International Journal of Energy Technology and Policy (24 papers in press)

Special Issue on: OA Multiscale Energy Systems for Renewable Energy Storage Part 3

• Open Access

  Current-sensing-array-based dielectric health monitoring system for secondary power circuits ( Free Full-text Access )

  by Hengchu Shi, Xiaofan Chen, Hao You, Shoudong Xu, Xiao Hu 
  Abstract: The secondary power system plays a crucial role in ensuring reliable power supply and safe production of the power system. The system consists of secondary equipment and secondary circuits, where the integrity of the secondary circuits and the correctness of wiring are crucial for the regular operation of protection and automation devices. If there are problems with the secondary circuit, especially wiring errors, it will directly affect the response of relay protection and automation devices, which may lead to system failures or accidents. In the actual operation of substations, multiple grounding points on the secondary sides of PT (voltage transformer) and current transformer (CT) often occur, which may cause misoperation or the refusal of relay protection devices, significantly increasing the risk of system operation. To address this issue, this paper proposes an online monitoring system based on a distributed zero-sequence current monitoring method for real-time monitoring of the insulation status of secondary AC circuits. This system effectively reduces the workload of operation and maintenance personnel, improves operational and maintenance efficiency and monitoring accuracy, thereby ensuring the reliable operation of the substation’s secondary circuit and providing strong guarantees for the stability and safety of the power system.
  Keywords: secondary power circuits; online monitoring; dielectric monitoring system; distributed sampling; current sensing.
  DOI: 10.1504/IJETP.2026.10076937
   
  

• Open Access

  Enabling holistic insulation monitoring in secondary AC protection circuits: a diagnostic algorithm ( Free Full-text Access )

  by Hengchu Shi, Hao You, Xiaofan Chen, Shoudong Xu, Jing Chen 
  Abstract: This paper addresses the problem of insulation status monitoring in secondary alternating current (AC) circuits of power system relay protection. A comprehensive diagnostic algorithm based on multi-dimensional feature fusion is proposed. This algorithm integrates distributed zero-sequence current monitoring, waveform similarity analysis, and third harmonic analysis techniques to construct a multi-dimensional feature fusion diagnostic model. By real-time acquisition of zero-sequence current in each branch, combined with the similarity changes of current waveforms under normal and abnormal operating conditions and the characteristics of third harmonic content, early identification and accurate location of typical faults such as insulation degradation, single-point grounding, and multi-point grounding are achieved. Experimental results show that the algorithm achieves a 98.6% detection rate for insulation faults, a 94.3% accuracy rate for multi-point grounding location, an average diagnostic latency of only 43.63 ms, and a false alarm rate of 1.5% under normal operating conditions. This significantly improves the timeliness and accuracy of insulation fault diagnosis, providing effective technical support for the safe and reliable operation of secondary circuits in substation relay protection.
  Keywords: secondary circuit; insulation status; zero-sequence current; waveform similarity analysis; harmonic analysis; comprehensive diagnosis.
  DOI: 10.1504/IJETP.2026.10077374
   
  

• Open Access

  Noise emission prediction technology for converter stations based on digital intelligent networks ( Free Full-text Access )

  by Zhihao Zhang, Yijie Chen, Qi Liu, Gangye Ding, Wanyi Zhu 
  Abstract: Traditional noise prediction techniques are mostly based on simplified physical models, which make it difficult to characterise the multi-source coupling and time-varying nonlinear noise emission characteristics of converter stations. This paper proposes a noise emission prediction technique for converter stations based on digital intelligent networks, and designs a hybrid deep learning model that combines graph neural networks (GNN) and long short-term memory (LSTM) to explicitly simulate the spatial relationships and temporal dynamics between devices. The experimental results show that a mean absolute error (MAE) of 1.9 dB was achieved in LAeq (equivalent continuous a-weighted sound pressure level) prediction, which is 27% and 21% lower than traditional support vector regression (SVR) and single LSTM models, respectively. In addition, even in complex scenarios such as load fluctuations and data loss of up to 10%, the fluctuation of model error is still less than 0.3 dB, indicating excellent stability of the model.
  Keywords: converter station; noise emission prediction; digital intelligent network; graph neural network; GNN; multi-source data fusion.
  DOI: 10.1504/IJETP.2026.10078426
   
  

• Open Access

  Carbon information disclosure and equity financing costs in China: evidence from the dual-carbon policy framework ( Free Full-text Access )

  by Xi Zhang, Yuxuan Wang 
  Abstract: Under the backdrop of the global trend of carbon reduction, the Chinese government is intensifying the oversight of carbon information disclosure by listed companies. In this study, we investigate the effect of carbon information disclosure on the company’s cost of equity, using a dataset of Chinese A-share listed companies for the period from 2018 to 2024. Employing text breakdown and the entropy method to develop a comprehensive carbon information disclosure index, this study examines the empirical relevance between disclosure quality and cost of equity. The findings indicate that better carbon information disclosure significantly reduces the cost of equity. Environmental, social, and governance rating is also identified as a partial mediator in the relationship between carbon transparency and cost of equity. Heterogeneity analysis reveals that the magnitude of this effect varies slightly across subsamples while the negative association remains robust across various ownership structures and carbon-market participation statuses. This study provides critical policy references for company officers and regulators, while enriching references about the efficiency of carbon trading and capital markets.
  Keywords: carbon information disclosure; CID; cost of equity; COE; ESG rating; China.
  DOI: 10.1504/IJETP.2026.10079535
   
  

Regular Issues

• Rural solar photovoltaic expansion and forest degradation in India: unpacking the Clean Energy Deforestation Paradox  
  by Usha Shukla, Manas Bajpai 
  Abstract: India’s renewable energy transition, driven primarily by the rapid growth of solar photovoltaic (PV) systems, is vital to achieving national goals of energy security, climate mitigation, and rural development. However, this study identifies a critical sustainability contradiction termed the Clean Energy Deforestation Paradox, wherein rural and forest fringe households often finance solar adoption through forest based and extractive livelihood activities such as fuel wood collection, charcoal production, and non-timber forest product (NTFP) trade. Drawing on an extensive review of literature, the paper reveals that while national policies on renewable energy and afforestation demonstrate significant progress, localised forest degradation and carbon stock depletion persist due to fragmented governance and socio-economic dependencies. To address these interlinked challenges, the study proposes the rural energy environment livelihood integration model (REELIM-India) a conceptual framework connecting renewable energy access, ecological sustainability, and livelihood resilience. The framework provides an original contribution by integrating livelihood financing behaviour with ecological and energy systems analysis specific to Indias rural context. The findings emphasise that achieving a truly just and sustainable energy transition requires forest sensitive financing, coordinated inter-ministerial planning, and inclusive livelihood strategies that align clean energy expansion with environmental conservation and social equity.
  Keywords: solar photovoltaics; forest degradation; rural livelihoods; energy poverty; deforestation; decentralised energy.
  DOI: 10.1504/IJETP.2026.10078631
   
  
• Robust optimisation-based load flow in uncertain DG-integrated unbalanced distribution systems  
  by Sanat Kumar Paul, Smriti Jaiswal, Dulal Chandra Das, Pukhrambam Devachandra Singh, Juwel Hossain, Chirabrata Debnath, Abanishwar Chakrabarti 
  Abstract: This paper proposes a robust load flow (LF) formulation for unbalanced, mutually coupled, three-phase active distribution systems operating under load and renewable generation uncertainties. Unlike conventional deterministic approaches, the proposed framework incorporates bounded uncertainty through a worst-case optimisation strategy to ensure reliable and secure system operation. The LF problem is formulated as a nonlinear optimisation model including robust power balance equations, voltage constraints, and distributed generator (DG) operating limits. The model is implemented in GAMS using the KNITRO solver and independently validated through a modified backward-forward sweep method developed in MATLAB to confirm solution feasibility and accuracy. The formulation is evaluated on the IEEE 33-bus, IEEE 34-bus, and IEEE 123-bus distribution systems. Simulation results demonstrate improved voltage estimation accuracy, effective power loss minimisation under uncertainty, and reduced computational burden compared with existing methods. For instance, the IEEE 33-bus system achieves a runtime of 0.021 seconds, while the robust solutions limit power losses to 2.65 pu and 0.32 pu for the IEEE 34- and IEEE 123-bus systems, respectively. These results confirm the scalability, robustness, and practical applicability of the proposed formulation for modern distribution system analysis and operation.
  Keywords: robust load flow; robust OPF; load flow; unbalanced distribution system; optimisation.
  DOI: 10.1504/IJETP.2026.10078704
   
  
• Collaborative optimisation of inter-regional new energy consumption based on Stackelberg and multi-timescale  
  by Gang Wang, Zhaoshi Zhang, Ying Yu, Rangle Wu, Xing Liu 
  Abstract: Addressing the lack of coordination mechanisms and the accumulation of deviations across multiple timescales in inter-regional new energy consumption, this paper proposes a collaborative framework integrating Stackelberg game theory and dual-timescale rolling optimisation. Through a ‘leader-followers’ architecture, adaptive adjustment based on deviation feedback, and robust tube-model predictive control (tube-MPC), the two-level game is equivalently transformed into an efficient mixed integer linear programming (MILP) solution. Experiments demonstrate that the proposed method achieves an average consumption rate of 93.3% and a root-mean-square error (RMSE) of 247 MW, providing support for the construction of an inter-regional collaborative operation paradigm.
  Keywords: inter-regional new energy consumption; new energy consumption; Stackelberg game theory; multi-timescale; tube-MPC robust control.
  DOI: 10.1504/IJETP.2026.10079412
   
  
• Cooperative UAV coverage path planning for power inspection  
  by He Tang, Jiangyi Wu, Guangdu Cen, Ke Wang 
  Abstract: With the expansion of the power grid and increasingly complex geographical conditions across regions, traditional manual power inspections face high risk, substantial labour costs, and low inspection efficiency. UAV-based power inspection offers a safer and more efficient means to monitor target areas and inspect key facilities, such as transmission lines and substations, using onboard sensor suites (cameras, LiDAR, and thermal infrared imagers). In this study, we propose a cooperative UAV power-inspection model, establish a cluster-level performance and inspection-mode framework, and compute multi-UAV cooperative inspection paths through coordinated area division and coverage-path planning. We validate the approach with real-data experiments conducted in Yangjiang City, Guangdong Province, demonstrating the effectiveness of the proposed inspection-path planning method. Specifically, the proposed regional and line-mode coverage approaches achieve 2.12.4 times higher coverage efficiency than state-of-the-art methods MFC and MSTC-STAR.
  Keywords: power inspection; multi-UAV system aerial photogrammetry; path planning; collaborative coverage path planning.
  DOI: 10.1504/IJETP.2026.10079499
   
  
• Distributed resource cloud edge collaborative control based on improved deep Q network  
  by Xin Yin, Jin Ma, Lipeng Cao, Yusen Wang 
  Abstract: In order to solve the problems of low task success rate, high average task processing time, and high bandwidth consumption in traditional distributed resource cloud edge collaborative control methods, a distributed resource cloud edge collaborative control method based on an improved deep Q network (DQN) is proposed. A multi-objective collaborative control model including total system cost, average task delay, system energy efficiency, and load balancing degree was constructed through real-time distributed resources and edge load state perception mechanism. An improved deep Q-Network model was designed by introducing a dual experience pool replay mechanism and bias correction strategy to enhance the stability and convergence efficiency of the model solution. The experimental results show that the proposed method has a task success rate of up to 98.21%, an average latency of as low as 0.33s, and bandwidth consumption controlled within 14.32Mbps, verifying its effectiveness and superiority.
  Keywords: improve deep Q network; distributed resources; cloud edge collaborative control; edge load state perception; load balancing degree.
  DOI: 10.1504/IJETP.2026.10079624
   
  
• Fuzzy optimisation for cooperative UAV-vehicle inspection of transmission towers with uncertain service times  
  by Yan Cui, Na Ren, Hongjiang Wang, Pizhen Zhang 
  Abstract: This paper addresses a cooperative UAV-ground vehicle routing problem for transmission tower inspection, focusing on two domain-specific challenges: the structural differences between tower types (suspension vs. strain towers) that lead to differentiated on-site service times, and the terrain-induced elevation variations that constrain UAV flight path feasibility. To tackle this, a fuzzy chance-constrained programming model is developed, which distinctively integrates tower type-differentiated fuzzy service times and a terrain-induced flight gradient constraint into a comprehensive routing optimisation framework. A two-layer algorithmic framework combining Compass-based area partitioning, OR-Tools, and fuzzy simulation is employed to solve the model. Computational experiments based on realistic 220kV transmission corridor scenarios validate the approach. The results successfully quantify the efficiency-reliability trade-off, demonstrate the algorithm's capability in attribute-aware task reallocation, and verify the generation of robust, physically feasible inspection plans. The proposed method provides a practical, domain-grounded decision-support tool for large-scale grid maintenance planning.
  Keywords: transmission tower inspection; vehicle-supported UAV routing problem; tower-type-differentiated service time; flight gradient constraint; compass-OR-tools algorithm.
  DOI: 10.1504/IJETP.2027.10079751
   
  
• Design and modelling of robust sliding mode controller for a high gain non-isolated DC-DC converter system powered by fuel cells  
  by Chintalapudi K. Krishna, Attuluri R. Vijay Babu 
  Abstract: To maintain stable voltage regulation during dynamic load fluctuations and changing fuel cell outputs, fuel cell-powered EVs require extremely reliable and efficient power converters. Conventional control techniques frequently have inadequate disturbance rejection and poor transient responsiveness, which results in observable output voltage ripple and deviations. This paper presents a sliding mode controller (SMC) for a single-switch non-isolated DC-DC boost converter designed for fuel cell-based EV applications in order to address these issues. The SMC approach provides great resilience against parameter uncertainties, external disturbances, and the nonlinear behaviour typical of fuel cells by ensuring that system trajectories converge to and stay on a predetermined sliding surface. Accurate voltage regulation, significant ripple reduction, and quick transient response are some of the main advantages. According to simulation results, the suggested SMC increases system efficiency to 98.3% while successfully maintaining output voltage firmly around 200 V, within ±0.7 V, during load transitions from 1.25 A to 2.5 A. SMC is a very dependable control technique for advanced fuel cell EV power systems since it performs better than conventional controllers in terms of stability, ripple suppression and dynamic response.
  Keywords: fuel cell; EV; voltage regulation; dynamic response; sliding mode controller; SMC; ripple reduction; non-isolated DC-DC converter.
  DOI: 10.1504/IJETP.2027.10079752
   
  
• A spatiotemporal framework for disaster anomaly identification and power grid impact prediction using multi-source meteorological monitoring data  
  by Saipeng Zhang, Yufeng Tai, Junbo Liu, Changlong Gao, Jingyao Luan, Chunshen Wang, Yufeng Liu 
  Abstract: This paper introduces a new spatiotemporal deep learning (DL) model that is artificial intelligence (AI)-driven and detects abnormal patterns related to disaster and power grid impact prediction based on multi-source meteorology and energy data. The design implements the tools of region embedding to identify features in space and long short-term memory (LSTM) networks to identify temporal patterns. Then a temporal convolutional network (TCN) is used to model the dependency at long-range and classify impacts, full pre-treatment, such as data cleaning, coding, and normalisation, interoperability between heterogeneous data sources and feature fusion allows complete spatiotemporal representation. The findings show a high level of performance based on the classification accuracy of 98.15%, R² = 0.9639, and low prediction errors (MAE = 0.1438, RMSE = 0.1736). The forecasts of the model are used in proactive decisions in the management of power systems. Its scalable, data-driven solution improves company effectiveness and power resilience.
  Keywords: temporal convolutional network; TCN; power forecasting; energy demand; disaster identification; monitoring data; impact prediction.
  DOI: 10.1504/IJETP.2026.10079819
   
  
• Path planning for urban transmission tower inspection using a cooperative UAV-vehicle team and a two-phase optimisation algorithm  
  by Yan Cui, Shun Yu, Hongjiang Wang, Pizhen Zhang, Na Ren 
  Abstract: Regular inspection of transmission towers is crucial for power grid safety, while manual inspections are hindered by safety risks, inaccuracies, and high cost. To address these challenges, this study employs a cooperative unmanned aerial vehicle (UAV) and ground vehicle system for urban tower inspection. The vehicle serves as a mobile base for UAV launch, landing, and battery replacement, while the UAV must inspect all assigned towers and return before its battery depletes. A mixed-integer nonlinear programming (MINLP) model is formulated to minimise the total operational distance under routing and endurance constraints. An efficient two-phase algorithmic framework is proposed: the convergent optimisation via most promising area stochastic search (COMPASS) algorithm partitions the inspection area around selected parking points; a linear programming model solved with Google OR-Tools determines the optimal inspection sequence within each partition. This decomposition converts the original MINLP into a simpler integer program and a set of binary programs, enhancing computational tractability. Numerical experiments on four scenarios demonstrate that the proposed method obtains effective high-quality solutions within reasonable computation time across various numbers of parking points and area sizes. The results also indicate that increased UAV endurance can further reduce total inspection time.
  Keywords: transmission tower inspection; UAV-vehicle cooperation; two-phase optimisation algorithm; COMPASS algorithm; cooperative path planning.
  DOI: 10.1504/IJETP.2027.10080010
   
  
• Research on core technology of regenerative braking for electric vehicles based on intelligent road condition recognition algorithm  
  by Zhiqiang Xu 
  Abstract: This research takes the intelligent road condition recognition algorithm as the core to explore the optimisation path of electric vehicle feedback braking technology. By adjusting the conduction sequence of the inverter switch tube, the precise control of the braking reverse torque is realised, and the braking kinetic energy is efficiently fed back to the energy storage components. The simulation and experimental results show that the technology can improve the braking energy recovery efficiency of electric vehicles by 18%-22%, and increase the endurance mileage by 15%-18% under urban conditions. As the key technology of vehicle intelligence, intelligent road condition recognition algorithm can not only improve driving safety, but also provide real-time road condition data for the cooperative operation of on-board systems and optimise the overall control logic of the system. The research adopts the method of literature research, combined with theoretical analysis and experimental verification, to ensure the feasibility and logic of the technical scheme, and provide practical support for improving the energy efficiency of electric vehicles.
  Keywords: intelligent road condition recognition algorithm; electric vehicle; regenerative braking; core technology.
  DOI: 10.1504/IJETP.2026.10078611
   
  
• Optimisation of new energy vehicle traffic flow and application of hybrid multi-objective evolutionary algorithm based on internet of things simulation of urban mobility simulation platform  
  by Tengfei Li 
  Abstract: The current optimisation of traffic flow faces challenges, such as increased congestion at intersections and the inability of traditional signal control strategies to adapt to dynamic mixed traffic. With the increasing proportion of new energy vehicles, the exhaust emission model needs to be reconstructed, and existing methods often focus on single objective optimisation, ignoring the synergistic trade-off between delay and emissions. This paper selects a multi-objective optimisation (MOO) model consisting of average vehicle delay and average exhaust emissions. It uses backpropagation neural network (BPNN) to improve non dominated sorting genetic algorithm II (NSGA-II), and utilises urban traffic simulation (SUMO) traffic model to establish and optimise micro road traffic models of intersections. According to the analysis of the optimisation results, the signal cycle at the optimised intersection has been shortened by 16.4%, the average delay time has been reduced by 14.9%, and the exhaust emissions have been reduced by 8.5%.
  Keywords: traffic flow optimisation; non-dominated sorting genetic algorithm II; simulation of urban mobility; backpropagation neural network; traffic signal time; multi-objective optimisation; MOO.
  DOI: 10.1504/IJETP.2026.10077886
   
  
• Intelligent fault area identification in distribution networks: a joint graph convolutional network approach  
  by Yunyun Ma 
  Abstract: Traditional fault identification methods face challenges such as topological blind spots, computational bottlenecks, and hyperparameter sensitivity, hindering accurate and rapid fault area localisation. To address these limitations, this paper proposes an intelligent identification method based on a joint graph convolutional neural network. The approach constructs a feature matrix integrating multi-source measurement data and introduces a topological adjacency matrix to characterise structural correlations among nodes. By incorporating Bayesian classification and decision tree algorithms, adaptive allocation of feature weights is achieved. Furthermore, a fuzzy optimisation layer is embedded into the network to quantify fault feature uncertainty using membership functions, significantly enhancing model robustness under noisy or incomplete data conditions. Experimental results demonstrate that the proposed method achieves up to 95% accuracy and a response time of 0.42 seconds in fault section identification.
  Keywords: graph convolutional neural network; distribution network; fault area identification; decision tree algorithm.
  DOI: 10.1504/IJETP.2026.10077828
   
  
• Study on large vision model for key feature recognition in power equipment  
  by Huaqing Cao 
  Abstract: In this paper, a key feature recognition algorithm for power equipment based on visual large model is proposed. Firstly, collecting multi-source image data from devices through a multidimensional perception network, using an improved side window guided filtering method for image enhancement, and combining OTSU threshold segmentation to achieve target area extraction; then, use the DINOv2 visual big model for self supervised feature extraction; finally, the optimised whale swarm algorithm is introduced for key feature selection, which improves the accuracy of identifying key features of power equipment while reducing feature dimensions. The experimental results show that the proposed algorithm has a maximum signal-to-noise ratio of 48.75 dB for power equipment images, a maximum accuracy of 97.5% for key feature recognition, and a minimum recognition time of only 3.16 s.
  Keywords: visual big model; power equipment; key feature recognition; improved side window guided filtering; OTSU threshold segmentation; DINOv2 visual big model; optimised whale swarm algorithm.
  DOI: 10.1504/IJETP.2026.10077827
   
  
• Adaptive correction of AC power measurement error based on quantum voltage  
  by Lihua Zhong, Jingming Zhao, Yuyao Yang, Lei Feng, Jianzhong Huang 
  Abstract: In complex power grid environments, traditional static calibration methods face challenges such as nonlinear interference and random noise, resulting in significant calibration errors. Therefore, a quantum voltage based adaptive correction method for AC power measurement error is proposed. Firstly, design a hybrid reconstruction mechanism based on 12 level step waves and 60 point differential sampling, combined with quantised averaging and FFT to achieve high-precision reproduction of AC power signals; secondly, the error transfer formula is derived using commutation differential measurement technology to suppress the impact of insufficient component accuracy; finally, an adaptive correction strategy based on EM-KF is proposed, which eliminates gross errors through the Romanovsky criterion and achieves real-time error correction in dynamic environments using Bayesian models. The experimental results show that the proposed method achieves a relative error reduction percentage of 32.45%-37.23% in the test, and only requires 86.78-95.67 ms in the mutation response correction time test.
  Keywords: quantum voltage; communication power; measurement error; adaptive correction.
  DOI: 10.1504/IJETP.2026.10078053
   
  
• Intelligent UAV pod for rapid inspection of power transmission lines  
  by Penghui Huang, He Tang, Jiangyi Wu, Jibin Zhang 
  Abstract: With the rapid growth of electricity demand, efficient inspection and maintenance of power transmission lines have become critical. Traditional manual inspections are costly, time-consuming, and constrained by weather, terrain, and safety risks. This paper presents an intelligent UAV pod for rapid inspection of power lines, integrating a visible-light camera, inertial measurement unit (IMU), GNSS receiver, and onboard computing unit. The system fuses visual-inertial and GNSS data to achieve high precision real-time pose estimation and 3D mapping, while its lightweight design extends UAV flight endurance and operational flexibility. Extensive tests in both conventional and low-texture environments demonstrate the system's ability to generate accurate maps under challenging conditions. Field experiments along transmission line corridors show that the pod can efficiently reconstruct georeferenced 3D point clouds, supporting timely fault detection and post-disaster assessment. The proposed system not only enhances inspection efficiency and accuracy but also provides a practical solution for digital and intelligent management of power infrastructure.
  Keywords: intelligent UAV pod; real-time mapping; positioning technology; power transmission line inspection; lightweight design.
  DOI: 10.1504/IJETP.2026.10078322
   
  
• Enhanced two-stage (photovoltaic-diesel) pumping system using DC-link voltage-based speed reference and backstepping MPPT controller  
  by Rachida Kebbache, Abdelhamid Ksentini, El-Bahi Azzag, Saliha Maarouf 
  Abstract: This paper presents a hybrid water pumping system that integrates photovoltaic (PV) and diesel sources to supply a three-phase induction motor. The motor is controlled using a field-oriented control (FOC) strategy, where the reference speed is derived from the DC-link voltage rather than form the PV panel power. This simplified approach ensures efficient operation over the entire speed range without requiring precise motor efficiency data. To further improve performance, a backstepping controller is implemented for maximum power point tracking (MPPT), thereby improving energy extraction and minimising power fluctuations. The proposed method is benchmarked against conventional techniques, including incremental conductance (INC) and sliding mode control (SMC). Simulation results demonstrate the superiority of the backstepping-based MPPT, achieving up to 99.82% efficiency, a rapid reference speed settling time of 0.42 seconds, and higher pump power under variable irradiation. Overall, the system ensures reliable, efficient operation while reducing fossil fuel dependency and CO₂ emissions.
  Keywords: PV water pumping system; three-phase induction motor; maximum power point tracking; MPPT; backstepping controller; DC-link voltage reference.
  DOI: 10.1504/IJETP.2027.10078425
   
  

Special Issue on: Advancing Sustainable Development Banking Strategies Energy Transition and Green Economies

• Empowering communities: a case study of sustainable solutions to address load shedding in South Africa  
  by Chané De Bruyn  
  Abstract: South Africans have been plagued by varying stages of load shedding, with 2023 seeing a record-breaking 332 days of load shedding. This prolonged crisis has had severe repercussions, impacting local economic development, water services, food security, education and healthcare. As it affects businesses across all sectors, productivity, employment, and overall growth, addressing this issue is crucial for sustainable development and maintaining a thriving local economy. Using a case study approach, this paper assesses South Africa’s first 'smart town’, that through collaboration and innovative measures have been able to manage their own electricity demand, ensuring the continuation of business and economic activity. This study examines the significance of empowering local communities, discusses important tactics for encouraging community involvement, and provides a compelling case study of sustainable development projects led by empowered communities.
  Keywords: community empowerment; loadshedding; community led development; sustainable development; community; South Africa.
  DOI: 10.1504/IJETP.2025.10071921
   
  
• The impact of AI on China’s energy policy for EVs  
  by Klemens Katterbauer, Sema Yilmaz, Hassan Syed, Gözde Meral 
  Abstract: China’s national energy policy is fundamentally oriented toward achieving a low-carbon economy, with electric vehicles (EVs) serving as a key pillar in this transition. AI plays a crucial role in enhancing energy efficiency, optimizing grid integration, accelerating the widespread adoption of EVs. This report provides a comprehensive analysis of AI’s contributions to China’s EV-related energy policies, examining its applications, benefits, challenges, future developments. AI technologies are instrumental in facilitating China’s ambition to achieve carbon neutrality by 2060. In the energy distribution, AI significantly enhances grid stability through the implementation of smart charging systems, vehicle-to-grid(V2G) technologies, predictive analytics. However, several challenges must be addressed to realize these advantages. These are data security, the digital divide in rural areas, the high costs, the need for regulatory frameworks that balance innovation with compliance. Consequently, AI represents a transformative tool in advancing China’s EV adoption and aligning its energy policies with long-term sustainability objectives.
  Keywords: artificial intelligence; energy policy; electric vehicles; China; carbon neutrality.
  DOI: 10.1504/IJETP.2025.10072079
   
  
• NEOM Smart City the urban oasis in Saudi desert (green energy technologies, policies and strategies)  
  by Somayya Madakam, Shidhar M. Samant, Pragya Bhawsar 
  Abstract: Today, the cities are facing an energy crisis as day-to-day urban operations including home automation, manufacturing, transportation, water, entertainment and others depend on fossil fuel energy. These urban challenges are not just faced by a particular city, nation but also across the globe including Saudi Arabia. In light of all the above challenges, the present manuscript highlights how the new urban energy solutions can meet the present needs and can also help in sustainable urban development. The paper is based on the secondary data collected through reports, white papers, blogs, snaps, and videos on ‘NEOM’. The insights from the content analysis explores NEOM Smart City’s commitment to sustainable energy technologies that reflects its ambition to set new standards in urban sustainability and environmental stewardship. By harnessing renewable energy sources, implementing smart grid technologies, promoting energy efficiency, and fostering innovation, NEOM aims to create a model city that balances environmental preservation.
  Keywords: circular economy; green building; green energy technologies; green hydrogen economy; NEOM Smart City; quality of life; QoL; smart cities; smart grids; sustainable development.
  DOI: 10.1504/IJETP.2026.10075557
   
  
• Exploring the validity of waste Kuznets curve hypothesis: evidence from Eastern European countries  
  by Durmus Çagri Yildirim, Murat Özdemir, Seda Yildirim 
  Abstract: This study focuses on examining the determinants of urban waste production in the context of the environmental Kuznets curve (EKC) theory using annual data for the period 2000-2021 for 21 Eastern European countries (Azerbaijan, Bulgaria, Czechia, Armenia, Croatia, Hungary, Moldova, Poland, Romania, Slovak Republic, Slovenia, Turkey, Greece). According to the results, it was seen while the environmental Kuznets curve (EKC) theory is valid for relatively low waste levels, it loses its validity at higher waste levels. The effects of other explanatory variables vary according to the waste level. It is thought to contribute the related literature by giving new empirical evidences for the link between economic well-being and waste generation, modelled by the environmental Kuznets curve (EKC), which predicts that waste production is inversely U-linked to economic development.
  Keywords: municipal waste management; Kuznets curve theory; sustainability.