Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124504
Moving away from a carbon-intensive power supply over the next few decades requires a commitment to both reduce power-related carbon emissions and ensure a secure and affordable power supply in China. However, thermal power still contributes to roughly 70 % of the electricity generated in China, and the carbon lock-in in thermal power hampers the realization of low carbon transition of power systems. In this context, this paper employs a tripartite evolutionary game model to explore the strategies of breaking carbon lock-in in thermal power generation among the power plants and China's central and local governments. Three scenarios were designed considering both CCS installation and economic incentives. Numerical simulations show neither power plants nor the local government has the motivation to decarbonize power generation in baseline scenario. Under the single decarbonization strategy scenario, only relying on technological progress, carbon trading or subsidy is infeasible to incentive decarbonization due to high cost. When the three strategies are combinedly used, carbon lock-in can be broken. Therefore, the combined decarbonization strategy that integrates reducing technology cost, increasing carbon trading price, and subsidy is recommended for the decarbonization of thermal power. Moreover, the selection of a proper decarbonization strategy should be based on local situation.
{"title":"How to break carbon lock-in of thermal power industry in China—A tripartite evolutionary game analysis","authors":"","doi":"10.1016/j.apenergy.2024.124504","DOIUrl":"10.1016/j.apenergy.2024.124504","url":null,"abstract":"<div><div>Moving away from a carbon-intensive power supply over the next few decades requires a commitment to both reduce power-related carbon emissions and ensure a secure and affordable power supply in China. However, thermal power still contributes to roughly 70 % of the electricity generated in China, and the carbon lock-in in thermal power hampers the realization of low carbon transition of power systems. In this context, this paper employs a tripartite evolutionary game model to explore the strategies of breaking carbon lock-in in thermal power generation among the power plants and China's central and local governments. Three scenarios were designed considering both CCS installation and economic incentives. Numerical simulations show neither power plants nor the local government has the motivation to decarbonize power generation in baseline scenario. Under the single decarbonization strategy scenario, only relying on technological progress, carbon trading or subsidy is infeasible to incentive decarbonization due to high cost. When the three strategies are combinedly used, carbon lock-in can be broken. Therefore, the combined decarbonization strategy that integrates reducing technology cost, increasing carbon trading price, and subsidy is recommended for the decarbonization of thermal power. Moreover, the selection of a proper decarbonization strategy should be based on local situation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124566
Most studies on hydrogen ejectors focus on enhancing recirculation capability and extending the working range through structural optimization or innovation. Its wide application is limited by its performance deterioration dramatically due to its operating point slipping into the subcritical region under dynamic or fluctuating conditions. However, the criterion for ensuring and evaluating its operational stability is still lacking. Therefore, this study proposes a design criterion for stable and efficient hydrogen ejector, and a critical mode ejector is designed under the criterion. The internal flow characteristics, recirculation capability, and operational stability of the designed ejector are analyzed using an experimentally validated 2D axisymmetric CFD model. The findings indicate that the increase in primary flow pressure may decrease the critical recirculation ratio (ω⁎), but effectively increase the critical back pressure (pc⁎). In addition, the ω⁎ increases roughly linearly as secondary flow pressure rises, while the pc⁎ is less affected by it. The ω⁎ is sensitive to anode gas relative humidity and increases with it, while its effect on pc⁎ can be disregarded. Considering both the recirculation capability and operational stability, the designed critical mode ejector can operate within a power range of 38.86–86.59 kW, with the corresponding stability margin of 5–90 kPa and minimum ω⁎ above 0.72. The designed ejector not only meets the requirement of ω but works in the critical mode over a wide power range compared to other hydrogen ejectors reported in the literature. Compared to ω⁎, pc⁎ should have a higher priority in designing hydrogen ejectors. This research may contribute to designing stable and efficient ejectors used in PEMFC and promote its wide application.
{"title":"Design criterion of critical mode ejector for PEMFC hydrogen supply and recycle system","authors":"","doi":"10.1016/j.apenergy.2024.124566","DOIUrl":"10.1016/j.apenergy.2024.124566","url":null,"abstract":"<div><div>Most studies on hydrogen ejectors focus on enhancing recirculation capability and extending the working range through structural optimization or innovation. Its wide application is limited by its performance deterioration dramatically due to its operating point slipping into the subcritical region under dynamic or fluctuating conditions. However, the criterion for ensuring and evaluating its operational stability is still lacking. Therefore, this study proposes a design criterion for stable and efficient hydrogen ejector, and a critical mode ejector is designed under the criterion. The internal flow characteristics, recirculation capability, and operational stability of the designed ejector are analyzed using an experimentally validated 2D axisymmetric CFD model. The findings indicate that the increase in primary flow pressure may decrease the critical recirculation ratio (<em>ω</em><sub>⁎</sub>), but effectively increase the critical back pressure (<em>p</em><sub>c⁎</sub>). In addition, the <em>ω</em><sub>⁎</sub> increases roughly linearly as secondary flow pressure rises, while the <em>p</em><sub>c⁎</sub> is less affected by it. The <em>ω</em><sub>⁎</sub> is sensitive to anode gas relative humidity and increases with it, while its effect on <em>p</em><sub>c⁎</sub> can be disregarded. Considering both the recirculation capability and operational stability, the designed critical mode ejector can operate within a power range of 38.86–86.59 kW, with the corresponding stability margin of 5–90 kPa and minimum <em>ω</em><sub>⁎</sub> above 0.72. The designed ejector not only meets the requirement of <em>ω</em> but works in the critical mode over a wide power range compared to other hydrogen ejectors reported in the literature. Compared to <em>ω</em><sub>⁎</sub>, <em>p</em><sub>c⁎</sub> should have a higher priority in designing hydrogen ejectors. This research may contribute to designing stable and efficient ejectors used in PEMFC and promote its wide application.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124558
The literature widely recognizes the importance of effective economic policies in achieving Sustainable Development Goal 7 (SDG 7). However, existing research fails to examine how these policies synergistically affect the achievement of SDG 7 targets. This paper addresses this gap by conducting a configurational analysis to identify various combinations of economic policies through which countries can reach SDG 7 targets. Drawing on empirical evidence from 41 OECD and European countries, the findings suggest that governments must (1) enhance coherence of their economic policies through collaboration among and within government bodies, (2) promote prosperity and innovation opportunities by fostering inclusive labor markets, and (3) implement international financial regulations to facilitate energy investments. Policymakers can utilize these findings to identifying key policy levers, enhance innovation in policy making, and develop appropriate strategies for achieving sustainable development goals.
{"title":"The role of economic policies in achieving sustainable development goal 7: Insights from OECD and European countries","authors":"","doi":"10.1016/j.apenergy.2024.124558","DOIUrl":"10.1016/j.apenergy.2024.124558","url":null,"abstract":"<div><div>The literature widely recognizes the importance of effective economic policies in achieving Sustainable Development Goal 7 (SDG 7). However, existing research fails to examine how these policies synergistically affect the achievement of SDG 7 targets. This paper addresses this gap by conducting a configurational analysis to identify various combinations of economic policies through which countries can reach SDG 7 targets. Drawing on empirical evidence from 41 OECD and European countries, the findings suggest that governments must (1) enhance coherence of their economic policies through collaboration among and within government bodies, (2) promote prosperity and innovation opportunities by fostering inclusive labor markets, and (3) implement international financial regulations to facilitate energy investments. Policymakers can utilize these findings to identifying key policy levers, enhance innovation in policy making, and develop appropriate strategies for achieving sustainable development goals.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S030626192401941X/pdfft?md5=b6e90044919c7c4097818dd85f5d063f&pid=1-s2.0-S030626192401941X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124533
This paper introduces a framework designed to optimize the configuration of a 15 MW floating offshore wind turbine (FOWT) with a focus on the cost. The proposed framework employs a multi-objective evolutionary algorithm, integrating frequency domain (FD) simulations with equilibrium analysis to assess the responses of the floating platform and its mooring system. The objective function of the optimization includes the steel structural mass of the floating platform, the pitch heeling angle, and the motion response amplitude operator (RAO) peak as determined by the FD simulation. Constraints pertinent to these objective functions, alongside the safety of the mooring system and the dynamic response and parameter settings of the FOWT, are meticulously enforced. The resulting optimized designs exhibit substantial improvements in the steel structural mass and pitch heeling angle compare to the initial design parameters. The reliability of this optimization framework is corroborated through time domain (TD) simulations, which elucidate the effects of the pitch heel angle and motion RAO peaks on the time domain response of the optimized structures. These insights offer reference for the future optimization of floating platforms and mooring systems in the realm of offshore wind energy.
{"title":"Multi-objective optimization design for a 15 MW semisubmersible floating offshore wind turbine using evolutionary algorithm","authors":"","doi":"10.1016/j.apenergy.2024.124533","DOIUrl":"10.1016/j.apenergy.2024.124533","url":null,"abstract":"<div><div>This paper introduces a framework designed to optimize the configuration of a 15 MW floating offshore wind turbine (FOWT) with a focus on the cost. The proposed framework employs a multi-objective evolutionary algorithm, integrating frequency domain (FD) simulations with equilibrium analysis to assess the responses of the floating platform and its mooring system. The objective function of the optimization includes the steel structural mass of the floating platform, the pitch heeling angle, and the motion response amplitude operator (RAO) peak as determined by the FD simulation. Constraints pertinent to these objective functions, alongside the safety of the mooring system and the dynamic response and parameter settings of the FOWT, are meticulously enforced. The resulting optimized designs exhibit substantial improvements in the steel structural mass and pitch heeling angle compare to the initial design parameters. The reliability of this optimization framework is corroborated through time domain (TD) simulations, which elucidate the effects of the pitch heel angle and motion RAO peaks on the time domain response of the optimized structures. These insights offer reference for the future optimization of floating platforms and mooring systems in the realm of offshore wind energy.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124488
With the rapid development of high-efficiency, long-distance, and large-capacity power interaction in multiple communities, prosumers in each community who can participate in three markets, i.e., green power market, electricity market and carbon market, may make decisions based on incomplete rational behaviors. The behaviors, e.g., purchasing plenty of power from the power plants through the independent system operator (ISO) at a certain time slot, may cause the problem that a certain power line cannot transmit the power since the amount of power intended to transmit via the power line is beyond the constraint of the physical network, which is regarded as the transmission congestion. How to realize the optimization of energy management for the prosumers and power plants in three markets considering transmission congestion arouse the public concern. In this paper, an optimal energy management method is proposed for the power plants and prosumers with community energy storage considering transmission congestion based on carbon emission flow. It is constructed with a three-level structure, i.e., prosumer level, ISO level and power plant level. At the first level, i.e., prosumer level, based on the cumulative prospect theory, an incomplete rational behavior model is developed for the prosumers who can store the excess power in community energy storage for backup. Due to the existing prosumer peer-to-peer energy trading approach, all prosumers in the same community can be aggregated into a community agent to participate in the three markets, which can deliver the power demand from the prosumers to the ISO at the second level. At the third level, power plant level, two energy trading models of power plants are established, which can deliver the power supply from the power plants to the ISO at the second level, i.e., ISO level, as well. One is presented for the coal-fired power plants according to the cost-benefit function theory, the other one is constructed for the renewable power plants considering the uncertainty of renewable output power. Then, at the second level, an energy management method considering transmission congestion is developed in respect of the power demand from the first-level behavior and the power supply from the third level. Finally, the optimization of energy management is solved under the Lagrange multiplier method with the improved differential evolution algorithm, which is verified in numerical simulations with the effectiveness of the proposed method.
随着多社区高效、远距离、大容量电力互动的快速发展，每个社区中可参与绿色电力市场、电力市场和碳市场三个市场的用户可能会做出基于不完全理性行为的决策。这些行为，例如在某个时间段通过独立系统运营商（ISO）从发电厂购买大量电力，可能会导致某条电力线无法传输电力的问题，因为打算通过该电力线传输的电力量超出了物理网络的限制，这被视为传输拥塞。如何在考虑输电拥塞的情况下，实现三个市场中用户和发电厂的能源管理优化，引起了公众的关注。本文基于碳排放流，提出了一种考虑输电拥塞的发电厂和社区储能用户的优化能源管理方法。该方法采用三层结构，即用户层、国际标准化组织层和发电厂层。在第一个层面，即消费者层面，基于累积前景理论，为消费者建立了一个不完全理性行为模型，消费者可将多余电力储存在社区储能中备用。由于现有的准用户点对点能源交易方式，同一社区的所有准用户可以聚合成一个社区代理，参与三个市场，从而在第二层面将准用户的电力需求输送给 ISO。在第三个层面，即发电厂层面，建立了两个发电厂能源交易模型，这两个模型也可以将发电厂的电力供应输送到第二层面的 ISO，即 ISO 层面。一个模型是根据成本效益函数理论为燃煤发电厂建立的，另一个模型是考虑到可再生能源输出功率的不确定性为可再生能源发电厂建立的。然后，在第二层，针对第一层行为的电力需求和第三层的电力供应，开发了一种考虑输电拥塞的能源管理方法。最后，利用改进的微分演化算法，在拉格朗日乘数法下对能源管理进行优化求解，并通过数值模拟验证了所提方法的有效性。
{"title":"Optimal energy management for prosumers and power plants considering transmission congestion based on carbon emission flow","authors":"","doi":"10.1016/j.apenergy.2024.124488","DOIUrl":"10.1016/j.apenergy.2024.124488","url":null,"abstract":"<div><div>With the rapid development of high-efficiency, long-distance, and large-capacity power interaction in multiple communities, prosumers in each community who can participate in three markets, i.e., green power market, electricity market and carbon market, may make decisions based on incomplete rational behaviors. The behaviors, e.g., purchasing plenty of power from the power plants through the independent system operator (ISO) at a certain time slot, may cause the problem that a certain power line cannot transmit the power since the amount of power intended to transmit via the power line is beyond the constraint of the physical network, which is regarded as the transmission congestion. How to realize the optimization of energy management for the prosumers and power plants in three markets considering transmission congestion arouse the public concern. In this paper, an optimal energy management method is proposed for the power plants and prosumers with community energy storage considering transmission congestion based on carbon emission flow. It is constructed with a three-level structure, i.e., prosumer level, ISO level and power plant level. At the first level, i.e., prosumer level, based on the cumulative prospect theory, an incomplete rational behavior model is developed for the prosumers who can store the excess power in community energy storage for backup. Due to the existing prosumer peer-to-peer energy trading approach, all prosumers in the same community can be aggregated into a community agent to participate in the three markets, which can deliver the power demand from the prosumers to the ISO at the second level. At the third level, power plant level, two energy trading models of power plants are established, which can deliver the power supply from the power plants to the ISO at the second level, i.e., ISO level, as well. One is presented for the coal-fired power plants according to the cost-benefit function theory, the other one is constructed for the renewable power plants considering the uncertainty of renewable output power. Then, at the second level, an energy management method considering transmission congestion is developed in respect of the power demand from the first-level behavior and the power supply from the third level. Finally, the optimization of energy management is solved under the Lagrange multiplier method with the improved differential evolution algorithm, which is verified in numerical simulations with the effectiveness of the proposed method.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124446
In rural areas of sub-Saharan countries, there is great potential for solar and biomass resources to achieve a reliable electricity supply, reduce the dependence on fossil fuels, and mitigate greenhouse gas emissions, thereby tackling energy poverty and promoting sustainable development. This work aims to address the lack of reliable electricity access in rural communities of sub-Saharan countries through biomass gasification assisted by solar photovoltaic (PV) energy and a small back-up diesel engine–generator set. The biomass gasification plant is designed to convert locally available agricultural waste into producer gas, which can then be used to generate electricity. A detailed analysis of the system components, including the PV array, battery bank, biomass gasifier with a combined cooling, heat and power generation unit (CCHP), is carried out to evaluate their performance and efficiency under different operating conditions. The results reveal a CCHP efficiency of 62% for the gasification CCHP unit, accompanied by a remarkable 93.8% reduction in CO2 emissions considering the whole hybrid system. From an economic standpoint under conservative assumptions, the proposed facility can generate a cumulative profit of $157,890 after 20 years, recovering the initial investment within a period of just under 7 years. This is reflected in a levelized cost of electricity (LCOE) of $0.287/kWh, comparable to that of related studies. The outcomes demonstrate that the PV-assisted biomass gasification plant offers a sustainable technical, economical and environmentally friendly solution for electrification of rural communities in sub-Saharan countries.
{"title":"Techno-economic assessment of a hybrid PV-assisted biomass gasification CCHP plant for electrification of a rural area in the Savannah region of Ghana","authors":"","doi":"10.1016/j.apenergy.2024.124446","DOIUrl":"10.1016/j.apenergy.2024.124446","url":null,"abstract":"<div><div>In rural areas of sub-Saharan countries, there is great potential for solar and biomass resources to achieve a reliable electricity supply, reduce the dependence on fossil fuels, and mitigate greenhouse gas emissions, thereby tackling energy poverty and promoting sustainable development. This work aims to address the lack of reliable electricity access in rural communities of sub-Saharan countries through biomass gasification assisted by solar photovoltaic (PV) energy and a small back-up diesel engine–generator set. The biomass gasification plant is designed to convert locally available agricultural waste into producer gas, which can then be used to generate electricity. A detailed analysis of the system components, including the PV array, battery bank, biomass gasifier with a combined cooling, heat and power generation unit (CCHP), is carried out to evaluate their performance and efficiency under different operating conditions. The results reveal a CCHP efficiency of 62% for the gasification CCHP unit, accompanied by a remarkable 93.8% reduction in CO<sub>2</sub> emissions considering the whole hybrid system. From an economic standpoint under conservative assumptions, the proposed facility can generate a cumulative profit of $157,890 after 20 years, recovering the initial investment within a period of just under 7 years. This is reflected in a levelized cost of electricity (LCOE) of $0.287/kWh, comparable to that of related studies. The outcomes demonstrate that the PV-assisted biomass gasification plant offers a sustainable technical, economical and environmentally friendly solution for electrification of rural communities in sub-Saharan countries.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124546
This study investigates the Lithium-ion battery degradation of battery electric vehicles (BEVs) and calculates the compensation cost when BEVs are used as primary energy storage systems using vehicle-to-grid (V2G) technology. We introduce a novel co-simulation interface in MATLAB Simulink, which includes a V2G model, a BEV mobility model, and a battery degradation model to estimate the BEV battery degradation. This study considers three V2G operational scenarios from the fleet perspective and three individual user profiles to examine different battery degradation levels under different scenarios. Unlike studies that analyse battery degradation for a shorter period (1 day to 1 year), with standard drive cycles under a constant C rate, the co-simulation model calculates the battery degradation with a variable C rate energy flow under multiple real-world drive cycles, variable V2G operational and participation scenarios until the end of the life of the battery, followed by the estimation of V2G economic compensation.
The results show that V2G increases the battery degradation rate by 9 % - 14 % over 10 years. Unlike the calender degradation process which contributes 85 % to 90 % of total degradation over 10 years without V2G, the cyclic degradation process contributes only 10 % to 15 %, which increases to 20 % - 25 % with V2G for different sub-scenarios. As V2G only contributes to cyclic degradation, the results show an average of 0.31 % increase in total degradation per year due to V2G for 33 charging/discharging cycles. To break even the degradation rate and infrastructure cost, the comprehensive economic analysis estimates the V2G compensation as €132/MWh of V2G energy flow in the 2030 scenario and €70/MWh of V2G energy flow in the 2050 scenario.
{"title":"Vehicle-to-grid impact on battery degradation and estimation of V2G economic compensation","authors":"","doi":"10.1016/j.apenergy.2024.124546","DOIUrl":"10.1016/j.apenergy.2024.124546","url":null,"abstract":"<div><div>This study investigates the Lithium-ion battery degradation of battery electric vehicles (BEVs) and calculates the compensation cost when BEVs are used as primary energy storage systems using vehicle-to-grid (V2G) technology. We introduce a novel co-simulation interface in MATLAB Simulink, which includes a V2G model, a BEV mobility model, and a battery degradation model to estimate the BEV battery degradation. This study considers three V2G operational scenarios from the fleet perspective and three individual user profiles to examine different battery degradation levels under different scenarios. Unlike studies that analyse battery degradation for a shorter period (1 day to 1 year), with standard drive cycles under a constant C rate, the co-simulation model calculates the battery degradation with a variable C rate energy flow under multiple real-world drive cycles, variable V2G operational and participation scenarios until the end of the life of the battery, followed by the estimation of V2G economic compensation.</div><div>The results show that V2G increases the battery degradation rate by 9 % - 14 % over 10 years. Unlike the calender degradation process which contributes 85 % to 90 % of total degradation over 10 years without V2G, the cyclic degradation process contributes only 10 % to 15 %, which increases to 20 % - 25 % with V2G for different sub-scenarios. As V2G only contributes to cyclic degradation, the results show an average of 0.31 % increase in total degradation per year due to V2G for 33 charging/discharging cycles. To break even the degradation rate and infrastructure cost, the comprehensive economic analysis estimates the V2G compensation as €132/MWh of V2G energy flow in the 2030 scenario and €70/MWh of V2G energy flow in the 2050 scenario.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306261924019299/pdfft?md5=10777112e0db653fbbea38c6a48913eb&pid=1-s2.0-S0306261924019299-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124392
With the advancement of policy initiatives and technological developments, vehicle-to-grid (V2G) interactions have emerged as a critical focus within power system research. Despite numerous current studies on V2G, the exploration of incentive mechanisms to encourage user participation in these systems remains comparatively underdeveloped. This study proposes an innovative demand response (DR) incentive mechanism for electric vehicles (EVs) grounded in the regulation of fast charging powers via strategically assigning and allocating virtual points defined as fast charging right (FCR). The primary objective of this mechanism is to meet the power regulation requirements of DR while simultaneously balancing the grid’s response costs and accommodating the charging demands of EV users. Through simulation and comparative analysis with the existing pilot DR incentive mechanisms, the results demonstrate that the proposed mechanism outperforms the current model in three critical aspects: power regulation efficacy, cost-effectiveness, and user experience. In addition, this paper investigates the performance of the incentive mechanisms in both online and offline scheduling environments. The findings reveal that the FCR mechanism maintains robust power regulation performance even under online scheduling conditions.
随着政策措施的推进和技术的发展，车联网（V2G）互动已成为电力系统研究中的一个重要焦点。尽管目前有许多关于 V2G 的研究，但对鼓励用户参与这些系统的激励机制的探索仍相对不足。本研究针对电动汽车（EV）提出了一种创新的需求响应（DR）激励机制，即通过战略性地分配和分配定义为快速充电权（FCR）的虚拟点来调节快速充电功率。该机制的主要目标是在满足 DR 功率调节要求的同时，平衡电网响应成本并满足电动汽车用户的充电需求。通过仿真以及与现有试点 DR 激励机制的比较分析，结果表明所提出的机制在电力调节效率、成本效益和用户体验三个关键方面均优于现有模式。此外，本文还研究了激励机制在在线和离线调度环境下的表现。研究结果表明，即使在在线调度条件下，FCR 机制也能保持稳健的功率调节性能。
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Pub Date : 2024-09-24DOI: 10.1016/j.apenergy.2024.124514
The low temperature environment poses a significant challenge to the application of electric vehicles (EVs). At low temperatures, the dynamic characteristics inside the battery become significantly different from those in the temperature range of 10–40 °C, resulting in high uncertainties in the estimation of state of charge (SOC). Experimental studies on two types of lithium-ion batteries have found that due to changes in battery polarization characteristics at low temperatures, the open circuit voltage (OCV) identified by the commonly used equivalent circuit models and parameter identification methods becomes more distorted. This is the reason for the failure of most SOC estimation methods based on OCV-SOC mapping. A part of polarization voltage is incorrectly involved in the OCV by online parameter identification. Based on this phenomenon, a novel method is proposed to achieve accurate SOC estimation at low temperatures by compensating this part of polarization voltage. The compensation voltage is calculated by a function, which is identified from experimental data using genetic algorithm. The validation against experimental results demonstrates that the proposed method can achieve a root mean square error and mean absolute error of less than 3 % for the SOC estimation in temperatures down to −20 °C. Moreover, this method only needs experimental data of dynamic operating conditions measured at two temperatures which cover most of the battery's working temperature range. And its computational complexity is low, making it suitable for onboard applications.
低温环境给电动汽车（EV）的应用带来了巨大挑战。在低温条件下，电池内部的动态特性与 10-40 °C 温度范围内的动态特性有很大不同，导致电量状态（SOC）估算的不确定性很高。对两种锂离子电池进行的实验研究发现，由于低温下电池极化特性的变化，常用等效电路模型和参数识别方法所识别的开路电压（OCV）变得更加失真。这是大多数基于 OCV-SOC 映射的 SOC 估算方法失效的原因。在线参数识别方法错误地将极化电压的一部分卷入了 OCV。基于这一现象，我们提出了一种新方法，通过补偿这部分极化电压，在低温条件下实现准确的 SOC 估算。补偿电压由一个函数计算，该函数通过遗传算法从实验数据中识别出来。根据实验结果进行的验证表明，在温度低至 -20 °C 的情况下，所提出的方法在 SOC 估算方面的均方根误差和平均绝对误差均小于 3%。此外，该方法只需要在两个温度下测量动态工作条件的实验数据，这两个温度覆盖了电池工作温度的大部分范围。而且它的计算复杂度低，适合车载应用。
{"title":"A novel method for state of charge estimation of lithium-ion batteries at low-temperatures","authors":"","doi":"10.1016/j.apenergy.2024.124514","DOIUrl":"10.1016/j.apenergy.2024.124514","url":null,"abstract":"<div><div>The low temperature environment poses a significant challenge to the application of electric vehicles (EVs). At low temperatures, the dynamic characteristics inside the battery become significantly different from those in the temperature range of 10–40 °C, resulting in high uncertainties in the estimation of state of charge (SOC). Experimental studies on two types of lithium-ion batteries have found that due to changes in battery polarization characteristics at low temperatures, the open circuit voltage (OCV) identified by the commonly used equivalent circuit models and parameter identification methods becomes more distorted. This is the reason for the failure of most SOC estimation methods based on OCV-SOC mapping. A part of polarization voltage is incorrectly involved in the OCV by online parameter identification. Based on this phenomenon, a novel method is proposed to achieve accurate SOC estimation at low temperatures by compensating this part of polarization voltage. The compensation voltage is calculated by a function, which is identified from experimental data using genetic algorithm. The validation against experimental results demonstrates that the proposed method can achieve a root mean square error and mean absolute error of less than 3 % for the SOC estimation in temperatures down to −20 °C. Moreover, this method only needs experimental data of dynamic operating conditions measured at two temperatures which cover most of the battery's working temperature range. And its computational complexity is low, making it suitable for onboard applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.apenergy.2024.124518
The increasing integration of renewable energy in power systems causes a decrease in the frequency stability of the system. Consequently, renewable energy stations, such as wind farms (WFs), must possess adequate frequency support capabilities. To maximize the frequency support capability of the WF, it is crucial to determine the frequency support capability boundaries (FSCB) of the WF. Due to the uneven distribution of wind resources and complex operating states of wind turbines, accurate evaluation of the FSCB of the WF is challenging. To address this issue, this paper proposes a knowledge and data-driven fusion Koopman method to assess the FSCB of the doubly fed induction generator (DFIG)-based WF. The characteristics of FSCB are analyzed and a multi-dimensional indicator system is defined to precisely quantify FSCB at both theoretical and practical levels. To accurately calculate the defined indicators, a knowledge and data-driven fusion method based on Koopman-mixed integer linear programming (MILP) is proposed. The knowledge of WF frequency regulation structures is integrated to construct Koopman dictionary functions. This allows the training of historical frequency regulation data to obtain the global linearized Koopman operator for the assessment object. Subsequently, it facilitates online assessment results using real-time data. Case studies are undertaken on the four-machine two-area power system including a DFIG-based WF. The assessment error of the proposed Koopman-MILP method is within 2%, with an assessment speed nearly 10 times faster than conventional nonlinear methods. The proposed dictionary function, compared to the one without integrated knowledge, improves assessment accuracy by nearly 5 times. Additionally, it reveals the impact of frequency regulation strategies, safety operation constraints, and wind resources on FSCB. Simulation results validate the rationality of the proposed indicators, the accuracy of the assessment method, and the practicality of the assessment outcomes under various operating conditions.
{"title":"Online assessment of frequency support capability of the DFIG-based wind farm using a knowledge and data-driven fusion Koopman method","authors":"","doi":"10.1016/j.apenergy.2024.124518","DOIUrl":"10.1016/j.apenergy.2024.124518","url":null,"abstract":"<div><div>The increasing integration of renewable energy in power systems causes a decrease in the frequency stability of the system. Consequently, renewable energy stations, such as wind farms (WFs), must possess adequate frequency support capabilities. To maximize the frequency support capability of the WF, it is crucial to determine the frequency support capability boundaries (FSCB) of the WF. Due to the uneven distribution of wind resources and complex operating states of wind turbines, accurate evaluation of the FSCB of the WF is challenging. To address this issue, this paper proposes a knowledge and data-driven fusion Koopman method to assess the FSCB of the doubly fed induction generator (DFIG)-based WF. The characteristics of FSCB are analyzed and a multi-dimensional indicator system is defined to precisely quantify FSCB at both theoretical and practical levels. To accurately calculate the defined indicators, a knowledge and data-driven fusion method based on Koopman-mixed integer linear programming (MILP) is proposed. The knowledge of WF frequency regulation structures is integrated to construct Koopman dictionary functions. This allows the training of historical frequency regulation data to obtain the global linearized Koopman operator for the assessment object. Subsequently, it facilitates online assessment results using real-time data. Case studies are undertaken on the four-machine two-area power system including a DFIG-based WF. The assessment error of the proposed Koopman-MILP method is within 2%, with an assessment speed nearly 10 times faster than conventional nonlinear methods. The proposed dictionary function, compared to the one without integrated knowledge, improves assessment accuracy by nearly 5 times. Additionally, it reveals the impact of frequency regulation strategies, safety operation constraints, and wind resources on FSCB. Simulation results validate the rationality of the proposed indicators, the accuracy of the assessment method, and the practicality of the assessment outcomes under various operating conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":10.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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