Pub Date : 2024-11-09DOI: 10.1016/j.apenergy.2024.124795
Dong-Yeon Lee , Alana Wilson , Melanie H. McDermott , Benjamin K. Sovacool , Robert Kaufmann , Raphael Isaac , Cutler Cleveland , Margaret Smith , Marilyn Brown , Jacob Ward
Based on high-resolution spatial and temporal analysis, we quantify and evaluate the equality of plug-in electric vehicle adoption and public charging infrastructure deployment in the United States, examining current and historical trends, as well as racial and income-based disparities. Our results show that the current and historical distribution of conventional vehicle ownership and gas stations shows much more equality, in contrast to electric vehicles and charging infrastructure. With regards to the distribution of electric vehicle adoption, the more electrified vehicle technology is adopted, the more significant income inequality becomes, on a national scale. Over the last several years, almost all states ameliorated income and racial/ethnic inequality for plug-in electric vehicle adoption, but that is not the case for charging infrastructure. The income inequality of the distribution of nationwide charging infrastructure is three times larger than that of gas stations. Individual states, as well as some of the largest urbanized areas, demonstrate a wide range of inequality associated with income and race/ethnicity. There is a need to better understand what drives this significant spatial heterogeneity, as it implies that additional strategies tailored to local and regional contexts may be necessary to achieve more equal distribution of infrastructure as electric vehicles become common beyond early adopters. Improving consistency and coordination of development of charging infrastructure across different states/regions would likely benefit inter-state travelers.
{"title":"Does electric mobility display racial or income disparities? Quantifying inequality in the distribution of electric vehicle adoption and charging infrastructure in the United States","authors":"Dong-Yeon Lee , Alana Wilson , Melanie H. McDermott , Benjamin K. Sovacool , Robert Kaufmann , Raphael Isaac , Cutler Cleveland , Margaret Smith , Marilyn Brown , Jacob Ward","doi":"10.1016/j.apenergy.2024.124795","DOIUrl":"10.1016/j.apenergy.2024.124795","url":null,"abstract":"<div><div>Based on high-resolution spatial and temporal analysis, we quantify and evaluate the equality of plug-in electric vehicle adoption and public charging infrastructure deployment in the United States, examining current and historical trends, as well as racial and income-based disparities. Our results show that the current and historical distribution of conventional vehicle ownership and gas stations shows much more equality, in contrast to electric vehicles and charging infrastructure. With regards to the distribution of electric vehicle adoption, the more electrified vehicle technology is adopted, the more significant income inequality becomes, on a national scale. Over the last several years, almost all states ameliorated income and racial/ethnic inequality for plug-in electric vehicle adoption, but that is not the case for charging infrastructure. The income inequality of the distribution of nationwide charging infrastructure is three times larger than that of gas stations. Individual states, as well as some of the largest urbanized areas, demonstrate a wide range of inequality associated with income and race/ethnicity. There is a need to better understand what drives this significant spatial heterogeneity, as it implies that additional strategies tailored to local and regional contexts may be necessary to achieve more equal distribution of infrastructure as electric vehicles become common beyond early adopters. Improving consistency and coordination of development of charging infrastructure across different states/regions would likely benefit inter-state travelers.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124795"},"PeriodicalIF":10.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662486","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 : 2024-11-09DOI: 10.1016/j.apenergy.2024.124806
Wei Li , Song Han , Xi Guo , Shufan Xie , Na Rong , Qingling Zhang
A transient model of a power-electronic-assisted on-load tap-changer (POLTC) based Sen transformer (POST) and its switching logic analysis are presented in this paper. Firstly, a thyristor switch model considering the reverse recovery process (RRP) is developed. Furthermore, the transient model of POST is constructed by integrating the proposed thyristor switch model, which incorporates the RRP with the transient model of the Sen transformer (ST) considering the multi-winding coupling (MWC) effect. Secondly, the fundamental switching logic is established according to the topology of the POLTC. Thirdly, the commutation overlap angle (COA) and the short-circuit current (SCC) of the POLTC are evaluated by the proposed transient model. Finally, a method for selecting the optimum switching angle (OSA) is illustrated by analyzing the switching processes under different power factors. With the help of MATLAB, ANSYS/Simplorer, and PSCAD/EMTDC, analytical calculations and time-domain simulations have been carried out to verify the effectivenesses of the proposed transient model of POST, the suggested switching logic, and the proposed OSA selection method. The results also show that the switching process can be completed in less than one power cycle. Moreover, the magnitude of RRC ranges from 2.13 % to 7.08 % of the transmission line current. The change in the amplitude of the short-duration (safe) SCC during switching is about 5.90 % due to MWC.
本文介绍了基于电力电子辅助有载分接开关（POLTC）的森式变压器（POST）的暂态模型及其开关逻辑分析。首先，建立了一个考虑反向恢复过程（RRP）的晶闸管开关模型。此外，考虑到多绕组耦合效应（MWC），将所提出的包含 RRP 的晶闸管开关模型与森式变压器（ST）的瞬态模型相结合，构建了 POST 的瞬态模型。其次，根据 POLTC 的拓扑结构建立基本开关逻辑。第三，通过提出的瞬态模型评估 POLTC 的换向重叠角 (COA) 和短路电流 (SCC)。最后，通过分析不同功率因数下的切换过程，说明了选择最佳切换角 (OSA) 的方法。在 MATLAB、ANSYS/Simplorer 和 PSCAD/EMTDC 的帮助下，进行了分析计算和时域仿真，以验证所提出的 POST 瞬态模型、建议的开关逻辑和 OSA 选择方法的有效性。结果还表明，开关过程可在一个功率周期内完成。此外，RRC 的幅度为输电线路电流的 2.13% 至 7.08%。在切换过程中，由于 MWC 的影响，短时（安全）SCC 的振幅变化约为 5.90%。
{"title":"Transient modeling and switching logic analysis of a power-electronic-assisted OLTC based Sen transformer","authors":"Wei Li , Song Han , Xi Guo , Shufan Xie , Na Rong , Qingling Zhang","doi":"10.1016/j.apenergy.2024.124806","DOIUrl":"10.1016/j.apenergy.2024.124806","url":null,"abstract":"<div><div>A transient model of a power-electronic-assisted on-load tap-changer (POLTC) based Sen transformer (POST) and its switching logic analysis are presented in this paper. Firstly, a thyristor switch model considering the reverse recovery process (RRP) is developed. Furthermore, the transient model of POST is constructed by integrating the proposed thyristor switch model, which incorporates the RRP with the transient model of the Sen transformer (ST) considering the multi-winding coupling (MWC) effect. Secondly, the fundamental switching logic is established according to the topology of the POLTC. Thirdly, the commutation overlap angle (COA) and the short-circuit current (SCC) of the POLTC are evaluated by the proposed transient model. Finally, a method for selecting the optimum switching angle (OSA) is illustrated by analyzing the switching processes under different power factors. With the help of MATLAB, ANSYS/Simplorer, and PSCAD/EMTDC, analytical calculations and time-domain simulations have been carried out to verify the effectivenesses of the proposed transient model of POST, the suggested switching logic, and the proposed OSA selection method. The results also show that the switching process can be completed in less than one power cycle. Moreover, the magnitude of RRC ranges from 2.13 % to 7.08 % of the transmission line current. The change in the amplitude of the short-duration (safe) SCC during switching is about 5.90 % due to MWC.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124806"},"PeriodicalIF":10.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662487","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 : 2024-11-09DOI: 10.1016/j.apenergy.2024.124835
Mohammad Naim Azimi , Mohammad Mafizur Rahman , Tek Maraseni
Energy security is a crucial determinant of sustainable economic growth, especially in the South Asian region, where persistent energy challenges and institutional shortcomings have stifled developmental potential. This study aims to elucidate the complex interplay between energy security and economic growth, with a focus on how institutional quality moderates and transforms these dynamics to promote more resilient growth in the region. Drawing on data from eight South Asian countries from 2000 to 2022, the study employs a panel-corrected standard error (PCSE) model, reinforced by robust feasible generalized least squares (FGLS) method. The results reveal that energy availability, energy supply capacity, energy demand, and energy efficiency exert negative impacts on economic growth, whereas energy development capacity contributes positively to economic growth. Additionally, the novel aggregate energy security index and institutional quality index demonstrate positive effects on growth, alongside urbanization and foreign direct investment. Conversely, trade openness is found to have a negative influence on economic growth. Crucially, the institutional quality index absorbs the adverse effects of energy availability, energy supply capacity, energy demand, and energy efficiency on growth, while amplifying the positive impacts of both individual elements of energy security and its aggregate index. These results highlight the necessity for urgent policy interventions to simultaneously address existing energy security and institutional quality concerns to achieve sustainable economic growth.
{"title":"Powering progress: The interplay of energy security and institutional quality in driving economic growth","authors":"Mohammad Naim Azimi , Mohammad Mafizur Rahman , Tek Maraseni","doi":"10.1016/j.apenergy.2024.124835","DOIUrl":"10.1016/j.apenergy.2024.124835","url":null,"abstract":"<div><div>Energy security is a crucial determinant of sustainable economic growth, especially in the South Asian region, where persistent energy challenges and institutional shortcomings have stifled developmental potential. This study aims to elucidate the complex interplay between energy security and economic growth, with a focus on how institutional quality moderates and transforms these dynamics to promote more resilient growth in the region. Drawing on data from eight South Asian countries from 2000 to 2022, the study employs a panel-corrected standard error (PCSE) model, reinforced by robust feasible generalized least squares (FGLS) method. The results reveal that energy availability, energy supply capacity, energy demand, and energy efficiency exert negative impacts on economic growth, whereas energy development capacity contributes positively to economic growth. Additionally, the novel aggregate energy security index and institutional quality index demonstrate positive effects on growth, alongside urbanization and foreign direct investment. Conversely, trade openness is found to have a negative influence on economic growth. Crucially, the institutional quality index absorbs the adverse effects of energy availability, energy supply capacity, energy demand, and energy efficiency on growth, while amplifying the positive impacts of both individual elements of energy security and its aggregate index. These results highlight the necessity for urgent policy interventions to simultaneously address existing energy security and institutional quality concerns to achieve sustainable economic growth.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124835"},"PeriodicalIF":10.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662399","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 : 2024-11-09DOI: 10.1016/j.apenergy.2024.124851
Chiagoziem C. Ukwuoma , Dongsheng Cai , Chibueze D. Ukwuoma , Mmesoma P. Chukwuemeka , Blessing O. Ayeni , Chidera O. Ukwuoma , Odeh Victor Adeyi , Qi Huang
To meet the difficulties of the current energy environment, hydrogen has enormous potential as a clean and sustainable energy source. Utilizing hydrogen's potential requires accurate hydrogen production prediction. Due to its capacity to identify intricate patterns in data, Machine learning alongside deep learning models has attracted considerable interest from a variety of industries, including the energy industry. Although these models yield an acceptable performance, there is still a need to improve their prediction results. Also, they are inherently black boxes, which makes it difficult to comprehend and interpret their predictions, particularly in important sectors like hydrogen generation. Sequel to the above, a sequential gated recurrent and self-attention network is proposed in this study to enhance hydrogen production prediction. The framework captures both sequential dependencies and contextual information enabling the model to effectively learn and represent temporal patterns in hydrogen production prediction. The biomass gasification dataset is used for the experiment including the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R2), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE) evaluation metrics. The proposed model recorded an optimal performance with an MAE of 0.102, MSE of 0.027, RMSE of 0.160, R2 of 0.999, MSLE of 0.001, and RMSLE of 0.030 based on K-cross validation. Among the input features, the percentage of plastics in the mixture(wt%) and RSS Particle Size(mm) are identified to be the most influential features in the proposed model prediction as identified by Shapley Additive Explanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Feature importance plot. With 99.99 % of the data points for H2 production found within the range of reliability, the model demonstrates robust predictive capability with the majority of observations exerting minimal leverage (0 ≤ u ≤ [leverage threshold]) and limited influence (0 ≤ H ≤ [cooks' threshold]) on the predictive outcome using the modified William plot. Furthermore, various visualization approaches like Matthews correlation coefficient and Tarloy charts were adapted for the result explanations. The proposed model results were compared with state-of-the-art models exploring the significance of the proposed model in providing insights into the underlying mechanisms and factors influencing hydrogen production processes hence improving human understanding of the relationships between input factors and hydrogen production outputs as well as bridging the gap between predicted accuracy and interpretability.
{"title":"Sequential gated recurrent and self attention explainable deep learning model for predicting hydrogen production: Implications and applicability","authors":"Chiagoziem C. Ukwuoma , Dongsheng Cai , Chibueze D. Ukwuoma , Mmesoma P. Chukwuemeka , Blessing O. Ayeni , Chidera O. Ukwuoma , Odeh Victor Adeyi , Qi Huang","doi":"10.1016/j.apenergy.2024.124851","DOIUrl":"10.1016/j.apenergy.2024.124851","url":null,"abstract":"<div><div>To meet the difficulties of the current energy environment, hydrogen has enormous potential as a clean and sustainable energy source. Utilizing hydrogen's potential requires accurate hydrogen production prediction. Due to its capacity to identify intricate patterns in data, Machine learning alongside deep learning models has attracted considerable interest from a variety of industries, including the energy industry. Although these models yield an acceptable performance, there is still a need to improve their prediction results. Also, they are inherently black boxes, which makes it difficult to comprehend and interpret their predictions, particularly in important sectors like hydrogen generation. Sequel to the above, a sequential gated recurrent and self-attention network is proposed in this study to enhance hydrogen production prediction. The framework captures both sequential dependencies and contextual information enabling the model to effectively learn and represent temporal patterns in hydrogen production prediction. The biomass gasification dataset is used for the experiment including the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R<sup>2</sup>), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE) evaluation metrics. The proposed model recorded an optimal performance with an MAE of 0.102, MSE of 0.027, RMSE of 0.160, R<sup>2</sup> of 0.999, MSLE of 0.001, and RMSLE of 0.030 based on K-cross validation. Among the input features, the percentage of plastics in the mixture(wt%) and RSS Particle Size(mm) are identified to be the most influential features in the proposed model prediction as identified by Shapley Additive Explanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Feature importance plot. With 99.99 % of the data points for H<sub>2</sub> production found within the range of reliability, the model demonstrates robust predictive capability with the majority of observations exerting minimal leverage (0 ≤ u ≤ [leverage threshold]) and limited influence (0 ≤ H ≤ [cooks' threshold]) on the predictive outcome using the modified William plot. Furthermore, various visualization approaches like Matthews correlation coefficient and Tarloy charts were adapted for the result explanations. The proposed model results were compared with state-of-the-art models exploring the significance of the proposed model in providing insights into the underlying mechanisms and factors influencing hydrogen production processes hence improving human understanding of the relationships between input factors and hydrogen production outputs as well as bridging the gap between predicted accuracy and interpretability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124851"},"PeriodicalIF":10.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662499","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 : 2024-11-08DOI: 10.1016/j.apenergy.2024.124825
Kerem Ziya Akdemir , Kendall Mongird , Jordan D. Kern , Konstantinos Oikonomou , Nathalie Voisin , Casey D. Burleyson , Jennie S. Rice , Mengqi Zhao , Cameron Bracken , Chris Vernon
There is growing recognition of the advantages of interregional transmission capacity to decarbonize electricity grids. A less explored benefit is potential performance improvements during extreme weather events. This study examines the impacts of cooperative transmission expansion planning using an advanced modeling chain to simulate power grid operations of the United States Western Interconnection in 2019 and 2059 under different levels of collaboration between transmission planning regions. Two historical heat waves in 2019 with varying geographical coverage are replayed under future climate change in 2059 to assess the transmission cooperation benefits during grid stress. The results show that cooperative transmission planning yields the best outcomes in terms of reducing wholesale electricity prices and minimizing energy outages both for the whole interconnection and individual transmission planning regions. Compared to individual planning, cooperative planning reduces wholesale electricity prices by 64.3 % and interconnection-wide total costs (transmission investments + grid operations) by 34.6 % in 2059. It also helps decrease greenhouse gas emissions by increasing renewable energy utilization. However, the benefits of cooperation diminish during the widespread heat wave when all regions face extreme electricity demand due to higher space cooling needs. Despite this, cooperative transmission planning remains advantageous, particularly for California Independent System Operator with significant diurnal solar generation capacity. This study suggests that cooperation in transmission planning is crucial for reducing costs and increasing reliability both during normal periods and extreme weather events. It highlights the importance of optimizing the strategic investments to mitigate challenges posed by wider-scale extreme weather events of the future.
{"title":"Investigating the effects of cooperative transmission expansion planning on grid performance during heat waves with varying spatial scales","authors":"Kerem Ziya Akdemir , Kendall Mongird , Jordan D. Kern , Konstantinos Oikonomou , Nathalie Voisin , Casey D. Burleyson , Jennie S. Rice , Mengqi Zhao , Cameron Bracken , Chris Vernon","doi":"10.1016/j.apenergy.2024.124825","DOIUrl":"10.1016/j.apenergy.2024.124825","url":null,"abstract":"<div><div>There is growing recognition of the advantages of interregional transmission capacity to decarbonize electricity grids. A less explored benefit is potential performance improvements during extreme weather events. This study examines the impacts of cooperative transmission expansion planning using an advanced modeling chain to simulate power grid operations of the United States Western Interconnection in 2019 and 2059 under different levels of collaboration between transmission planning regions. Two historical heat waves in 2019 with varying geographical coverage are replayed under future climate change in 2059 to assess the transmission cooperation benefits during grid stress. The results show that cooperative transmission planning yields the best outcomes in terms of reducing wholesale electricity prices and minimizing energy outages both for the whole interconnection and individual transmission planning regions. Compared to individual planning, cooperative planning reduces wholesale electricity prices by 64.3 % and interconnection-wide total costs (transmission investments + grid operations) by 34.6 % in 2059. It also helps decrease greenhouse gas emissions by increasing renewable energy utilization. However, the benefits of cooperation diminish during the widespread heat wave when all regions face extreme electricity demand due to higher space cooling needs. Despite this, cooperative transmission planning remains advantageous, particularly for California Independent System Operator with significant diurnal solar generation capacity. This study suggests that cooperation in transmission planning is crucial for reducing costs and increasing reliability both during normal periods and extreme weather events. It highlights the importance of optimizing the strategic investments to mitigate challenges posed by wider-scale extreme weather events of the future.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124825"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662401","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 : 2024-11-08DOI: 10.1016/j.apenergy.2024.124765
Yan He , Jiang-Wen Xiao , Yan-Wu Wang , Zhi-Wei Liu , Shi-Yuan He
In recent years, shared energy storage has gained significant attention for mitigating the supply and demand imbalance caused by the intermittency of distributed renewable energy. Considering the subjective perception of prosumers when facing uncertainty, this paper proposes a new dynamic competitive on-demand renting framework for energy storage capacity (ESC) sharing to increase energy storage utilization, increase energy storage operator (ESO) profits, and reduce prosumer costs. In this framework, a demand-based dynamic capacity pricing mechanism is introduced, modeling the relationship between ESO and prosumers as a Stackelberg game while establishing a generalized Nash equilibrium (GNE) problem among prosumers. ESO determines the dynamic capacity pricing mechanism, while prosumers determine the hourly renting capacity based on demand. In capacity sharing, prospect theory is introduced for the first time to describe the subjective perceptions of prosumers when facing the uncertainty of renewable energy. Moreover, the existence of SE and the uniqueness of GNE are analyzed, followed by a summary and proposal of a method to determine the existence of equilibrium in a nested generalized non-cooperative Stackelberg game. Simulations show the effectiveness of the proposed framework on improving the ESC utilization rate, the impact of subjective perceptions on prosumers’ decision-making, and the profit favorability of the correct estimation of subjective perceptions on ESO. Specifically, the framework increases ESO utilization by 24.07% and profit by 13.73%.
近年来，共享储能在缓解分布式可再生能源间歇性导致的供需失衡方面受到了广泛关注。考虑到用户在面对不确定性时的主观感受，本文提出了一种新的动态竞争性按需租用储能容量（ESC）共享框架，以提高储能利用率、增加储能运营商（ESO）利润并降低用户成本。在该框架中，引入了基于需求的动态容量定价机制，将ESO和消费者之间的关系建模为斯泰克尔伯格博弈，同时在消费者之间建立广义纳什均衡（GNE）问题。ESO 决定动态容量定价机制，而 prosumers 则根据需求决定每小时的租用容量。在容量共享中，首次引入了前景理论来描述准消费者在面对可再生能源的不确定性时的主观感受。此外，还分析了 SE 的存在性和 GNE 的唯一性，随后总结并提出了确定嵌套广义非合作斯塔克尔伯格博弈中均衡存在性的方法。模拟显示了所提框架在提高ESC利用率、主观认知对消费者决策的影响以及正确估计主观认知对ESO的有利影响方面的有效性。具体而言，该框架使ESO利用率提高了24.07%，利润提高了13.73%。
{"title":"Subjective-uncertainty-oriented dynamic renting framework for energy storage sharing","authors":"Yan He , Jiang-Wen Xiao , Yan-Wu Wang , Zhi-Wei Liu , Shi-Yuan He","doi":"10.1016/j.apenergy.2024.124765","DOIUrl":"10.1016/j.apenergy.2024.124765","url":null,"abstract":"<div><div>In recent years, shared energy storage has gained significant attention for mitigating the supply and demand imbalance caused by the intermittency of distributed renewable energy. Considering the subjective perception of prosumers when facing uncertainty, this paper proposes a new dynamic competitive on-demand renting framework for energy storage capacity (ESC) sharing to increase energy storage utilization, increase energy storage operator (ESO) profits, and reduce prosumer costs. In this framework, a demand-based dynamic capacity pricing mechanism is introduced, modeling the relationship between ESO and prosumers as a Stackelberg game while establishing a generalized Nash equilibrium (GNE) problem among prosumers. ESO determines the dynamic capacity pricing mechanism, while prosumers determine the hourly renting capacity based on demand. In capacity sharing, prospect theory is introduced for the first time to describe the subjective perceptions of prosumers when facing the uncertainty of renewable energy. Moreover, the existence of SE and the uniqueness of GNE are analyzed, followed by a summary and proposal of a method to determine the existence of equilibrium in a nested generalized non-cooperative Stackelberg game. Simulations show the effectiveness of the proposed framework on improving the ESC utilization rate, the impact of subjective perceptions on prosumers’ decision-making, and the profit favorability of the correct estimation of subjective perceptions on ESO. Specifically, the framework increases ESO utilization by 24.07% and profit by 13.73%.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124765"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662481","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}
District heating networks are considered crucial for enabling emission-free heat supply, yet many existing networks still rely heavily on fossil fuels. With network pipes often lasting over 30 years, retrofitting heat producers in existing networks offers significant potential for decarbonization. This paper presents an automated design approach, to decarbonize existing heating networks through optimal producer retrofit and ultimately enabling 4th generation operation. Using multi-objective, mathematical optimization, it balances emissions and costs by assessing different prices. The optimization selects producer types, capacities, and for each period their heat supply and supply temperature. The considered heat producers are a natural gas boiler, an air-source heat pump, a solar thermal collector, and an electric boiler. A non-linear heat transport model ensures accurate accounting of heat and momentum losses throughout the network, and operational feasibility. The multi-period formulation incorporates temporal changes in heat demand and environmental conditions throughout the year. By formulating a continuous problem and using adjoint-based optimization, the automated approach remains scalable towards large scale applications. The design approach was assessed on a medium-sized 3rd generation district heating network case and was able to optimally retrofit the heat producers. The retrofit study highlights a strong influence of the price on the optimal heat producer design and operation. Increasing prices shift the design towards a heat supply dominated by an energy-efficient and low-emission heat pump. Furthermore, it was observed that even for the highest explored price of 0.3, the low-emission heat pump, electric boiler and solar thermal collector cannot fully replace the natural gas boiler in an economic way.
{"title":"Decarbonization of existing heating networks through optimal producer retrofit and low-temperature operation","authors":"Martin Sollich , Yannick Wack , Robbe Salenbien , Maarten Blommaert","doi":"10.1016/j.apenergy.2024.124796","DOIUrl":"10.1016/j.apenergy.2024.124796","url":null,"abstract":"<div><div>District heating networks are considered crucial for enabling emission-free heat supply, yet many existing networks still rely heavily on fossil fuels. With network pipes often lasting over 30 years, retrofitting heat producers in existing networks offers significant potential for decarbonization. This paper presents an automated design approach, to decarbonize existing heating networks through optimal producer retrofit and ultimately enabling 4th generation operation. Using multi-objective, mathematical optimization, it balances <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions and costs by assessing different <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> prices. The optimization selects producer types, capacities, and for each period their heat supply and supply temperature. The considered heat producers are a natural gas boiler, an air-source heat pump, a solar thermal collector, and an electric boiler. A non-linear heat transport model ensures accurate accounting of heat and momentum losses throughout the network, and operational feasibility. The multi-period formulation incorporates temporal changes in heat demand and environmental conditions throughout the year. By formulating a continuous problem and using adjoint-based optimization, the automated approach remains scalable towards large scale applications. The design approach was assessed on a medium-sized 3rd generation district heating network case and was able to optimally retrofit the heat producers. The retrofit study highlights a strong influence of the <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> price on the optimal heat producer design and operation. Increasing <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> prices shift the design towards a heat supply dominated by an energy-efficient and low-emission heat pump. Furthermore, it was observed that even for the highest explored <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> price of 0.3<span><math><mrow><mspace></mspace><mtext>€</mtext><mspace></mspace><msup><mrow><mi>kg</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, the low-emission heat pump, electric boiler and solar thermal collector cannot fully replace the natural gas boiler in an economic way.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124796"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662482","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 : 2024-11-08DOI: 10.1016/j.apenergy.2024.124812
Qiulei Wang , Zilong Ti , Shanghui Yang , Kun Yang , Jiaji Wang , Xiaowei Deng
With the increasing demand for electric power, the size of wind farms is becoming much larger than ever before. Power and load prediction are two of the most essential topics in wind farm layout optimization. Traditional wake modeling methods, such as analytic models and CFD simulations, struggle to handle such large-scale problems accurately and efficiently. In this study, a novel hierarchical dynamic wake modeling approach for wind turbines using generative deep learning, PHOENIX (PHysics-infOrmed gEnerative deep learniNg for hIerarchical dynamic wake modeling eXploration), is proposed to capture the spatial–temporal features of the unsteady wake field in wind turbine clusters. The dynamic wake meandering (DWM) model is employed to generate the corresponding datasets for training, testing, and validating the deep learning-based wake prediction framework. This research is expected to accelerate the prediction process and improve accuracy, and it can be further applied to wind turbine design and wind farm layout optimization.
随着电力需求的不断增长，风电场的规模也变得比以往大得多。功率和负荷预测是风电场布局优化中最重要的两个课题。传统的尾流建模方法，如分析模型和 CFD 模拟，难以准确高效地处理此类大规模问题。本研究提出了一种使用生成式深度学习的新型风力涡轮机分层动态尾流建模方法 PHOENIX（PHysics-infOrmed gEnerative deep learniNg for hIerarchical dynamic wake modeling eXploration），以捕捉风力涡轮机群中不稳定尾流场的时空特征。利用动态尾流蜿蜒（DWM）模型生成相应的数据集，用于训练、测试和验证基于深度学习的尾流预测框架。这项研究有望加速预测过程，提高预测精度，并可进一步应用于风机设计和风电场布局优化。
{"title":"Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning","authors":"Qiulei Wang , Zilong Ti , Shanghui Yang , Kun Yang , Jiaji Wang , Xiaowei Deng","doi":"10.1016/j.apenergy.2024.124812","DOIUrl":"10.1016/j.apenergy.2024.124812","url":null,"abstract":"<div><div>With the increasing demand for electric power, the size of wind farms is becoming much larger than ever before. Power and load prediction are two of the most essential topics in wind farm layout optimization. Traditional wake modeling methods, such as analytic models and CFD simulations, struggle to handle such large-scale problems accurately and efficiently. In this study, a novel hierarchical dynamic wake modeling approach for wind turbines using generative deep learning, <span>PHOENIX</span> (PHysics-infOrmed gEnerative deep learniNg for hIerarchical dynamic wake modeling eXploration), is proposed to capture the spatial–temporal features of the unsteady wake field in wind turbine clusters. The dynamic wake meandering (DWM) model is employed to generate the corresponding datasets for training, testing, and validating the deep learning-based wake prediction framework. This research is expected to accelerate the prediction process and improve accuracy, and it can be further applied to wind turbine design and wind farm layout optimization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124812"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662483","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 : 2024-11-08DOI: 10.1016/j.apenergy.2024.124744
Yanru Yang , Yu Liu , Yihang Zhang , Shaolong Shu , Junsheng Zheng
Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and storage system optimization. However, due to the intermittent and fluctuating nature of PV power generation, achieving accurate PV power forecasting remains a challenge. In this paper, we propose a novel approach for multi-site intra-hour PV power forecasting. Different from current work which predicts the power of each PV station independently, we predict the power of each PV station simultaneously by considering the inherent spatio-temporal correlation with other PV stations and develop a novel graph network named DEST-GNN. In DEST-GNN, an undirected graph is used to represent the dependence of these PV stations. Each PV station is represented by a node and the spatio-temporal correlation of any two PV stations is represented by an edge between them. To improve the accuracy of prediction, sparse spatio-temporal attention is adopted to filter out the weak associations of these PV stations. We then develop an adaptive graph convolution network (GCN) that adopts an adaptive adjacency matrix and a temporal convolution network to capture the hidden spatio-temporal dependency of these PV stations. Experimental studies using datasets from Alabama and California, provided by the National Renewable Energy Laboratory (NREL), demonstrate the effectiveness of DEST-GNN. For the Alabama dataset, DEST-GNN achieves a mean absolute error (MAE) of 0.49 over a 12-mon training scale. Furthermore, DEST-GNN attains an MAE of 0.42 on the California dataset, continuing to exhibit its strong forecasting capabilities.
{"title":"DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting","authors":"Yanru Yang , Yu Liu , Yihang Zhang , Shaolong Shu , Junsheng Zheng","doi":"10.1016/j.apenergy.2024.124744","DOIUrl":"10.1016/j.apenergy.2024.124744","url":null,"abstract":"<div><div>Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and storage system optimization. However, due to the intermittent and fluctuating nature of PV power generation, achieving accurate PV power forecasting remains a challenge. In this paper, we propose a novel approach for multi-site intra-hour PV power forecasting. Different from current work which predicts the power of each PV station independently, we predict the power of each PV station simultaneously by considering the inherent spatio-temporal correlation with other PV stations and develop a novel graph network named DEST-GNN. In DEST-GNN, an undirected graph is used to represent the dependence of these PV stations. Each PV station is represented by a node and the spatio-temporal correlation of any two PV stations is represented by an edge between them. To improve the accuracy of prediction, sparse spatio-temporal attention is adopted to filter out the weak associations of these PV stations. We then develop an adaptive graph convolution network (GCN) that adopts an adaptive adjacency matrix and a temporal convolution network to capture the hidden spatio-temporal dependency of these PV stations. Experimental studies using datasets from Alabama and California, provided by the National Renewable Energy Laboratory (NREL), demonstrate the effectiveness of DEST-GNN. For the Alabama dataset, DEST-GNN achieves a mean absolute error (MAE) of 0.49 over a 12-mon training scale. Furthermore, DEST-GNN attains an MAE of 0.42 on the California dataset, continuing to exhibit its strong forecasting capabilities.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124744"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662478","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 : 2024-11-08DOI: 10.1016/j.apenergy.2024.124846
Yunpeng Sun , Tonxin Li , Usman Mehmood
The Group of Twenty (G-20) nations are accountable for most of the global pollution and environmental degradation. Their contribution to global GDP and economic complexity (EC) significantly reflects the environmental degradation they have instigated. The G-20 nations are addressing environmental issues by emphasizing green finance (GFN) and fintech (FIN), with enhanced institutional integrity. Therefore, it becomes important to know that how economic complexity, renewable energy (RE), natural resources (NTR), GDP, green finance, green growth (GRW), fintech, and institutional quality (GOV) contribute to environmental sustainability in G-20 countries. In doing so, this work employed the Method of Moments Quantile Regression (MMQR) on the annual data from 2000 to 2021. The findings demonstrate that EC (−0.094 to −0.019), economic growth GDP (−0.660 to −0.458), and FIN (−0.017 to −0.008) are diminishing ecological footprints (EF) over four quantiles. Conversely, RE (0.019 to 0.076), NTR (0.084 to 0.109), and GOV (0.084 to 0.115) significantly influence the enhancement of EF. GFN (−0.148 to −0.109) concurrently reduces EF, but GRW (−0.061 to −0.007) exhibits a subtle effect. In the G-20, green growth and green finance can be essential drivers of environmental sustainability. It is advised that governments employ carbon taxes in tandem with environmental performance subsidies to enhance their sustainability initiatives. The governments of the G-20 nations need to make use of Fintech's advancements to make sure that businesses observe it appealing to employ sustainable practices to maintain their development trajectory.
{"title":"Balancing acts: Assessing the roles of renewable energy, economic complexity, Fintech, green finance, green growth, and economic performance in G-20 countries amidst sustainability efforts","authors":"Yunpeng Sun , Tonxin Li , Usman Mehmood","doi":"10.1016/j.apenergy.2024.124846","DOIUrl":"10.1016/j.apenergy.2024.124846","url":null,"abstract":"<div><div>The Group of Twenty (G-20) nations are accountable for most of the global pollution and environmental degradation. Their contribution to global GDP and economic complexity (EC) significantly reflects the environmental degradation they have instigated. The G-20 nations are addressing environmental issues by emphasizing green finance (GFN) and fintech (FIN), with enhanced institutional integrity. Therefore, it becomes important to know that how economic complexity, renewable energy (RE), natural resources (NTR), GDP, green finance, green growth (GRW), fintech, and institutional quality (GOV) contribute to environmental sustainability in G-20 countries. In doing so, this work employed the Method of Moments Quantile Regression (MMQR) on the annual data from 2000 to 2021. The findings demonstrate that EC (−0.094 to −0.019), economic growth GDP (−0.660 to −0.458), and FIN (−0.017 to −0.008) are diminishing ecological footprints (EF) over four quantiles. Conversely, RE (0.019 to 0.076), NTR (0.084 to 0.109), and GOV (0.084 to 0.115) significantly influence the enhancement of EF. GFN (−0.148 to −0.109) concurrently reduces EF, but GRW (−0.061 to −0.007) exhibits a subtle effect. In the G-20, green growth and green finance can be essential drivers of environmental sustainability. It is advised that governments employ carbon taxes in tandem with environmental performance subsidies to enhance their sustainability initiatives. The governments of the G-20 nations need to make use of Fintech's advancements to make sure that businesses observe it appealing to employ sustainable practices to maintain their development trajectory.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124846"},"PeriodicalIF":10.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662485","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号