Empowering distribution system operators: A review of distributed energy resource forecasting techniques.

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.

A high-efficiency poly-input boost DC-DC converter for energy storage and electric vehicle applications.

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.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

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.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

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.

A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape.

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.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience.

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.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon's grid-connected systems.

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.

State-of-the-art review on energy sharing and trading of resilient multi microgrids.

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.

Chitosan nanoencapsulated exogenous trypsin biomimics zymogen-like enzyme in fish gastrointestinal tract.

Exogenous proteolytic enzyme supplementation is required in certain disease conditions in humans and animals and due to compelling reasons on use of more plant protein ingredients and profitability in animal feed industry. However, limitations on their utility in diet are imposed by their pH specificity, thermolabile nature, inhibition due to a variety of factors and the possibility of intestinal damage. For enhancing the efficacy and safety of exogenous trypsin, an efficient chitosan (0.04%) nanoencapsulation-based controlled delivery system was developed. An experiment was conducted for 45 days to evaluate nanoencapsulated trypsin (0.01% and 0.02%) along with 0.02% bare trypsin and 0.4% chitosan nanoparticles against a control diet on productive efficiency (growth rate, feed conversion and protein efficiency ratio), organo-somatic indices, nutrient digestibility, tissue enzyme activities, hematic parameters and intestinal histology of the fish Labeo rohita. All the synthesized nanoparticles were of desired characteristics. Enhanced fish productive efficiency using nanoencapsulated trypsin over its bare form was noticed, which corresponded with enhanced (P<0.01) nutrient digestibility, activity of intestinal protease, liver and muscle tissue transaminases (alanine and aspartate) and dehydrogenases (lactate and malate), serum blood urea nitrogen and serum protein profile. Intestinal tissues of fish fed with 0.02% bare trypsin showed broadened, marked foamy cells with lipid vacuoles. However, villi were healthier in appearance with improved morphological features in fish fed with nanoencapsulated trypsin than with bare trypsin, and the villi were longer in fish fed with 0.01% nanoencapsulated trypsin than with 0.02% nanoencapsulated trypsin. The result of this premier experiment shows that nanoencapsulated trypsin mimics zymogen-like proteolytic activity via controlled release, and hence the use of 0.01% nanoencapsulated trypsin (in chitosan nanoparticles) over bare trypsin can be favored as a dietary supplement in animals and humans.

Haplotype-dependent differential activation of the human IL-10 gene promoter in macrophages and trophoblasts: implications for placental IL-10 deficiency and pregnancy complications.

PROBLEM: polymorphic changes in the IL-10 gene promoter have been identified that lead to altered IL-10 production. We hypothesized that because of these genotypic changes, the IL-10 promoter might be expressed in a cell type-specific manner and may respond differentially to inflammatory triggers. METHOD OF STUDY: we created reporter gene promoter constructs containing GCC, ACC, and ATA haplotypes using DNA from patients harboring polymorphic changes at -1082 (G→A), -819 (C→T), and -592 (C→A) sites in the IL-10 promoter. These individual luciferase reporter constructs were transiently transfected into either primary term trophoblasts or THP1 monocytic cells. DNA-binding studies were performed to implicate the role of the Sp1 transcription factor in response to differential promoter activity. RESULTS: our results suggest that the GCC promoter construct was activated in trophoblast cells in response to lipopolysaccharide (LPS), as demonstrated by reporter gene expression, but not in monocytic cells. The ACC construct showed weaker activation in both cell types. Importantly, while the ATA promoter was constitutively activated in both cell types, its expression was selectively repressed in response to LPS, but only in trophoblasts. DNA-nuclear protein binding assays with nuclear extracts from LPS treated or untreated cells suggested a functional relevance for Sp1 binding differences at the -592 position. CONCLUSIONS: these results demonstrate cell type-specific effects of the genotypic changes in the IL-10 gene promoter. These responses may be further modulated by bacterial infections or other inflammatory conditions to suppress IL-10 production in human trophoblasts.

TSPY gene copy number as a potential new risk factor for male infertility.

The human TSPY (testis-specific protein, Y-linked) gene family (30-60 copies) is situated in the MSY (male-specific) region of the Y chromosome. Testis-specific expression indicates that the gene plays a role in spermatogenesis. Refined quantitative fluorescence PCR (polymerase chain reaction) was applied to evaluate the relative number of TSPY copies compared with AMELY/X (amelogenin gene, Y-linked) genes in 84 stratified infertile men and in 40 controls. A significantly higher number of TSPY copies was found in infertile men compared with the controls (P = 0.002). The diagnostic discrimination potential of the relative number of TSPY copies was evaluated by receiver operating characteristic curve analysis. TSPY/AMELY was unambiguously found to be powerful in the diagnostic separation of both the control samples and the infertile men, reaching a good level of specificity (0.642) and sensitivity (0.732) at a cut-off point of 0.46. The findings were supported by independently repeated studies of randomly selected positive samples and controls. Evaluation of the TSPY copy number offers a completely new diagnostic approach in relation to the genetic cause of male infertility. The possible effect of the copy number of TSPY genes on spermatogenesis may explain indiscrete pathological alterations of spermatid quality and quantity.