International Journal of Energy Technology and Policy (17 papers in press)

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
   
  
• Transformer-customer relationship identification under limited observability: a smart meter data-driven Gaussian mixture model approach  
  by Limin Yin, Fengyue Zhao, Ruifeng Li, Dongbo Guo, Guanyu Yan, Mulin Han, Yinghua Sun, Kostas Soumalas 
  Abstract: To address the problems of low identification accuracy and poor performance in traditional distribution-network topology identification methods based on the similarity of voltage and power-change trends after high-penetration distributed photovoltaics (DPV) integration, this paper proposes a new data-driven topology identification method for partially visible distribution networks using smart-meter data. With load-side smart meter data, an enhanced agglomerative nesting (AGNES) clustering algorithm is employed to precisely distinguish between PV and non-PV users within distribution-transformer areas. By analysing the similarity of electricity-consumption behaviours among users in adjacent transformer areas and fitting the PV generation sequences of these areas, the Gaussian mixture model (GMM) is introduced to extract user-load features, fit the power generation of PV users based on the PV generation sequence of the transformer areas, and estimate the load-consumption data of PV users. On this basis, a multiple linear regression model is constructed and solved, achieving accurate identification of the transformer-customer relationship in the transformer areas. Verification with a real-world case in Zhejiang Province, China demonstrates that this method achieves high identification accuracy and has strong application value in partially visible distribution networks.
  Keywords: distributed PV; low-voltage distribution network; partially visible distribution networks; transformer-customer relationship.
  DOI: 10.1504/IJETP.2027.10080156
   
  
• Artificial intelligence-based SCD file validity and virtual circuit correctness detection technology in energy and power scenarios  
  by Yunfei Hong, Yiqiang Jiang, Gang Bai, Qing Hu, Xiaoyang Deng, Shurong Ling 
  Abstract: This paper proposes an AI-based approach to address inefficiencies in rule-based methods and the trade-off between model accuracy and lightweight edge deployment for defect detection in SCD files in smart substations. A high-speed XML parser constructs a ‘signal source-signal sink-associated IED’ path chain from SCD files and converts their topology into a 32x32 feature matrix. The SMOTE algorithm balances the number of defect samples represented as classified signals along the path chain. A four-layer convolutional neural network extracts local features and classifies defects. To reduce computational complexity, a knowledge distillation framework based on CodeBERT is introduced for model compression. Experimental results show 93.8% accuracy, with the compressed model reduced to 2.8M parameters and a detection time of 5.7 seconds per file. The method supports all voltage levels and enables intelligent edge-side diagnostics and early warning for secure and stable power grid operation.
  Keywords: SCD files; virtual loop detection; convolutional neural networks; CNNs; knowledge distillation; smart substations.
  DOI: 10.1504/IJETP.2027.10080301
   
  
• Graph neural network-driven anomaly detection framework for UAV inspection data  
  by Jinming He, Yida Gao, Ping Wang, Ruijun Li 
  Abstract: UAV-based inspection has become essential for monitoring power and industrial infrastructures, yet the noisy, multi-modal nature of UAV data makes reliable anomaly detection challenging. This paper proposes a graph neural network-driven framework that integrates spatiotemporal graph modelling with a spectral-attention hybrid GNN to jointly capture global manifold structure and local defect-sensitive variations. A graph autoencoder computes anomaly scores via reconstruction divergence, and a theoretical analysis proves that the hybrid propagation amplifies deviations from graph smoothness, ensuring anomaly separability. Experiments on three real-world UAV datasets — UAV-Grid, UAV-Tower, and UAV-Industrial— show that the proposed method consistently surpasses classical baselines (Isolation Forest, DeepSVDD) and graph-based models (GCN-AE, GAT-AE). Robustness and ablation studies further confirm the necessity of both spectral and attention components, while volcano-plot analysis demonstrates significant improvements in discriminative margin and statistical confidence. The results highlight the framework’s effectiveness for intelligent UAV-based monitoring.
  Keywords: graph neural networks; GNNs; UAV inspection; spatiotemporal graph modelling; anomaly detection; deep learning; intelligent monitoring.
  DOI: 10.1504/IJETP.2027.10080320
   
  

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.
  DOI: 10.1504/IJETP.2026.10080081
   
  
• Smart solutions for sustainable tourism with the internet of things  
  by Rúben Folha, António Abreu, Manuel Pérez Cota, Agostinho Sousa Pinto, Maria José Angélico Gonçalves  
  Abstract: Sustainable tourism seeks to minimise environmental impacts while enhancing social and economic benefits. Internet of things (IoT)-enabled smart tourism supports these goals through real-time data collection, resource optimisation, automated energy control, waste management and improved visitor experiences. This paper presents a systematic literature review, following the SALSA framework, of studies published between 1997 and 2024 and retrieved from Scopus, Web of Science, IEEE Xplore, Google Scholar, e³, and the International Journal of Energy Technology and Policy. It examines IoT applications across tourism domains, their sustainability contributions, and emerging trends and challenges. Findings show that applications predominantly concern smart hospitality, destination management and environmentally efficient operations. However, empirical validation remains limited, and sustainability benefits are frequently implied rather than systematically assessed. By integrating technological, environmental and service-oriented perspectives, the review identifies research gaps, calls for closer alignment between digital innovation and sustainability frameworks, and provides directions for researchers, policymakers and tourism stakeholders.
  Keywords: tourism; sustainable tourism; smart tourism; digital transformation; internet of things; IoT.