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}
Pub Date : 2024-06-27DOI: 10.1016/j.apenergy.2024.123797
Juan P. Laporte, Rocío Román-Collado, José M. Cansino
Energy consumption in universities is a crucial issue as they aim to balance growing operational demands with environmental sustainability. This study employs the Logarithmic Mean Divisia Index method to assess energy consumption variations at Universidad Autónoma de Chile from 2017 to 2022, demonstrating the method's efficacy and simplicity in decomposing energy use into its determinants. The analysis reveals a 19% increase in energy consumption, primarily fueled by heightened energy intensity from increased research activities and rising enrollment. However, weather conditions and infrastructural efficiencies have mitigated this increase. Notably, the 2020 remote learning period saw a 45% decrease in energy consumption, largely due to reduced energy intensity. This study validates the LMDI method for individual institutions and provides a clear, interpretable framework for understanding energy variations. It highlights the impact of the Chilean accreditation system, which indirectly induces energy consumption expansions in universities by requiring enlargements in gross floor area. The findings also emphasize the significant effect of weather on energy usage in extreme climates. Recommendations for Universidad Autónoma de Chile include implementing behavioral change programs, enhancing climate control, and lighting systems, conducting energy audits, pursuing building retrofitting, and considering a partial shift to remote learning to further reduce energy consumption.
{"title":"Key driving forces of energy consumption in a higher education institution using the LMDI approach: The case of the Universidad Autónoma de Chile.","authors":"Juan P. Laporte, Rocío Román-Collado, José M. Cansino","doi":"10.1016/j.apenergy.2024.123797","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123797","url":null,"abstract":"Energy consumption in universities is a crucial issue as they aim to balance growing operational demands with environmental sustainability. This study employs the Logarithmic Mean Divisia Index method to assess energy consumption variations at Universidad Autónoma de Chile from 2017 to 2022, demonstrating the method's efficacy and simplicity in decomposing energy use into its determinants. The analysis reveals a 19% increase in energy consumption, primarily fueled by heightened energy intensity from increased research activities and rising enrollment. However, weather conditions and infrastructural efficiencies have mitigated this increase. Notably, the 2020 remote learning period saw a 45% decrease in energy consumption, largely due to reduced energy intensity. This study validates the LMDI method for individual institutions and provides a clear, interpretable framework for understanding energy variations. It highlights the impact of the Chilean accreditation system, which indirectly induces energy consumption expansions in universities by requiring enlargements in gross floor area. The findings also emphasize the significant effect of weather on energy usage in extreme climates. Recommendations for Universidad Autónoma de Chile include implementing behavioral change programs, enhancing climate control, and lighting systems, conducting energy audits, pursuing building retrofitting, and considering a partial shift to remote learning to further reduce energy consumption.","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":"141525135","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}
The “LiCoNiMnO-LiFePO” hybrid battery pack is employed to mitigate drawbacks and fully leverage the distinct advantages of these two types of cells. Nonetheless, the fundamental statistics of the two types of cells diverge significantly, and the different cell configurations lead to the different output performances of the hybrid battery pack. Therefore, the different capacity and initial electricity thresholds configurations of the hybrid battery pack for various configurations are researched to achieve the diverse output performances. Firstly, a new approach based on electrochemical equivalent circuit model is introduced to simplify the parameter calibration process. Secondly, a series-connected hybrid battery pack simulation model with electrical-thermal-aging coupling is established to achieve the high-precision of capacity and aging simulation, which effectively replaces the physical experiments. Finally, the unchanged available electricity theory of the hybrid battery pack under a wide range of temperature scenarios is constructed based on the configurations of heterogeneous cells under different configuration, which provides a reference to the development of equalization algorithms. Furthermore, even if the aging rate of the heterogeneous cells is inconsistent, the configuration of the hybrid battery pack still could maintain the consistent output characteristics.
{"title":"Study on the configuration of LiCoxNiyMn1-x-yO2 - LiFePO4 hybrid battery pack","authors":"Mingzhu Wang, Guan Wang, Qi Luo, Suran Li, Shuai Yao, Wenkang Bao, Yuedong Sun, Yuejiu Zheng","doi":"10.1016/j.apenergy.2024.123744","DOIUrl":"https://doi.org/10.1016/j.apenergy.2024.123744","url":null,"abstract":"The “LiCoNiMnO-LiFePO” hybrid battery pack is employed to mitigate drawbacks and fully leverage the distinct advantages of these two types of cells. Nonetheless, the fundamental statistics of the two types of cells diverge significantly, and the different cell configurations lead to the different output performances of the hybrid battery pack. Therefore, the different capacity and initial electricity thresholds configurations of the hybrid battery pack for various configurations are researched to achieve the diverse output performances. Firstly, a new approach based on electrochemical equivalent circuit model is introduced to simplify the parameter calibration process. Secondly, a series-connected hybrid battery pack simulation model with electrical-thermal-aging coupling is established to achieve the high-precision of capacity and aging simulation, which effectively replaces the physical experiments. Finally, the unchanged available electricity theory of the hybrid battery pack under a wide range of temperature scenarios is constructed based on the configurations of heterogeneous cells under different configuration, which provides a reference to the development of equalization algorithms. Furthermore, even if the aging rate of the heterogeneous cells is inconsistent, the configuration of the hybrid battery pack still could maintain the consistent output characteristics.","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":"141525143","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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