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Abstract While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult. Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents the Hybrid approach based on the Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing approach to identifying cyber threats to the IoT. The HSVMR-D employs SVM to categorize known and unknown threats using attributes acquired from IoT data. Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. Moreover, protecting vital infrastructure and sensitive data, HSVMR-D provides a thorough and adaptable solution to improve the security posture of IoT deployments. Comprehensive experiment analysis and simulation results compared to the baseline study have confirmed the efficiency of the proposed HSVMR-D. Furthermore, increased resilience to completely novel changing threats, fewer false positives, and improved accuracy in threat detection are all outcomes that show the proposed work outperforms others. The HSVMR-D approach is helpful where the primary objective is a secure environment in the Internet of Things (IoT) when resources are limited.

Abstract This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Abstract This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.

Abstract DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

The transport sector is experiencing a notable transition towards sustainability, propelled by technological progress, innovative materials, and a dedication to environmental preservation. This study explicitly examines the incorporation of electric vehicles (EVs) into the power grid, with a particular emphasis on passenger automobiles. Our analysis emphasises the vital importance of updated transport infrastructure in decreasing greenhouse gas emissions and aiding carbon reduction efforts in electricity networks. The analysis uncovers that adopting electric vehicles offers significant advantages, including enhanced grid efficiency and decreased emissions. However, it also brings issues concerning the design and operation of power systems at both the transmission and distribution levels. Key players are crucial in tackling these difficulties to improve electric vehicle integration into the grid. The study determines the most effective ways for distributing and providing electric vehicle charging infrastructure, and investigates the efforts made to establish common standards in order to solve current challenges. This research contributes to the advancement of sustainable mobility and energy systems by conducting a thorough examination of the impact of electric vehicles on power systems and offering appropriate integration solutions.

Abstract This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

Abstract The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Abstract This research paper introduces an avant-garde poly-input DC–DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources—solar energy and fuel cells—with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85–90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91–95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC’s adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC–DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Abstract As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Abstract Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

Abstract Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Nowadays business has shifted from product-centric to customer-centric due to the industrial revolution. As a result, the customer’s problem and experience need to be studied and inspected thoroughly to find solutions. Charging stations implementing different charging technologies are reinforced in various topographical locations. Varied charging methods, including AC slow charging, AC fast charging, DC level 1 charging, DC level 2 charging, wireless charging is implemented so far to reduce the charging time. Various business models are established and reported by researchers to support the EV ecosystem. This paper presents one such business model for an electric vehicle (EV) charging system. Design Thinking is one of the problem-solving approaches discussed here. How to tackle the charging issues by implementing different technologies is also discussed. Different phases of Design Thinking are used to find the possible solution to the problems faced by customers. Different groups of customers’ problems and how the proposed solution can fulfil a major group of customers’ needs are discussed here.

Abstract This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

Effective management of Distributed Energy Resources (DERs) and optimization of grid operations are crucial responsibilities of Distribution System Operators (DSOs). Hence, this comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. These goals underscore the significance of clean and accessible energy, advancements in infrastructure, sustainable urban development, climate change mitigation, and collaborative partnerships. The review's core focuses on the DER forecasting techniques employed by DSOs. It explores various aspects, including data collection methods, load forecasting models, DER generation forecasting, aggregation and integration techniques, and the role of advanced technologies like machine learning and artificial intelligence. The review highlights the critical role of accurate DER forecasts in optimizing grid operations, managing energy flows, and facilitating the integration of renewable energy sources. Furthermore, the review examines the implications of DER forecasting for DSOs in achieving the SDGs. It discusses how DER forecasting facilitates the transition to affordable and clean energy, enhances industry innovation and infrastructure, builds sustainable cities and communities, drives climate action efforts, and fosters stakeholder partnerships. However, the review also identifies challenges and limitations in DER forecasting, including data availability, forecasting accuracy, uncertainty management, and regulatory barriers. It emphasizes further research and development in improved forecasting algorithms, advanced data analytics, and enhanced communication and coordination mechanisms. Finally, this comprehensive critical review highlights the importance of DER forecasting for DSOs in achieving the SDGs. Accurate forecasting can promote sustainable and clean energy practices, drive innovation, build resilient communities, mitigate climate change, and foster collaborative partnerships. The review emphasizes the necessity of advancing DER forecasting techniques and addressing associated challenges to fully realize the potential of DERs in contributing to a sustainable and inclusive future.This comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. As Distributed Energy Resources (DERs) play an increasingly significant role in the transition to sustainable energy systems, accurate forecasting techniques are essential for optimizing grid operations and facilitating the integration of renewable energy sources. By effectively managing DERs, Distribution System Operators (DSOs) contribute to the advancement of several SDGs, including affordable and clean energy (SDG 7), sustainable infrastructure (SDG 9), climate action (SDG 13), and partnerships for the goals (SDG 17). This review explores the intersection of DER forecasting with the SDGs, highlighting how forecasting initiatives can support national and global efforts toward sustainable development by providing insights into energy demand, grid stability, and renewable energy integration. The goals and targets are derived from an analysis of current trends and the identification of potential development scenarios by 2030. Both optimistic and pessimistic projections are utilized for communicating with the general public and national governments concerning DSM network planning. Utilizing data from various nations enables the identification of effective strategies and the prediction of similar trends in other areas. Simultaneously, the magnitude of activities related to Sustainable Development Goals (SDGs) enables the improvement and efficient organization of data gathering on a global basis. This, in turn, provides a foundation for future forecasting endeavours.

Abstract This paper proposes an innovative approach for improving the charging efficiency of electric vehicles (EVs) by combining photovoltaic (PV) systems with AC–DC Power Factor Correction (PFC). The proposed approach employs bi-directional power flow management within the PFC system, allowing for enhanced resource utilization and EV battery capacity under a variety of environmental circumstances. A modified Lyapunov-based robust model reference adaptive controller (M-LRMRAC) is developed to provide real-time Maximum Power Point Tracking (MPPT) for the PV array. By quickly recording the MPP, this controller skilfully adjusts to shifting radiation and temperature dynamics. A noteworthy accomplishment is that the M-LRMRAC outperforms traditional Perturb and Observe (P&O) techniques by achieving quick MPP convergence (0.54 s). Additionally, the benefits of this integrated system go beyond effective MPPT. The method achieves operating at unity power factor and reduces total harmonic distortion, which results in improved power quality when charging EV Batteries (EVB). The entire solution provided by this multifaceted architecture improves the quality of electricity delivered to EV batteries while also increasing energy efficiency. This research helps to the evolution of sustainable and dependable EV charging infrastructure by solving difficulties and optimising performance. The combination of PV systems with AC–DC PFC, aided by the M-LRMRAC technology, presents a viable route for attaining efficient, clean, and high-quality EV charging, hence supporting the shift to a greener and more sustainable transportation landscape.

Summary: Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange with the grid. Combining numerous MGs to form a multi-microgrid (MMG) is a viable approach to enhance smart distribution networks' operational and financial performance. However, the correlation and coordination of intermittent power generation within each MG network pose many techno-economic challenges for energy sharing and trading. This review offers a comprehensive analysis of these challenges within the framework of MMG operations. It examines state-of-the-art methodologies for optimizing multi-energy dispatch and scrutinizes contemporary strategies within energy markets that contribute to the resilience of power systems. The discourse extends to the burgeoning role of blockchain technology in revolutionizing decentralized market frameworks and the intricacies of MMG coordination for reliable and cost-effective energy distribution. Overall, this study provides ample inspiration for theoretical and practical research to the new entrants and experts alike to develop new concepts for energy markets, scheduling and novel operating models for future resilient multi-energy networked systems/MMGs.

This article gives an in-depth review of the integration of the Internet of Things (IoT) and cloud computing in power systems (PS), to improve power distribution sustainability and efficiency. IoT provides seamless communication throughout the electrical grid by leveraging modern Information and Communication Technology (ICT) and embedded technologies, ushering in an information revolution. The Internet of Energy (IoE) emphasizes the convergence of ICT and energy generation, highlighting IoT's disruptive potential in the electric power industry. Cloud computing, on the other hand, effectively handles data processing, storage, and computational resources. Cloud of Things (CoT), the merging of IoT with cloud computing, provides huge processing capabilities and quick access to computer resources, enabling novel applications and analytics in power distribution. The influence of IoT and cloud-based applications in the distributed generation and renewable energy industries is demonstrated via case studies and real-world scenarios. The introduction of new parameters and optimization strategies highlights the potential for future developments in power technology. To achieve smooth integration and effective resource utilization, proposals for additional research in electric cars and grid-to-vehicle technologies have been made. The integration of CoT into PS has an enormous influence, opening the door for increased efficiency, sustainability, and reliability in the energy sector, as shown by the study's overall findings. Future generations will advance thanks to the CoT, which presents exciting potential for a more eco-friendly and technologically sophisticated energy landscape.

A DC–DC converter in an EV charging station is to regulate the DC voltage from the rectifier to the required voltage for battery charging. This ensures that a constant, stable, and safe charging voltage is provided to the battery However, if the EV charging is done only with a DC–DC​ converter, it results in poor Power Quality (PQ), with high levels of harmonic distortion and a low power factor. This can lead to longer charging times, increased energy costs, decreased battery performance and life, and introduce serious PQ issues. To address these issues, the Ferdowsi Converter is proposed is a combination of two interleaved Buck-Boost converters with a common input inductor as a solution with simplified PT control strategy presented, operated in operating in discontinuous conduction mode (DCM) is presented, which can achieve nearly unity power factor (PF) over universal input voltage range studied. The converter performs with fewer components, providing a charger input current that is in phase with the mains voltage, reducing the THD to below 5% under steady state and during variations in line and load side.

Abstract This proposed work deals with the implementation of a single-phase topology with using hybrid for multilevel inverters. It is observed that the proposed structure improves the performance of the hybrid multilevel inverter with high-frequency switches for positive levels and reverse voltage with negative levels. This paper studies a novel construction for an asymmetrical hybrid single-phase multilevel inverter. This paper also studies the operation of multi level inverter under depreciating sources condition due to fluctuation in input. The PWM method, i.e. APOD and CO, has been incorporated into the simulation of single-phase 7-level, 9-level, and the 11-level hybrid inverter. These two methods are compared to one another and to standard methods in terms of THD for different values of the modulation index, and they are found to be superior. Overall loss of 50% of the THD is almost equal to rated output voltage, it is observed in cascaded multilevel inverters. The working and performance of the proposed model are simulated in MATLAB Simulink software and the results are discussed in detail.

Combined heat and power (CHP) systems are widely used in industries for their high energy efficiency and reduced carbon emissions. The optimal dispatch of CHP systems involves scheduling the operation of various equipment to minimize the total operational cost while meeting the heat and power demand of the facility. In this research work, a novel quantum-inspired optimization algorithm is proposed for the first time to solve the optimal dispatch problem of CHP systems. The proposed algorithm combines the principles of quantum mechanics with classical optimization algorithms to achieve a better solution. The algorithm uses quantum gates to perform quantum operations on the optimization variables, which allows for the exploration of a larger search space and potentially better solutions than classical algorithms. The proposed algorithm also incorporates a classical optimizer to refine the numerical evaluations acquired from the quantum operations. The performance of the adopted optimization technique was demonstrated by associating it with various other optimization techniques based on factors such as the speed of convergence, computational time, and the quality of the solution. The comparison is made on two standard CHP systems subjected to various quality and inequality constraints. The simulation results indicate that the quantum-inspired optimization technique surpassed the other algorithms in both solution quality and computational efficiency. The implemented algorithm provides a promising solution to the optimal dispatch problem of CHP systems. Future research can further explore the application of quantum-inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.

This paper aims to present a novel Direct Torque Control (DTC) strategy based on twelve sectors and a new Modified Switching Table (12-MST-DTC) used to control the Doubly Fed Induction Generator (DFIG) powered by Wind Turbine (WT) and operating under random wind speed. Robustness and fast dynamic performances have made Conventional DTC (C-DTC) popular and widely used, but presents major drawbacks which are high torque and flux ripples. Therefore, the 12-DTC has been proposed in the literature as an improvement but suffers from significant oscillations during super-synchronous operation mode as well as the case of overspeed. The proposed control strategy maintains the performances that offer (C-DTC and 12-DTC) and furthermore minimizes those drawbacks. The newly modified switching table is designed with additional input to cover all WT-DFIG system operation modes (sub-, super- and synchronous modes in addition to the case of over-speed) in order to diminish the flux, torque ripples and THD of the AC-grid injected currents. For optimal operating of variable-speed WT, MPPT algorithm and blade pitch control are invested. Simulation results of the 12-MST-DTC are compared with those of C-DTC and 12-DTC using MATLAB/Simulink environment treating all operation modes of WT-DFIG system and even in case of wind over speeds. The comparison results demonstrate the efficiency of the proposed control strategy in terms of flux/torque ripples and THD reduction, therefore fewer DFIG mechanical vibration and acoustic noise.

The utility's utilization of communication technology and renewable energy sources has paved the path for self-sustaining microgrids (MGs). However, the intermittency of solar and wind energies raises concerns about meeting demand effectively. To ensure optimal performance of distributed MGs, an efficient energy management system (EMS) is crucial to tackle this uncertainty. Historically, MGs have primarily achieved operational cost reduction through optimal functioning. Integrating demand response (DR) into the EMS could further enhance operational efficiency and peak reduction. This research work addresses this challenge by incorporating DR programs into grid-connected MGs' energy management. Stochastic programming is employed to account for the unpredictable solar and wind behaviours. Flexible price elasticity is used to calculate price elasticity coefficients, portraying customer responses effectively. The implemented research work compares the Dragon Fly Algorithm with other heuristic approaches, resulting in a 12.42 % reduction in overall operating costs and the efficacy of the proposed algorithm is shown.. Using the Analytic Hierarchy Process (AHP), the User Satisfaction Index is assessed, revealing that the CPP demand response initiative tops the satisfaction scale with a score of 0.92881.. Moreover, this research offers an exhaustive evaluation of techno-economic markers for each scenario, systematically ranked using the proposed AHP methodology..

Background: Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. Material and method: We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selector-based feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. Results: The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. Conclusions: Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.

This paper presents a new single-source switched capacitor- (SC-) based multilevel inverter (MLI) design with a boosting potential of three times the supply voltage. To produce a waveform with seven output voltage levels, the suggested switching capacitor inverter consists of eight switches, single diode, and two capacitors. Because capacitors are inherently balanced, there is no need for a balancing circuit or sensor. The structure can be expanded using the provided generalized equations. In addition, the technique for switching control and loss analyses is explored. A fair comparison with the most recent SCMLI topologies has been conducted to demonstrate the merits of the proposed work. Furthermore, the proposed topology is evaluated using the MATLAB/SIMULINK tool, and experiments under both transient and steady-state situations are performed to demonstrate its feasibility. At dynamic-loaded situations, the performance of the proposed SCMLI with dynamic modulation index and switching frequency is tested.

This work presents the two space vector pulse width modulation (SVPWM-I and SVPWM-II) strategies for eleven-level (11L) asymmetric cascaded H-bridge (CHB) multilevel inverter (MLI). Depending on the isolated structure and nonappearance of capacitor voltage balancing issues, the CHB MLI structure is favoured. These days, the SVPWM control method is accomplished superior consideration among the diverse PWM methods. In common, the SVPWM strategy is realized based on deteriorating higher-level hexagons into a lower level hexagon (2-level). Compared to the classical SVPWM strategy, the proposed SVPWM-I strategy decreases the memory and mathematical burden necessity included within the demonstration of eleven-level SVPWM devoid of losing the inverter output voltage (AC) contour by diminution the number of two-level hexagons. Also, the SVPWM-II strategy is presented, which incredibly diminishes the mathematical endeavours. The presented two SVPWM methods performed on an eleven-level asymmetric CHB multilevel inverter (MLI) by utilizing SIMULINK/MATLAB program tool and are compared with conventional sinusoidal PWM and Third harmonic injection (THI) PWM methods to confirm the proposed SVPWM methods. The proposed SVPWM methods give higher AC RMS voltage and lower harmonic distortion when compared to SPWM and THIPWM methods. To validate the presented two SVPWM control schemes, hardware results are taken on asymmetric eleven-level CHB MLI.

The failure probability of the wave energy converters is exceptionally high, which again increases the operation cost of the entities. The cause of this high cost lies in the fact that various factors influence the production efficiency of the converters. To solve this problem, multiple converters are utilized in series and parallel formation to produce energy simultaneously. This multiple converter system, known as wave energy farms, also fails to increase efficiency and decrease the cost of operation sufficiently. The reason for this is that not only technical but socio-economic as well as different environmental factors have a significant role in this aspect, which remains undetected or under- or over-detected while calculating the potential wave energy. The present investigation tries to classify the different factors which are most influential in controlling the transfer efficiency of wave energy farms to solve the problem of erroneously detecting significant factors. The authors offer a new indicator for estimating the failure likelihood of wave energy farms in converting ocean wave energy into electricity by combining Multi-Criteria Decision Making and Polynomial Neural Networks with information collected from an unbiased ranking technique.

This paper proposes a step-up 3-Ф switched-capacitor multilevel inverter topology with minimal switch count and voltage stresses. The proposed topology is designed to provide five distinct output voltage levels from a single isolated dc source, making it suitable for medium and low-voltage applications. Each leg of the proposed topology contains four switches, one power diode, and a capacitor. The switching signals are also generated using a staircase universal modulation method. As a result, the proposed topology will operate at both low and high switching frequencies. To highlight the proposed topology’s advantages, a comparison of three-phase topologies wasperformed in terms of the switching components, voltage stress, component count per level factor, and cost function withthe recent literature. The topology achieved an efficiency of about 96.7% with dynamic loading, and 75% of the switches experienced half of the peak output voltage (VDC), whereas the remaining switches experienced peak output voltage (2VDC) as voltage stress. The MATLAB/Simulink environment was used to simulate the proposed topology, and a laboratory prototype was also built to verify the inverter’s theoretical justifications and real-time performance.

An escalating energy demand can be seen especially in developing and fast-growing economies such as India. Conventional energy resources meet most of the energy demand. The alarming issue of global warming and the dependency on fossil fuels to meet the energy demand has motivated the use of clean energy sources. In this context, the educational institutions with high electricity consumption in India have been planning to opt for locally available renewable energy sources to meet their electricity demand. Even one of the most crucial issues in such academic institutions is the food waste management. Most of the institutes, give up their food wastage to piggeries which are directly fed to animals or discarded or dumped irrationally. In this paper, an optimal microgrid solution using locally available energy resources for a real physical location considering its real time-power demand is proposed. Various scenarios and different combinations of energy sources, such as solar photovoltaic, food waste based biogas plant, and a diesel generator as backup have been considered along with batteries as storage in off-grid and grid-connected systems. Hybrid optimization of multiple electric renewables (HOMER) PRO software package is utilized for detailed technical and financial analysis with a multiyear growth approach to determine the optimal energy system, which is unlikely observed in literature so far. The detailed analysis results illustrate that photovoltaic contributes most of the electricity being generated in all the scenarios. The renewable fraction is comparatively high in the off-grid system in the range of 92% to 100% as compared with 63% to 80% in grid-connected systems. The results obtained also show that levelized cost of electricity is low in case of grid-connected systems varying between 0.18 Indian National Rupee (INR)/kWh to 1.39 INR/kWh in contrast to 11.96 INR/kWh to 18.47 INR/kWh for off-grid systems.

Sustainable electrification planning for remote locations especially in developing countries is very complex in nature while considering different traits such as social, economic, technical, and environmental. To address these issues related to current energy needs depending upon the end user requirements, a coherent, translucent, efficient, and rational energy planning framework has to be identified. This paper presents a comprehensive generalized methodological framework based on the synergies of decision analysis and optimization models for the design of a reliable, robust, and economic microgrid system based on locally available resources for rural communities in developing nations. The framework consists of three different stages. First, decision analysis considering various criterions (technical, social, economic, and environmental) for the selection of suitable energy alternative for designing the microgrid considering multiple scenarios are carried out. Second, the optimal sizing of the various energy resources in different microgrid structures is illustrated. Third, hybrid decision analysis methods are used for selection of the best sustainable microgrid energy system. Finally, the framework presented is then utilized for the design of a sustainable rural microgrid for a remote community located in the Himalayas in India to illustrate its effectiveness. The results obtained show that decision analysis tools provide a real-time solution for rural electrification by binding the synergy between various criteria considering different scenarios. The feasibility analysis using proposed multiyear scalable approach shows its competence not only in determining the suitable size of the microgrid, but also by reducing the net present cost and the cost of electricity significantly.

This paper presents a new methodology of reformed electric system cascade analysis (RESCA) for the optimization of hybrid renewable energy system (HRES) with wind energy conversion system, PV system, battery energy storage system, non-intermittent source, and grid as system components. Four different configurations of HRES in islanding and grid-connected mode is considered for analysis. The four configurations of HRES is optimized using RESCA with constraints, such as final excess energy, renewable energy fraction, energy generation ratio, loss of power supply probability, and annual system cost (ASC). The optimization is realized for a residential load in Newark, USA consisting of 100 households with a daily consumption of 30 kWh for each house. The paper successfully presents the application of the RESCA methodology for the optimization of HRES based on reliability and economic constraints for both isolated and grid connected mode of applications.

Public transport is very influential industry affecting our society due to its significant environmental and social impact. Policy makers have seen electric vehicle as a tool to reduce greenhouse gas emissions. Cleanliness of power from the electricity grid plays a vital role in environmental impact in the transition to electric vehicle. In countries like China, USA, India etc., majority of electricity generation is still dominated by fossil fuel fired generators. Thus, in some cases, the environmental impact of electric vehicle may be more adverse in comparison to conventional internal combustion engine vehicles (ICEV). This paper tries to study the environmental effect of incorporation of 100 Electric Vehicle taxies in Johannesburg (JNB), South Africa. Firstly, the current electricity generation scenario and vehicles standards of SA with its carbon emission is discussed. Secondly, it is shown that with the current generation mix the adoption of EV is not sustainable. Finally, penetration of Renewable Energy (RE) system at charging stations is proposed with Renewable Energy Fraction (REF) of 40% and 58% to make carbon mission from EV equivalent to Euro III and Euro V standards of petrol ICEV. The optimised size of RE system is found using the Electric System Cascade Analysis.