Pub Date : 2024-06-28DOI: 10.1016/j.apenergy.2024.123789
Sijan Devkota, Pratistha Karmacharya, Sherila Maharjan, Dilip Khatiwada, Bibek Uprety
This study reports a comprehensive techno-economic and environmental assessment of a realistic pathway for decarbonizing the urea industry. The proposed green urea synthesis plant utilizes hydroelectricity-powered electrolysis process and carbon capture from cement flue gas to create sustainable and environmentally friendly production process. Utilizing Aspen Plus and MATLAB, this study first, models the electrolysis, air separation, ammonia synthesis, carbon capture and urea synthesis units, and then evaluates the economic and environmental parameters of the synthesis process. Furthermore, the study highlights the transformative impacts of carbon credit and the renewable energy prices on the profitability metrics of the green urea plant. For the proposed 220 kt/year urea plant, the total energy consumption is 8.18 × 10 GJ/year with the electrolysis unit accounting for half of the energy demand. The estimated total capital investment for the urea plant is 510.79 million USD, with an annual operating expenditure of 156.02 million USD. The urea synthesis unit accounted for half of the total capital expenditure, while electricity contributed to the largest proportion (73%) of the operating expenses. The levelized cost for urea (LCOU) is estimated to be 570.96 USD/t which is approximately 62.2% higher than the urea obtained from conventional process. The electrolyzer unit contributed to 34.4% of the total LCOU. Sensitivity analysis showed that 30% decrease in the electricity price from the base case could lower the LCOU by 27%. The global warming potential of the proposed green urea process is 326.11 kg CO/t of urea. Lower hydroelectricity prices and carbon credit opportunities significantly improve the economic viability of the green urea production process.
{"title":"Decarbonizing urea: Techno-economic and environmental analysis of a model hydroelectricity and carbon capture based green urea production","authors":"Sijan Devkota, Pratistha Karmacharya, Sherila Maharjan, Dilip Khatiwada, Bibek Uprety","doi":"10.1016/j.apenergy.2024.123789","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123789","url":null,"abstract":"This study reports a comprehensive techno-economic and environmental assessment of a realistic pathway for decarbonizing the urea industry. The proposed green urea synthesis plant utilizes hydroelectricity-powered electrolysis process and carbon capture from cement flue gas to create sustainable and environmentally friendly production process. Utilizing Aspen Plus and MATLAB, this study first, models the electrolysis, air separation, ammonia synthesis, carbon capture and urea synthesis units, and then evaluates the economic and environmental parameters of the synthesis process. Furthermore, the study highlights the transformative impacts of carbon credit and the renewable energy prices on the profitability metrics of the green urea plant. For the proposed 220 kt/year urea plant, the total energy consumption is 8.18 × 10 GJ/year with the electrolysis unit accounting for half of the energy demand. The estimated total capital investment for the urea plant is 510.79 million USD, with an annual operating expenditure of 156.02 million USD. The urea synthesis unit accounted for half of the total capital expenditure, while electricity contributed to the largest proportion (73%) of the operating expenses. The levelized cost for urea (LCOU) is estimated to be 570.96 USD/t which is approximately 62.2% higher than the urea obtained from conventional process. The electrolyzer unit contributed to 34.4% of the total LCOU. Sensitivity analysis showed that 30% decrease in the electricity price from the base case could lower the LCOU by 27%. The global warming potential of the proposed green urea process is 326.11 kg CO/t of urea. Lower hydroelectricity prices and carbon credit opportunities significantly improve the economic viability of the green urea production process.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506485","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}
Co-firing of waste biomass as low-carbon fuels in combined heat and power (CHP) units is an indispensable energy supply technology providing regional electricity and heat, especially in industrial parks, that can combat climate change and support the achievement of Sustainable Development Goals (SDGs) in China. In this study, we proposed a life cycle material-energy-carbon integrated GHG accounting framework for CHP units converting waste to energy with co-firing of sludge and biomass pellets, including supply and value chains. This framework enables the prediction of multi-scope GHG emissions and environmental impacts associated with co-firing based on the properties of biomass fuels. It facilitates the selection of suitable biomass fuels according to decarbonization or reduced environmental impact objectives. When considering CO emissions alone, co-firing with sludge appears to be a beneficial option due to the economic gains from waste disposal. However, this also leads to increased GHG emissions unless avoided emissions from original disposal are considered, thus highlighting the need to expand the regulatory framework pertaining to GHG emissions that recognizes non-CO GHG emissions. Additionally, the practice of co-firing biomass fuels presents a complex interplay between enhanced decarbonization effects but potentially more significant environmental impacts when the co-firing ratio increases, highlighting the trade-offs between enhanced decarbonization and environmental challenges. Leveraging this framework, our study evaluated the national decarbonization potential through the deployment of CHP units with sludge or biomass co-firing across various provinces, indicating that biomass pellets co-firing could lead to more substantial GHG emission reduction.
在热电联产（CHP）机组中将废弃生物质作为低碳燃料进行联合燃烧是一项不可或缺的能源供应技术，可提供区域电力和热能，尤其是在工业园区，可应对气候变化并支持中国可持续发展目标（SDGs）的实现。在本研究中，我们提出了一个利用污泥和生物质颗粒联合燃烧将废物转化为能源的热电联产机组的生命周期物质-能源-碳综合温室气体核算框架，包括供应链和价值链。该框架可根据生物质燃料的特性，预测与联合燃烧相关的多范围温室气体排放和环境影响。它有助于根据脱碳或减少环境影响的目标选择合适的生物质燃料。如果仅考虑二氧化碳排放量，使用污泥联合燃烧似乎是一个有益的选择，因为可以从废物处置中获得经济收益。然而，这也会导致温室气体排放量的增加，除非考虑到原始处置所避免的排放量，因此需要扩大温室气体排放的监管框架，承认非 CO 温室气体排放。此外，共同燃烧生物质燃料的做法在增强脱碳效果与共同燃烧比例增加时可能产生的更大环境影响之间呈现出复杂的相互作用，突出了增强脱碳效果与环境挑战之间的权衡。利用这一框架，我们的研究通过在各省部署污泥或生物质联合燃烧的热电联产机组，对全国的脱碳潜力进行了评估，结果表明，生物质颗粒联合燃烧可实现更大幅度的温室气体减排。
{"title":"Multi-scope decarbonization and environmental impacts evaluation for biomass fuels co-firing CHP units in China","authors":"Ying Wang, Yuxin Yan, Qingyang Lin, Hanxiao Liu, Xiang Luo, Chenghang Zheng, Tao Wu, Xiang Gao","doi":"10.1016/j.apenergy.2024.123793","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123793","url":null,"abstract":"Co-firing of waste biomass as low-carbon fuels in combined heat and power (CHP) units is an indispensable energy supply technology providing regional electricity and heat, especially in industrial parks, that can combat climate change and support the achievement of Sustainable Development Goals (SDGs) in China. In this study, we proposed a life cycle material-energy-carbon integrated GHG accounting framework for CHP units converting waste to energy with co-firing of sludge and biomass pellets, including supply and value chains. This framework enables the prediction of multi-scope GHG emissions and environmental impacts associated with co-firing based on the properties of biomass fuels. It facilitates the selection of suitable biomass fuels according to decarbonization or reduced environmental impact objectives. When considering CO emissions alone, co-firing with sludge appears to be a beneficial option due to the economic gains from waste disposal. However, this also leads to increased GHG emissions unless avoided emissions from original disposal are considered, thus highlighting the need to expand the regulatory framework pertaining to GHG emissions that recognizes non-CO GHG emissions. Additionally, the practice of co-firing biomass fuels presents a complex interplay between enhanced decarbonization effects but potentially more significant environmental impacts when the co-firing ratio increases, highlighting the trade-offs between enhanced decarbonization and environmental challenges. Leveraging this framework, our study evaluated the national decarbonization potential through the deployment of CHP units with sludge or biomass co-firing across various provinces, indicating that biomass pellets co-firing could lead to more substantial GHG emission reduction.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506486","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 : 2024-06-28DOI: 10.1016/j.apenergy.2024.123735
Tianbiao He, Jie Ma, Ning Mao, Meng Qi, Tao Jin
Utilizing LNG cold energy for power generation is critical for improving energy efficiency of LNG supply chain. Current studies on power generation systems that use LNG cold energy primarily focus on steady-state simulations and optimizing key parameters. However, there is a notable gap in research regarding dynamic simulations to understand the dynamic behaviors of these systems. To address this, a dynamic model for a dual-stage series ORC system that harnesses LNG cold energy was proposed focusing on its dynamic responses. A comparative analysis of its stability under two different control strategies were conducted identifying the cascade control strategy as the superior method. The effects of various parameters, such as LNG temperature, mass flow, and composition, along with exhaust gas pressure, temperature, and composition, on the stability and dynamic response of the system were investigated. The results indicate that fluctuations in LNG mass flow have the most significant impact on system stability, while exhaust gas pressure has the least. Additionally, most parameters effectively returned to their setpoints after disturbances when managed by the cascaded control strategy. This research provides valuable insights into the operational characteristics of the dual-stage ORC, demonstrating its potential for sustainable power generation by leveraging the recovery of LNG cold energy.
{"title":"Exploring the stability and dynamic responses of dual-stage series ORC using LNG cold energy for sustainable power generation","authors":"Tianbiao He, Jie Ma, Ning Mao, Meng Qi, Tao Jin","doi":"10.1016/j.apenergy.2024.123735","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123735","url":null,"abstract":"Utilizing LNG cold energy for power generation is critical for improving energy efficiency of LNG supply chain. Current studies on power generation systems that use LNG cold energy primarily focus on steady-state simulations and optimizing key parameters. However, there is a notable gap in research regarding dynamic simulations to understand the dynamic behaviors of these systems. To address this, a dynamic model for a dual-stage series ORC system that harnesses LNG cold energy was proposed focusing on its dynamic responses. A comparative analysis of its stability under two different control strategies were conducted identifying the cascade control strategy as the superior method. The effects of various parameters, such as LNG temperature, mass flow, and composition, along with exhaust gas pressure, temperature, and composition, on the stability and dynamic response of the system were investigated. The results indicate that fluctuations in LNG mass flow have the most significant impact on system stability, while exhaust gas pressure has the least. Additionally, most parameters effectively returned to their setpoints after disturbances when managed by the cascaded control strategy. This research provides valuable insights into the operational characteristics of the dual-stage ORC, demonstrating its potential for sustainable power generation by leveraging the recovery of LNG cold energy.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525134","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 : 2024-06-28DOI: 10.1016/j.apenergy.2024.123762
Jinbo Qu, Yongming Feng, Binyang Wu, Yuanqing Zhu, Jiaqi Wang
Finite time thermodynamics is applied to carry out the thermodynamic analysis of integrated system including solid oxide fuel cell (SOFC) and supercritical CO Brayton Carnot battery (CB). SOFC-CB integration can keep SOFC-based system high flexibility in terms of load changing, but research methods used in the past studies focus on classical equilibrium thermodynamics. The large deviations have been caused from calculated and practical points. Therefore, this paper considers finite time of thermodynamic process and finite size of heat exchangers to find out the realistic regulations from pinch point and performances. The comparison results show the finite time thermodynamic model shows more precise, in which the average error of finite time thermodynamic model can reach 4.08%, 2.02 times smaller than that of classical equilibrium thermodynamic model. It can be significantly observed that the increase of power output can lead to the decrease of efficiency. In addition, the finite time thermodynamic analysis of CB system is also performed. The results show that in the finite time thermodynamic theoretical framework, optimization round-trip electric efficiency of CB can reach 214.8%. Meanwhile, the multi-objective optimization based on TOPSIS combined with weight entropy method and non-dominated sorting genetic algorithm-II is performed. The optimal results show that the net efficiency, net power output and charging power of SOFC system can be achieved by 47.82%, 3159 kW, and 402 kW, while the overall energy utilization efficiency during the whole operation can reach 60.89% at fuel utilization of 0.70. Furthermore, the configuration optimization results show that the net efficiency, net power output and charging power of SOFC system can be achieved by 59.01%, 3989 kW, and 128 kW, while the overall efficiency can reach 62.88%. The improved system can show more feasibility of the actual application.
{"title":"Understanding the thermodynamic behaviors of integrated system including solid oxide fuel cell and Carnot battery based on finite time thermodynamics","authors":"Jinbo Qu, Yongming Feng, Binyang Wu, Yuanqing Zhu, Jiaqi Wang","doi":"10.1016/j.apenergy.2024.123762","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123762","url":null,"abstract":"Finite time thermodynamics is applied to carry out the thermodynamic analysis of integrated system including solid oxide fuel cell (SOFC) and supercritical CO Brayton Carnot battery (CB). SOFC-CB integration can keep SOFC-based system high flexibility in terms of load changing, but research methods used in the past studies focus on classical equilibrium thermodynamics. The large deviations have been caused from calculated and practical points. Therefore, this paper considers finite time of thermodynamic process and finite size of heat exchangers to find out the realistic regulations from pinch point and performances. The comparison results show the finite time thermodynamic model shows more precise, in which the average error of finite time thermodynamic model can reach 4.08%, 2.02 times smaller than that of classical equilibrium thermodynamic model. It can be significantly observed that the increase of power output can lead to the decrease of efficiency. In addition, the finite time thermodynamic analysis of CB system is also performed. The results show that in the finite time thermodynamic theoretical framework, optimization round-trip electric efficiency of CB can reach 214.8%. Meanwhile, the multi-objective optimization based on TOPSIS combined with weight entropy method and non-dominated sorting genetic algorithm-II is performed. The optimal results show that the net efficiency, net power output and charging power of SOFC system can be achieved by 47.82%, 3159 kW, and 402 kW, while the overall energy utilization efficiency during the whole operation can reach 60.89% at fuel utilization of 0.70. Furthermore, the configuration optimization results show that the net efficiency, net power output and charging power of SOFC system can be achieved by 59.01%, 3989 kW, and 128 kW, while the overall efficiency can reach 62.88%. The improved system can show more feasibility of the actual application.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525132","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}
Owing to the urgency of energy demand, an enhanced supervisory control scheme (ESCS) is proposed for hybrid microgrids (HMGs) integrating AC and DC grids. This system optimizes energy management within a virtual power plant (VPP) setup, facilitating smart charging stations for electric vehicles (EVs) and enabling vehicle-to-grid (V2G) and grid-to-vehicle (G2V) interactions. The proposed ESCS combines three sub-controllers: a sliding mode approach-based maximum power algorithm (SMA-MPA), active current detection technique (ACDT), and state of charge (SOC) regulation scheme. In this proposed approach, the SMA-MPA method is employed to extract maximum power with necessary stability confirmation. Moreover, ACDT is utilized to mitigate harmonics from nonlinear loads through the DC-AC inverter, thereby improving power quality (PQ). To enhance SOC regulation of the VPP, a detailed flow chart of appropriate converting mode selection associated with SOC controller design is proposed for smoother operation and improved dynamics. The coordination between sub-controllers is achieved by analyzing power demand and supply, DC-link voltage conditions, and SOC states of the VPP. The proposed ESCS approach enhances PQ even during PV shutdown conditions. Through software simulations and real-time Hardware-in-the-Loop (HIL-402) validation, the ESCS's superior power management, PQ, and regulatory compliance are demonstrated against conventional PQ methods. The findings exhibit excellent power management, improved PQ, and better voltage/frequency regulation in accordance with prescribed international IEEE 519 standards.
{"title":"Enhanced supervisory control scheme for hybrid microgrid operation with virtual power plants","authors":"Buddhadeva Sahoo, Subhransu Ranjan Samantaray, Pravat Kumar Rout","doi":"10.1016/j.apenergy.2024.123741","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123741","url":null,"abstract":"Owing to the urgency of energy demand, an enhanced supervisory control scheme (ESCS) is proposed for hybrid microgrids (HMGs) integrating AC and DC grids. This system optimizes energy management within a virtual power plant (VPP) setup, facilitating smart charging stations for electric vehicles (EVs) and enabling vehicle-to-grid (V2G) and grid-to-vehicle (G2V) interactions. The proposed ESCS combines three sub-controllers: a sliding mode approach-based maximum power algorithm (SMA-MPA), active current detection technique (ACDT), and state of charge (SOC) regulation scheme. In this proposed approach, the SMA-MPA method is employed to extract maximum power with necessary stability confirmation. Moreover, ACDT is utilized to mitigate harmonics from nonlinear loads through the DC-AC inverter, thereby improving power quality (PQ). To enhance SOC regulation of the VPP, a detailed flow chart of appropriate converting mode selection associated with SOC controller design is proposed for smoother operation and improved dynamics. The coordination between sub-controllers is achieved by analyzing power demand and supply, DC-link voltage conditions, and SOC states of the VPP. The proposed ESCS approach enhances PQ even during PV shutdown conditions. Through software simulations and real-time Hardware-in-the-Loop (HIL-402) validation, the ESCS's superior power management, PQ, and regulatory compliance are demonstrated against conventional PQ methods. The findings exhibit excellent power management, improved PQ, and better voltage/frequency regulation in accordance with prescribed international IEEE 519 standards.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525133","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 : 2024-06-27DOI: 10.1016/j.apenergy.2024.123763
Qingqing Li, Jinbo Shi, Wenxiang Li, Siyun Xiao, Ke Song, Yongbo Zhang, Zhenqi Wang, Jie Gu, Bo Liu, Xiaoming Lai
Current carbon neutralization systems are time-consuming, which generally require at least one to two months. We propose a highly efficient real-time carbon neutralization mechanism, a Modelx+MRV + O system based on the Internet of Things and blockchain technology for all the carbon reduction scenarios. This mechanism includes an accounting model for a certain distributed carbon reduction scenario, and real-time M, RV, and O systems, enabling enterprises, products, or individuals to reliably reach carbon neutrality in real time. We demonstrated how to build a real-time model (Modelx) by proposing a carbon emission reduction methodology for the returnable packaging scenario and a photovoltaic power generation scenario combining IoT technology for a traditional methodology for polar electricity. We found that the proposed system can achieve real-time analysis based on the monitored turnover number and electricity generated and avoid falsified values. Because carbon neutrality is essential to reduce carbon emissions and combat climate change globally, this system can accelerate the sustainable transformation by managing carbon neutrality globally.
{"title":"An efficient tool for real-time global carbon neutrality with credibility of delicacy management: A Modelx + MRV + O system","authors":"Qingqing Li, Jinbo Shi, Wenxiang Li, Siyun Xiao, Ke Song, Yongbo Zhang, Zhenqi Wang, Jie Gu, Bo Liu, Xiaoming Lai","doi":"10.1016/j.apenergy.2024.123763","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123763","url":null,"abstract":"Current carbon neutralization systems are time-consuming, which generally require at least one to two months. We propose a highly efficient real-time carbon neutralization mechanism, a Modelx+MRV + O system based on the Internet of Things and blockchain technology for all the carbon reduction scenarios. This mechanism includes an accounting model for a certain distributed carbon reduction scenario, and real-time M, RV, and O systems, enabling enterprises, products, or individuals to reliably reach carbon neutrality in real time. We demonstrated how to build a real-time model (Modelx) by proposing a carbon emission reduction methodology for the returnable packaging scenario and a photovoltaic power generation scenario combining IoT technology for a traditional methodology for polar electricity. We found that the proposed system can achieve real-time analysis based on the monitored turnover number and electricity generated and avoid falsified values. Because carbon neutrality is essential to reduce carbon emissions and combat climate change globally, this system can accelerate the sustainable transformation by managing carbon neutrality globally.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525140","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 : 2024-06-27DOI: 10.1016/j.apenergy.2024.123749
Weipeng Zhan, Zhenpo Wang, Junjun Deng, Peng Liu, Dingsong Cui
As the growing deployment towards transportation electrification, a critical focus has emerged on quantifying the reduction contribution of greenhouse gas emissions from electric vehicles towards achieving carbon neutrality under diverse policy scenarios in the future. This necessitates a dynamic model that captures the evolving composition of the vehicle fleet and accurately forecasts the penetration and developmental trajectory of the electric vehicles in the car market. However, previous studies have largely overlooked the heterogeneity in user usage attributes, rendering them less effective in evaluating the impact of usage-based incentives on electric vehicle market penetration. To bridge this research gap, this study introduces an innovative, data-driven framework that integrates system dynamics and agent-based model. The proposed model can predict levels of electric vehicle penetration and corresponding greenhouse gas emission reductions within the private passenger vehicle sector, under a variety of policy scenarios. Our findings indicate that usage-based incentives, when implemented with optimal intensity, yield more significant emission reduction impacts and long-term economic benefits compared to conventional purchase-based subsidy. These insights not only furnish actionable policy suggestions to expedite the electric vehicle industry's growth in China but also offer valuable implications for other countries seeking to implement effective strategies for combating climate change and fostering sustainable transportation initiatives.
{"title":"Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios","authors":"Weipeng Zhan, Zhenpo Wang, Junjun Deng, Peng Liu, Dingsong Cui","doi":"10.1016/j.apenergy.2024.123749","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123749","url":null,"abstract":"As the growing deployment towards transportation electrification, a critical focus has emerged on quantifying the reduction contribution of greenhouse gas emissions from electric vehicles towards achieving carbon neutrality under diverse policy scenarios in the future. This necessitates a dynamic model that captures the evolving composition of the vehicle fleet and accurately forecasts the penetration and developmental trajectory of the electric vehicles in the car market. However, previous studies have largely overlooked the heterogeneity in user usage attributes, rendering them less effective in evaluating the impact of usage-based incentives on electric vehicle market penetration. To bridge this research gap, this study introduces an innovative, data-driven framework that integrates system dynamics and agent-based model. The proposed model can predict levels of electric vehicle penetration and corresponding greenhouse gas emission reductions within the private passenger vehicle sector, under a variety of policy scenarios. Our findings indicate that usage-based incentives, when implemented with optimal intensity, yield more significant emission reduction impacts and long-term economic benefits compared to conventional purchase-based subsidy. These insights not only furnish actionable policy suggestions to expedite the electric vehicle industry's growth in China but also offer valuable implications for other countries seeking to implement effective strategies for combating climate change and fostering sustainable transportation initiatives.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525141","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 : 2024-06-27DOI: 10.1016/j.apenergy.2024.123774
Yiming Bie, Wei Qin, Jiabin Wu
Currently, the charging energy of electric buses (EBs) primarily relies on the power grid (PG), and the production of the electricity for the power grid still results in carbon emissions. In recent years, a remarkable development has been observed in the photovoltaic (PV) technology. If EBs can be charged using electricity generated from PV, it has great potential to significantly reduce carbon emissions for EB systems at the source. Considering the inherent output power fluctuations from PV source, we propose a hybrid electricity supply mode named “Photovoltaic-Energy Storage System-Power Grid” (PV-ESS-PG). Firstly, considering the characteristics of different electricity supply modes, we introduce charging strategies tailored to different scenarios and formulate a cooperative optimization model for EB dispatching and charging plans. Secondly, we decompose this model into two sub-problems: bus dispatching and charging scheduling. To solve these two sub-problems, we employ the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to obtain the optimization results of bus dispatching plan, charging mode, charging start time, and charging duration. Finally, we validate the proposed method using real-world data of EB operation and PV output power. We further analyze the influences of weather conditions, ESS capacity, and EB rated battery capacity on the optimization results. We find that, compared to the conventional unitary power grid electricity supply mode, the proposed method reduces daily charging costs by 25.48% and carbon emissions by 68.71% of the whole bus route.
{"title":"Optimal electric bus scheduling method under hybrid energy supply mode of photovoltaic-energy storage system-power grid","authors":"Yiming Bie, Wei Qin, Jiabin Wu","doi":"10.1016/j.apenergy.2024.123774","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123774","url":null,"abstract":"Currently, the charging energy of electric buses (EBs) primarily relies on the power grid (PG), and the production of the electricity for the power grid still results in carbon emissions. In recent years, a remarkable development has been observed in the photovoltaic (PV) technology. If EBs can be charged using electricity generated from PV, it has great potential to significantly reduce carbon emissions for EB systems at the source. Considering the inherent output power fluctuations from PV source, we propose a hybrid electricity supply mode named “Photovoltaic-Energy Storage System-Power Grid” (PV-ESS-PG). Firstly, considering the characteristics of different electricity supply modes, we introduce charging strategies tailored to different scenarios and formulate a cooperative optimization model for EB dispatching and charging plans. Secondly, we decompose this model into two sub-problems: bus dispatching and charging scheduling. To solve these two sub-problems, we employ the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to obtain the optimization results of bus dispatching plan, charging mode, charging start time, and charging duration. Finally, we validate the proposed method using real-world data of EB operation and PV output power. We further analyze the influences of weather conditions, ESS capacity, and EB rated battery capacity on the optimization results. We find that, compared to the conventional unitary power grid electricity supply mode, the proposed method reduces daily charging costs by 25.48% and carbon emissions by 68.71% of the whole bus route.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531966","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 : 2024-06-27DOI: 10.1016/j.apenergy.2024.123781
Yukun Fan, Weifeng Liu, Feilin Zhu, Sen Wang, Hao Yue, Yurou Zeng, Bin Xu, Ping-an Zhong
Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.
{"title":"Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties","authors":"Yukun Fan, Weifeng Liu, Feilin Zhu, Sen Wang, Hao Yue, Yurou Zeng, Bin Xu, Ping-an Zhong","doi":"10.1016/j.apenergy.2024.123781","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123781","url":null,"abstract":"Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525139","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 : 2024-06-27DOI: 10.1016/j.apenergy.2024.123783
Dejian Zhou, Ke Li, Huhao Gao, Alexandru Tatomir, Martin Sauter, Leonhard Ganzer
High-temperature aquifer thermal storage (HT-ATES) is an effective method to mitigate the increasing greenhouse gas emissions, and it is attracting industry attention as an alternative to traditional fossil fuels for heating and cooling. However, the uncertainty of exploration and long profit cycles impede the popularization of HT-ATES technology. In this paper, to optimize HT-ATES evaluation and make the results more convictive, we demonstrate a numerical study based on a real district and a proven aquifer. An integrated HT-ATES model includes the wellbore and aquifer is used to simulate the fluid flow and heat transfer. Moreover, a dynamic economic assessment is demonstrated depending on the HT-ATES fluctuation performance. A 30-year HT-ATES cycling simulation shows that the wellbore and aquifer have had a continuous heating loss since the operation started. Working well and balancing the well lost 2.7% and 2.2% of total energy through the wellbore. The aquifer lost 4.1% of total energy due to heating transfer to overburden and other layers. HT-ATES could recover around 90% of stored total energy. The HT-ATES economic performance is affected by the heating store and production cycling, the benefit mainly comes from the heating production season. The initial investment and heat exchange efficiency between the HT-ATES & end-application system determines the levelized heat (LCOH) cost and payback time, the optimist case still needs 3 years to be profitable. HT-ATES have considerable green benefits, it could reduce local CO emissions 1937 t/year.
{"title":"Techno-economic assessment of high-temperature aquifer thermal energy storage system, insights from a study case in Burgwedel, Germany","authors":"Dejian Zhou, Ke Li, Huhao Gao, Alexandru Tatomir, Martin Sauter, Leonhard Ganzer","doi":"10.1016/j.apenergy.2024.123783","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123783","url":null,"abstract":"High-temperature aquifer thermal storage (HT-ATES) is an effective method to mitigate the increasing greenhouse gas emissions, and it is attracting industry attention as an alternative to traditional fossil fuels for heating and cooling. However, the uncertainty of exploration and long profit cycles impede the popularization of HT-ATES technology. In this paper, to optimize HT-ATES evaluation and make the results more convictive, we demonstrate a numerical study based on a real district and a proven aquifer. An integrated HT-ATES model includes the wellbore and aquifer is used to simulate the fluid flow and heat transfer. Moreover, a dynamic economic assessment is demonstrated depending on the HT-ATES fluctuation performance. A 30-year HT-ATES cycling simulation shows that the wellbore and aquifer have had a continuous heating loss since the operation started. Working well and balancing the well lost 2.7% and 2.2% of total energy through the wellbore. The aquifer lost 4.1% of total energy due to heating transfer to overburden and other layers. HT-ATES could recover around 90% of stored total energy. The HT-ATES economic performance is affected by the heating store and production cycling, the benefit mainly comes from the heating production season. The initial investment and heat exchange efficiency between the HT-ATES & end-application system determines the levelized heat (LCOH) cost and payback time, the optimist case still needs 3 years to be profitable. HT-ATES have considerable green benefits, it could reduce local CO emissions 1937 t/year.","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525136","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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