Research Articles (Electrical, Electronic and Computer Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1693

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The κ-μ/gamma-Rayleigh fading model : a composite fading model for powerline-wireless communication channels
Mokise, Kealeboga L.; Myburgh, Hermanus Carel (Wiley, 2025-11-25)
Statistical distributions are frequently used to model fading effects introduced by the communication channel on the received signal. Some distributions are directly derived from physical propagation models, while others are adapted from statistics and applied to model fading based on their goodness-of-fit to measurements or on account of their mathematical simplicity. In this paper, a line-of-sight (LOS) shadowed κ-µ/gamma-Rayleigh (κ-µ/GR) is proposed and thoroughly investigated. The GR distribution was selected for its mathematical simplicity and flexibility. Closed-form expressions for fundamental statistics such as the probability density function (PDF) and cumulative distribution function (CDF) are derived for the κ-µ/GR fading model. Additionally, analytical expressions for higher-order moments, including the amount of fading (AF) and the moment generating function (MGF), are provided in closed-form expressions. Performance measures of interest, such as outage probability (OP), average symbol error probability (ASEP), and average channel capacity, are derived in closed-form for communication systems operating under the κ-µ/GR channel fading conditions. The validity and utility of the proposed composite fading model for characterizing composite fading behavior observed in hybrid powerline-wireless communication (PLC-WLC) channels are demonstrated through an extensive series of theoretical comparisons with experimental PLC-WLC measurements. Hybrid PLC-WLC channel measurements were performed in various environments, and PLC-WLC propagation scenarios were classified according to the cable branching characteristics of the PLC segment of the hybrid PLC-WLC channel. The goodness-of-fit of the proposed composite fading model was evaluated using the Kullback-Leibler (KL) divergence test. The results revealed that the proposed composite fading model exhibited an excellent fit to the fading conditions encountered in hybrid PLC-WLC channels. Compared with other existing composite fading models, the κ-µ/GR model provided the most accurate fitting results for measurements in large indoor environments, for which the propagation conditions present strong LOS signal components and weak scattered signal components. Furthermore, it was concluded on the basis of the obtained results that increased branching and terminations in the PLC channel of a PLC-WLC propagation environment lead to increased shadowing and multipath fading effects on the received signal and, consequently, to increased composite fading.
A two-level hierarchical optimization framework for grid-connected photovoltaic-wind-battery systems in greenhouse energy management
Ren, Zhiling; Xu, Meng; Zhao, Zilong; Wang, Xinran; Guo, Jia; Dong, Yun (Elsevier, 2026-06)
Greenhouse operations are energy-intensive and face increasing pressure from high operational costs, carbon emissions, and grid reliability constraints. This study develops a grid-connected photovoltaic-wind-battery hybrid energy system and proposes a two-level hierarchical optimization framework for greenhouse energy management. At the upper level, greenhouse operations are optimized using two alternative strategies: energy demand minimization, which aims to reduce heating, cooling, and ventilation loads, and energy expense minimization, which focuses on minimizing energy costs under time-of-use electricity tariffs. At the lower level, energy system scheduling is addressed through renewable energy utilization maximization and comprehensive cost minimization strategies, the latter accounting for electricity purchases, battery degradation, and carbon emissions. Simulation results demonstrate that the comprehensive cost minimization strategy achieves the best overall balance between economic performance and environmental benefits, reducing total operational costs by 45.30% and carbon emissions by 69.25% compared with the baseline. Sensitivity analysis further reveals that the battery unit cost is the most influential factor affecting the economic performance of the system. The proposed framework provides practical guidance for designing cost-effective and low-carbon greenhouse energy systems, supporting reliable and sustainable energy networks.
Power flow and reliability analysis of a non-isolated PV/grid connected quasi resonant converter for off-board EV charging station
Harini, S.; Chellammal, N.; Bansal, Ramesh C. (Taylor and Francis, 2026)
Growing awareness of greenhouse gas emissions and exhaustion of fossil fuels leads to the adoption of electric vehicles (EVs). However, the major limitations with respect to EV technology are the driving range and charging time. Also, the proliferation of EVs overloads the power grid and paves the way of incorporating renewable energy sources and energy storage devices. The use of multiple sources necessitates the deployment of compact, low-cost, and high-power electronic converters. This paper proposes a novel configuration of a Multi-Source Non-Isolated Quasi-Resonant Converter (MSNQRC) to overcome the limitations in the charging infrastructure. Due to the presence of a quasi-resonant network, the proposed MSNQRC with a single switch can achieve high voltage gain even at low-duty cycles, provide continuous current and low voltage stress. In addition, this paper elaborates on the parametric design of the converter along with PV modeling based on load ratings and the reliability research of MSNQRC by analysing component failures. A prototype model of 300W is built to verify the efficacy of the designed converter model. The results thus obtained validate the designed model and prove that the proposed system can be used for off-board battery charging systems.
Climate-adaptive energy strategies for sustainable greenhouse systems : a Köppen-based systematic review
Dong, Yun; Ye, Xianming; Lin, Dong; Zhang, Lijun; Xia, Xiaohua (Elsevier, 2026-03)
Greenhouses are essential for enhancing crop yields and enabling year-round production, but their high energy intensity and climate-sensitive demand challenge sustainability. To address the lack of climate-stratified evidence, we conduct a systematic review of climate-adaptive energy approaches for greenhouse systems structured by the Köppen climate classification (KCC). We searched the Web of Science (2019–2024) using Topic “greenhouse”, limiting to articles and refining by the “Citation Topic Micro: Greenhouse” filter; 276 records were identified and 268 articles were retained after title and abstract screening. The evidence is organized into four domains: (1) microclimate modeling and decision-support tools, (2) passive design and device-assisted enhancements, (3) active operational optimization, and (4) renewable energy integration. Results reveal climate-specific patterns: cold and arid regions most consistently benefit from insulation, thermal screens, phase-change storage, and solar–thermal-assisted heating; temperate and tropical climates increasingly adopt advanced control, including model predictive control and data-driven/learning-based controllers, to coordinate multi-variable microclimate-energy trade-offs. Renewable integration is expanding across zones, yet harmonized techno-economic and life-cycle assessments remain limited. This KCC-based synthesis supports region-specific design and operation decisions and highlights priorities for future research and deployment. HIGHLIGHTS • Climate-based framework for greenhouse energy. • Köppen classification applied to greenhouse sustainability. • Passive, active, and renewable approaches systematically reviewed. • Climate-specific energy patterns identified across global zones. • Guidance for sustainable, low-carbon greenhouse production.
Comparative analysis of various reconfiguration strategies of PV array in partial shading conditions: a review
Gautam, Vaishali; Khatoon, Shahida; Jalil, Mohd Faisal; Bansal, Ramesh C. (Taylor and Francis, 2026)
Photovoltaic system performance depends on factors like bypass diode topology, array size, arrangement, and shading intensity. Partial shading causes mismatch losses and reduced power production. Though many methods address this, they may not fully optimize power. Reconfiguring PV modules offers a promising solution to minimize power loss. In this paper, major Static and Dynamic reconfiguration strategies have been discussed. The methodology, benefits, and limitations of each approach are compared and evaluated. The research set out to compare and contrast the effectiveness of static and dynamic reconfiguration approaches with regards to energy output, shading losses, and mismatch losses. The results showed that dynamic reconfiguration was more effective in reducing shading losses and increasing energy yield compared to static reconfiguration. This article aims to help researchers and readers in gaining an understanding of the available reconfiguration strategies and factors that affect their selection. Overall, this article is a valuable resource for researchers, engineers, and professionals working in the field of renewable energy, particularly in the area of PV systems.
A systematic review of hierarchical control frameworks in resilient microgrids : South Africa focus
Wattegama, Rajitha; Short, Michael; Aggarwal, Geetika; Al-Greer, Maher; Naidoo, Raj (MDPI, 2026-02)
This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure.
Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions
Konduru, Sudharshan; Naveen, C.; Bansal, Ramesh C. (Taylor and Francis, 2026)
Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.
Fuzzy logic-based data flow control for long-range wide area networks in Internet of Military Things
Kufakunesu, Rachel; Myburgh, Hermanus Carel; De Freitas, Allan (MDPI, 2026-02)
The Internet of Military Things (IoMT) relies on Long-Range Wide Area Networks (LoRaWAN) for low-power, long-range communication in critical applications like border security and soldier health monitoring. However, conventional priority-based flow control mechanisms, which rely on static classification thresholds, lack the adaptability to handle the nuanced, continuous nature of physiological data and dynamic network states. To overcome this rigidity, this paper introduces a novel, domain-adaptive Fuzzy Logic Flow Control (FFC) protocol specifically tailored for LoRaWAN-based IoMT. While employing established Mamdani inference, the FFC system innovatively fuses multi-parameter physiological data (body temperature, blood pressure, oxygen saturation, and heart rate) into a continuous Health Score, which is then mapped via a context-optimised sigmoid function to dynamic transmission intervals. This represents a novel application-layer semantic integration with LoRaWAN’s constrained MAC and PHY layers, enabling cross-layer flow optimisation without protocol modification. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency relative to traditional static priority architectures. Seamlessly integrated into the NS-3 LoRaWAN simulation framework, the FFC protocol demonstrates superior performance in IoMT communications. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency compared with traditional static priority-based architectures. It achieves this by prioritising high-priority health telemetry, proactively mitigating network congestion, and optimising energy utilisation, thereby offering a robust solution for emergent, health-critical scenarios in resource-constrained environments.
Speeding up sequential Markov chain Monte Carlo methods in the context of large volumes of data from distributed sensor networks
De Freitas, Allan; Septier, Francois; Mihaylova, Lyudmila (Wiley, 2026-02)
Advances in digital sensors, digital data storage, and communications have resulted in systems being capable of accumulating large collections of data. In light of dealing with the challenges that large volumes of data present, this work proposes solutions to inference and filtering problems within the Bayesian framework. Two novel sequential Markov chain Monte Carlo (SMCMC) frameworks are proposed for nonlinear and non-Gaussian state space models, able to deal with large volumes of data (or observations). These are SMCMC frameworks relying on two key ideas: (1) a divide-and-conquer type approach computing local filtering distributions, each using a subset of the data, and (2) subsampling the large data and utilizing a smaller subset for filtering and inference. Simulation results highlight the large computational savings that can reach 90% by the proposed algorithms when compared with a state-of-the-art SMCMC approach.
Using system dynamics modelling to optimize energy and water efficiency of decoupled aquaponic systems : a South African perspective
Roux, Adriaan J.G.; Ayomoh, Michael Kweneojo; Yadavalli, Venkata S. Sarma; Bansal, Ramesh C. (Elsevier, 2025-07-11)
Despite being energy and water proficient, the practice of aquaponics has remained underdeveloped and underutilized in a poor power generating continent like the African continent and a water scarce society like the Republic of South Africa. As the population of humans on the globe continues to grow geometrically with climate change also being aided, more proficient and safe means of food security premised on energy and water efficiency is becoming the prerogative of governments across different nations. This research has presented a system dynamics model of a decoupled aquaponics system to investigate the sensitivity of parameters in the design of aquaponics systems in the Republic of South Africa. Two major driving variables considered in this research include energy and water utilization for efficient design. A couple of ventilation flow, heating and energy based models were built into the system dynamics model for the conduct of simulation. The results revealed that the top performing countries in respect of energy and water efficiency include locations with hot humid climates such as Brazil, Nigeria and Malaysia. In South Africa, Durban was the best performing city with a peak energy demand of 18.4 MW and a total yearly energy usage of 4550 MW. Durban had a 7.3 % higher cumulative energy compared to Brazil. Durban had a net water return of 124.8. Given the humid and hot climate in the city of Durban, it is considered to be competitively suitable for aquaponics operations. Other regions in South Africa could still be suitable to operate aquaponics systems however, this might be less energy and water efficient. The outcome of this research can be utilized by local governing authorities to ensure sustainable policy design and implementation.
Innovative dust detection and efficient cleaning of PV panels : a CNN-RF approach using I-V curve data transformed into RGB mosaics
Bashir, Safia Babikir; Farag, Mena Maurice; Hamid, Abdul-Kadir; Adam, Ali A.; Bansal, Ramesh C.; Mbungu, Nsilulu T.; Elnady, A.; Abo-Khalil, Ahmed G.; Hussein, Mousa (Elsevier, 2025-07)
Photovoltaic (PV) panels are vital for renewable energy generation, yet their efficiency is critically hindered by environmental challenges such as dust accumulation, especially in arid regions like the UAE. Dust buildup can reduce efficiency by up to 30% within a month, threatening the sustainability of solar power, which is projected to supply 10% of global energy by 2030. Existing cleaning methods are unsustainable, consuming an estimated 10 billion gallons of water annually, enough to meet the drinking needs of 2 million people, necessitating the development of a cost-effective, resource-efficient alternative. This research presents a novel machine learning-based system to automate dust detection and optimize cleaning, significantly reducing water consumption while improving power generation efficiency. The methodology transforms I-V curve electrical parameters into RGB mosaic images, enabling precise classification of operational states such as normal operation, dust accumulation, shading, and faults without relying on external imaging devices. The system is built on a hybrid model combining Convolutional Neural Networks (CNN) and Random Forest (RF) classifiers (CNN-RF), where the CNN extracts high-level features from RGB mosaic images, and the RF classifier accurately categorizes operational states. Upon detecting dust accumulation, a secondary CNN-RF model classifies the severity into low, moderate, or heavy, guiding an optimized cleaning process that minimizes water usage while maintaining cleaning effectiveness. The primary CNN-RF model achieved 100% accuracy in classifying operational states using RGB mosaic images, surpassing the 97% accuracy achieved by I–V curve-based methods. Furthermore, the secondary CNN-RF model for dust severity classification attained an accuracy of 98% using RGB mosaic images, compared to only 68% when using traditional I–V curves, highlighting the superior performance of RGB mosaic images in detecting fine-grained dust levels. This optimized classification approach guides an automated cleaning system that minimizes water usage while maintaining PV panel efficiency.
Multi-objective optimization of load flow in power systems: an overview
Nyingu, Bansendeka Theo; Masike, Lebogang; Mbukani, Mwana Wa Kalaga (MDPI, 2025-11-20)
The expanding complexity of power systems—driven by the motivation to reduce their carbon footprint by integrating renewable energy sources (RESs) in the grid, the increasing energy demand, grid scalability, and the necessity for reliable and sustainable operation—has made the optimal power flow (OPF) problem the main issue in power systems. Hence, the concept of muti-objective optimal power flow (MOOPF) in power systems has become a crucial tool for power system management and planning. This article provides an overview of recent optimization techniques in power systems that have MOOPF as their central problem, as well as their applications in power systems, with the purpose of identifying significant approaches, challenges and trends when it comes to large-scale probabilistic MOOPF. This overview was developed based on an in-depth analysis of MOOPF techniques, the classification of their applications, and the formulation of the problem in power systems. This overview contributes to the existing literature by highlighting the evolution of optimization techniques, and the need for robust, probabilistic hybrid optimization techniques that can address variability, uncertainty, reliability, and sustainability in power systems. These findings are significant because they emphasize the current transition towards more adaptive and intelligent optimization strategies, which are essential to developing sustainable, dependable, and effective power systems, especially as we move towards smart grids and low-carbon energy systems.
A review of wind power generation steady-state reactive power support requirements and improvement strategies
Ncwane, Siyanda; Bansal, Ramesh C. (Elsevier, 2026-05)
The penetration of wind power generation (WPG) facilities into power systems continues to increase globally. Wind power generation facilities have become increasingly important in providing reactive power support to help regulate power system voltage. To ensure that WPG facilities can provide adequate support, grid codes have been developed with specific requirements that must be met before they can reach commercial operation. However, WPG facilities are sometimes unable to meet the required reactive power support levels. Controller based solutions are commonly used to improve the reactive power capability of WPG facilities. This paper reviews recent developments in control strategies. Their response speed, benefits, and limitations are discussed to identify gaps and to propose future improvements. Current control strategies are not implemented using hybrid control structures, and mostly rely on classical and metaheuristic optimization algorithms. These control strategies can be slow, and sometimes increase the operation of the WPG facility's grid integration transformer on-load tap changer. Machine learning based hybrid control strategies have the potential to improve performance and enable WPG facilities to efficiently provide reactive power support. HIGHLIGHTS • Wind power generation reactive power control grid code requirements are reviewed. • Wind power generation reactive power control strategies are discussed. • Reactive power control strategies are classified into three control structures. • Machine learning based hybrid control strategies are proposed to control reactive power production.
Level-crossing downsampling for quantization error reduction in sine wave estimation
De Beer, Dirk Johannes; Joubert, Trudi-Heleen (Institute of Electrical and Electronics Engineers, 2026-02)
This work introduces a digital postprocessing algorithm—level-crossing downsampling (LC-DS)—for estimating sine wave parameters from sequences of quantized values acquired by standard ADCs. LC-DS emulates level-crossing sampling by retaining only transition points, reducing correlated quantization error, and accelerating least-squares regression (LSR). Its performance is benchmarked against uniform LSR and calibrated sinefit to highlight accuracy and computational tradeoffs. Across a wide dynamic range, LC-DS consistently outperforms uniform sampling and approaches the accuracy of calibrated sinefit for low-level signals, while remaining up to two orders of magnitude faster for large datasets. Unlike conventional methods, LC-DS scales efficiently with data size, enabling real-time estimation without hardware modifications. Practical and simulated experiments, including electrochemical impedance spectroscopy, confirm robustness under conditions such as signal saturation. These results position LC-DS as a compelling alternative for applications requiring both high precision and computational efficiency.
Learning-based moving horizon autonomous control of a chemical reactor
Sun, Bei; Kong, Peng; Le Roux, Johan Derik; Craig, Ian Keith; He, Mingfang; Yang, Chunhua (Elsevier, 2025-12)
This paper proposes a learning-based moving horizon autonomous control of a chemical reactor (LMHAC) approach for chemical reactor with multiple operating conditions. In the proposed LMHAC scheme, model-based control, model-free control and process modeling are integrated in a moving horizon framework. A control switching logic makes a selection between model predictive control (MPC) and adaptive dynamic programming (ADP) depending on whether the model parameters are known or unknown under the current operating condition. To be compatible with the moving horizon framework, the conventional ADP is fitted into a finite horizon composed of two different stages, namely a learning stage and a control-identification stage. In the learning stage, a constrained finite-horizon ADP (CFADP) first learns an approximated optimal controller from the collected input-state information pair generated by an initial admissible control. In the control-identification stage, the approximated optimal control is applied to the process to generate a sequence of input-state information pairs which is then utilized in turn to identify the unknown model parameters. The LMHAC framework is capable of providing the optimal or nearly optimal control for different operating conditions online and incrementally enlarge the known domain of system dynamics. The feasibility and performance of the proposed approach are illustrated via a case study.
Gaussian process modelling of an industrial flotation bank
Lindqvist, Johan; Atta, Khalid; Le Roux, Johan Derik; Johansson, Andreas (Elsevier, 2026-05)
A control-oriented Gaussian process regression (GPR) model of froth flotation is developed and compared to a previously developed parametric model. The model aims to predict the behaviour of froth flotation, taking into consideration which state variables are available from measurements: air recovery, top of froth bubble size, and pulp level. The framework encodes prior knowledge of a published flotation model. Each state is modelled using a separate GP, with a custom covariance function whose form is given by the flotation model. These kernels capture the interaction between the relevant state variables and manipulated variables. The model aims to balance the complexity required to explain such a complex process with the uncertainty of its instrumentation. To evaluate the ability of the GPR model to capture the process dynamics, the GP model is assessed using an industrial data set, demonstrating its capacity to improve the performance of state prediction. The purpose of the GPR model is to enable supervisory and advanced model-based control. HIGHLIGHTS • A Gaussian process regression (GPR) model is developed using industrial online froth flotation data. • The kernels for the GPR model are based on modelling insights. • The predictive capacity of the GPR model is better than that of a dynamic semi-mechanistic model. • The GPR model shows potential for use in model predictive process control.
FPEVO : fused point-edge visual odometry for low-structured and low-textured scenes
Brown, Dylan; Grobler, Hans; De Villiers, Johan Pieter (Elsevier, 2025-11)
Visual odometry is an essential component of vision-based robotic navigation systems. A primary limitation of existing visual odometry solutions is their inability to achieve satisfactory performance in both high- and low-textured regions. In this paper, a robust RGB-D visual odometry method is proposed that fuses point and edge features. By combining the descriptiveness of feature points with the structure provided by edge data, a method that is robust to low-textured scenes is developed. Edge features are first detected and grouped based on the Gestalt principles of continuity and proximity. Edge groups are then associated between the current and previous frames using point features in the vicinity of the edges. Pose estimation is thereafter performed by first matching points between associated edge groups, filtering these points based on structural constraints imposed by the edges, and estimating the motion of the agent. Compared to state-of-the-art alternatives, such as REVO, MSC-VO, DROID-VO and SplaTAM on the TUM RGB-D, ICL-NUIM and Tartan-Air datasets, the resulting method reduces the root mean square absolute trajectory error, and translational and rotational relative pose errors by up to 58%, 75%, and 82%, respectively. This indicates that our method is not only more accurate than current approaches, but also more consistent, especially in low-structured and low-textured environments.
Integrating computational modelling into the ecosystem of cochlear implantation : advancing access to diagnostics, decision-making, and post-implantation outcomes on a global scale
Hanekom, Tania (MDPI, 2025-11-08)
Disabling hearing loss affects more than 5% of the global population, with numbers expected to double by 2050. The burden is especially high in low- and middle-income countries, where access to cochlear implant (CI) technology and the required follow-up care is limited. While CIs are a proven treatment for certain types of hearing loss, their adoption in these countries is hindered by high costs, the need for specialised rehabilitation, and the financial and time commitment required for long-term device maintenance. Although remote programming has improved accessibility to standard care, specialised interventions for complications remain restricted mainly to areas with clinical centres. Computational modelling offers a promising solution to this access-to-care dilemma. The models may be used to simulate complications, such as non-auditory stimulation (NAS), to investigate and plan personalised interventions, and ultimately predict device parameters, without requiring the recipient’s physical presence. Both phenomenological and biophysical models have already demonstrated useful application in CIs: the former streamlines clinical workflows and aims to establish consistency in device fitting, and the latter provides insights into patient-specific auditory biophysiology. Despite decades of research, clinical translation of biophysical models has been limited by data constraints, parameter uncertainty, and validation challenges. In this perspective piece, it is argued that biophysical models have now reached sufficient maturity to be integrated into routine CI care. Apart from the advantages that this approach will bring to the overall advancement of person-centred CI care, it is envisioned to improve accessibility, personalisation, and long-term outcomes for CI recipients in low- and middle-income countries.
Two-stage optimization of appliance scheduling and BESS capacity with comfort level
Ren, Zhiling; Chen, Xi (Dong, Yun, 2026-06)
Residential photovoltaic (PV)–battery energy storage system (BESS) planning often neglects impact of comfort level. We propose a two-stage framework that jointly selects BESS capacity and appliance start-up schedules while accounting for appliance-use comfort, thermal comfort, and BESS degradation. Annual days are clustered by k-means to select a set of typical days. Stage 1 performs a discrete capacity scan (coarse grid plus local refinement). For each candidate, Stage 2 solves a typical day mixed-integer linear programming problem with a linear fractional cost–comfort objective via Dinkelbach’s method, maps the resulting binary schedules to all natural days by cluster membership, and then solves an annual rolling linear programming problem for dispatch and degradation-inclusive evaluation. In the baseline case, the selected capacity is 10.75 kWh with an annual total cost of 2623.2 CNY and comfort indices of 0.793 and 0.788; degradation cost is 404.3 CNY. Without the comfort term, the preferred capacity increases to 16.5 kWh and the annual total cost decreases to 2265.9 CNY, while degradation cost rises to 555.1 CNY. Sensitivity analyses show that outcomes vary with comfort settings and time-of-use prices. Overall, the framework quantifies annual cost-comfort-degradation trade-offs and recommends a feasible BESS size and appliance schedules. HIGHLIGHTS • Two-stage framework jointly optimizes BESS capacity and appliance schedules with comfort. • k-means typical days enable tractable annual MILP and rolling LP evaluation. • Baseline selects 10.75 kWh, 2623.2 CNY, with comfort indices 0.793 and 0.788. • Removing comfort changes preferred BESS capacity and appliance start-up schedules. • Sensitivity analyses quantify impacts of comfort weights, bounds, price.
A carbon subsidy framework for coordinated low-carbon operation in industrial park with multiple users
Ren, Zhiling; Chen, Xi; Zhao, Boya; Dong, Yun (Elsevier, 2026-05)
As the global impacts of carbon emissions intensify, power markets are shifting from profit-driven models to frameworks that integrate economic performance and carbon mitigation. Current low-carbon approaches commonly exhibit insufficient user response, elevated abatement costs, complex implementation, and limited flexibility. To bridge this gap, we propose a carbon subsidy framework for industrial parks. In this framework, the Industrial Park Operator (IPO) announces real-time shared electricity, carbon, and subsidy prices, while users optimize load scheduling and market participation. The interaction is formulated as a Stackelberg game and solved using a Differential Evolution-Mixed-Integer Quadratic Programming (DE-MIQP) approach. Case studies demonstrate that, compared with a baseline without subsidies, the IPO provides a total subsidy of 1,295.57 CNY, the proposed framework reduces industrial-park CO2 emissions by 40.13 %, and the IPO’s profit decreases by 217.39 CNY, while the users’ aggregate profit increases by 608.3 CNY. Finally, two sensitivity analyses are conducted: one investigates parameter sensitivity, and the other is a ten-user case study that exhibits emission-reduction behavior similar to that of the four-user case. These findings confirm that the carbon subsidy framework effectively incentivizes user participation, reduces emissions, and offers a practical pathway for coordinated low-carbon operation in multi-user industrial parks. HIGHLIGHTS • Carbon subsidy framework for coordinated low-carbon industrial park operation. • Stackelberg game with DE-MIQP preserves user privacy while finding equilibrium. • Subsidy cuts park CO2 emissions by 40.13 % and SPP output by 47.01 % versus baseline. • Profit shifts: IPO -217.39 CNY, users +608.3 CNY, subsidies total 1,295.57 CNY. • Sensitivity and ten-user studies confirm robustness and scalability of the scheme.

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