Gengsheng He, Yu Huang, Guori Huang, Xi Liu, Pei Li, Yan Zhang
Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.
虚拟发电厂（VPP）通过物联网技术汇聚大量分布式能源资源（DER），为电网提供灵活性。它是促进可再生能源利用、实现未来电力系统碳中和的有效手段。本文针对 DERs 的低碳效益评估问题，提出了自组织 VPP 的灵活性评估模型，以量化 DERs 在不同时段响应行为的低碳价值。首先，介绍了基于可再生能源与负荷需求曲线同步率的零碳指数定义。然后，我们建立了虚拟电厂的多级自组织聚合方法，定义了 DER 的基本规则，并将其自组织聚合表征为马尔可夫博弈过程。此外，我们还利用 QMIX 实现了虚拟电厂自下而上、由简到繁的分层构建。实验结果表明，当用户追踪零碳曲线时，可以在不降低总体负荷的情况下实现零碳排放，从而显著增强电网的调节能力和可再生能源的消耗。此外，自组织算法还能优化 DER 的组合，提高 VPP 在复杂环境中的协调效率。
{"title":"Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning","authors":"Gengsheng He, Yu Huang, Guori Huang, Xi Liu, Pei Li, Yan Zhang","doi":"10.3390/en17153688","DOIUrl":"https://doi.org/10.3390/en17153688","url":null,"abstract":"Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.
{"title":"A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer","authors":"Deng-ao Li, Qi Liu, Ding Feng, Zhichao Chen","doi":"10.3390/en17153676","DOIUrl":"https://doi.org/10.3390/en17153676","url":null,"abstract":"Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory Autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model’s reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings.
{"title":"Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting","authors":"Yeeun Moon, Younjeong Lee, Yejin Hwang, J. Jeong","doi":"10.3390/en17153666","DOIUrl":"https://doi.org/10.3390/en17153666","url":null,"abstract":"Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory Autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model’s reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leng Tian, X. Chai, Lei Zhang, Wenbo Zhang, Yuan Zhu, Jiaxin Wang, Jianguo Wang
Increasing oil production is crucial for multilayer co-production. When there are significant differences in the permeability of each layer, an interlayer contradiction arises that can impact the recovery efficiency. After a number of tests and the establishment of a mathematical model, the effects of permeability contrast on oil production for water flooding were revealed. In the meantime, the developed mathematical model was solved using the Buckley–Lever seepage equation. Ultimately, the accuracy of the established model was confirmed by comparing the simulated outcomes of the mathematical model with the experimental results. The findings indicate that when permeability contrast increases, the production ratio of the high-permeability layer will improve. This is primarily due to the low-permeability layer’s production contribution rate decreasing. The accuracy of the established model is ensured by an error of less than 5% between the results of the experiment and the simulation. When the permeability contrast is less than three, the low-permeability layer can be effectively used for three-layer commingled production. However, when the permeability contrast exceeds six, the production coefficient of the low-permeability layer will be less than 5%, which has a significant impact on the layer’s development.
{"title":"Study on Compatibility Evaluation of Multilayer Co-Production to Enhance Recovery of Water Flooding in Oil Reservoir","authors":"Leng Tian, X. Chai, Lei Zhang, Wenbo Zhang, Yuan Zhu, Jiaxin Wang, Jianguo Wang","doi":"10.3390/en17153667","DOIUrl":"https://doi.org/10.3390/en17153667","url":null,"abstract":"Increasing oil production is crucial for multilayer co-production. When there are significant differences in the permeability of each layer, an interlayer contradiction arises that can impact the recovery efficiency. After a number of tests and the establishment of a mathematical model, the effects of permeability contrast on oil production for water flooding were revealed. In the meantime, the developed mathematical model was solved using the Buckley–Lever seepage equation. Ultimately, the accuracy of the established model was confirmed by comparing the simulated outcomes of the mathematical model with the experimental results. The findings indicate that when permeability contrast increases, the production ratio of the high-permeability layer will improve. This is primarily due to the low-permeability layer’s production contribution rate decreasing. The accuracy of the established model is ensured by an error of less than 5% between the results of the experiment and the simulation. When the permeability contrast is less than three, the low-permeability layer can be effectively used for three-layer commingled production. However, when the permeability contrast exceeds six, the production coefficient of the low-permeability layer will be less than 5%, which has a significant impact on the layer’s development.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. A. Bolaños-Navarrete, J. Bastidas‐Rodríguez, Gustavo Osorio
This paper presents a single-phase Full-Bridge (FB) inverter with a hybrid commutation technique designed to reduce the harmonic distortion caused by the loss of the controller capability around the zero-crossing point in the unipolar commutation region. The hybrid modulation changes from unipolar to bipolar commutation under the loss of the reference control, improving the robustness and efficiency of the method. The commutation technique improves the switching performance and reduces the switching losses. Simulation models are developed in MATLAB/Simulink R2023b to evaluate their performance under different operating conditions. The results show that the proposed commutation technique can achieve high efficiency, low total harmonic distortion (THD), and fast dynamic response. The experimental implementation of sliding mode control (SMC) implemented in an STM32 microcontroller confirms that the hybrid commutation technique can reduce the THD by 0.96 percentage points for local (off-grid) loads and up to 2.45 in an industrial grid-tie network, compared with unipolar commutation. These findings highlight the potential of the proposed modulation technique for applications like solar panels and offer crucial insights for ongoing research and development in this field.
{"title":"A Hybrid Commutation Technique for Reducing Zero-Crossing Distortion in a Sliding Mode Controller for Single-Phase Grid-Tied Full-Bridge Inverters","authors":"M. A. Bolaños-Navarrete, J. Bastidas‐Rodríguez, Gustavo Osorio","doi":"10.3390/en17153671","DOIUrl":"https://doi.org/10.3390/en17153671","url":null,"abstract":"This paper presents a single-phase Full-Bridge (FB) inverter with a hybrid commutation technique designed to reduce the harmonic distortion caused by the loss of the controller capability around the zero-crossing point in the unipolar commutation region. The hybrid modulation changes from unipolar to bipolar commutation under the loss of the reference control, improving the robustness and efficiency of the method. The commutation technique improves the switching performance and reduces the switching losses. Simulation models are developed in MATLAB/Simulink R2023b to evaluate their performance under different operating conditions. The results show that the proposed commutation technique can achieve high efficiency, low total harmonic distortion (THD), and fast dynamic response. The experimental implementation of sliding mode control (SMC) implemented in an STM32 microcontroller confirms that the hybrid commutation technique can reduce the THD by 0.96 percentage points for local (off-grid) loads and up to 2.45 in an industrial grid-tie network, compared with unipolar commutation. These findings highlight the potential of the proposed modulation technique for applications like solar panels and offer crucial insights for ongoing research and development in this field.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on an electromagnetic induction heating system that was recently developed in a previous work, an orthogonal test with three elements and nine levels was carried out to improve the heating effect of the system. This was intended to achieve a balance between the heating rate and temperature uniformity, where the electrochemical and thermal behaviors of the heated lithium-ion battery could be characterized by a high-accuracy electrochemical–thermal coupling model. This was validated against constant-current discharge and HPPC test data at room temperature and different low temperatures. Under the optimal parameter combination that was found in the orthogonal test, the battery temperature could rise to 293.15 K from 243.15 K in 494 s, with a maximum temperature rise rate of 0.133 K·s−1. The temperature difference after heating reached 4.21 K, which resulted from the heat conductivity of the battery material due to the skin depth of the battery shell and the material properties inside the battery. Due to the internal resistance, which decreased to no more than a quarter of the low-temperature level, both the usable energy and pulse power were increased more than 2.5 and 3 times, respectively. The enhancement of the energy output ability could provide a greater cruise range and improved dynamics for electric vehicles. The capacity calibration results obtained during the heating cycles indicated that there was only a 3.61% reduction in capacity retention after 120 repetitive heating cycles, which was 0.008 Ah below the normal cycle at 293.15 K, even compared with room-temperature capacity calibration, thus reducing the effect on the battery’s lifetime. Therefore, the electromagnetic induction heating system with a heating strategy could achieve a beneficial compromise between the temperature rise behavior, cycle lifetime, and working ability, indicating considerable potential for the optimization of the heating effect.
{"title":"Research on the Optimization of the Heating Effect of Lithium-Ion Batteries at a Low Temperature Based on an Electromagnetic Induction Heating System","authors":"Borui Wang, Mingyin Yan","doi":"10.3390/en17153678","DOIUrl":"https://doi.org/10.3390/en17153678","url":null,"abstract":"Based on an electromagnetic induction heating system that was recently developed in a previous work, an orthogonal test with three elements and nine levels was carried out to improve the heating effect of the system. This was intended to achieve a balance between the heating rate and temperature uniformity, where the electrochemical and thermal behaviors of the heated lithium-ion battery could be characterized by a high-accuracy electrochemical–thermal coupling model. This was validated against constant-current discharge and HPPC test data at room temperature and different low temperatures. Under the optimal parameter combination that was found in the orthogonal test, the battery temperature could rise to 293.15 K from 243.15 K in 494 s, with a maximum temperature rise rate of 0.133 K·s−1. The temperature difference after heating reached 4.21 K, which resulted from the heat conductivity of the battery material due to the skin depth of the battery shell and the material properties inside the battery. Due to the internal resistance, which decreased to no more than a quarter of the low-temperature level, both the usable energy and pulse power were increased more than 2.5 and 3 times, respectively. The enhancement of the energy output ability could provide a greater cruise range and improved dynamics for electric vehicles. The capacity calibration results obtained during the heating cycles indicated that there was only a 3.61% reduction in capacity retention after 120 repetitive heating cycles, which was 0.008 Ah below the normal cycle at 293.15 K, even compared with room-temperature capacity calibration, thus reducing the effect on the battery’s lifetime. Therefore, the electromagnetic induction heating system with a heating strategy could achieve a beneficial compromise between the temperature rise behavior, cycle lifetime, and working ability, indicating considerable potential for the optimization of the heating effect.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuqing Wang, Chaochen Yan, Zhaozhen Wang, Jiaxing Wang
With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China’s 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable.
{"title":"Adaptability Evaluation of Power Grid Planning Scheme for Novel Power System Considering Multiple Decision Psychology","authors":"Yuqing Wang, Chaochen Yan, Zhaozhen Wang, Jiaxing Wang","doi":"10.3390/en17153672","DOIUrl":"https://doi.org/10.3390/en17153672","url":null,"abstract":"With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China’s 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reza Derakhshani, L. Lankof, Amin GhasemiNejad, Alireza Zarasvandi, Mohammad Mahdi Amani Zarin, M. Zaresefat
This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.
{"title":"A Novel Sustainable Approach for Site Selection of Underground Hydrogen Storage in Poland Using Deep Learning","authors":"Reza Derakhshani, L. Lankof, Amin GhasemiNejad, Alireza Zarasvandi, Mohammad Mahdi Amani Zarin, M. Zaresefat","doi":"10.3390/en17153677","DOIUrl":"https://doi.org/10.3390/en17153677","url":null,"abstract":"This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project’s profitability as, without this revenue stream, the project would not be profitable in any analysed scenario.
{"title":"Decarbonizing Hard-to-Abate Sectors with Renewable Hydrogen: A Real Case Application to the Ceramics Industry","authors":"Jorge Sousa, Inês Azevedo, Cristina Camus, Luís Mendes, Carla Viveiros, Filipe Barata","doi":"10.3390/en17153661","DOIUrl":"https://doi.org/10.3390/en17153661","url":null,"abstract":"Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project’s profitability as, without this revenue stream, the project would not be profitable in any analysed scenario.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper analyses the state of the issue related to the reliability of power supply for selected electronic security systems employed in buildings and over vast areas constituting so-called state critical infrastructure. The authors conducted operational tests covering power supply systems, developed power supply system models, executed a functional safety reliability analysis for such technical facilities, and worked out graphs, as well as drew conclusions arising from the conducted computer simulation. The article also contains element (fuse) redundancy tests, which are the fundamental components of each security system power supply device. In addition, the operation process analysis covering power supply devices functioning within a given environment was conducted for selected representative electronic security systems operated in buildings. Analysis results enabled determining basic operation process indices for selected power supply systems, i.e., failure rate λ and recovery rate μ. Then, reliability models for devices powering electronic security systems were developed, and a computer simulation to work out reliability parameters was conducted for the determined operation process indices (λ, μ). Basic reliability indices for electronic security systems responsible for the life, health and property accumulated within the buildings and vast areas in question were determined for power supply models developed this way. Data for reliability computer simulations were developed on the basis of proprietary system tests. The authors also tested selected activation times of redundant components protecting power supplies.
{"title":"Selected Reliability Aspects Related to the Power Supply of Security Systems","authors":"J. Łukasiak, Jacek Paś, A. Rosiński","doi":"10.3390/en17153665","DOIUrl":"https://doi.org/10.3390/en17153665","url":null,"abstract":"The paper analyses the state of the issue related to the reliability of power supply for selected electronic security systems employed in buildings and over vast areas constituting so-called state critical infrastructure. The authors conducted operational tests covering power supply systems, developed power supply system models, executed a functional safety reliability analysis for such technical facilities, and worked out graphs, as well as drew conclusions arising from the conducted computer simulation. The article also contains element (fuse) redundancy tests, which are the fundamental components of each security system power supply device. In addition, the operation process analysis covering power supply devices functioning within a given environment was conducted for selected representative electronic security systems operated in buildings. Analysis results enabled determining basic operation process indices for selected power supply systems, i.e., failure rate λ and recovery rate μ. Then, reliability models for devices powering electronic security systems were developed, and a computer simulation to work out reliability parameters was conducted for the determined operation process indices (λ, μ). Basic reliability indices for electronic security systems responsible for the life, health and property accumulated within the buildings and vast areas in question were determined for power supply models developed this way. Data for reliability computer simulations were developed on the basis of proprietary system tests. The authors also tested selected activation times of redundant components protecting power supplies.","PeriodicalId":11557,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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