 22 kV overhead distribution line in Thailand is utilized as a model to analyze voltage drop improvement. The study is conducted using PSCAD software to simulate and evaluate the effectiveness of various capacitor bank allocation techniques in enhancing voltage drop in the distribution line. In addition, the impact of capacitor bank placement and sizing on voltage drop mitigation in a distribution line is analyzed through a proposed mathematical framework. This analysis considers the interaction between reactive current compensation and impedance of a distribution line, ensuring an optimized approach for capacitor bank deployment to enhance voltage drop. The results emphasize that the capacitor bank installation at the end of the distribution line yields the most substantial voltage drop improvement. This is attributed to the utilization of larger capacitor bank capacities compared to the 1/2-kvar and 2/3-kvar methods. Furthermore, the mathematical analysis confirms that both the placement and sizing of capacitor banks play a crucial role in voltage drop mitigation.

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