Jhon Brajhan Benites Quispe, M. Mezaroba, A. Batschauer, Jean Marcos de Souza Ribeiro
This paper analyzes, designs and implements a reconfigurable phase-shifted full-bridge (PSFB) converter. It adopts the topology of the traditional PSFB converter and incorporates clamping circuits to solve some fundamental problems of conventional topology. In addition, auxiliary switches are employed for output reconfiguration, which allows expanding the output voltage range without compromising the system efficiency. Single pole double throw (SPDT) mechanical switches are used to realize series and parallel connections. In this paper, the characterization of the PSFB converter with clamping circuit and its design considerations are discussed. A 10 kW prototype with a power density of 0.485 W/cm3, 900 V input voltage and 400/800 V nominal output voltage was manufactured. The experimental results validated the analysis and confirmed the high conversion efficiency for a wide load range; an efficiency of 96.69% was obtained for the full load condition.
{"title":"A Reconfigurable Phase-Shifted Full-Bridge DC–DC Converter with Wide Range Output Voltage","authors":"Jhon Brajhan Benites Quispe, M. Mezaroba, A. Batschauer, Jean Marcos de Souza Ribeiro","doi":"10.3390/en17143483","DOIUrl":"https://doi.org/10.3390/en17143483","url":null,"abstract":"This paper analyzes, designs and implements a reconfigurable phase-shifted full-bridge (PSFB) converter. It adopts the topology of the traditional PSFB converter and incorporates clamping circuits to solve some fundamental problems of conventional topology. In addition, auxiliary switches are employed for output reconfiguration, which allows expanding the output voltage range without compromising the system efficiency. Single pole double throw (SPDT) mechanical switches are used to realize series and parallel connections. In this paper, the characterization of the PSFB converter with clamping circuit and its design considerations are discussed. A 10 kW prototype with a power density of 0.485 W/cm3, 900 V input voltage and 400/800 V nominal output voltage was manufactured. The experimental results validated the analysis and confirmed the high conversion efficiency for a wide load range; an efficiency of 96.69% was obtained for the full load condition.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yichao Zhang, A. Anvari‐Moghaddam, S. Peyghami, F. Blaabjerg
As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.
{"title":"Novel Frequency Regulation Scenarios Generation Method Serving for Battery Energy Storage System Participating in PJM Market","authors":"Yichao Zhang, A. Anvari‐Moghaddam, S. Peyghami, F. Blaabjerg","doi":"10.3390/en17143479","DOIUrl":"https://doi.org/10.3390/en17143479","url":null,"abstract":"As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A design toolbox has been developed for hybrid energy storage systems (HESSs) that employ both batteries and supercapacitors, primarily focusing on optimizing the system sizing/cost and mitigating battery aging. The toolbox incorporates the BaSiS model, a non-empirical physical–electrochemical degradation model for lithium-ion batteries that enables accurate simulations of battery performance and degradation under realistic operating conditions. The paper presents a detailed description of the parameterization, and validation process for the battery model, emphasizing the high accuracy and strong reliability of the battery aging prediction. The HESS design toolbox can be used to investigate the impact of various battery/supercapacitor configurations and energy management algorithms on the design, battery degradation, and system investment cost of the hybrid storage system. To illustrate the effectiveness of the design toolbox, a case study on Dynamic Moderation frequency support in the UK grid was conducted. For this use case, the application of hybrid storage energy systems is well suited due to the highly dynamic power regulation requirements in island grids with low inertia. By utilizing the fast response of supercapacitors, the stress on the battery caused by short-term high-power peaks can be significantly alleviated. In this way, the hybrid storage system effectively reduces either the battery size or the battery aging rate. In summary, this research highlights the crucial role of a comprehensive analysis in the design of hybrid energy storage systems, addressing both battery aging and overall system costs. The design toolbox can provide transparency regarding the design space and assist in determining the most suitable HESS configuration for a given application.
{"title":"A Design Tool for Battery/Supercapacitor Hybrid Energy Storage Systems Based on the Physical–Electrochemical Degradation Battery Model BaSiS","authors":"Weiwei Shan, Michael Schwalm, Martin Shan","doi":"10.3390/en17143481","DOIUrl":"https://doi.org/10.3390/en17143481","url":null,"abstract":"A design toolbox has been developed for hybrid energy storage systems (HESSs) that employ both batteries and supercapacitors, primarily focusing on optimizing the system sizing/cost and mitigating battery aging. The toolbox incorporates the BaSiS model, a non-empirical physical–electrochemical degradation model for lithium-ion batteries that enables accurate simulations of battery performance and degradation under realistic operating conditions. The paper presents a detailed description of the parameterization, and validation process for the battery model, emphasizing the high accuracy and strong reliability of the battery aging prediction. The HESS design toolbox can be used to investigate the impact of various battery/supercapacitor configurations and energy management algorithms on the design, battery degradation, and system investment cost of the hybrid storage system. To illustrate the effectiveness of the design toolbox, a case study on Dynamic Moderation frequency support in the UK grid was conducted. For this use case, the application of hybrid storage energy systems is well suited due to the highly dynamic power regulation requirements in island grids with low inertia. By utilizing the fast response of supercapacitors, the stress on the battery caused by short-term high-power peaks can be significantly alleviated. In this way, the hybrid storage system effectively reduces either the battery size or the battery aging rate. In summary, this research highlights the crucial role of a comprehensive analysis in the design of hybrid energy storage systems, addressing both battery aging and overall system costs. The design toolbox can provide transparency regarding the design space and assist in determining the most suitable HESS configuration for a given application.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The environmental burden is a global problem affecting the European Union. A comprehensive analysis of the environmental burden is essential for creating strategies supporting sustainable economic development. This study attempts to answer the question of why, despite the continuously decreasing energy consumption of the EU, the environmental burden of this region is not substantially decreasing. This study provides novel insights into this research area by integrating EU economic dynamics and environmental efficiency indicators. In this study, we used the IPAT method. Before the main analysis, the researcher conducted cross-sectional dependence, slope heterogeneity, and Westerlund cointegration tests using the primary data. Based on the results, the EU member states were classified into clusters, and a linear trend model analysis was carried out. The results show that the total environmental load of the EU did not decrease significantly between 2012 and 2022. The fact that the environmental burden remained at the same level is explained by the fact that there were 16 member countries whose total environmental load increased but whose economic output was lower during this time period. This was offset by 11 member countries with high economic outputs, whose total environmental load decreased. This study proved that GDP growth was the main driving force maintaining the total environmental load at the same level. The EU should encourage member states to continue to implement environmental protection rules to limit and eliminate costly environmental burdens on their societies and economies. This study can be helpful to researchers, political decision-makers, and experts working on environmental public policies for the EU.
{"title":"The Trend in Environmental Load in the European Union during the Period of 2012–2022","authors":"László Török","doi":"10.3390/en17143473","DOIUrl":"https://doi.org/10.3390/en17143473","url":null,"abstract":"The environmental burden is a global problem affecting the European Union. A comprehensive analysis of the environmental burden is essential for creating strategies supporting sustainable economic development. This study attempts to answer the question of why, despite the continuously decreasing energy consumption of the EU, the environmental burden of this region is not substantially decreasing. This study provides novel insights into this research area by integrating EU economic dynamics and environmental efficiency indicators. In this study, we used the IPAT method. Before the main analysis, the researcher conducted cross-sectional dependence, slope heterogeneity, and Westerlund cointegration tests using the primary data. Based on the results, the EU member states were classified into clusters, and a linear trend model analysis was carried out. The results show that the total environmental load of the EU did not decrease significantly between 2012 and 2022. The fact that the environmental burden remained at the same level is explained by the fact that there were 16 member countries whose total environmental load increased but whose economic output was lower during this time period. This was offset by 11 member countries with high economic outputs, whose total environmental load decreased. This study proved that GDP growth was the main driving force maintaining the total environmental load at the same level. The EU should encourage member states to continue to implement environmental protection rules to limit and eliminate costly environmental burdens on their societies and economies. This study can be helpful to researchers, political decision-makers, and experts working on environmental public policies for the EU.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs’ productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells’ production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs.
{"title":"A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network","authors":"Kunming Tian, Zhihong Kang, Zhijiang Kang","doi":"10.3390/en17143482","DOIUrl":"https://doi.org/10.3390/en17143482","url":null,"abstract":"Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs’ productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells’ production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rita Teixeira, A. Cerveira, E. J. S. Pires, José Baptista
Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.
{"title":"Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods","authors":"Rita Teixeira, A. Cerveira, E. J. S. Pires, José Baptista","doi":"10.3390/en17143480","DOIUrl":"https://doi.org/10.3390/en17143480","url":null,"abstract":"Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed Darwish, Mohamed Elgenedy, Barry W. Williams
Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems.
{"title":"A Review of Modular Electrical Sub-Systems of Electric Vehicles","authors":"Ahmed Darwish, Mohamed Elgenedy, Barry W. Williams","doi":"10.3390/en17143474","DOIUrl":"https://doi.org/10.3390/en17143474","url":null,"abstract":"Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hemin Hu, Tao Wang, Fan Zhang, Bing Zhang, Jian Qi
Characterizing the optimal operating parameters for a heat pump with a specific refrigerant is paramount, as it provides valuable guidance for refrigerant selection. The temperature mismatch between cold and hot fluids in the evaporator and condenser can lead to degraded thermal performance in heat pumps with large temperature variations. To address these two key issues, we selected several pure refrigerants with varying critical temperature levels for use in a large temperature variation heat pump configuration. The corresponding thermal performance was then investigated using the Ebsilon code under fixed temperature lift conditions as the operating temperature varied. It indicates that the maximum coefficient of performance (COP) is typically achieved when the deviation factors of temperature and pressure from their critical parameters fall within the ranges of 0.62~0.71 and 0.36~0.5, respectively. Our research recommends the binary refrigerant mixture of R152a/R1336mzz(z) (COP = 3.54) for the current operating conditions, as it significantly improves thermal performance compared to pure R1336mzz (z) (COP = 2.87) and R152a (COP = 3.01). Through research on the impact of the compositional ratio of R152a/R1336mzz(z) on the thermal performance of the heat pump, we found that that the optimal ratio of R1336mzz(z) component to R152a component is 0.5/0.5. This study offers valuable guidance for selecting the most suitable refrigerants for heat pumps in practical engineering design scenarios.
{"title":"Matching Characteristics of Refrigerant and Operating Parameters in Large Temperature Variation Heat Pump","authors":"Hemin Hu, Tao Wang, Fan Zhang, Bing Zhang, Jian Qi","doi":"10.3390/en17143477","DOIUrl":"https://doi.org/10.3390/en17143477","url":null,"abstract":"Characterizing the optimal operating parameters for a heat pump with a specific refrigerant is paramount, as it provides valuable guidance for refrigerant selection. The temperature mismatch between cold and hot fluids in the evaporator and condenser can lead to degraded thermal performance in heat pumps with large temperature variations. To address these two key issues, we selected several pure refrigerants with varying critical temperature levels for use in a large temperature variation heat pump configuration. The corresponding thermal performance was then investigated using the Ebsilon code under fixed temperature lift conditions as the operating temperature varied. It indicates that the maximum coefficient of performance (COP) is typically achieved when the deviation factors of temperature and pressure from their critical parameters fall within the ranges of 0.62~0.71 and 0.36~0.5, respectively. Our research recommends the binary refrigerant mixture of R152a/R1336mzz(z) (COP = 3.54) for the current operating conditions, as it significantly improves thermal performance compared to pure R1336mzz (z) (COP = 2.87) and R152a (COP = 3.01). Through research on the impact of the compositional ratio of R152a/R1336mzz(z) on the thermal performance of the heat pump, we found that that the optimal ratio of R1336mzz(z) component to R152a component is 0.5/0.5. This study offers valuable guidance for selecting the most suitable refrigerants for heat pumps in practical engineering design scenarios.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The stability of energy generation is crucial for the functioning of every country. Currently, the EU policy is moving towards becoming independent of fossil energy sources, which can be replaced with sources that are not exhaustible, for example, energy from the sun. Public awareness of renewable energy is increasing. People are willing to invest in natural solutions. However, planning large photovoltaic farm projects is difficult due to complex location requirements. The study aimed to analyse the interferences/barriers to be considered when searching for a suitable location to install a photovoltaic farm. The analysis was conducted for the territory of Poland. The study used a literature and local legislation query and the Delphi method. The Delphi method identified the most important interferences from the investor’s perspective. Eleven interferences have been identified, classified into legal, spatial, technical, social, and financial groups. Several are locally determined and only exist in selected locations (e.g., technical determinants of the power grid condition, etc.). In contrast, others are unitary (e.g., concerns about the impact of PV on human health, etc.). The decision-makers are aware of the existing interferences/barriers, and the proposed administrative, legal, and technical solutions marginally mitigate barriers. System solutions are recommended, allowing an easier way to find a suitable location for a PV system.
{"title":"Regional Interferences to Photovoltaic Development: A Polish Perspective","authors":"Katarzyna Kocur-Bera","doi":"10.3390/en17143484","DOIUrl":"https://doi.org/10.3390/en17143484","url":null,"abstract":"The stability of energy generation is crucial for the functioning of every country. Currently, the EU policy is moving towards becoming independent of fossil energy sources, which can be replaced with sources that are not exhaustible, for example, energy from the sun. Public awareness of renewable energy is increasing. People are willing to invest in natural solutions. However, planning large photovoltaic farm projects is difficult due to complex location requirements. The study aimed to analyse the interferences/barriers to be considered when searching for a suitable location to install a photovoltaic farm. The analysis was conducted for the territory of Poland. The study used a literature and local legislation query and the Delphi method. The Delphi method identified the most important interferences from the investor’s perspective. Eleven interferences have been identified, classified into legal, spatial, technical, social, and financial groups. Several are locally determined and only exist in selected locations (e.g., technical determinants of the power grid condition, etc.). In contrast, others are unitary (e.g., concerns about the impact of PV on human health, etc.). The decision-makers are aware of the existing interferences/barriers, and the proposed administrative, legal, and technical solutions marginally mitigate barriers. System solutions are recommended, allowing an easier way to find a suitable location for a PV system.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gaith Baccouche, Mohamed Haikel Chehab, Chokri Ben Salah, M. Tlija, A. Rabhi
The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.
{"title":"Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles","authors":"Gaith Baccouche, Mohamed Haikel Chehab, Chokri Ben Salah, M. Tlija, A. Rabhi","doi":"10.3390/en17143470","DOIUrl":"https://doi.org/10.3390/en17143470","url":null,"abstract":"The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.","PeriodicalId":504870,"journal":{"name":"Energies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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