Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.006
Huayi Wu , Zhao Xu , Youwei Jia
To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources, energy storage systems are being deployed in microgrids. Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day. To mitigate this challenge, an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed, with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses. The SoC ranges of the battery energy storage (BES) are determined in the day- ahead stage. Concurrently, the power generated by fuel cells and consumed by electrolysis device are optimized. This is followed by the intraday stage, where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized. To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage, we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG). Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.
{"title":"Optimal hydrogen-battery energy storage system operation in microgrid with zero-carbon emission","authors":"Huayi Wu , Zhao Xu , Youwei Jia","doi":"10.1016/j.gloei.2024.10.006","DOIUrl":"10.1016/j.gloei.2024.10.006","url":null,"abstract":"<div><div>To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources, energy storage systems are being deployed in microgrids. Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day. To mitigate this challenge, an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed, with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses. The SoC ranges of the battery energy storage (BES) are determined in the day- ahead stage. Concurrently, the power generated by fuel cells and consumed by electrolysis device are optimized. This is followed by the intraday stage, where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized. To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage, we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG). Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 616-628"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658736","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}
Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.005
Ning Zhou , Bowen Shang , Mingming Xu , Lei Peng , Guang Feng
Improving the accuracy of solar power forecasting is crucial to ensure grid stability, optimize solar power plant operations, and enhance grid dispatch efficiency. Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties, challenges such as labor-intensive parameter adjustments and complex optimization processes persist. Thus, this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN), long short- term memory (LSTM), and attention mechanisms. The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy. To prepare high-quality training data, the solar power data were first preprocessed, including feature selection, data cleaning, imputation, and smoothing. The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture, followed by hyperparameter optimization employing Bayesian methods. The experimental results indicated that within acceptable model training times, the CNN-LSTM-attention model outperformed the LSTM, GRU, CNN-LSTM, CNN-LSTM with autoencoders, and parallel CNN-LSTM attention models. Furthermore, following Bayesian optimization, the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model, as evidenced by MRE evaluations. This highlights the clear advantage of the optimized model in forecasting fluctuating data.
{"title":"Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization","authors":"Ning Zhou , Bowen Shang , Mingming Xu , Lei Peng , Guang Feng","doi":"10.1016/j.gloei.2024.10.005","DOIUrl":"10.1016/j.gloei.2024.10.005","url":null,"abstract":"<div><div>Improving the accuracy of solar power forecasting is crucial to ensure grid stability, optimize solar power plant operations, and enhance grid dispatch efficiency. Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties, challenges such as labor-intensive parameter adjustments and complex optimization processes persist. Thus, this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN), long short- term memory (LSTM), and attention mechanisms. The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy. To prepare high-quality training data, the solar power data were first preprocessed, including feature selection, data cleaning, imputation, and smoothing. The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture, followed by hyperparameter optimization employing Bayesian methods. The experimental results indicated that within acceptable model training times, the CNN-LSTM-attention model outperformed the LSTM, GRU, CNN-LSTM, CNN-LSTM with autoencoders, and parallel CNN-LSTM attention models. Furthermore, following Bayesian optimization, the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model, as evidenced by MRE evaluations. This highlights the clear advantage of the optimized model in forecasting fluctuating data.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 667-681"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658718","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}
Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.012
Tianyu Zhang , Xiangjun Li , Hanning Li , hangyu Sun , Weisen Zhao
As the proportion of renewable energy infiltrating the power grid increases, suppressing its randomness and volatility, reducing its impact on the safe operation of the power grid, and improving the level of new energy consumption are increasingly important. For these purposes, energy storage stations (ESS) are receiving increasing attention. This article discusses the structure, working principle, and control methods of grid-following and grid-forming energy-storage converters, which are currently commonly used. A simulation analysis was conducted to investigate their dynamic response characteristics. The advantages and disadvantages of two types of energy storage power stations are discussed, and a configuration strategy for hybrid ESS is proposed. This paper presents research on and a simulation analysis of grid- forming and grid-following hybrid energy storage systems considering two types of energy storage according to different capacity scenarios. Finally, a comparative analysis between the systems is presented. A simulation model was established using PSD-BPA (Power System Department-Bonneville Power Administration) to analyze the impact of the capacity ratio of grid-following and grid-forming ESS on their dynamic response characteristics in a hybrid ESS. In addition, a development direction for future ESSs is indicated.
{"title":"Simulation and application analysis of a hybrid energy storage station in a new power system","authors":"Tianyu Zhang , Xiangjun Li , Hanning Li , hangyu Sun , Weisen Zhao","doi":"10.1016/j.gloei.2024.10.012","DOIUrl":"10.1016/j.gloei.2024.10.012","url":null,"abstract":"<div><div>As the proportion of renewable energy infiltrating the power grid increases, suppressing its randomness and volatility, reducing its impact on the safe operation of the power grid, and improving the level of new energy consumption are increasingly important. For these purposes, energy storage stations (ESS) are receiving increasing attention. This article discusses the structure, working principle, and control methods of grid-following and grid-forming energy-storage converters, which are currently commonly used. A simulation analysis was conducted to investigate their dynamic response characteristics. The advantages and disadvantages of two types of energy storage power stations are discussed, and a configuration strategy for hybrid ESS is proposed. This paper presents research on and a simulation analysis of grid- forming and grid-following hybrid energy storage systems considering two types of energy storage according to different capacity scenarios. Finally, a comparative analysis between the systems is presented. A simulation model was established using PSD-BPA (Power System Department-Bonneville Power Administration) to analyze the impact of the capacity ratio of grid-following and grid-forming ESS on their dynamic response characteristics in a hybrid ESS. In addition, a development direction for future ESSs is indicated.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 553-562"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658751","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}
Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.010
Yansong Gao , A. Lifu , Chenxu Zhao , Xiaodong Qin , Ri Na , An Wang , Shangshang Wei
This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters.
{"title":"Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting","authors":"Yansong Gao , A. Lifu , Chenxu Zhao , Xiaodong Qin , Ri Na , An Wang , Shangshang Wei","doi":"10.1016/j.gloei.2024.10.010","DOIUrl":"10.1016/j.gloei.2024.10.010","url":null,"abstract":"<div><div>This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 653-666"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658739","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}
Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.011
Weiming Peng , Yanhui Xu , Gendi Li , Jie Song , Guizhi Xu , Xiaona Xu , Yan Pan
Hydrogen energy, with its abundant reserves, green and low-carbon characteristic, high energy density, diverse sources, and wide applications, is gradually becoming an important carrier in the global energy transformation and development. In this paper, the off-grid wind power hydrogen production system is considered as the research object, and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell, including underload, overload, variable load, and start- stop are analyzed. On this basis, the characteristic extraction of wind power output data after noise reduction is carried out, and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study, and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems, and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.
{"title":"Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell","authors":"Weiming Peng , Yanhui Xu , Gendi Li , Jie Song , Guizhi Xu , Xiaona Xu , Yan Pan","doi":"10.1016/j.gloei.2024.10.011","DOIUrl":"10.1016/j.gloei.2024.10.011","url":null,"abstract":"<div><div>Hydrogen energy, with its abundant reserves, green and low-carbon characteristic, high energy density, diverse sources, and wide applications, is gradually becoming an important carrier in the global energy transformation and development. In this paper, the off-grid wind power hydrogen production system is considered as the research object, and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell, including underload, overload, variable load, and start- stop are analyzed. On this basis, the characteristic extraction of wind power output data after noise reduction is carried out, and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study, and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems, and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 642-652"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658738","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}
Pub Date : 2024-10-01DOI: 10.1016/j.gloei.2024.10.009
Mohd Zaidi Mohd Tumari , Mohd Ashraf Ahmad , Mohd Riduwan Ghazali , Mohd Helmi Suid
Automatic voltage regulators (AVR) are designed to manipulate a synchronous generator’s voltage level automatically. Proportional integral derivative (PID) controllers are typically used in AVR systems to regulate voltage. Although advanced PID tuning methods have been proposed, the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties. This requires continuous fine tuning of the PID parameters. However, manual adjustment of these parameters can compromise the stability and robustness of the AVR system. This study focuses on the online self-tuning of PID controllers called indirect design approach-2 (IDA-2) in AVR systems while preserving robustness. In particular, we indirectly tune the PID controller by shifting the frequency response. The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters. Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously, thereby improving the control performance and robustness. We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins (GMs) and phase margins (PMs) with previously optimized PID parameters during parameter uncertainties. The proposed method is further evaluated in terms of disturbance rejection, measurement noise, and frequency response analysis during parameter uncertainty calculations against existing methods. Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system. In summary, online self-tuning enables automated PID parameter adjustment in an AVR system, while maintaining stability and robustness.
{"title":"Optimizing PID controller parameters for robust automatic voltage regulator system through indirect design approach-2","authors":"Mohd Zaidi Mohd Tumari , Mohd Ashraf Ahmad , Mohd Riduwan Ghazali , Mohd Helmi Suid","doi":"10.1016/j.gloei.2024.10.009","DOIUrl":"10.1016/j.gloei.2024.10.009","url":null,"abstract":"<div><div>Automatic voltage regulators (AVR) are designed to manipulate a synchronous generator’s voltage level automatically. Proportional integral derivative (PID) controllers are typically used in AVR systems to regulate voltage. Although advanced PID tuning methods have been proposed, the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties. This requires continuous fine tuning of the PID parameters. However, manual adjustment of these parameters can compromise the stability and robustness of the AVR system. This study focuses on the online self-tuning of PID controllers called indirect design approach-2 (IDA-2) in AVR systems while preserving robustness. In particular, we indirectly tune the PID controller by shifting the frequency response. The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters. Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously, thereby improving the control performance and robustness. We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins (GMs) and phase margins (PMs) with previously optimized PID parameters during parameter uncertainties. The proposed method is further evaluated in terms of disturbance rejection, measurement noise, and frequency response analysis during parameter uncertainty calculations against existing methods. Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system. In summary, online self-tuning enables automated PID parameter adjustment in an AVR system, while maintaining stability and robustness.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 5","pages":"Pages 682-696"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658752","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}
Pub Date : 2024-08-01DOI: 10.1016/j.gloei.2024.08.009
Mahmood Swadi , Dheyaa Jasim Kadhim , Mohamed Salem , Firas Mohammed Tuaimah , Ammar Sabri. Majeed , Ali Jawad Alrubaie
The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels. This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar, wind, and hydrogen. This transition economically challenges traditional energy sectors while fostering new industries, promoting job growth, and sustainable economic development. The transition to renewable energy demands social equity, ensuring universal access to affordable energy, and considering community impact. The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint. This study highlights the rapid growth of the global wind power market, which is projected to increase from $112.23 billion in 2022 to $278.43 billion by 2030, with a compound annual growth rate of 13.67%. In addition, the demand for hydrogen is expected to increase, significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions. By 2028, renewables are predicted to account for 42% of global electricity generation, with significant contributions from wind and solar photovoltaic (PV) technology, particularly in China, the European Union, the United States, and India. These developments signify a global commitment to diversifying energy sources, reducing emissions, and moving toward cleaner and more sustainable energy solutions. This review offers stakeholders the insights required to smoothly transition to sustainable energy, setting the stage for a resilient future.
{"title":"Investigating and predicting the role of photovoltaic, wind, and hydrogen energies in sustainable global energy evolution","authors":"Mahmood Swadi , Dheyaa Jasim Kadhim , Mohamed Salem , Firas Mohammed Tuaimah , Ammar Sabri. Majeed , Ali Jawad Alrubaie","doi":"10.1016/j.gloei.2024.08.009","DOIUrl":"10.1016/j.gloei.2024.08.009","url":null,"abstract":"<div><p>The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels. This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar, wind, and hydrogen. This transition economically challenges traditional energy sectors while fostering new industries, promoting job growth, and sustainable economic development. The transition to renewable energy demands social equity, ensuring universal access to affordable energy, and considering community impact. The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint. This study highlights the rapid growth of the global wind power market, which is projected to increase from $112.23 billion in 2022 to $278.43 billion by 2030, with a compound annual growth rate of 13.67%. In addition, the demand for hydrogen is expected to increase, significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions. By 2028, renewables are predicted to account for 42% of global electricity generation, with significant contributions from wind and solar photovoltaic (PV) technology, particularly in China, the European Union, the United States, and India. These developments signify a global commitment to diversifying energy sources, reducing emissions, and moving toward cleaner and more sustainable energy solutions. This review offers stakeholders the insights required to smoothly transition to sustainable energy, setting the stage for a resilient future.</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 4","pages":"Pages 429-445"},"PeriodicalIF":1.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096511724000690/pdf?md5=eff79ff67e3a3421e6aab2553ce9b73a&pid=1-s2.0-S2096511724000690-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095114","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}
Pub Date : 2024-08-01DOI: 10.1016/j.gloei.2024.08.012
Ziyang Li , Xujun Lang , Bo Yang , Xiaolin Liu , Hao Wang , Zhang Li
Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at Kdc = 0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB.
{"title":"Vibration and noise mechanism of a 110 kV transformer under DC bias based on finite element method","authors":"Ziyang Li , Xujun Lang , Bo Yang , Xiaolin Liu , Hao Wang , Zhang Li","doi":"10.1016/j.gloei.2024.08.012","DOIUrl":"10.1016/j.gloei.2024.08.012","url":null,"abstract":"<div><p>Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at <em>K</em>dc = 0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB.</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 4","pages":"Pages 503-512"},"PeriodicalIF":1.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096511724000720/pdf?md5=664a1104150c92fc42fb9015d7421a6a&pid=1-s2.0-S2096511724000720-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095024","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}
Pub Date : 2024-08-01DOI: 10.1016/j.gloei.2024.08.003
Songyu Wu , Xiaoyan Qi , Xiang Li , Xuanyu Liu , Bolin Tong , Feiyu Zhang , Zhong Zhang
The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.
{"title":"Collaborative robust dispatch of electricity and carbon under carbon allowance trading market","authors":"Songyu Wu , Xiaoyan Qi , Xiang Li , Xuanyu Liu , Bolin Tong , Feiyu Zhang , Zhong Zhang","doi":"10.1016/j.gloei.2024.08.003","DOIUrl":"10.1016/j.gloei.2024.08.003","url":null,"abstract":"<div><p>The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 4","pages":"Pages 391-401"},"PeriodicalIF":1.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209651172400063X/pdf?md5=db99d0f4250e25bcb0b52d6852b124ca&pid=1-s2.0-S209651172400063X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095112","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}
Pub Date : 2024-08-01DOI: 10.1016/j.gloei.2024.08.011
Yu Li , Yixi Cuomu , Yiming Gao , Guoqin Lv , Weiwei Lin , Sirui Li , Changchun Zhou
In this study, the present situation and characteristics of power supply in remote areas are summarized. By studying the cases of power supply projects in remote areas, the experience is analyzed and described, and the applicability of related technologies, such as grid-forming storage and power load management, is studied, including grid-connection technologies, such as grid-forming converters and power load management. On this basis, three power-supply modes were proposed. The application scenarios and advantages of the three modes were compared and analyzed. Based on the local development situation, the temporal sequences of the three schemes are described, and a case study was conducted. The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.
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