International Journal of Energy Technology and Policy (12 papers in press)

Regular Issues

• Open Access

  Analysis on the Development Path of Rural Energy Digital Economy from the Perspective of Artificial Intelligence ( Free Full-text Access )

  by Lu Chen 
  Abstract: This article aimed to explore the development path of the energy digital economy (DE) from the perspective of artificial intelligence (AI) and predict its future development trends. With a particular emphasis on the rural context, the study examined how intelligent technologies can reshape the rural energy structure and improve the efficiency and sustainability of rural energy systems. This can promote the digitization and intelligence process of the energy industry, improve energy efficiency, and promote sustainable development of the energy industry. Therefore, this article selected Tangsteel Group as the research object, divided the energy DE development using AI technology into Group A and the energy DE development without using AI technology into Group B, and compared and analyzed Groups A and B from four aspects: efficiency, safety, cost, and accuracy. From experimental analysis, this article drew the following conclusion: the efficiency in Group A was generally above 70%, with a maximum of 91.35%, which was equivalent to the enterprise having strong control over all aspects, maximizing efficiency.
  Keywords: Digital Economy; Energy Development; Artificial Intelligence; Path Analysis; Tangshan Steel.
  DOI: 10.1504/IJETP.2025.10074603
   
  

• Open Access

  Quality Inspection of Power Transmission Towers Based on Point Cloud Registration ( Free Full-text Access )

  by Xuan Qi, Honglin Yan, Xiao Tu, Yinying Liu, Weihua Ding 
  Abstract: To address the inefficiency in feature point extraction and registration caused by the complex structure of power transmission towers, this study proposes a feature registration strategy incorporating curvature feature. Given the critical role of power transmission infrastructure in smart grid systems, accurate and efficient tower modeling is essential for ensuring structural safety and operational reliability. First, an algorithm based on normal vector angles is employed to obtain an initial set of feature points. Subsequently, high-curvature points rich in geometric information are identified and retained through Gaussian curvature analysis. This approach enhances feature distinctiveness compared to uniform sampling or intensity-based selection methods. To further enhance registration efficiency, Gaussian curvature parameters are introduced into the Random Sample Consensus (RANSAC) algorithm for preliminary matching. This integration significantly reduces the number of incorrect correspondences compared to standard RANSAC implementations. Additionally, a symmetric objective function optimizes the Iterative Closest Point (ICP) algorithm to achieve precise registration across surfaces with varying characteristics. Unlike conventional ICP, which assumes consistent surface normals, the proposed method handles asymmetric structures more effectively.
  Keywords: Iterative Closest Point (ICP); power transmission; Gaussian curvature; Random Sample Consensus (RANSAC); 3D inspection.
  DOI: 10.1504/IJETP.2025.10074604
   
  

• Open Access

  Flow Field Analysis of Agitated Displacement Tank for Cementing Equipment ( Free Full-text Access )

  by Lin Hou, Chunxia Chen, Rongxing Jiang, Wei Ji, Biao Lei, Mengfei Zhang, Yunrui Huang 
  Abstract: To address the problems of low mixing efficiency and uneven dispersion of additives in the mixing displacement tank of cementing equipment, this paper uses CFD simulation to analyse the influence of impeller parameters on flow field characteristics and optimise the mixing performance. This paper takes the cementing equipment stirring displacement tank as the research object and uses computational fluid dynamics (CFD) to analyse the mixing effect of the stirring displacement tank on liquid additives and clean water. The distribution results of liquid additives, clean water, and fluids with a flow rate greater than 0.5 m/s are obtained. Comparative analysis of the simulation results showed that increasing the rotational speed is the best way to enhance the fluidity of the fluid in the tank. The inclined impeller is conducive to the lateral flow of the fluid, while the vertically arranged impeller is conducive to the vertical upward movement of the fluid.
  Keywords: Agitated displacement tank; Agitating performance; Rotational speed; Impeller.
  DOI: 10.1504/IJETP.2025.10074704
   
  

• Open Access

  Reinforcement Learning-Based Optimization of Intelligent Battery Thermal Management System data ( Free Full-text Access )

  by Junda Ge 
  Abstract: Effective battery thermal management (BTMS) is vital for safety, longevity, and performance, yet rule-based or PID schemes falter under rapid operational changes. We propose an RL-driven BTMS that learns control policies in a high-fidelity thermal simulator. The agent observes cell temperature, state of charge, and ambient conditions, and outputs continuous cooling/heating commands. We adopt deep deterministic policy gradient to cope with nonlinear dynamics and continuous actions. For safety and generalisation, the learned policy is fused with a rule-based controller via a confidence-aware hybrid scheme. Tests on real driving cycles show 3.2 faster response, 18.4% lower temperature-tracking MAE, and 18.0% less cooling energy than conventional BTMS, improving regulation efficiency and robustness. These results indicate deep RL with hybrid control is a scalable, adaptive, and safety-aware solution for intelligent BTMS.
  Keywords: Battery Thermal Management System; Reinforcement Learning; Deep Deterministic Policy Gradient; Energy Efficiency; Electric Vehicles; Intelligent Control; Thermal Optimization.
  DOI: 10.1504/IJETP.2025.10074834
   
  
• Sustainable fuels for thermal power generation for sustainable energy supply in a low-density population cluster  
  by Izuchukwu Francis Okafor, Nwachukwu Paul Nwachukwu, Ifeanyi Wilfred Okonkwo, Ikenna David Okeke 
  Abstract: Fossil fuels for thermal power generation have been the dominant fuels for energy generation, which are unsustainable and harmful to the environment. This study examined thermal power generation with biomass briquette fuel and solar thermal energy for sustainable power supply in a low-density population cluster. The power supply situation in Nigeria was highlighted. Regenerative Rankine thermal plant with biomass fuel and concentrated solar thermal power (CSTP) plant was examined for improvement in thermal efficiency. Engineering equation solver was used in solving the mathematical formulations generated in this study. It was found that the thermal efficiency of the plant increased with temperature. Potentially, the plant can operate at peak thermal efficiency if the operating parameters are optimized, and can switch to either biomass fuel to handle solar intermittency issues or to solar thermal to conserve biomass fuel, indicating the novelty of hybrid fuel sources for sustainable thermal power generation.
  Keywords: solar thermal power; regenerative Rankine; power plant; thermal efficiency; power generation.
  DOI: 10.1504/IJETP.2025.10069114
   
  
• Supply chain governance for sustainable solar energy system: impact of artificial intelligence  
  by Monica Bhatia, Pradyumn Chaturvedi, Vikas Khare 
  Abstract: This paper delves into various facets of supply chain management for solar energy systems, with a particular focus on the profound impact of AI. Paper explore the current state of solar energy supply chain management, emphasizing the need for improved efficiency, environmental sustainability, and reliability. The creation of a semantic network for supply chain management of solar energy systems highlight the significance of structured knowledge representation, fostering intelligent decision- making and real-time responsiveness to dynamic operational challenges. Paper examine into the push-pull view of the solar energy supply chain, where AI plays a pivotal role in orchestrating demand-driven and efficient operations. Additionally, this paper includes a comprehensive SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of supply chain management in the solar energy sector. The SWOT analysis identifies critical areas for improvement and underscores the transformative potential of AI.
  Keywords: logistic management; inventory management; SWOT analysis; supplier selection; artificial intelligence.
  DOI: 10.1504/IJETP.2025.10070456
   
  
• Solar PV panel efficiency enhancement through particle swarm optimisation assisted adaptive reconfigurable connection system  
  by Amarendra Kumar  
  Abstract: This paper presents a novel scheme for efficiency enhancement of solar PV arrays through optimisation assisted adaptive connection scheme. Leveraging the power of particle swarm optimisation (PSO), the proposed scheme focuses on dynamically adapting electrical connections of solar panels to maximise efficiency. The potential connection configuration scheme allows real-time adjustments based on environmental factors such as shading, temperature variations, and panel mismatches. The comparative study presented in this paper demonstrates the efficacy of the ARCS-PSO approach in achieving remarkable efficiency improvements compared to static or fixed connection configurations. The simulation results show that the ARCS-PSO scheme consistently outperformed fixed connection configurations under varying conditions. Under uniform irradiance, the proposed method results in higher energy yield. In challenging cases like partial shading and temperature variations, ARCS-PSO demonstrated increased power output. These findings validate the potential of ARCS-PSO in optimising solar energy systems under diverse operating conditions.
  Keywords: adaptive reconfigurable connection system; ARCS; particle swarm optimisation; PSO; solar panel arrays; energy efficiency; dynamic connectivity.
  DOI: 10.1504/IJETP.2025.10072551
   
  
• Optimal operation and profit maximisation of a wind-integrated energy hub under risk-taking strategy: a case study on renewable energy integration and storage capacity analysis  
  by Hailiang Rong, Jia Wang 
  Abstract: This study investigates the optimal operation of a wind-integrated local energy hub aimed at maximising profit while coordinating with electrical, thermal, and natural gas networks. The system comprises combined heat and power (CHP) units, heat pumps, and both electrical and thermal energy storage, with wind energy as the sole renewable source. To address wind speed uncertainty, information gap decision theory (IGDT) was applied under both risk-averse and risk-taking strategies. A mixed-integer linear programming (MILP) model was developed and solved using GAMS, incorporating detailed constraints related to energy balance, storage, ramping, and market exchanges. Sensitivity analysis across twenty scenarios revealed that risk-taking strategies yielded higher profits but required more operational adjustments. Notably, particularly electrical, significantly enhanced economic outcomes. Demand response programs such as time-of-use (TOU) and direct load control (DLC) were also integrated to improve operational flexibility. The findings demonstrate that robust, market-responsive strategies enhance both profitability and renewable energy utilisation.
  Keywords: wind energy; profit maximisation; energy storage; info-gap decision theory; risk-taking strategy; demand response; mixed-integer linear programming; MILP; renewable energy integration.
  DOI: 10.1504/IJETP.2025.10074011
   
  
• Energy-saving design and new energy utilisation evaluation of building engineering, heating, ventilation, and air conditioning based on internet of things technology  
  by Xiaowen Bian, Peimin Zhao, Bingqing Xue, Song Zhou 
  Abstract: As building sizes expand and demands for indoor comfort continue to rise, traditional heating, ventilation, and air conditioning (HVAC) systems face challenges such as energy waste, inefficiency, and a lack of intelligent control, making them unable to meet the energy-saving, environmentally friendly, and efficient operation requirements of modern buildings. This paper leverages internet of things (IoT) technology to connect heating stations, employees, and a central control system to form an integrated heating and energy-saving system. Furthermore, a ten-fold cross-validation method is introduced to optimise parameters, which are selected based on the characteristics of the optimal input. The experimental results showed that the model root mean squared error (RMSE) corresponding to the results of the energy prediction model decreased from 8.9338 to 4.2271, while the residual standard error (RSE) decreased from 0.3202 to 0.1105. It could be seen that the predictive ability of the model was significantly improved.
  Keywords: construction engineering; heating; ventilation; and air conditioning; numerical simulation; internet of things; IoT; support vector regression algorithm; SVR; cross-validation method.
  DOI: 10.1504/IJETP.2025.10074093
   
  
• Carbon Neutrality: Evaluating the Impact of Carbon Emissions Trading Policy on Green Energy and Environmental Economic Efficiency  
  by Yanmei Li, Yi Zhang 
  Abstract: Amid rising global concern over climate change, China launched its carbon emissions trading pilot (CETP) in 2011 to promote green energy and low-carbon development. This study investigates the impact of CETP on green energy environmental and economic efficiency (GEEEE), using a new measurement index and the slack-based measure super-efficiency model. Treating the policy as a quasi-natural experiment and analysing its implementation across seven pilot provinces between 2009 and 2023, a difference-in-differences approach is employed to assess its effectiveness. We evaluate both the dynamic and spatial heterogeneity of the policys outcomes by examining variations across regions and industries. The findings suggest that the CETP significantly enhances GEEEE performance in the designated pilot areas. Moreover, this effect is amplified by three key mechanisms: technological innovation, energy and industrial restructuring, and regional policy responsiveness. These findings provide theoretical and practical insights for policymakers in designing carbon reduction strategies and advancing global climate goals.
  Keywords: Carbon emissions trading policy; Carbon neutrality; Low carbon development; Energy Green Transition; Super-efficiency model with undesired outputs; DID model.
  DOI: 10.1504/IJETP.2025.10074895
   
  

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