Energy Reports最新文献

{"title":"Forecasting capacitor banks for improving efficiency of grid-integrated PV plants: A machine learning approach","authors":"Saurabh Kumar Rajput ,&nbsp;Deepansh Kulshrestha ,&nbsp;Nikhil Paliwal ,&nbsp;Vivek Saxena ,&nbsp;Saibal Manna ,&nbsp;Mohammed H. Alsharif ,&nbsp;Mun-Kyeom Kim","doi":"10.1016/j.egyr.2024.12.011","DOIUrl":"10.1016/j.egyr.2024.12.011","url":null,"abstract":"<div><div>Grid-connected rooftop PV systems are becoming more popular to promote renewable energy. The rooftop PV may diminish the system's energy efficiency by lowering the power factor (PF) on the grid side. The current work provides a machine learning approach that estimates the necessary capacitor banks to boost the PF to unity, enabling proactive remedial action for energy savings. Various machine learning models, such as linear regression, ridge regression, lasso regression, random forest, decision tree, XGBoost, Adaboost, and gradient boosting, are evaluated to improve the system's efficiency. The best model is Lasso Regression, which produces a high R² score of 0.89 with low MSE and MAPE values. The model is based on real-time data collected from a 100 kWp PV plant connected to an 11 kV grid supply and an institutional building load. The model undergoes validation by implementing the forecasted capacitor banks. According to the findings, a 10.60 kVAR-rated shunt capacitor is required to maintain the PF at unity and save an average of 1673.52 kWh of energy per month. This work highlights the necessity of implementing Lasso regression in energy management systems to improve PF, decrease electricity costs, and reduce environmental impacts.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 140-160"},"PeriodicalIF":4.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing all-solid-state sodium-ion batteries: Insights from a P2D Model on NaSICON-based polymer–ceramic electrolyte","authors":"F. Gerbig,&nbsp;J. Kühn,&nbsp;H. Nirschl","doi":"10.1016/j.egyr.2024.11.077","DOIUrl":"10.1016/j.egyr.2024.11.077","url":null,"abstract":"<div><div>Rechargeable batteries are integral to modern technology, with lithium-ion batteries (LIBs) leading in portable electronics and electric vehicles. However, the abundance and global distribution of sodium have renewed interest in sodium-ion batteries (SIBs) as a sustainable alternative, particularly for stationary energy storage and applications with less stringent energy density needs. This study develops a pseudo-two-dimensional (P2D) model to investigate the performance of all-solid-state sodium-ion batteries (ASSSIBs) with hybrid polymer–ceramic electrolytes. We compare this model with a particle-resolved microstructure model to derive effective transport parameters. Our results highlight the significance of electrolyte composition and cell design to mitigate transport limitation in the electrolyte and maximize battery performance. Optimal cell design varies with C-rate, requiring lower active material fractions and more uneven particle distributions for higher rates. Optimization shows that the charge process can harness more cell capacity than discharging, suggesting a bottleneck in the discharge process. These insights guide the development of more efficient and reliable ASSSIBs, emphasizing the importance of fast-ion conducting solid electrolytes for future advancements.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 105-116"},"PeriodicalIF":4.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal power distribution in DC/AC microgrids with electric vehicles using flow direction algorithm tuned CNN","authors":"P. Prasanna Lakshmi ,&nbsp;L. Premalatha","doi":"10.1016/j.egyr.2024.11.082","DOIUrl":"10.1016/j.egyr.2024.11.082","url":null,"abstract":"<div><div>In this paper, a new approach for optimal power distribution in DC/AC microgrids integrated with electric vehicles (EVs) using a Flow Direction Algorithm (FDA) tuned Convolutional Neural Network (CNN) is proposed. The increasing adoption of microgrids and EVs necessitates advanced energy management systems capable of efficiently handling power flow to ensure stability, reliability, and efficiency. Traditional approaches often battle with the difficulty and dynamic nature of power distribution in such systems. Our proposed approach leverages the predictive capabilities of CNNs, tuned by the FDA, to dynamically optimize power flow in DC/AC microgrid. The Flow Direction Algorithm enhances CNN's ability to predict optimal power distribution by learning from historical data, considering factors such as load demand, generation capacity, and EV charging requirements. This integration allows for adaptive and intelligent decision-making, reducing energy losses and improving the overall system. The proposed method is validated through extensive simulations in MATLAB/Simulink, demonstrating significant improvements in power distribution efficiency compared to Fuzzy logic system, Artificial Neural network and conventional CNN. The results indicate that the FDA-tuned CNN effectively balances the power between DC and AC microgrid, loads, and manages EV charging and discharging processes, and mitigates potential grid disturbances. The proposed method has prediction accuracy of 99.4 %, overall efficiency of 99.1 %.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 196-216"},"PeriodicalIF":4.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution network fault identification method based on multimodal ResNet with recorded waveform-driven feature extraction","authors":"Jian Wang ,&nbsp;Bo Zhang ,&nbsp;Dong Yin ,&nbsp;Jinxin Ouyang","doi":"10.1016/j.egyr.2024.12.012","DOIUrl":"10.1016/j.egyr.2024.12.012","url":null,"abstract":"<div><div>The existing distribution network fault identification research mainly focuses on the identification of single-cause faults or high impedance fault, and lacks of comprehensive identification of fault types and fault causes due to insufficient fault samples for reference. In this paper, a fault identification method for distribution networks based on recorded waveform-driven feature extraction and multimodal ResNet is proposed. First, the waveform characteristics are analyzed according to the typical fault recording data, and the faults of different grounding media are modeled with the fault mechanism, which are used to generate the dataset of unbalanced faults for fault waveform inversion and fault feature extraction. Second, the three-phase and zero sequence Volt-Ampere curves from the head end of a feeder are used as the feature inputs. Then, a multimodal ResNet model based on RGB normalization and attention mechanism is constructed to extract the fault features. Finally, experimental results show that the proposed model achieves better fault identification compared to other neural networks and feature extraction methods. The proposed method performs well by transfer learning without extensive re-training for different distribution systems, and can identify actual fault data for small samples. Moreover, the proposed model is properly adapted to both noise and sampling frequency.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 90-104"},"PeriodicalIF":4.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study on photovoltaic module-integrated planar air-core inductors to facilitate embedded power electronics","authors":"David A. van Nijen,&nbsp;Saurabh Chakravarty,&nbsp;Jim Voorn,&nbsp;Miro Zeman,&nbsp;Olindo Isabella,&nbsp;Patrizio Manganiello","doi":"10.1016/j.egyr.2024.12.003","DOIUrl":"10.1016/j.egyr.2024.12.003","url":null,"abstract":"<div><div>Photovoltaic modules are typically not optimized for conditions of partial shading. One proposed approach to improve their shade tolerance is to implement maximum power point tracking on different strings of cells within the modules. However, this approach increases the demand for sub-module power converters, which poses a challenge. To address this, researchers have suggested integrating power electronic components directly into the module laminate, or even within the solar cells themselves. Despite these advancements, limited research has focused on integrating the most bulky component: the inductor. This study investigates through simulations whether planar air-core inductors can yield the required properties to support sub-module power conversion. The simulated inductors have an area that is as large as an industrial crystalline silicon solar cell. First, it is shown how the interplay between the different design parameters, such as track spacing, track width, number of turns, and middle gap size, plays an important role in the inductor properties at high frequency. The coil geometries that are simulated yield inductance values between 0.3 <span><math><mi>μ</mi></math></span>H and 3.2 <span><math><mi>μ</mi></math></span>H. Subsequently, the feasibility of implementing these inductors into an exemplary DC–DC boost converter is evaluated. To adequately reduce the ripple current, a significant switching frequency of at least several hundred kHz is required. Additionally, at 500 kHz, an inductor thickness of around 0.5 mm is necessary to keep the ohmic losses in the inductor below 2% of the total generated power in standard test conditions. While demonstrating feasible combinations, these findings also present significant challenges.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 82-89"},"PeriodicalIF":4.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization scheduling method for multi-energy complementary based on green certificate-carbon trading mechanism and comprehensive demand response","authors":"Jun Zhang ,&nbsp;Tianren Zhang ,&nbsp;Feng Pan ,&nbsp;Yuyao Yang ,&nbsp;Lei Feng ,&nbsp;Yuping Huang","doi":"10.1016/j.egyr.2024.11.071","DOIUrl":"10.1016/j.egyr.2024.11.071","url":null,"abstract":"<div><div>To achieve the goal of carbon neutrality,effectively managing load fluctuations while controlling total carbon emissions presents a significant challenge for the new power system. This study addresses issues such as carbon reduction and efficiency enhancement, improving the integration of clean energy, and reducing energy costs for enterprise clusters integrated energy systems. We innovatively combine Dynamic Green Certificate Trading (GCT), Carbon Emission Trading (CET) mechanisms, and Integrated Demand Response (IDR) to propose a multi-energy complementarity optimization scheduling method that considers the GCT-CET mechanisms and IDR. First, we establish an optimization model for an integrated energy system that accounts for the coupling of electricity, gas, heat, and cold, with the goal of minimizing system operational costs, carbon emissions, and green certificate trading costs. Based on optimizing the energy supply side's outputs, we utilize energy conversion devices to enhance multi-energy complementary capabilities. On the demand side, we develop an integrated demand response model to smooth load curves. By introducing demand-side integrated energy response and the carbon emission trading (CET) mechanism, we aim to achieve carbon reduction and peak shaving, resulting in a significant carbon reduction effect of 21.6 %, with peak-to-valley differences in electricity, heat, and cold loads reduced by 23.47 %, 23.47 %, and 20.99 %, respectively. Additionally, we incorporate a dynamic green certificate trading mechanism to explore its impact on carbon emissions and the integrated energy supply structure. The results demonstrate that the optimization model for enterprise clusters' integrated energy systems, which considers the GCT-CET mechanisms and IDR, effectively enhances the economic efficiency of the system, providing important insights for the sustainable development and environmental protection of future energy systems.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 40-58"},"PeriodicalIF":4.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new approach towards analysis of life cycle of energy storage systems: An intuitionistic fuzzy rough based TODIM approach","authors":"Amir Hussain ,&nbsp;Kifayat Ullah ,&nbsp;Nezir Aydin ,&nbsp;Oludolapo Akanni Olanrewaju","doi":"10.1016/j.egyr.2024.11.091","DOIUrl":"10.1016/j.egyr.2024.11.091","url":null,"abstract":"<div><div>The role of energy storage systems (ESS) in balancing supply and demand, integrating renewable sources, and enhancing stability is crucial. The life cycle of the ESS may vary due to the involvement of multiple environmental, economic, and social factors. During the life cycle analysis (LCA) of the ESS, the evaluation of these factors is very important. This study conducts a thorough and comprehensive analysis of the life cycles of various energy storage technologies based on multiple factors. A well-known framework called intuitionist fuzzy rough set (IFRS) is utilized to extract information from experts for ESS based on multiple factors. Then, an acronym in Portuguese for interactive and multi-criteria decision-making (TODIM) is utilized for symmetric LCA for ESS. The results, which varied using different weights, were compared with similar models to demonstrate the robustness and adaptability of the approach.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 59-67"},"PeriodicalIF":4.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of urban morphology integrated building envelope materials in achieving zero emissions: A simulation-based study in complex-shaped urban blocks","authors":"Daranee Jareemit ,&nbsp;Manat Srivanit ,&nbsp;Samustpon Tanapant ,&nbsp;Bundit Limeechokchai","doi":"10.1016/j.egyr.2024.11.088","DOIUrl":"10.1016/j.egyr.2024.11.088","url":null,"abstract":"<div><div>Cities significantly impact global energy demand and play a crucial role in the challenges posed by global climate change. This research investigates how building envelope materials integrating urban forms influence the carbon neutrality of nine urban blocks in Bangkok's commercial district. The urban energy consumption and potential solar energy simulations were conducted using the parametric platform in the Rhino-Grasshopper with microclimate and energy simulation plugins. Forty-eight envelope designs with varying window-to-wall ratios, walls, and glass U-values were examined. Results indicate that the average energy use intensity (EUI) ranged from 67.12 to 114.89 kWh/m²·yr with estimated CO₂ emissions ranging from 26.7 to 60.3 kgCO₂e/m²·yr. The solar energy production intensity (PVI) was 17.7–235.5 kWh/m²·yr. The areas with a high residential-to-commercial ratio exhibit lower average EUI than those with low residential-to-commercial ratios. Buildings with a lower floor area ratio (FAR) demonstrate substantial potential for solar energy production of twice their consumption, achieving zero energy consumption. Conversely, higher-density blocks face challenges due to limited roof space. To achieve a zero-energy building, the urban block requires the roof area to be at least 50 % of the building floor area. The novelty of this study provides valuable insights for architects, urban planners, and policymakers to maximize energy efficiency and move towards zero-emission in irregular urban patterns with mixed-use functions. The findings could be used to inform policymakers, architects, and city planners in the early stage of building construction, to avoid the carbon lock-in.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 27-39"},"PeriodicalIF":4.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A U-MIDAS modeling framework for forecasting carbon dioxide emissions based on LSTM network and LASSO regression","authors":"Chunzi Wang ,&nbsp;Fusheng Xie ,&nbsp;Junpeng Yan ,&nbsp;Yiqing Xia","doi":"10.1016/j.egyr.2024.11.069","DOIUrl":"10.1016/j.egyr.2024.11.069","url":null,"abstract":"<div><div>Accurately predicting CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions holds profound significance for the Chinese government as it shapes policies aimed at realizing the China’s “dual carbon” goals of carbon peaking and carbon neutrality. This research focuses on improving the dynamic forecasting accuracy of CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions while including mixed-frequency independent variables. We propose a novel hybrid model, U-LSTM-LASSO, which combines the advantages of LSTM network for non-linear pattern recognition and LASSO regression for efficient feature selection within the unrestricted mixed data sampling (U-MIDAS) regression framework. Then U-LSTM-LASSO model is compared with two separate models, U-LSTM and U-LASSO, across all combinations of forecast horizons (<span><math><mi>h</mi></math></span>) ranging from 1 to 8 and maximum lag orders (<span><math><mi>K</mi></math></span>) set at 2, 5, and 8. The forecast performance is evaluated using root mean squared error (RMSE), the Diebold–Mariano (DM) test, and the cumulative sum of squared forecast error loss differential (CUMSFE). Empirical findings demonstrate that U-LSTM-LASSO exhibits lower testing RMSE values than U-LSTM and U-LASSO across all <span><math><mi>h</mi></math></span> and <span><math><mi>K</mi></math></span> combinations. The results of DM test indicate that U-LSTM-LASSO shows statistically more accurate forecasts than U-LSTM across all <span><math><mi>h</mi></math></span> and <span><math><mi>K</mi></math></span> combinations. Similarly, U-LSTM-LASSO statistically outperforms U-LASSO in all but two cases, when <span><math><mrow><mi>h</mi><mo>=</mo><mn>3</mn><mo>,</mo><mi>K</mi><mo>=</mo><mn>8</mn></mrow></math></span> and <span><math><mrow><mi>h</mi><mo>=</mo><mn>6</mn><mo>,</mo><mi>K</mi><mo>=</mo><mn>8</mn></mrow></math></span>. Further, the advancement of U-LSTM-LASSO model remains consistent throughout the entire testing period, as evidenced by the average CUMSFE values, all of which exceed 0 at every timestamp in the testing set. Additionally, U-LSTM generally outperforms U-LASSO in terms of RMSE under most situations, yet it does not exhibit a statistically significant difference in forecast accuracy across all <span><math><mi>h</mi></math></span> and <span><math><mi>K</mi></math></span> combinations.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 16-26"},"PeriodicalIF":4.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-step wind energy forecasting in the Mexican Isthmus using machine and deep learning","authors":"Angel A. Galarza-Chavez ,&nbsp;Jose L. Martinez-Rodriguez ,&nbsp;René Fernando Domínguez-Cruz ,&nbsp;Esmeralda López-Garza ,&nbsp;Ana B. Rios-Alvarado","doi":"10.1016/j.egyr.2024.11.074","DOIUrl":"10.1016/j.egyr.2024.11.074","url":null,"abstract":"<div><div>Wind energy has gained more presence in Mexico, specifically in the Isthmus region of Oaxaca. Due to the intermittency of environmental conditions, predicting power generation across various wind farms in the area is essential for making informed decisions. However, there is currently a lack of strategies that provide energy predictions for wind farms in this region over a specific period, particularly using a multi-step forecasting approach. This paper proposes a methodology and implementation for forecasting energy generation in wind farms within the Isthmus region. The methodology includes stages for data analysis and exploration, preprocessing, configuring regression models, evaluation and simulation, and multi-step forecasting (24-hour period). Five regression algorithms were analyzed: Linear Regression (LR), Support Vector Regression (SVR), Multiple-SVR (M-SVR), General Regression Neural Network (GRNN), and Long Short-Term Memory (LSTM). Additionally, multi-step forecasting strategies such as recursive and Multi-Input Multi-Output (MIMO) were examined. Among these models, the LR and M-SVR models using the MIMO strategy yielded the best results in this study, achieving a Root Mean Square Error (RMSE) of 0.10 and a Mean Absolute Error (MAE) of 0.08. We also analyze daily forecasts to demonstrate the monthly model performance fluctuations during a whole year. Furthermore, the proposed model is based on actual wind conditions in the area, enhancing its effectiveness and feasibility.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 1-15"},"PeriodicalIF":4.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}