Effective energy management is essential for the performance and sustainability of hybrid renewable energy systems (HRES), which face challenges due to the variability of renewable sources and load demands. With such strategies in place, the system undergoes processes aimed at mitigation of renewable energy source and load demand variations. This review explores a wide range of management techniques, from traditional rule-based and optimization methods to advanced AI-driven approaches. The review also analyzes the strategies on the most basic measures of performance, which include scalability, flexibility, cost effectiveness, fragmentation, and structural defensibility. Special attention is given to the transformative role of AI in enhancing forecasting accuracy and real-time decision-making. This review provides energy management resources on HRES to help guide further progress and enhancement of energy strategy and conservation practices.
{"title":"Comprehensive review of classical and ai-driven energy management strategies for hybrid renewable energy systems","authors":"Manal Kouihi, Souhaila Bikndaren, Mohamed Moutchou, Abdelhafid Ait ElMahjoub, Radouane Majdoul","doi":"10.1016/j.prime.2025.101085","DOIUrl":"10.1016/j.prime.2025.101085","url":null,"abstract":"<div><div>Effective energy management is essential for the performance and sustainability of hybrid renewable energy systems (HRES), which face challenges due to the variability of renewable sources and load demands. With such strategies in place, the system undergoes processes aimed at mitigation of renewable energy source and load demand variations. This review explores a wide range of management techniques, from traditional rule-based and optimization methods to advanced AI-driven approaches. The review also analyzes the strategies on the most basic measures of performance, which include scalability, flexibility, cost effectiveness, fragmentation, and structural defensibility. Special attention is given to the transformative role of AI in enhancing forecasting accuracy and real-time decision-making. This review provides energy management resources on HRES to help guide further progress and enhancement of energy strategy and conservation practices.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101085"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1016/j.prime.2025.101068
A. Valderrabano-Gonzalez , H.R. Robles-Campos , F. Beltran-Carbajal , R. Tapia-Olvera , J.C. Rosas-Caro , O. Aguilar-Mejia
Sensitivity Analysis plays a crucial role in the design, control, and optimization of multi-pulse Voltage Source Converters. It helps engineers assess how variations in parameters influence system performance, enabling the development of more efficient and robust converters. This study presents some Failure Modes and Effects Analysis that intends to identify the most affected components when a gate disconnection failure occurs, making them critical points for stress management. Additionally, the most vulnerable components when gates remain connected to high value are distinguished. Verifying the voltage output shape, it can be noticed that RMS voltage measurements are not a reliable indicator for tracking failure, whereas THD offers a more effective solution. Understanding these failure modes is essential for refining the design and control strategies of electronic converters, particularly in applications such as motor control and StatCom. Sensitivity analysis also strengthens control algorithms, ensuring that they can effectively accommodate parameter fluctuations while aiding in fault diagnosis and failure prediction.
The early detection of switch malfunctions in power converters is essential for maintaining system reliability, safety, performance, and cost efficiency. Timely identification allows for proactive maintenance, preventing extensive damage and ensuring continuous operation. As the converters become increasingly integral to various applications, implementing reliable fault detection mechanisms is essential for sustaining their optimal performance and long-term functionality.
{"title":"Failure mode and effects analysis and sensitivity analysis for a neutral point re-injection multi-pulse voltage source converter","authors":"A. Valderrabano-Gonzalez , H.R. Robles-Campos , F. Beltran-Carbajal , R. Tapia-Olvera , J.C. Rosas-Caro , O. Aguilar-Mejia","doi":"10.1016/j.prime.2025.101068","DOIUrl":"10.1016/j.prime.2025.101068","url":null,"abstract":"<div><div>Sensitivity Analysis plays a crucial role in the design, control, and optimization of multi-pulse Voltage Source Converters. It helps engineers assess how variations in parameters influence system performance, enabling the development of more efficient and robust converters. This study presents some Failure Modes and Effects Analysis that intends to identify the most affected components when a gate disconnection failure occurs, making them critical points for stress management. Additionally, the most vulnerable components when gates remain connected to high value are distinguished. Verifying the voltage output shape, it can be noticed that RMS voltage measurements are not a reliable indicator for tracking failure, whereas THD offers a more effective solution. Understanding these failure modes is essential for refining the design and control strategies of electronic converters, particularly in applications such as motor control and StatCom. Sensitivity analysis also strengthens control algorithms, ensuring that they can effectively accommodate parameter fluctuations while aiding in fault diagnosis and failure prediction.</div><div>The early detection of switch malfunctions in power converters is essential for maintaining system reliability, safety, performance, and cost efficiency. Timely identification allows for proactive maintenance, preventing extensive damage and ensuring continuous operation. As the converters become increasingly integral to various applications, implementing reliable fault detection mechanisms is essential for sustaining their optimal performance and long-term functionality.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101068"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1016/j.prime.2025.101082
Samarendra Pratap Singh, Neeraj Kanwar, Amit Saraswat
The Soft Open Point (SOP) is a cutting-edge electronic apparatus consisting of voltage source converters linked by a DC capacitor, designed to connect neighbouring distribution feeders. The primary roles of SOPs include managing active power flow and offering reactive power assistance. By incorporating energy storage, this device gains the ability to regulate active power flow over time. This study proposes a framework that takes into account the capabilities of an energy storage-equipped SOP (ESOP) across temporal and spatial dimensions, along with demand side management (DSM) and coordinated dispatch of active and reactive power from solar PV and wind turbine generators. The aim is to optimise the operational costs of an active distribution network (ADN). To accurately depict demand, a voltage-sensitive exponential load model is employed. The suggested framework is formulated as a mixed integer nonlinear programming (MINLP) model, implemented on a modified IEEE 33 bus distribution network, and solved using the DICOPT solver in GAMS.
{"title":"Optimal operation of active distribution network considering energy storage-equipped soft open points","authors":"Samarendra Pratap Singh, Neeraj Kanwar, Amit Saraswat","doi":"10.1016/j.prime.2025.101082","DOIUrl":"10.1016/j.prime.2025.101082","url":null,"abstract":"<div><div>The Soft Open Point (SOP) is a cutting-edge electronic apparatus consisting of voltage source converters linked by a DC capacitor, designed to connect neighbouring distribution feeders. The primary roles of SOPs include managing active power flow and offering reactive power assistance. By incorporating energy storage, this device gains the ability to regulate active power flow over time. This study proposes a framework that takes into account the capabilities of an energy storage-equipped SOP (ESOP) across temporal and spatial dimensions, along with demand side management (DSM) and coordinated dispatch of active and reactive power from solar PV and wind turbine generators. The aim is to optimise the operational costs of an active distribution network (ADN). To accurately depict demand, a voltage-sensitive exponential load model is employed. The suggested framework is formulated as a mixed integer nonlinear programming (MINLP) model, implemented on a modified IEEE 33 bus distribution network, and solved using the DICOPT solver in GAMS.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101082"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.1016/j.prime.2025.101069
Imene Drici , Hicham Allag , Mohammed Chebout , Hocine Bouchekhou , Abdellah Kouzou , Nicolas Bracikowski
<div><div>Maintaining resonance is essential for maximizing the efficiency of resonant wireless power transfer (RWPT) systems, especially under varying load and coil alignment conditions. Frequency tracking plays a crucial role in this context, allowing the system to dynamically adjust to resonance shifts and sustain optimal energy transmission. However, many existing studies either remain theoretical or do not implement real-time, experimentally validated control strategies. To bridge this gap, this work presents a practical and adaptable solution based on frequency tracking combined with pulse-width modulation (PWM), validated through both experimentation and simulation. The investigation begins with the design and implementation of an experimental setup, which includes a voltage inverter controlled using a triangular-sinusoidal pulse width modulation (TS-PWM) technique. A fixed ratio of 39 is maintained between the reference signal and the carrier signal, as defined by the DMAH860 inverter, whose implementation in this context represents a novel application for real-time control of resonant converters. The transfer system employs pancake-shaped coils, carefully aligned and modularly spaced using custom mechanical supports. A series-series (SS) compensation topology is utilized, with electrical parameters selected to operate efficiently in the frequency range of 10 kHz to 100 kHz. Although many RWPT systems have been studied experimentally, the practical integration of frequency tracking with PWM remains relatively underexplored, particularly in terms of real-time dynamic adaptation under varying operating conditions. Experimental measurements of inverter output voltage and resonant current were digitized and analyzed using Fourier Transform methods. These results were compared with simulations conducted in the Simulink-MATLAB environment, demonstrating strong agreement between practical and theoretical outcomes. Harmonic distortion rates were also calculated for both experimental and simulated cases to validate system performance. To fill this gap, this work proposes a novel, experimentally validated control approach that combines PWM and real-time frequency tracking to maximize active power transfer in resonant inductive systems. A significant contribution of this study is the development of a robust and adaptive frequency-tracking algorithm, which dynamically adjusts the carrier signal while preserving a fixed ratio with the reference signal. Implemented in Simulink-MATLAB, the model uses a single-phase full-bridge inverter to drive the resonant circuit. Active power, derived from the instantaneous voltage and current of the resonant load, is compared with delayed power values to fine-tune the operating frequency. The delay parameter ensures system stability, while the exponential step size enhances convergence toward optimal resonance. The algorithm demonstrates excellent tracking performance maintaining efficient operation across different
{"title":"Efficiency maximization of resonant wireless power transfer using an advanced dynamic frequency tracking approach: Experimental and simulation analysis","authors":"Imene Drici , Hicham Allag , Mohammed Chebout , Hocine Bouchekhou , Abdellah Kouzou , Nicolas Bracikowski","doi":"10.1016/j.prime.2025.101069","DOIUrl":"10.1016/j.prime.2025.101069","url":null,"abstract":"<div><div>Maintaining resonance is essential for maximizing the efficiency of resonant wireless power transfer (RWPT) systems, especially under varying load and coil alignment conditions. Frequency tracking plays a crucial role in this context, allowing the system to dynamically adjust to resonance shifts and sustain optimal energy transmission. However, many existing studies either remain theoretical or do not implement real-time, experimentally validated control strategies. To bridge this gap, this work presents a practical and adaptable solution based on frequency tracking combined with pulse-width modulation (PWM), validated through both experimentation and simulation. The investigation begins with the design and implementation of an experimental setup, which includes a voltage inverter controlled using a triangular-sinusoidal pulse width modulation (TS-PWM) technique. A fixed ratio of 39 is maintained between the reference signal and the carrier signal, as defined by the DMAH860 inverter, whose implementation in this context represents a novel application for real-time control of resonant converters. The transfer system employs pancake-shaped coils, carefully aligned and modularly spaced using custom mechanical supports. A series-series (SS) compensation topology is utilized, with electrical parameters selected to operate efficiently in the frequency range of 10 kHz to 100 kHz. Although many RWPT systems have been studied experimentally, the practical integration of frequency tracking with PWM remains relatively underexplored, particularly in terms of real-time dynamic adaptation under varying operating conditions. Experimental measurements of inverter output voltage and resonant current were digitized and analyzed using Fourier Transform methods. These results were compared with simulations conducted in the Simulink-MATLAB environment, demonstrating strong agreement between practical and theoretical outcomes. Harmonic distortion rates were also calculated for both experimental and simulated cases to validate system performance. To fill this gap, this work proposes a novel, experimentally validated control approach that combines PWM and real-time frequency tracking to maximize active power transfer in resonant inductive systems. A significant contribution of this study is the development of a robust and adaptive frequency-tracking algorithm, which dynamically adjusts the carrier signal while preserving a fixed ratio with the reference signal. Implemented in Simulink-MATLAB, the model uses a single-phase full-bridge inverter to drive the resonant circuit. Active power, derived from the instantaneous voltage and current of the resonant load, is compared with delayed power values to fine-tune the operating frequency. The delay parameter ensures system stability, while the exponential step size enhances convergence toward optimal resonance. The algorithm demonstrates excellent tracking performance maintaining efficient operation across different","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101069"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-20DOI: 10.1016/j.prime.2025.101071
Ankita Sharma, Rajasekharareddy Chilipi, Kunisetti V. Praveen Kumar
This paper introduces a novel control technique for the modular multilevel converter-based power electronic transformer (MMC-PET). MMC-PET under consideration encompasses an integrated solar photovoltaic (SPV) system, an MMC-based isolated DC–DC converter (MIDC), and a three-phase MMC interconnected with a power grid. A constant switching frequency model predictive control (CSF-MPC) is developed for the grid-tied MMC of the MMC-PET. The developed CSF-MPC is designed to efficiently handle multiple control objectives of the MMC-PET, including active power injection, common DC-link voltage regulation, harmonic current compensation, and low voltage ride-through (LVRT) support. Further, to ensure effective control of the MMC-PET during both LVRT and normal operating modes, a seamless transition control technique is developed. This developed technique enables the MIDC to operate the SPV system at its maximum power point under normal conditions whereas during LVRT mode, dynamically shifts its control strategy to regulate the common DC-link voltage and limit solar power generation. In addition to this, an arm current sensorless voltage balance control technique is implemented to regulate submodule capacitor voltages. The developed control technique offers benefits such as a predictive nature, reduced tuning efforts, minimized sensing variables, and constant-frequency switching pulses. Additionally, it enhances transient response and adaptability to varying operating conditions. Simulation and experimental results validate the performance of the developed control method in various operating scenarios, such as variable solar irradiation, linear and non-linear load, unbalanced load conditions, and LVRT events.
{"title":"Constant switching frequency model predictive control of MMC-PET for grid integrated solar photovoltaic system","authors":"Ankita Sharma, Rajasekharareddy Chilipi, Kunisetti V. Praveen Kumar","doi":"10.1016/j.prime.2025.101071","DOIUrl":"10.1016/j.prime.2025.101071","url":null,"abstract":"<div><div>This paper introduces a novel control technique for the modular multilevel converter-based power electronic transformer (MMC-PET). MMC-PET under consideration encompasses an integrated solar photovoltaic (SPV) system, an MMC-based isolated DC–DC converter (MIDC), and a three-phase MMC interconnected with a power grid. A constant switching frequency model predictive control (CSF-MPC) is developed for the grid-tied MMC of the MMC-PET. The developed CSF-MPC is designed to efficiently handle multiple control objectives of the MMC-PET, including active power injection, common DC-link voltage regulation, harmonic current compensation, and low voltage ride-through (LVRT) support. Further, to ensure effective control of the MMC-PET during both LVRT and normal operating modes, a seamless transition control technique is developed. This developed technique enables the MIDC to operate the SPV system at its maximum power point under normal conditions whereas during LVRT mode, dynamically shifts its control strategy to regulate the common DC-link voltage and limit solar power generation. In addition to this, an arm current sensorless voltage balance control technique is implemented to regulate submodule capacitor voltages. The developed control technique offers benefits such as a predictive nature, reduced tuning efforts, minimized sensing variables, and constant-frequency switching pulses. Additionally, it enhances transient response and adaptability to varying operating conditions. Simulation and experimental results validate the performance of the developed control method in various operating scenarios, such as variable solar irradiation, linear and non-linear load, unbalanced load conditions, and LVRT events.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101071"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-20DOI: 10.1016/j.prime.2025.101079
Abishek Pandey Chettri , Narayanamoorthi R
Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanographic research, military surveillance, and offshore energy maintenance but face the constraint of limited battery capacity and recharging difficulties in terms of endurance during operation. Wireless Power Transfer (WPT) appears to be a promising answer in providing remote, contactless charging, thereby extending mission duration. This review covers recent advancements in WPT technologies for AUVs, focusing on resonant inductive coupling, magnetic resonance, and acoustic-based power transfer systems. Among these, resonant inductive and magnetic resonance coupling methods have demonstrated the highest practical suitability for AUV applications due to their efficiency, misalignment tolerance, and compatibility with submerged operational conditions. The specific problem of energy loss with salinity and conductivity from the water, alignment issues, and the use of durable corrosion-resistant materials are analyzed in detail. This paper reviews the key advancements in inductive coupling techniques, coil configurations, as well as hybrid power transfer modes combining electromagnetic, acoustic and optical methods. No sole WPT technology can solely address the variety of underwater power transfer application needs, but hybrid schemes are promising. Hybrid designs can be used to support short-range, high efficiency transfer modes with complementary long-range acoustic or optical transfer techniques for improved performances. Some future research areas include adaptive control systems, metamaterials, and novel energy harvesting innovations, as well as integrated energy storage and management in AUV docking stations. WPT technologies have advanced significantly, but innovation is still needed to optimize AUVs for long-duration underwater missions that are critical to marine resource management and subsea infrastructure monitoring.
{"title":"Comprehensive review of wireless power transfer for autonomous underwater vehicles: technological innovations, challenges, and future prospects","authors":"Abishek Pandey Chettri , Narayanamoorthi R","doi":"10.1016/j.prime.2025.101079","DOIUrl":"10.1016/j.prime.2025.101079","url":null,"abstract":"<div><div>Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanographic research, military surveillance, and offshore energy maintenance but face the constraint of limited battery capacity and recharging difficulties in terms of endurance during operation. Wireless Power Transfer (WPT) appears to be a promising answer in providing remote, contactless charging, thereby extending mission duration. This review covers recent advancements in WPT technologies for AUVs, focusing on resonant inductive coupling, magnetic resonance, and acoustic-based power transfer systems. Among these, resonant inductive and magnetic resonance coupling methods have demonstrated the highest practical suitability for AUV applications due to their efficiency, misalignment tolerance, and compatibility with submerged operational conditions. The specific problem of energy loss with salinity and conductivity from the water, alignment issues, and the use of durable corrosion-resistant materials are analyzed in detail. This paper reviews the key advancements in inductive coupling techniques, coil configurations, as well as hybrid power transfer modes combining electromagnetic, acoustic and optical methods. No sole WPT technology can solely address the variety of underwater power transfer application needs, but hybrid schemes are promising. Hybrid designs can be used to support short-range, high efficiency transfer modes with complementary long-range acoustic or optical transfer techniques for improved performances. Some future research areas include adaptive control systems, metamaterials, and novel energy harvesting innovations, as well as integrated energy storage and management in AUV docking stations. WPT technologies have advanced significantly, but innovation is still needed to optimize AUVs for long-duration underwater missions that are critical to marine resource management and subsea infrastructure monitoring.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101079"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1016/j.prime.2025.101070
Usman Gani Joy , Shahadat Kabir , A.F.M. Farhad , Asraful Islam
Accurate time series forecasting is crucial for energy management and resource allocation, aligning with the United Nations Sustainable Development Goals (SDGs) for affordable and clean energy (SDG 7). However, dataset complexity and heterogeneity challenge existing models, such as Principal Component Analysis-Transformer (PCA-Transformer), which rely on static techniques and struggle with feature extraction, scalability, and adaptability to evolving patterns. This study introduces a framework integrating a Wavelet-Inspired Multi-Scale Transform (WIMST), Adaptive Transform (AT), Residual Blocks (RB), and Attention Mechanism (AM). The WIMST dynamically extracts multi-scale features, AT models complex interactions, RB stabilize deep training, and AM captures long-range dependencies, synergistically addressing non-linear patterns in dynamic datasets. Evaluated on the University of California, Irvine (UCI) Appliances Energy and U.S. Energy Information Administration (EIA) Renewable Energy datasets, the model achieves a Root Mean Square Error (RMSE) of 0.1899, Mean Absolute Error (MAE) of 0.1478, and coefficient of determination () of 0.9998 on UCI (versus baselines’ RMSE of 0.350–14.39), and RMSE values of 0.4393 (Hydro), 0.3093 (Waste), 2.9767 (Solar), and 0.1081 (Geothermal) on EIA, reducing errors by 70%–97% compared to Autoregressive Integrated Moving Average (ARIMA). These results highlight superior accuracy and robustness for energy forecasting across diverse applications.
{"title":"An adaptive multi-scale attention-based framework for robust energy and resource forecasting across heterogeneous datasets","authors":"Usman Gani Joy , Shahadat Kabir , A.F.M. Farhad , Asraful Islam","doi":"10.1016/j.prime.2025.101070","DOIUrl":"10.1016/j.prime.2025.101070","url":null,"abstract":"<div><div>Accurate time series forecasting is crucial for energy management and resource allocation, aligning with the United Nations Sustainable Development Goals (SDGs) for affordable and clean energy (SDG 7). However, dataset complexity and heterogeneity challenge existing models, such as Principal Component Analysis-Transformer (PCA-Transformer), which rely on static techniques and struggle with feature extraction, scalability, and adaptability to evolving patterns. This study introduces a framework integrating a Wavelet-Inspired Multi-Scale Transform (WIMST), Adaptive Transform (AT), Residual Blocks (RB), and Attention Mechanism (AM). The WIMST dynamically extracts multi-scale features, AT models complex interactions, RB stabilize deep training, and AM captures long-range dependencies, synergistically addressing non-linear patterns in dynamic datasets. Evaluated on the University of California, Irvine (UCI) Appliances Energy and U.S. Energy Information Administration (EIA) Renewable Energy datasets, the model achieves a Root Mean Square Error (RMSE) of 0.1899, Mean Absolute Error (MAE) of 0.1478, and coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.9998 on UCI (versus baselines’ RMSE of 0.350–14.39), and RMSE values of 0.4393 (Hydro), 0.3093 (Waste), 2.9767 (Solar), and 0.1081 (Geothermal) on EIA, reducing errors by 70%–97% compared to Autoregressive Integrated Moving Average (ARIMA). These results highlight superior accuracy and robustness for energy forecasting across diverse applications.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101070"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1016/j.prime.2025.101078
Rupsha Bhattacharyya , KK Singh , K Bhanja , RB Grover
The expansion of electricity generation capacity in a nation targeting net zero emissions is a long-term planning exercise involving multiple considerations and tradeoffs. In this study, multi-criteria assessments of statistically generated supply side scenarios have been performed for the specific case of India in 2070, considering 6 low carbon power generation technologies. The objective is to identify (i) an optimal portfolio of generators to meet demand in 2070 and (ii) auxiliary systems like water electrolysers, hydrogen storage and battery electricity storage to serve as flexible loads for using surplus power without curtailment or modulation of generator output. Based on current techno-commercial parameters, optimal mixes for net zero India by 2070 are identified under different policy preferences. The electricity generation mixes with 2.27–11.39 % solar PV, 1.56–18.89 % wind power, 0.02–1.52 % hydel power, 0.04–0.90 % biomass-based power, 45.8–72 % fossil fuels with carbon capture and 22.5–24.6 % nuclear power are determined to be optimal, depending on priorities in different policies. Targeting a generation mix in this range enables an 87–93 % reduction in the carbon emissions intensity of the electricity sector from present levels.
{"title":"Multi-criteria assessments for identification of optimal low-carbon electricity generation mixes in net zero India","authors":"Rupsha Bhattacharyya , KK Singh , K Bhanja , RB Grover","doi":"10.1016/j.prime.2025.101078","DOIUrl":"10.1016/j.prime.2025.101078","url":null,"abstract":"<div><div>The expansion of electricity generation capacity in a nation targeting net zero emissions is a long-term planning exercise involving multiple considerations and tradeoffs. In this study, multi-criteria assessments of statistically generated supply side scenarios have been performed for the specific case of India in 2070, considering 6 low carbon power generation technologies. The objective is to identify (i) an optimal portfolio of generators to meet demand in 2070 and (ii) auxiliary systems like water electrolysers, hydrogen storage and battery electricity storage to serve as flexible loads for using surplus power without curtailment or modulation of generator output. Based on current techno-commercial parameters, optimal mixes for net zero India by 2070 are identified under different policy preferences. The electricity generation mixes with 2.27–11.39 % solar PV, 1.56–18.89 % wind power, 0.02–1.52 % hydel power, 0.04–0.90 % biomass-based power, 45.8–72 % fossil fuels with carbon capture and 22.5–24.6 % nuclear power are determined to be optimal, depending on priorities in different policies. Targeting a generation mix in this range enables an 87–93 % reduction in the carbon emissions intensity of the electricity sector from present levels.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101078"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.prime.2025.101062
Vivek Sahu, Pratim Kundu
Modern grid codes require renewable energy source (RES) plants to remain connected with the grid even during fault scenarios. Fault current being comparable to rated full-load output of RES plants, makes timely fault analysis a more challenging task. In this work, fault detection and classification on a network based fully on inverter-based resources (IBRs) is proposed. Discrete Fourier transformation is utilized to extract fundamental component of three-phase voltages and currents. Two separate indices for fault classification, based on Spherical Coordinate System (SCS)-based representation is proposed. Mathematical derivations form the underlying basis for simplistic threshold settings. Based on the indices, decision variables are set. Sign-based identification of faulty phase(s), without the need of prior fault-type identification, is done by a new category of power variables. Final decision is reached through a proposed trip logic. Timely response of fault analysis is identified to be within practical limits to suit industrial requirements. The methodology is tested on a modified IEEE 9-bus model using PSCAD/EMTDC. Different fault types, fault distance, inception angle and resistance are found to validate the threshold settings. Satisfactory performance during switching events like load, generator and line tripping ensures its robustness.
{"title":"Fault classification in inverter based resources system using spherical coordinate system","authors":"Vivek Sahu, Pratim Kundu","doi":"10.1016/j.prime.2025.101062","DOIUrl":"10.1016/j.prime.2025.101062","url":null,"abstract":"<div><div>Modern grid codes require renewable energy source (RES) plants to remain connected with the grid even during fault scenarios. Fault current being comparable to rated full-load output of RES plants, makes timely fault analysis a more challenging task. In this work, fault detection and classification on a network based fully on inverter-based resources (IBRs) is proposed. Discrete Fourier transformation is utilized to extract fundamental component of three-phase voltages and currents. Two separate indices for fault classification, based on Spherical Coordinate System (SCS)-based representation is proposed. Mathematical derivations form the underlying basis for simplistic threshold settings. Based on the indices, decision variables are set. Sign-based identification of faulty phase(s), without the need of prior fault-type identification, is done by a new category of power variables. Final decision is reached through a proposed trip logic. Timely response of fault analysis is identified to be within practical limits to suit industrial requirements. The methodology is tested on a modified IEEE 9-bus model using PSCAD/EMTDC. Different fault types, fault distance, inception angle and resistance are found to validate the threshold settings. Satisfactory performance during switching events like load, generator and line tripping ensures its robustness.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101062"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.prime.2025.101066
Alice Jansson, Olof Samuelsson, Francisco J. Márquez-Fernández
The increasing global concern for greenhouse gas emissions has led to a fast pace in the transition away from fossil fuels in the transport sector. The share of electric passenger cars is increasing quickly, introducing new loads on the power grid. These loads primarily appear at the distribution grid level, where most of the charging occurs in the form of overnight charging at home. Although the impact on the distribution grid capacity from electric car charging is well researched, little effort has been put into understanding the aggregated impact at higher voltage levels. This study develops a probabilistic methodology for estimating the aggregated home charging demand of a 100 % electrified car fleet at the sub-transmission grid level. The resulting risk of overload in the primary substation transformers is estimated through a probabilistic load flow simulation. The full methodology is applied on a case study in southern Sweden using the best available vehicle and transport data, and a real grid model of the area. The results of the study show how aggregated home charging at low voltage levels may increase the average substation transformer loading by up to 72 %. This indicates that the aggregated home charging loads cannot be ignored at the sub-transmission level. More specifically, 11 out of 86 studied substations do not have enough capacity to feed the simulated home charging without transformer overloads.
{"title":"Impact of aggregated electric vehicle home charging in the sub-transmission grid","authors":"Alice Jansson, Olof Samuelsson, Francisco J. Márquez-Fernández","doi":"10.1016/j.prime.2025.101066","DOIUrl":"10.1016/j.prime.2025.101066","url":null,"abstract":"<div><div>The increasing global concern for greenhouse gas emissions has led to a fast pace in the transition away from fossil fuels in the transport sector. The share of electric passenger cars is increasing quickly, introducing new loads on the power grid. These loads primarily appear at the distribution grid level, where most of the charging occurs in the form of overnight charging at home. Although the impact on the distribution grid capacity from electric car charging is well researched, little effort has been put into understanding the aggregated impact at higher voltage levels. This study develops a probabilistic methodology for estimating the aggregated home charging demand of a 100 % electrified car fleet at the sub-transmission grid level. The resulting risk of overload in the primary substation transformers is estimated through a probabilistic load flow simulation. The full methodology is applied on a case study in southern Sweden using the best available vehicle and transport data, and a real grid model of the area. The results of the study show how aggregated home charging at low voltage levels may increase the average substation transformer loading by up to 72 %. This indicates that the aggregated home charging loads cannot be ignored at the sub-transmission level. More specifically, 11 out of 86 studied substations do not have enough capacity to feed the simulated home charging without transformer overloads.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101066"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号