Cleaner Energy Systems最新文献_第3页

A comparative study of floating and ground-mounted photovoltaic power generation in Indian contexts 印度浮动和地面光伏发电比较研究

Cleaner Energy Systems Pub Date : 2024-08-17 DOI: 10.1016/j.cles.2024.100140

Anusuya K , Vijayakumar K

{"title":"A comparative study of floating and ground-mounted photovoltaic power generation in Indian contexts","authors":"Anusuya K , Vijayakumar K","doi":"10.1016/j.cles.2024.100140","DOIUrl":"10.1016/j.cles.2024.100140","url":null,"abstract":"<div><p>The escalating global demand for energy and growing environmental concerns have stimulated the development of renewable energy-based power systems. In this context, solar power has gained significant attention, notably in the form of floating photovoltaic systems. These systems, installed on water bodies, not only boost efficiency but also reduce water evaporation from reservoirs. This research explores the power generation capabilities of floating photovoltaic systems in comparison to ground-mounted photovoltaic systems, considering a 250-watt monocrystalline photovoltaic panel. This study utilizes typical meteorological year data to comprehensively analyze four distinct locations in India. By using a single-diode model, this study finds that floating photovoltaic systems provide 6–7 % more power output than ground-mounted photovoltaic systems. This efficiency gain is because the floating photovoltaic panels operate at a lower temperature (4–6 °C) than their ground-mounted photovoltaic counterparts, positively influencing the overall performance. Furthermore, the degradation and soiling of ground-mounted photovoltaic and floating photovoltaic systems were also compared. The financial analysis reveals that ground-mounted photovoltaic systems typically have a lower levelized cost of electricity and shorter payback periods. Even though the financial indicators of floating photovoltaic systems are not favorable compared to ground-mounted photovoltaic systems, these results show how vital floating photovoltaic technology is for achieving the United Nations’ Sustainable Development Goals and how it could be used as an efficient technique to reduce land requirements for solar photovoltaic solutions in various geographical conditions.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000347/pdfft?md5=a477c0b72f5feb0b833a97cf97875b34&pid=1-s2.0-S2772783124000347-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term forecasting of floating photovoltaic power generation using machine learning models 利用机器学习模型对浮动光伏发电进行短期预测

Cleaner Energy Systems Pub Date : 2024-08-16 DOI: 10.1016/j.cles.2024.100137

Mohd Herwan Sulaiman , Mohd Shawal Jadin , Zuriani Mustaffa , Mohd Nurulakla Mohd Azlan , Hamdan Daniyal

{"title":"Short-Term forecasting of floating photovoltaic power generation using machine learning models","authors":"Mohd Herwan Sulaiman , Mohd Shawal Jadin , Zuriani Mustaffa , Mohd Nurulakla Mohd Azlan , Hamdan Daniyal","doi":"10.1016/j.cles.2024.100137","DOIUrl":"10.1016/j.cles.2024.100137","url":null,"abstract":"<div><p>Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000311/pdfft?md5=7ce96141a620bc0a687d5ccbf423c62a&pid=1-s2.0-S2772783124000311-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing energy access in rural areas: Intelligent microgrid management for universal telecommunications and electricity 加强农村地区的能源供应：普及电信和电力的智能微电网管理

Cleaner Energy Systems Pub Date : 2024-08-16 DOI: 10.1016/j.cles.2024.100136

Kanlou Zandjina Dadjiogou , Ayité Sénah Akoda Ajavon , Yao Bokovi

{"title":"Enhancing energy access in rural areas: Intelligent microgrid management for universal telecommunications and electricity","authors":"Kanlou Zandjina Dadjiogou , Ayité Sénah Akoda Ajavon , Yao Bokovi","doi":"10.1016/j.cles.2024.100136","DOIUrl":"10.1016/j.cles.2024.100136","url":null,"abstract":"<div><p>In rural areas lacking an electricity grid, cell phone operators use generators to power their facilities. At the same time, however, the local population is finding it difficult to use the cell phones and other electronic devices for which these operators are deploying their efforts. This situation, due to the problem of access to energy, hinders universal access to telecommunications. The present study aims to solve this problem using microgrid techniques. A microgrid consisting of photovoltaic panels, a genset and storage batteries has been designed to meet the needs of cell phone operators' sites in Bapure, a rural locality in Togo. The focus is on managing energy flows between the various sources of the microgrid, and between the needs of the cell phone operators' site and those of the local population. To resolve the lack of solar irradiation data at Bapure, hourly solar irradiation was predicted using the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to obtain a realistic result. Optimization studies were then carried out using the Particle Swarm Optimization (PSO) algorithm to determine the optimum system configuration to ensure continuity of service at the operator's site. The simulation results show that the proposed system has a surplus of energy production at all times, which can be used to supply electricity to the population at a cost equal to 0.0185 USD, with a solar energy utilization rate of 98,95 % and a generator that only needs to operate at 0.15 % throughout the year. The results obtained indicate that a renewable energy system can provide a more efficient solution for electrifying the rural mobile operator's sites and the local population, and can improve the quality of service for the telecommunications industries.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277278312400030X/pdfft?md5=ae5f5c5d0670b19a102b4e6150630ef8&pid=1-s2.0-S277278312400030X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models 利用机器学习模型分析发电过程中温度对光伏组件表面的影响

Cleaner Energy Systems Pub Date : 2024-08-13 DOI: 10.1016/j.cles.2024.100135

S. M. Rezaul Karim , Debasish Sarker , Md. Monirul Kabir

{"title":"Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models","authors":"S. M. Rezaul Karim , Debasish Sarker , Md. Monirul Kabir","doi":"10.1016/j.cles.2024.100135","DOIUrl":"10.1016/j.cles.2024.100135","url":null,"abstract":"<div><p>Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly dependent on the production of clean energy. According to the IEA solar energy has a huge potential and will contribute up to 16 % of the global electricity by 2050. Hence, prediction of solar energy production has a great deal of demand in renewable energy sector. This paper compares machine-learning algorithms to evaluate the impact of PV module back surface temperature (degC) on the generated power. Support Vector Machine for Regression (SMOreg), Multilayer Perceptron (ANN), Linear Regression, M5 Rules, k-Nearest-Neighbor (Ibk) and Random Forest methods are employed to test their performance in different ratio of training and testing data. The dataset comprises five independent parameters such as PV module back surface temperature (degC), Dry bulb temperature (degC), Relative humidity (%RH), Atmospheric pressure (mb), and Precipitation (mm). The dependent parameter is Maximum power of PV module (W). The correlation coefficient was determined by varying the percentage of training data from 60 % to 85 %. The numerical tests were done for two data sets, one dataset includes all the independent variables and another one excluded the PV module back surface temperature. Except for M5 Rules, other models exhibit consistent correlation coefficients with several of training data. All models demonstrate a dependency on the PV module back surface temperature, with Random Forest surpassing others in overall performance with a correlation coefficient of 0.9713 at 75 % of training set.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000293/pdfft?md5=e94b18d5f59eadc7771707204dcf1063&pid=1-s2.0-S2772783124000293-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics 智能农业技术：可再生能源、基于物联网的能源管理和精准机器人技术的综合框架

Cleaner Energy Systems Pub Date : 2024-08-11 DOI: 10.1016/j.cles.2024.100132

Anis Ur Rehman , Yasser Alamoudi , Haris M. Khalid , Abdennabi Morchid , S.M. Muyeen , Almoataz Y. Abdelaziz

{"title":"Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics","authors":"Anis Ur Rehman , Yasser Alamoudi , Haris M. Khalid , Abdennabi Morchid , S.M. Muyeen , Almoataz Y. Abdelaziz","doi":"10.1016/j.cles.2024.100132","DOIUrl":"10.1016/j.cles.2024.100132","url":null,"abstract":"<div><p>Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000268/pdfft?md5=1b376a814067d1973d6aa4c7ad6eeee0&pid=1-s2.0-S2772783124000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data 通过深度学习推进电动汽车电池充电状态估算：使用真实世界驾驶数据的综合研究

Cleaner Energy Systems Pub Date : 2024-08-01 DOI: 10.1016/j.cles.2024.100131

Mohd Herwan Sulaiman , Zuriani Mustaffa , Saifudin Razali , Mohd Razali Daud

{"title":"Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data","authors":"Mohd Herwan Sulaiman , Zuriani Mustaffa , Saifudin Razali , Mohd Razali Daud","doi":"10.1016/j.cles.2024.100131","DOIUrl":"10.1016/j.cles.2024.100131","url":null,"abstract":"<div><p>Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000256/pdfft?md5=7448e81d1f5c13869ef75b0f12b0f078&pid=1-s2.0-S2772783124000256-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm 利用带优化算法的混合深度学习模型进行实时运行的极短期风力发电预测

Cleaner Energy Systems Pub Date : 2024-07-27 DOI: 10.1016/j.cles.2024.100129

Md. Omer Faruque , Md. Alamgir Hossain , Md. Rashidul Islam , S.M. Mahfuz Alam , Ashish Kumar Karmaker

{"title":"Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm","authors":"Md. Omer Faruque , Md. Alamgir Hossain , Md. Rashidul Islam , S.M. Mahfuz Alam , Ashish Kumar Karmaker","doi":"10.1016/j.cles.2024.100129","DOIUrl":"10.1016/j.cles.2024.100129","url":null,"abstract":"<div><p>This paper proposes a new hybrid deep learning model to enhance the accuracy of forecasting very short-term wind power generation. The proposed model comprises a convolutional layer, a long-short-term memory (LSTM) unit, and fully connected neural network. Convolution layer can automatically learn complicated features from the raw input, whereas the LSTM layers can retain useful information through which gradient information may flow over extended periods. To obtain the best performance from the forecasting model, a random search optimization technique has been developed for tuning hyper-parameters of the model developed. The 5 min datasets from the White Rock wind farm, Australia are used to investigate the effectiveness of the proposed model as wind farms are participating in spot electricity market. To compare the effectiveness, the proposed model is compared with the existing models, such as convolution neural network (CNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), artificial neural network (ANN), and support vector machine (SVM). The root-mean-square error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (TIC) are used to analyze and compare the performances of the predictive models. Based on RMSE and MAE, the proposed model exhibits a higher accuracy of approximately 23.79% and 28.63% compared to other forecasting methods, respectively.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000232/pdfft?md5=e04018332afecc00662ddb1533359b4e&pid=1-s2.0-S2772783124000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A SWOT Analysis Approach for the Development of Photovoltaic (PV) Energy in Northern Nigeria 尼日利亚北部光伏能源开发的 SWOT 分析方法

Cleaner Energy Systems Pub Date : 2024-07-24 DOI: 10.1016/j.cles.2024.100128

Anas A. Bisu , Tariq G. Ahmed , Umar S. Ahmad , Abubakar D. Maiwada

{"title":"A SWOT Analysis Approach for the Development of Photovoltaic (PV) Energy in Northern Nigeria","authors":"Anas A. Bisu , Tariq G. Ahmed , Umar S. Ahmad , Abubakar D. Maiwada","doi":"10.1016/j.cles.2024.100128","DOIUrl":"10.1016/j.cles.2024.100128","url":null,"abstract":"<div><p>This research employs a comprehensive Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to investigate the advancement of photovoltaic (PV) energy in Northern Nigeria. The study delves into the intricacies of introducing PV systems within the context of economic challenges, including issues such as currency volatility and inflation, which amplify costs and impede capital investments. Environmental factors, such as dust and sandstorms, are identified as obstacles diminishing the efficiency of solar panels. Additionally, security concerns in remote areas elevate operational costs and influence investment decisions. This paper proposes effective mitigation strategies, encompassing widespread public awareness campaigns to augment market engagement, the establishment of mini-grid systems for enhanced energy distribution, customised on-the-job training programs to foster local expertise in PV technology, and the utilisation of micro-grid systems as experimental grounds for regulatory and policy testing. By synthesising these components, the study offers a comprehensive overview of the prerequisites essential for the successful proliferation of PV energy in Northern Nigeria. Emphasis is placed on the potential for solar energy to significantly contribute to the region's sustainable development and achieve energy independence when the identified strength, and opportunities are exploited. The key strength identified are the average Global horizontal irradiance (GHI) of 5.436 kWh/m<sup>2</sup>, Direct Normal Irradiance (DNI) of 1534–1680 kWh/m<sup>2</sup>, Levelised Cost of Electricity (LCoE) of $ 0.1, and an opportunity to fully utilise the over $ 7.88 million grant authorised by the African Development Bank (AfDB) from the Sustainable Energy Fund for Africa.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000220/pdfft?md5=5ad76d8050fa4517244a11767e5a1f55&pid=1-s2.0-S2772783124000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141961799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing energy expenditure in agricultural autonomous ground vehicles through a GPU-accelerated particle swarm optimization-artificial neural network framework 通过 GPU 加速的粒子群优化-人工神经网络框架优化农业自主地面车辆的能源消耗

Cleaner Energy Systems Pub Date : 2024-07-24 DOI: 10.1016/j.cles.2024.100130

Ambuj, Rajendra Machavaram

{"title":"Optimizing energy expenditure in agricultural autonomous ground vehicles through a GPU-accelerated particle swarm optimization-artificial neural network framework","authors":"Ambuj, Rajendra Machavaram","doi":"10.1016/j.cles.2024.100130","DOIUrl":"10.1016/j.cles.2024.100130","url":null,"abstract":"<div><p>The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000244/pdfft?md5=95d29beea2a66736f8e26b5d92c843c0&pid=1-s2.0-S2772783124000244-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Techno-economic analysis and dynamic power simulation of a hybrid solar-wind-battery system for power supply in rural areas in Pakistan 用于巴基斯坦农村地区供电的太阳能-风能-电池混合系统的技术经济分析和动态功率模拟

Cleaner Energy Systems Pub Date : 2024-06-11 DOI: 10.1016/j.cles.2024.100127

Rafiq Ahmad , Hooman Farzaneh

{"title":"Techno-economic analysis and dynamic power simulation of a hybrid solar-wind-battery system for power supply in rural areas in Pakistan","authors":"Rafiq Ahmad , Hooman Farzaneh","doi":"10.1016/j.cles.2024.100127","DOIUrl":"10.1016/j.cles.2024.100127","url":null,"abstract":"<div><p>This study presents the optimal design and operation of a proposed hybrid renewable energy system (HRES) for the electrification of a residential building in rural areas in Pakistan. The main contributions of this study are twofold. Firstly, it develops a size optimization model based on the particle swarm optimization (PSO) technique to determine the optimal configuration for two hybrid renewable energy systems (HRES), including both grid-tied and off-grid modes, integrating wind and photovoltaic (PV) systems with battery storage. The optimal configuration is determined by minimizing the levelized cost of electricity, using local meteorological and electricity load data, along with technical specifications of the main HRES components. Secondly, dynamic simulations of two HRES configurations are conducted, using MATLAB Simulink, ensuring the optimal energy balance between multiple energy sources and the load at each operation hour. To meet an annual electrical demand of 131.035 MWh, the grid-tied HRES yields 146.081 MWh annually, with solar contributing 68.85 MWh and wind 77.272 MWh. Conversely, the off-grid system generates 133.533 MWh annually, with solar and wind output power at 43.932 MWh and 89.601 MWh, respectively. The grid-tied system achieves an LCOE of approximately 0.29 $/kWh, with optimal wind turbine and PV capacities of 11 kW and 29 kW, respectively. While in off-grid configuration, the off-grid scenario exhibits an LCOE of 0.91 $/kWh, with optimal capacities of 10 kW for wind turbine, 20 kW for PV, and 2437.5 AH for batteries. The findings provide insights relevant to diverse locations, emphasizing the importance of local meteorological and geographical data. Multiple case studies ensure the robustness and applicability of the proposed system under varying conditions.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000219/pdfft?md5=e59e664382a1c7350082b74cc2fb9e65&pid=1-s2.0-S2772783124000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141392712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0