• Energy Research

Dans les zones rurales ou sous-développées, les défaillances de micro-réseaux sont fréquentes, ce qui souligne l'importance de mettre en œuvre un plan de gestion de l'énergie efficace. Le système de stockage d'énergie par batterie d'un microgrid est un élément essentiel d'un tel plan. Le système peut réguler les tensions, atténuer les déséquilibres et augmenter la fiabilité du système, ce qui le rend essentiel pour maximiser les avantages du stockage d'énergie. Cette étude propose une méthode de gestion du stockage d'énergie et de contrôle des opérations de charge et de décharge des batteries en fonction des exigences de charge dans un micro-réseau connecté à un système solaire. Le problème de la gestion de l'énergie sur une période de 24 heures est abordé, et le modèle est simulé à l'aide du logiciel Matlab R2022a, en utilisant une analyse des flux d'état et des méthodes de programmation linéaire pour minimiser le prix total variable de l'électricité avant et après l'îlotage. La méthode heuristique entraîne des dépenses d'exploitation plus élevées de 4,567 % par temps clair et de 8,5232 % par temps nuageux par rapport à l'approche de programmation linéaire lorsque la charge est satisfaite pendant 24 heures. En las zonas rurales o subdesarrolladas, las fallas de la microrred son comunes, lo que subraya la importancia de implementar un plan de gestión energética eficaz. El sistema de almacenamiento de energía de la batería de una microrred es un componente crítico de dicho plan. El sistema puede regular los voltajes, mitigar los desequilibrios y aumentar la confiabilidad del sistema, por lo que es vital maximizar los beneficios del almacenamiento de energía. Este estudio propone un método para gestionar el almacenamiento de energía y controlar las operaciones de carga y descarga de la batería en función de los requisitos de carga en una microrred conectada a un sistema solar. Se aborda el problema de la gestión de la energía en un periodo de 24 horas, y se simula el modelo utilizando el software MATLAB R2022a, empleando análisis de flujo de estado y métodos de programación lineal para minimizar el precio variable total de la electricidad antes y después del aislamiento. El método heurístico da como resultado mayores gastos operativos en un 4.567% en clima despejado y un 8.5232% en clima nublado en comparación con el enfoque de programación lineal cuando la carga se satisface durante 24 horas. In rural or underdeveloped areas, microgrid failures are common occurrences, which underscores the importance of implementing an effective energy management plan. A microgrid's battery energy storage system is a critical component of such a plan. The system can regulate voltages, mitigate imbalances, and increase system reliability, making it vital to maximize the benefits of energy storage. This study proposes a method for managing energy storage and controlling battery charge and discharge operations based on load requirements in a microgrid connected to a solar system. The problem of energy management over a 24-hour period is addressed, and the model is simulated using MATLAB R2022a software, employing state flow analysis and linear programming methods to minimize the total variable electricity price before and after islanding. The heuristic method results in higher operating expenses by 4.567% in clear weather and 8.5232% in cloudy weather compared to the linear programming approach when the load is satisfied for 24 hours. في المناطق الريفية أو المتخلفة، تعتبر أعطال الشبكات الدقيقة حوادث شائعة، مما يؤكد أهمية تنفيذ خطة فعالة لإدارة الطاقة. يعد نظام تخزين طاقة البطارية الخاص بالشبكة الدقيقة مكونًا حاسمًا في مثل هذه الخطة. يمكن للنظام تنظيم الفولتية وتخفيف الاختلالات وزيادة موثوقية النظام، مما يجعل من الضروري تعظيم فوائد تخزين الطاقة. تقترح هذه الدراسة طريقة لإدارة تخزين الطاقة والتحكم في شحن البطارية وعمليات التفريغ بناءً على متطلبات الحمل في شبكة صغيرة متصلة بنظام شمسي. تتم معالجة مشكلة إدارة الطاقة على مدار 24 ساعة، ويتم محاكاة النموذج باستخدام برنامج MATLAB R2022a، باستخدام تحليل تدفق الحالة وطرق البرمجة الخطية لتقليل إجمالي سعر الكهرباء المتغير قبل وبعد الجزيرة. تؤدي طريقة الاستدلال إلى ارتفاع نفقات التشغيل بنسبة 4.567 ٪ في الطقس الصافي و 8.5232 ٪ في الطقس الغائم مقارنة بنهج البرمجة الخطية عند استيفاء الحمل لمدة 24 ساعة.

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.

This study introduces the Worst Moth Disruption Strategy (WMFO) to enhance the Moth Fly Optimization (MFO) algorithm, specifically addressing challenges related to population stagnation and low diversity. The WMFO aims to prevent local trapping of moths, fostering improved global search capabilities. Demonstrating a remarkable efficiency of 66.6 %, WMFO outperforms the MFO on CEC15 benchmark test functions. The Friedman and Wilcoxon tests further confirm WMFO's superiority over state-of-the-art algorithms. Introducing a hybrid model, WMFO-MLP, combining WMFO with a Multi-Layer Perceptron (MLP), facilitates effective parameter tuning for carbon emission prediction, achieving an outstanding total accuracy of 97.8 %. Comparative analysis indicates that the MLP-WMFO model surpasses alternative techniques in precision, reliability, and efficiency. Feature importance analysis reveals that variables such as Oil Efficiency and Economic Growth significantly impact MLP-WMFO's predictive power, contributing up to 40 %. Additionally, Gas Efficiency, Renewable Energy, Financial Risk, and Political Risk explain 26.5 %, 13.6 %, 8 %, and 6.5 %, respectively. Finally, WMFO-MLP performance offers advancements in optimization and predictive modeling with practical applications in carbon emission prediction.

AbstractPower flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology‐based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability.

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2 W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2 W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2 W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2 W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.