Pub Date : 2026-01-20DOI: 10.1016/j.ijepes.2026.111596
Pengning Zhang , Pengyang Li , Xiaohong Li , Dengji Miao , Jian Zhang , Ying Zhan
As the key component of modern energy conversion systems, high-frequency transformer (HFT) directly affects the reliability of the system, and the design parameters of HFT, such as power density, loss, and temperature rise, are coupled with each other. Therefore, optimizing the design of HFT while considering multiple parameters has important engineering significance. In order to reduce the operating temperature rise without compromising the optimization outcomes, this article establishes a coupled design model of HFT and heat dissipation fins, and proposes a multi-objective optimization design method for HFT considering heat dissipation based on multi-objective particle swarm optimization (MOPSO). Finally, a 10 kHz/20kVA litz-wire HFT prototype is designed, and the proposed optimization design method is verified through modeling simulation and experimental testing.
{"title":"Multi-objective optimization design method for electromagnetic structure and heat dissipation of litz-wire high-frequency transformer","authors":"Pengning Zhang , Pengyang Li , Xiaohong Li , Dengji Miao , Jian Zhang , Ying Zhan","doi":"10.1016/j.ijepes.2026.111596","DOIUrl":"10.1016/j.ijepes.2026.111596","url":null,"abstract":"<div><div>As the key component of modern energy conversion systems, high-frequency transformer (HFT) directly affects the reliability of the system, and the design parameters of HFT, such as power density, loss, and temperature rise, are coupled with each other. Therefore, optimizing the design of HFT while considering multiple parameters has important engineering significance. In order to reduce the operating temperature rise without compromising the optimization outcomes, this article establishes a coupled design model of HFT and heat dissipation fins, and proposes a multi-objective optimization design method for HFT considering heat dissipation based on multi-objective particle swarm optimization (MOPSO). Finally, a 10 kHz/20kVA litz-wire HFT prototype is designed, and the proposed optimization design method is verified through modeling simulation and experimental testing.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111596"},"PeriodicalIF":5.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ijepes.2026.111578
Guiqing Ma , Haixin Wang , Mingchao Xia , Hassan Bevrani , Zhe Chen , Junyou Yang
The growing of renewable energy integration in the power systems requires precise inertia estimation to ensure the overall stability. Current data-driven methods exhibit physical inconsistency and limited mode interpretability. To address these challenges, this paper proposes a cyber-physical framework that combines physical laws with a model for data-driven mode extraction. First, an inertia distribution model incorporating a nonlinear correction term is developed to enhance nonlinear parameter identification accuracy. Then, a physics-informed dynamic mode decomposition (PIDMD) is proposed, integrating physical laws to improve inertia behavior tracking capability while enabling inertia distribution interpretation through a modal mapping. Finally, evaluations of the proposed framework on a modified IEEE 39-bus test system demonstrate a 6.3% improvement in inertia estimation accuracy, and achieving a normalized error of 0.4%. Field validations using phasor measurement units successfully capture multi-frequency oscillations at 0.1 Hz, 0.31 Hz and 0.08 Hz. These results clearly show the important causal relationships between oscillatory modes and the distribution of inertia in renewable-dominated power systems.
{"title":"A cyber-physical framework for estimating inertia distribution in power systems","authors":"Guiqing Ma , Haixin Wang , Mingchao Xia , Hassan Bevrani , Zhe Chen , Junyou Yang","doi":"10.1016/j.ijepes.2026.111578","DOIUrl":"10.1016/j.ijepes.2026.111578","url":null,"abstract":"<div><div>The growing of renewable energy integration in the power systems requires precise inertia estimation to ensure the overall stability. Current data-driven methods exhibit physical inconsistency and limited mode interpretability. To address these challenges, this paper proposes a cyber-physical framework that combines physical laws with a model for data-driven mode extraction. First, an inertia distribution model incorporating a nonlinear correction term is developed to enhance nonlinear parameter identification accuracy. Then, a physics-informed dynamic mode decomposition (PIDMD) is proposed, integrating physical laws to improve inertia behavior tracking capability while enabling inertia distribution interpretation through a modal mapping. Finally, evaluations of the proposed framework on a modified IEEE 39-bus test system demonstrate a 6.3% improvement in inertia estimation accuracy, and achieving a normalized error of 0.4%. Field validations using phasor measurement units successfully capture multi-frequency oscillations at 0.1 Hz, 0.31 Hz and 0.08 Hz. These results clearly show the important causal relationships between oscillatory modes and the distribution of inertia in renewable-dominated power systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111578"},"PeriodicalIF":5.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ijepes.2026.111576
Irati Ibanez-Hidalgo , Ricardo P. Aguilera , Alain Sanchez-Ruiz , Angel Perez-Basante , Asier Zubizarreta , Salvador Ceballos
Active power filters are widely used to improve power quality and mitigate harmonic distortion caused by non-linear loads and grid-connected generation units. In high-power and medium-voltage applications, conventional high-frequency carrier-based pulse width modulation techniques results in excessive switching losses, reduced efficiency, and converter current derating. This work proposes a novel optimal harmonic control strategy for high-power/medium-voltage active power filters operating at low-switching frequencies. The proposed approach reduces switching losses, increases power density, and lowers system costs. Unlike existing low-switching-frequency methods that primarily regulate the fundamental voltage, the proposed strategy enables precise control of both the magnitude and phase of multiple low-order harmonics, ensuring full active power filter functionality. A Kalman filter provides accurate real-time estimation of grid voltage and current harmonic distortions, which are processed by an optimal model predictive controller. This controller is integrated with an advanced selective harmonic control pulse width modulation technique to regulate current harmonics efficiently. To reduce the computational burden of real-time selective harmonic control pulse width modulation, an artificial neural network is employed for fast and efficient execution. The proposed strategy is compared with a conventional proportional-integral control approach and validated experimentally using a three-level neutral point clamped converter-based active power filter.
{"title":"Model predictive control based selective harmonic control-PWM strategy for active power filters","authors":"Irati Ibanez-Hidalgo , Ricardo P. Aguilera , Alain Sanchez-Ruiz , Angel Perez-Basante , Asier Zubizarreta , Salvador Ceballos","doi":"10.1016/j.ijepes.2026.111576","DOIUrl":"10.1016/j.ijepes.2026.111576","url":null,"abstract":"<div><div>Active power filters are widely used to improve power quality and mitigate harmonic distortion caused by non-linear loads and grid-connected generation units. In high-power and medium-voltage applications, conventional high-frequency carrier-based pulse width modulation techniques results in excessive switching losses, reduced efficiency, and converter current derating. This work proposes a novel optimal harmonic control strategy for high-power/medium-voltage active power filters operating at low-switching frequencies. The proposed approach reduces switching losses, increases power density, and lowers system costs. Unlike existing low-switching-frequency methods that primarily regulate the fundamental voltage, the proposed strategy enables precise control of both the magnitude and phase of multiple low-order harmonics, ensuring full active power filter functionality. A Kalman filter provides accurate real-time estimation of grid voltage and current harmonic distortions, which are processed by an optimal model predictive controller. This controller is integrated with an advanced selective harmonic control pulse width modulation technique to regulate current harmonics efficiently. To reduce the computational burden of real-time selective harmonic control pulse width modulation, an artificial neural network is employed for fast and efficient execution. The proposed strategy is compared with a conventional proportional-integral control approach and validated experimentally using a three-level neutral point clamped converter-based active power filter.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111576"},"PeriodicalIF":5.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ijepes.2026.111601
Shuaibing Li , Xinchen Li , Hongyu Li , Yi Cui , Lixia Yang , Yongqiang Kang , Xiping Ma , Junming Zhu , Hongwei Li
In response to the challenges of an accelerated aging rate and excessive life loss of distribution transformers resulting from the integration of distributed photovoltaics and electric vehicles, this paper proposes a flexible strategy to actively prolong the insulation life of transformers by reforming transformer cooling systems. An experimental platform consisting of a 2kVA prototype transformer with complete temperature control functionality was first built in the laboratory. A heat dissipation system was designed to facilitate the dynamic temperature rise control of the transformer, and the heat dissipation performance was verified through numerical simulation. Afterward, temperature rise experiments were conducted on distribution transformers under different load conditions. On this basis, an active temperature rise control strategy was proposed. Finally, the relative aging rate and remaining useful life with and without the active transformer temperature control system were calculated. The results show that the proposed active temperature control can effectively reduce the aging rate of transformers and prolong the life expectancy of distribution transformers.
{"title":"Measurement-based adaptive temperature control for Lifetime extension of distribution transformers under dynamic loading","authors":"Shuaibing Li , Xinchen Li , Hongyu Li , Yi Cui , Lixia Yang , Yongqiang Kang , Xiping Ma , Junming Zhu , Hongwei Li","doi":"10.1016/j.ijepes.2026.111601","DOIUrl":"10.1016/j.ijepes.2026.111601","url":null,"abstract":"<div><div>In response to the challenges of an accelerated aging rate and excessive life loss of distribution transformers resulting from the integration of distributed photovoltaics and electric vehicles, this paper proposes a flexible strategy to actively prolong the insulation life of transformers by reforming transformer cooling systems. An experimental platform consisting of a 2kVA prototype transformer with complete temperature control functionality was first built in the laboratory. A heat dissipation system was designed to facilitate the dynamic temperature rise control of the transformer, and the heat dissipation performance was verified through numerical simulation. Afterward, temperature rise experiments were conducted on distribution transformers under different load conditions. On this basis, an active temperature rise control strategy was proposed. Finally, the relative aging rate and remaining useful life with and without the active transformer temperature control system were calculated. The results show that the proposed active temperature control can effectively reduce the aging rate of transformers and prolong the life expectancy of distribution transformers.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111601"},"PeriodicalIF":5.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.ijepes.2025.111557
Danyang Xu , Zeyu Liu , Kai Hou , Lewei Zhu , Yunfei Mu , Hongjie Jia , Ruifeng Zhao
The frequency security of low-inertia microgrids (MGs) is a critical concern following unintentional islanding events (UIEs). To tackle this challenge, this paper proposes a seamless islanding-aware frequency-constrained MG scheduling model. An equivalent condition for the maximum frequency deviation (MFD) constraint after a UIE is established to guide the scheduling of frequency support resources within the MG. Specifically, the post-UIE frequency dynamics are approximated by a quadratic function, which allows for the analytical derivation of primary frequency response (PFR) reserve contributions from diverse resources. Moreover, the occurrence time of the MFD is conservatively estimated and introduced as a decision variable into the scheduling model. Building on these two approximations, an equivalent enforcement condition of the MFD constraint is derived. To address the non-convexity of the MFD occurrence time, a convex relaxation is employed, and an iterative algorithm is further designed to reduce the relaxation gap. In addition, a distributionally robust chance-constrained (DRCC) approach is incorporated to capture the uncertainty of renewable energy sources (RESs). Numerical studies on a modified IEEE 33-bus MG verify that the proposed scheduling model effectively maintains post-UIE frequency trajectories within 49.5–50.5 Hz, thereby ensuring the seamless islanding capability of the MG.
{"title":"Seamless islanding-aware frequency constrained microgrid scheduling","authors":"Danyang Xu , Zeyu Liu , Kai Hou , Lewei Zhu , Yunfei Mu , Hongjie Jia , Ruifeng Zhao","doi":"10.1016/j.ijepes.2025.111557","DOIUrl":"10.1016/j.ijepes.2025.111557","url":null,"abstract":"<div><div>The frequency security of low-inertia microgrids (MGs) is a critical concern following unintentional islanding events (UIEs). To tackle this challenge, this paper proposes a seamless islanding-aware frequency-constrained MG scheduling model. An equivalent condition for the maximum frequency deviation (MFD) constraint after a UIE is established to guide the scheduling of frequency support resources within the MG. Specifically, the post-UIE frequency dynamics are approximated by a quadratic function, which allows for the analytical derivation of primary frequency response (PFR) reserve contributions from diverse resources. Moreover, the occurrence time of the MFD is conservatively estimated and introduced as a decision variable into the scheduling model. Building on these two approximations, an equivalent enforcement condition of the MFD constraint is derived. To address the non-convexity of the MFD occurrence time, a convex relaxation is employed, and an iterative algorithm is further designed to reduce the relaxation gap. In addition, a distributionally robust chance-constrained (DRCC) approach is incorporated to capture the uncertainty of renewable energy sources (RESs). Numerical studies on a modified IEEE 33-bus MG verify that the proposed scheduling model effectively maintains post-UIE frequency trajectories within 49.5–50.5 Hz, thereby ensuring the seamless islanding capability of the MG.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111557"},"PeriodicalIF":5.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.ijepes.2026.111575
Weijie Xia , Gao Peng , Chenguang Wang , Peter Palensky , Eric Pauwels , Pedro P. Vergara
Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains, e.g., households with a moderate amount of data, and thousands of target domains, e.g., households that ECP are required to be modeled. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning. To address these limitations, this paper proposes a novel FSL framework that integrates Transformers with Gaussian Mixture Models (GMMs) for ECP modeling. The proposed approach is fine-tuning-free, computationally efficient, and robust even with extremely limited data. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6% of the complete domain dataset) and outperforms state-of-the-art time series modeling methods in the context of ECP modeling.
{"title":"Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains","authors":"Weijie Xia , Gao Peng , Chenguang Wang , Peter Palensky , Eric Pauwels , Pedro P. Vergara","doi":"10.1016/j.ijepes.2026.111575","DOIUrl":"10.1016/j.ijepes.2026.111575","url":null,"abstract":"<div><div>Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains, e.g., households with a moderate amount of data, and thousands of target domains, e.g., households that ECP are required to be modeled. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning. To address these limitations, this paper proposes a novel FSL framework that integrates Transformers with Gaussian Mixture Models (GMMs) for ECP modeling. The proposed approach is fine-tuning-free, computationally efficient, and robust even with extremely limited data. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6% of the complete domain dataset) and outperforms state-of-the-art time series modeling methods in the context of ECP modeling.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111575"},"PeriodicalIF":5.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.ijepes.2026.111573
Shuaishuai Feng , Deping Ke , Liangzhong Yao , Huanhuan Yang , Jianxin Zhang , Guanghu Xu , Jian Xu
This paper presents a robust emergency frequency control framework that leverages coordinated rapid regulation by wind turbines and loads to pre-generate control strategies for large hypothetical frequency sag faults and activate control to stabilize system frequency during faults. The underlying optimization problem considers multi-type resources with discrete and continuous control modes, while capturing uncertainties from environmental and modeling errors, nonlinearities, and non-analytical computations. Accordingly, this paper proposes a two-step successive solution approach to address the formulated mixed-integer nonlinear robust optimization problem efficiently. In step one, uncertainties are temporarily ignored, and the resulting mixed-integer nonlinear and non-analytical problem is solved using proposed simplified methods, such as differential discretization, which serves as a warm start for the original problem. In step two, the solution from step one is treated as a reference, and trajectory sensitivities are used to quantify the impact of uncertainties on frequency security and other constraints. The original problem is ultimately reformulated as a bi-level mixed-integer linear optimization with independent decision variables, enabling efficient solution. Finally, simulations on the modified IEEE 39-bus and 118-bus systems demonstrate that incorporating rapid regulation of wind turbines and loads significantly reduces control costs. Additionally, the proposed method ensures high solving efficiency and reliable control effects against modeling uncertainties.
{"title":"Robust control by a novel two-step successive solution approach to mitigate emergency frequency sag in power systems with modeling uncertainties","authors":"Shuaishuai Feng , Deping Ke , Liangzhong Yao , Huanhuan Yang , Jianxin Zhang , Guanghu Xu , Jian Xu","doi":"10.1016/j.ijepes.2026.111573","DOIUrl":"10.1016/j.ijepes.2026.111573","url":null,"abstract":"<div><div>This paper presents a robust emergency frequency control framework that leverages coordinated rapid regulation by wind turbines and loads to pre-generate control strategies for large hypothetical frequency sag faults and activate control to stabilize system frequency during faults. The underlying optimization problem considers multi-type resources with discrete and continuous control modes, while capturing uncertainties from environmental and modeling errors, nonlinearities, and non-analytical computations. Accordingly, this paper proposes a two-step successive solution approach to address the formulated mixed-integer nonlinear robust optimization problem efficiently. In step one, uncertainties are temporarily ignored, and the resulting mixed-integer nonlinear and non-analytical problem is solved using proposed simplified methods, such as differential discretization, which serves as a warm start for the original problem. In step two, the solution from step one is treated as a reference, and trajectory sensitivities are used to quantify the impact of uncertainties on frequency security and other constraints. The original problem is ultimately reformulated as a bi-level mixed-integer linear optimization with independent decision variables, enabling efficient solution. Finally, simulations on the modified IEEE 39-bus and 118-bus systems demonstrate that incorporating rapid regulation of wind turbines and loads significantly reduces control costs. Additionally, the proposed method ensures high solving efficiency and reliable control effects against modeling uncertainties.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111573"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.ijepes.2026.111589
Zoltan Čorba, Bane Popadić, Dragan Milićević, Boris Dumnić, Vladan Krsman
The aim of the study is to analyse the impact of climate change on the operation of the rooftop photovoltaic (PV) power plant with the rated power of 8.0 kW. To analyse the impact of climate change on the operation of a photovoltaic power plant, the most important factors affecting the power plant’s production were investigated; the solar radiation and air temperature, the power degradation and some anomalies of PV modules. The mean annual sum of solar irradiation was 1519.1 kWh/m2 with a growth tendency of 13.3 kWh/m2 per year. The mean value of the temperature is 15.4 °C with an annual growth tendency of 0.15 °C. In order to assess the degradation of PV module power, measurements of the parameters of all PV modules in the power plant were carried out during the summer of 2024 to determine the current state after 13 years of operation. The average annual degradation of power is 0.76 %. The average annual electricity production in the observed period is 11582.7 kWh with an annual growth tendency of 6 kWh/year. Before the construction of the power plant the production was estimated at 11,265 kWh/year using the PVSyst software. Since 2012 when the power plant production has been monitored this software has been improved and the meteorological databases have been updated. Therefore, the power plant production was estimated with newer versions of the software. In all cases the PVGIS meteorological database was used. The input horizontal radiation data show an increasing trend of 1347 kWh/m2 (2012), 1367 kWh/m2 (2019) and 1402 kWh/m2 (2023). Therefore, the annual production estimate increases to 11,531 kWh (2019) and to 11,824 kWh in the simulation in 2023. Despite the degradation of the power of the PV panels, the expected drop in production during long-term operation is not observed. This phenomenon contributes to the further reduction of CO2 emissions.
{"title":"Impact of climate change on electricity production of rooftop photovoltaic system for powering laboratory data","authors":"Zoltan Čorba, Bane Popadić, Dragan Milićević, Boris Dumnić, Vladan Krsman","doi":"10.1016/j.ijepes.2026.111589","DOIUrl":"10.1016/j.ijepes.2026.111589","url":null,"abstract":"<div><div>The aim of the study is to analyse the impact of climate change on the operation of the rooftop photovoltaic (PV) power plant with the rated power of 8.0 kW. To analyse the impact of climate change on the operation of a photovoltaic power plant, the most important factors affecting the power plant’s production were investigated; the solar radiation and air temperature, the power degradation and some anomalies of PV modules. The mean annual sum of solar irradiation was 1519.1 kWh/m<sup>2</sup> with a growth tendency of 13.3 kWh/m<sup>2</sup> per year. The mean value of the temperature is 15.4 °C with an annual growth tendency of 0.15 °C. In order to assess the degradation of PV module power, measurements of the parameters of all PV modules in the power plant were carried out during the summer of 2024 to determine the current state after 13 years of operation. The average annual degradation of power is 0.76 %. The average annual electricity production in the observed period is 11582.7 kWh with an annual growth tendency of 6 kWh/year. Before the construction of the power plant the production was estimated at 11,265 kWh/year using the PVSyst software. Since 2012 when the power plant production has been monitored this software has been improved and the meteorological databases have been updated. Therefore, the power plant production was estimated with newer versions of the software. In all cases the PVGIS meteorological database was used. The input horizontal radiation data show an increasing trend of 1347 kWh/m<sup>2</sup> (2012), 1367 kWh/m<sup>2</sup> (2019) and 1402 kWh/m<sup>2</sup> (2023). Therefore, the annual production estimate increases to 11,531 kWh (2019) and to 11,824 kWh in the simulation in 2023. Despite the degradation of the power of the PV panels, the expected drop in production during long-term operation is not observed. This phenomenon contributes to the further reduction of CO<sub>2</sub> emissions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111589"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.ijepes.2026.111579
Jae-Kyeong Kim
This paper presents a practical method to assess the impact of multiple uncertainties in dynamic load models and renewable generation on short-term voltage stability (STVS), with seamless integration into diverse analysis environments and common industry workflows. The proposed method targets deterministic worst-case analysis and does not rely on assumed probability distributions; probabilistic uncertainty quantification is considered complementary but is outside the scope of this paper. Building on trajectory sensitivity analysis, the proposed approach extends conventional sensitivity-based indices by introducing a new metric that accounts for parameter variability. The resulting index quantifies the influence of uncertain parameters, and additional descriptive measures are incorporated to support systematic interpretation of uncertainty impacts. Using the index information, the most influential parameter sets associated with worst-impact conditions are identified and evaluated via targeted nonlinear simulations. This workflow mitigates the limitations of sensitivity-based approximations and enables the assessment of extreme scenarios under substantial uncertainties by capturing nonlinear system behavior. Case studies on the IEEE 39-bus system and a large-scale real-world Korean power system demonstrate that the proposed index-based approach supports reliable uncertainty-aware STVS analysis and improves the practicality of stability studies.
{"title":"A practical method for short-term voltage stability assessment under multiple and large uncertainties in load models and renewable generation","authors":"Jae-Kyeong Kim","doi":"10.1016/j.ijepes.2026.111579","DOIUrl":"10.1016/j.ijepes.2026.111579","url":null,"abstract":"<div><div>This paper presents a practical method to assess the impact of multiple uncertainties in dynamic load models and renewable generation on short-term voltage stability (STVS), with seamless integration into diverse analysis environments and common industry workflows. The proposed method targets deterministic worst-case analysis and does not rely on assumed probability distributions; probabilistic uncertainty quantification is considered complementary but is outside the scope of this paper. Building on trajectory sensitivity analysis, the proposed approach extends conventional sensitivity-based indices by introducing a new metric that accounts for parameter variability. The resulting index quantifies the influence of uncertain parameters, and additional descriptive measures are incorporated to support systematic interpretation of uncertainty impacts. Using the index information, the most influential parameter sets associated with worst-impact conditions are identified and evaluated via targeted nonlinear simulations. This workflow mitigates the limitations of sensitivity-based approximations and enables the assessment of extreme scenarios under substantial uncertainties by capturing nonlinear system behavior. Case studies on the IEEE 39-bus system and a large-scale real-world Korean power system demonstrate that the proposed index-based approach supports reliable uncertainty-aware STVS analysis and improves the practicality of stability studies.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111579"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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