Pub Date : 2024-11-16DOI: 10.1016/j.apenergy.2024.124916
Muhammad Akbar , Qi An , Yulian Ye , Lichao Wu , Chang Wu , Tianlong Bu , Wenjing Dong , Xunying Wang , Baoyuan Wang , Hao Wang , Chen Xia
Solid oxide fuel cells (SOFCs) can generate high-efficiency and clean power but face a high-temperature bottleneck that hinders their widespread application. If alternative electrolytes can be developed to reduce the operating temperatures, the application of SOFCs will possibly be expanded to more scenarios, such as power sources for the Internet of Things (IoT). Herein, as a proof of the concept, a 400 °C operable SOFC is developed based on a precipitation-method prepared CeO2 electrolyte for powering wireless sensor in IoT system. Material studies indicate the CeO2 electrolyte sample forms a coating structure with a thin layer of amorphous carbonate covering the surface of CeO2 particles, which could result in fast hybrid proton and oxygen ion transport. The fabricated CeO2 electrolyte-based SOFCs exhibit promising power densities of 0.275–0.650 W cm−2 with open circuit voltages of 1.04–1.11 V at 400–500 °C, indicative of feasible cell operation at 400 °C. It is also found the cell has high repeatability and good stability for 150 h under different current densities. With the aid of a power management unit, the developed SOFC is further applied to charge a supercapacitor, for powering a customized IoT system to monitor environmental parameters. The charge process is fast and stable. Our study thus developed a 400 °C operable SOFC based on CeO2 electrolyte and demonstrates the feasibility of SOFC as power sources for LoT technology for the first time.
固体氧化物燃料电池（SOFC）可以产生高效清洁的电力，但却面临着高温瓶颈，阻碍了其广泛应用。如果能开发出降低工作温度的替代电解质，SOFC 的应用将有可能扩展到更多场景，例如物联网（IoT）的电源。作为概念验证，本文基于沉淀法制备的 CeO2 电解质开发了一种可在 400 °C 下工作的 SOFC，用于为物联网系统中的无线传感器供电。材料研究表明，CeO2 电解质样品在 CeO2 颗粒表面形成了一层无定形碳酸盐薄层的涂层结构，可实现质子和氧离子的快速混合传输。所制备的基于 CeO2 电解质的 SOFC 在 400-500 ℃ 下的功率密度为 0.275-0.650 W cm-2，开路电压为 1.04-1.11 V，表明电池可在 400 ℃ 下工作。研究还发现，在不同的电流密度下，该电池具有 150 小时的高重复性和良好稳定性。在电源管理单元的帮助下，开发的 SOFC 进一步应用于超级电容器充电，为定制的物联网系统供电，以监测环境参数。充电过程快速而稳定。因此，我们的研究开发出了基于 CeO2 电解质的 400 °C 可操作 SOFC，并首次证明了 SOFC 作为 LoT 技术电源的可行性。
{"title":"400 °C operable SOFCs based on ceria electrolyte for powering wireless sensor in internet of things","authors":"Muhammad Akbar , Qi An , Yulian Ye , Lichao Wu , Chang Wu , Tianlong Bu , Wenjing Dong , Xunying Wang , Baoyuan Wang , Hao Wang , Chen Xia","doi":"10.1016/j.apenergy.2024.124916","DOIUrl":"10.1016/j.apenergy.2024.124916","url":null,"abstract":"<div><div>Solid oxide fuel cells (SOFCs) can generate high-efficiency and clean power but face a high-temperature bottleneck that hinders their widespread application. If alternative electrolytes can be developed to reduce the operating temperatures, the application of SOFCs will possibly be expanded to more scenarios, such as power sources for the Internet of Things (IoT). Herein, as a proof of the concept, a 400 °C operable SOFC is developed based on a precipitation-method prepared CeO<sub>2</sub> electrolyte for powering wireless sensor in IoT system. Material studies indicate the CeO<sub>2</sub> electrolyte sample forms a coating structure with a thin layer of amorphous carbonate covering the surface of CeO<sub>2</sub> particles, which could result in fast hybrid proton and oxygen ion transport. The fabricated CeO<sub>2</sub> electrolyte-based SOFCs exhibit promising power densities of 0.275–0.650 W cm<sup>−2</sup> with open circuit voltages of 1.04–1.11 V at 400–500 °C, indicative of feasible cell operation at 400 °C. It is also found the cell has high repeatability and good stability for 150 h under different current densities. With the aid of a power management unit, the developed SOFC is further applied to charge a supercapacitor, for powering a customized IoT system to monitor environmental parameters. The charge process is fast and stable. Our study thus developed a 400 °C operable SOFC based on CeO<sub>2</sub> electrolyte and demonstrates the feasibility of SOFC as power sources for LoT technology for the first time.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124916"},"PeriodicalIF":10.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654008","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 : 2024-11-16DOI: 10.1016/j.apenergy.2024.124802
Baixue Wang, Maosheng Duan
The effectiveness of China's carbon emissions trading systems (ETSs) in reducing emissions is crucial for achieving carbon neutrality and global climate targets. However, existing research presents conflicting results regarding their impact on the carbon intensity of output, likely due to data and methodological challenges. This study examines the impact of China's regional ETSs on carbon emissions and carbon intensity in the power sector, utilizing verified firm-level carbon emission data and annually updated regulatory lists of firms. The findings indicate that the ETSs significantly reduced carbon emissions primarily through reduced power generation, with little impact on carbon intensity. Nonetheless, large allowance shortages and high carbon prices contributed to the reduction of carbon intensity, while large market size, high liquidity, and stringent penalties had a limited impact on emission reductions. The study also found that firms with lower levels of technology were under more pressure, while those with advanced technology were under less pressure, suggesting that the ETSs served to “reward the good and punish the bad.” This implies that although the ETSs did not increase the efficiency of coal-fired firms, they contributed to optimizing the power generation structure by reducing the output of coal-fired firms and incentivizing the replacement of outdated capacity. Moreover, no evidence of carbon leakage to neighboring provinces or within the same ownership networks was found. These insights are valuable for designing both China's national ETS and ETSs in other countries with similar conditions.
{"title":"Have China's emissions trading systems reduced carbon emissions? Firm-level evidence from the power sector","authors":"Baixue Wang, Maosheng Duan","doi":"10.1016/j.apenergy.2024.124802","DOIUrl":"10.1016/j.apenergy.2024.124802","url":null,"abstract":"<div><div>The effectiveness of China's carbon emissions trading systems (ETSs) in reducing emissions is crucial for achieving carbon neutrality and global climate targets. However, existing research presents conflicting results regarding their impact on the carbon intensity of output, likely due to data and methodological challenges. This study examines the impact of China's regional ETSs on carbon emissions and carbon intensity in the power sector, utilizing verified firm-level carbon emission data and annually updated regulatory lists of firms. The findings indicate that the ETSs significantly reduced carbon emissions primarily through reduced power generation, with little impact on carbon intensity. Nonetheless, large allowance shortages and high carbon prices contributed to the reduction of carbon intensity, while large market size, high liquidity, and stringent penalties had a limited impact on emission reductions. The study also found that firms with lower levels of technology were under more pressure, while those with advanced technology were under less pressure, suggesting that the ETSs served to “reward the good and punish the bad.” This implies that although the ETSs did not increase the efficiency of coal-fired firms, they contributed to optimizing the power generation structure by reducing the output of coal-fired firms and incentivizing the replacement of outdated capacity. Moreover, no evidence of carbon leakage to neighboring provinces or within the same ownership networks was found. These insights are valuable for designing both China's national ETS and ETSs in other countries with similar conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124802"},"PeriodicalIF":10.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654007","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 : 2024-11-15DOI: 10.1016/j.apenergy.2024.124797
Jongki Lee, Akram Syed Ali, Afshin Farmarzi, Urwa Irfan, Christopher Riley, Brent Stephens, Mohammad Heidarinejad
This study evaluated the HVAC energy end-use performance of 39 motorized insulating interior shades installed on the 37th floor of a high-rise commercial building in Chicago, Illinois, USA operating under realistic conditions. The study lasted for 44 weeks (spanning all four seasons). Three motorized control strategies of On-schedule, Dynamic (‘smart’), and Manual, as well as one typical mini-blind Baseline control strategy, were developed and implemented to assess energy performance of the interior automated shades. The three controlled motorized control strategies are compared to the Baseline strategy as a reference. The results showed that the motorized interior insulating shades reduced daily energy consumption by up to 20.5 % with the automated control strategy, which includes the Dynamic and On-schedule strategies, and up to 11.8 % without the control, which includes only the Manual control strategy. A weather normalized energy consumption analysis, which translates the actual energy consumption values to typical year energy consumption values, indicates that the automated shades are expected to save 20–35 % in energy consumption compared to the Baseline strategy. The calculated payback period for a defined “best practice” scenario is 21.9 years considering an initial electricity rate of $0.0897/kWh in 2021. Accounting for a future utility incentive program that provides a one-time rebate of $0.25/kWh savings during the installation, the simple payback period for the “best practice” scenario was estimated to be 12.3 years with the 2021 electricity rate and 4.4 years with assumptions for future electricity rates. Results suggest that the shades are a promising energy efficiency measure, especially for buildings for which building envelope retrofits or new construction are cost prohibitive or infeasible.
{"title":"Assessing the long-term energy performance of automated interior insulating window shades in a high-rise commercial building","authors":"Jongki Lee, Akram Syed Ali, Afshin Farmarzi, Urwa Irfan, Christopher Riley, Brent Stephens, Mohammad Heidarinejad","doi":"10.1016/j.apenergy.2024.124797","DOIUrl":"10.1016/j.apenergy.2024.124797","url":null,"abstract":"<div><div>This study evaluated the HVAC energy end-use performance of 39 motorized insulating interior shades installed on the 37th floor of a high-rise commercial building in Chicago, Illinois, USA operating under realistic conditions. The study lasted for 44 weeks (spanning all four seasons). Three motorized control strategies of On-schedule, Dynamic (‘smart’), and Manual, as well as one typical mini-blind Baseline control strategy, were developed and implemented to assess energy performance of the interior automated shades. The three controlled motorized control strategies are compared to the Baseline strategy as a reference. The results showed that the motorized interior insulating shades reduced daily energy consumption by up to 20.5 % with the automated control strategy, which includes the Dynamic and On-schedule strategies, and up to 11.8 % without the control, which includes only the Manual control strategy. A weather normalized energy consumption analysis, which translates the actual energy consumption values to typical year energy consumption values, indicates that the automated shades are expected to save 20–35 % in energy consumption compared to the Baseline strategy. The calculated payback period for a defined “best practice” scenario is 21.9 years considering an initial electricity rate of $0.0897/kWh in 2021. Accounting for a future utility incentive program that provides a one-time rebate of $0.25/kWh savings during the installation, the simple payback period for the “best practice” scenario was estimated to be 12.3 years with the 2021 electricity rate and 4.4 years with assumptions for future electricity rates. Results suggest that the shades are a promising energy efficiency measure, especially for buildings for which building envelope retrofits or new construction are cost prohibitive or infeasible.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124797"},"PeriodicalIF":10.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653942","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 : 2024-11-15DOI: 10.1016/j.apenergy.2024.124844
Yuan Gao , Zehuan Hu , Wei-An Chen , Mingzhe Liu , Yingjun Ruan
Deep learning models are increasingly being used to predict renewable energy-related variables, such as solar radiation and outdoor temperature. However, the black-box nature of these models results in a lack of interpretability in their predictions, and the design of deep network architectures significantly impacts the final prediction outcomes. The introduction of Kolmogorov–Arnold Network (KAN) provides an excellent solution to both of these issues. We hope that the KAN mechanism can provide fully interpretable neural network models, enhancing the potential for practical deployment. At the same time, KAN is capable of achieving good prediction results across various network architectures and neuron counts. We conducted case studies using real-world data from the Tokyo Meteorological Observatory to predict solar radiation and outdoor temperature, comparing the results with those of commonly used recurrent neural network baseline models. The results indicate that KAN can maintain model performance regardless of the chosen number of neurons. For instance, in the solar radiation prediction task, the KAN with a single hidden neuron reduces the MSE error by 75.33% compared to the baseline model. More importantly, KAN allows for the quantification of each step in the network’s computations, thereby enhancing overall interpretability.
深度学习模型越来越多地被用于预测可再生能源相关变量，如太阳辐射和室外温度。然而，这些模型的黑箱性质导致其预测结果缺乏可解释性，而且深度网络架构的设计会对最终预测结果产生重大影响。柯尔莫哥洛夫-阿诺德网络（KAN）的引入很好地解决了这两个问题。我们希望 KAN 机制能够提供完全可解释的神经网络模型，从而提高实际部署的潜力。同时，KAN 能够在不同的网络架构和神经元数量下取得良好的预测结果。我们利用东京气象台的实际数据进行了案例研究，预测太阳辐射和室外温度，并将结果与常用的递归神经网络基线模型进行了比较。结果表明，无论选择多少神经元，KAN 都能保持模型的性能。例如，在太阳辐射预测任务中，与基线模型相比，只有一个隐藏神经元的 KAN 可将 MSE 误差降低 75.33%。更重要的是，KAN 可以量化网络计算的每一步，从而提高整体可解释性。
{"title":"A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting","authors":"Yuan Gao , Zehuan Hu , Wei-An Chen , Mingzhe Liu , Yingjun Ruan","doi":"10.1016/j.apenergy.2024.124844","DOIUrl":"10.1016/j.apenergy.2024.124844","url":null,"abstract":"<div><div>Deep learning models are increasingly being used to predict renewable energy-related variables, such as solar radiation and outdoor temperature. However, the black-box nature of these models results in a lack of interpretability in their predictions, and the design of deep network architectures significantly impacts the final prediction outcomes. The introduction of Kolmogorov–Arnold Network (KAN) provides an excellent solution to both of these issues. We hope that the KAN mechanism can provide fully interpretable neural network models, enhancing the potential for practical deployment. At the same time, KAN is capable of achieving good prediction results across various network architectures and neuron counts. We conducted case studies using real-world data from the Tokyo Meteorological Observatory to predict solar radiation and outdoor temperature, comparing the results with those of commonly used recurrent neural network baseline models. The results indicate that KAN can maintain model performance regardless of the chosen number of neurons. For instance, in the solar radiation prediction task, the KAN with a single hidden neuron reduces the MSE error by 75.33% compared to the baseline model. More importantly, KAN allows for the quantification of each step in the network’s computations, thereby enhancing overall interpretability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124844"},"PeriodicalIF":10.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662524","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}
Energy models have been a valuable tool in support of well-informed decision-making towards the transition to climate neutrality in the European Union. However, considering the extra levels of detail required when designing a system based on intermittent renewables, modelling approaches in the field often lack the necessary time resolution, or are not open source, raising concerns of transparency and scientific reproducibility. This article addresses this gap by introducing a novel bidirectional soft-linking approach between two open-source energy models to generate long-term scenarios in the power sector and evaluate their feasibility, allowing for the optimisation of investments over a 30-year period and the sector's hourly operation at different snapshots. To demonstrate the applicability of this modelling approach, the Greek power sector is used as a testing ground in order to study the capacity and flexibility requirements of different transition pathways by 2050. Simulation outcomes show that a more ambitious variable renewable energy and storage capacity expansion than the one projected by the National Energy and Climate Plan is required to achieve the targets of 2050, while also highlighting a path dependency on gas at least until 2033. The latter could either result in a lock-in effect or to stranded assets if the decision to phase out gas is not taken rapidly. On the other hand, there is the potential to achieve carbon neutrality by 2035, if significant investments take place in time. Finally, switching from natural gas to hydrogen could be an effective solution for new gas power plants to avoid becoming stranded assets.
{"title":"Bidirectional soft-linking of a Capacity Expansion Model with a Production Cost Model to evaluate the feasibility of transition pathways towards carbon neutrality in the power sector","authors":"Nikos Kleanthis, Vassilis Stavrakas, Alexandros Flamos","doi":"10.1016/j.apenergy.2024.124843","DOIUrl":"10.1016/j.apenergy.2024.124843","url":null,"abstract":"<div><div>Energy models have been a valuable tool in support of well-informed decision-making towards the transition to climate neutrality in the European Union. However, considering the extra levels of detail required when designing a system based on intermittent renewables, modelling approaches in the field often lack the necessary time resolution, or are not open source, raising concerns of transparency and scientific reproducibility. This article addresses this gap by introducing a novel bidirectional soft-linking approach between two open-source energy models to generate long-term scenarios in the power sector and evaluate their feasibility, allowing for the optimisation of investments over a 30-year period and the sector's hourly operation at different snapshots. To demonstrate the applicability of this modelling approach, the Greek power sector is used as a testing ground in order to study the capacity and flexibility requirements of different transition pathways by 2050. Simulation outcomes show that a more ambitious variable renewable energy and storage capacity expansion than the one projected by the National Energy and Climate Plan is required to achieve the targets of 2050, while also highlighting a path dependency on gas at least until 2033. The latter could either result in a lock-in effect or to stranded assets if the decision to phase out gas is not taken rapidly. On the other hand, there is the potential to achieve carbon neutrality by 2035, if significant investments take place in time. Finally, switching from natural gas to hydrogen could be an effective solution for new gas power plants to avoid becoming stranded assets.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124843"},"PeriodicalIF":10.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653941","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}
A newly introduced colour correction transmittance factor (CCTF) and an innovative probabilistic-to-deterministic approach were applied to create virtual clones of coloured building-integrated photovoltaic (BIPV) systems. These virtual clones calculate the current at maximum power point () by adjusting the plane-of-array irradiance according to the transmittance properties of the coloured layer, which are governed by the CCTF. An ensemble of 200 randomly combined physical photovoltaic model chains was implemented (probabilistic approach), and the median of the diverse outputs was calculated to provide a deterministic estimations. The virtual clones were validated against observations from two BIPV facades located in Zwolle (The Netherlands), where black (CCTF=1.00), light-grey (CCTF=0.89), and terracotta (CCTF=0.70) photovoltaic modules were mounted. Hourly data were collected from June 2023 to May 2024. The performance of different regression techniques was evaluated for the calibration of the virtual clones. The non-calibrated virtual clones showed similar accuracy throughout the year, with the determination coefficient () that ranged from 0.594 (light-grey) to 0.613 (terracotta). Although the models generally overestimated , the results demonstrated that such a tendency was accentuated during overcast days. Consistent biases were also observed for solar elevations greater than 30°. Finally, the façade orientation influenced the simulation performance. Indeed, the non-calibrated models overestimated by circa 150 the annual from the south-facing façade, and by more than 700 the annual from the façade oriented south-west, regardless of the colour. However, calibration, particularly with Random Forest and Gradient Boosting, consistently reduced cumulative error across all scenarios.
{"title":"Implementation and validation of virtual clones of coloured building-integrated photovoltaic facades","authors":"Mattia Manni , Tom Melkert , Gabriele Lobaccaro , Bjørn Petter Jelle","doi":"10.1016/j.apenergy.2024.124845","DOIUrl":"10.1016/j.apenergy.2024.124845","url":null,"abstract":"<div><div>A newly introduced colour correction transmittance factor (CCTF) and an innovative probabilistic-to-deterministic approach were applied to create virtual clones of coloured building-integrated photovoltaic (BIPV) systems. These virtual clones calculate the current at maximum power point (<span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub></math></span>) by adjusting the plane-of-array irradiance according to the transmittance properties of the coloured layer, which are governed by the CCTF. An ensemble of 200 randomly combined physical photovoltaic model chains was implemented (probabilistic approach), and the median of the diverse outputs was calculated to provide a deterministic <span><math><mrow><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub><mo>,</mo></mrow></math></span> estimations. The virtual clones were validated against observations from two BIPV facades located in Zwolle (The Netherlands), where black (CCTF=1.00), light-grey (CCTF=0.89), and terracotta (CCTF=0.70) photovoltaic modules were mounted. Hourly <span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub></math></span> data were collected from June 2023 to May 2024. The performance of different regression techniques was evaluated for the calibration of the virtual clones. The non-calibrated virtual clones showed similar accuracy throughout the year, with the determination coefficient (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) that ranged from 0.594 (light-grey) to 0.613 (terracotta). Although the models generally overestimated <span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub></math></span>, the results demonstrated that such a tendency was accentuated during overcast days. Consistent biases were also observed for solar elevations greater than 30°. Finally, the façade orientation influenced the simulation performance. Indeed, the non-calibrated models overestimated by circa 150 <span><math><mi>A</mi></math></span> the annual <span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub></math></span> from the south-facing façade, and by more than 700 <span><math><mi>A</mi></math></span> the annual <span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>m</mi><mi>p</mi><mi>p</mi></mrow></msub></math></span> from the façade oriented south-west, regardless of the colour. However, calibration, particularly with Random Forest and Gradient Boosting, consistently reduced cumulative error across all scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124845"},"PeriodicalIF":10.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662527","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}
Accelerating the deployment of renewable energy (RE) is one of the most important strategies to achieve the 2060 carbon neutrality goal in China. In this context, it is crucial to understand the RE investment needs at the provincial level to better allocate resources and develop policies to facilitate RE development at the local level. In this paper, we estimate the wind and solar investment needs of Chinese provinces between 2020 and 2060 under four alternative pathways towards China's 2060 carbon neutrality, using a global integrated assessment model with provincial details of China combined with the most updated cost data for each province, and explicitly considering national and local investment market conditions. Results show that the average annual wind and solar investment needs are $317 billion per year between 2020 and 2060, or 2.3 % of China's 2020 GDP. We find large spatial and temporal variations in the needed RE investment and identify that technologies, resource endowment, and financial conditions are the three primary contributors to the regional disparity in investment needs. This study delves into the local factors constraining RE deployment in China, providing insights applicable not only to the country but also holding implications for studying global RE investment dynamics in alignment with the collective pursuit of heightened clean energy transition goals.
{"title":"A provincial analysis on wind and solar investment needs towards China's carbon neutrality","authors":"Jiehong Lou, Sha Yu, Ryna Yiyun Cui, Andy Miller, Nathan Hultman","doi":"10.1016/j.apenergy.2024.124841","DOIUrl":"10.1016/j.apenergy.2024.124841","url":null,"abstract":"<div><div>Accelerating the deployment of renewable energy (RE) is one of the most important strategies to achieve the 2060 carbon neutrality goal in China. In this context, it is crucial to understand the RE investment needs at the provincial level to better allocate resources and develop policies to facilitate RE development at the local level. In this paper, we estimate the wind and solar investment needs of Chinese provinces between 2020 and 2060 under four alternative pathways towards China's 2060 carbon neutrality, using a global integrated assessment model with provincial details of China combined with the most updated cost data for each province, and explicitly considering national and local investment market conditions. Results show that the average annual wind and solar investment needs are $317 billion per year between 2020 and 2060, or 2.3 % of China's 2020 GDP. We find large spatial and temporal variations in the needed RE investment and identify that technologies, resource endowment, and financial conditions are the three primary contributors to the regional disparity in investment needs. This study delves into the local factors constraining RE deployment in China, providing insights applicable not only to the country but also holding implications for studying global RE investment dynamics in alignment with the collective pursuit of heightened clean energy transition goals.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124841"},"PeriodicalIF":10.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662526","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 : 2024-11-14DOI: 10.1016/j.apenergy.2024.124822
Xuhui Huang , Tao Zhou , Ning Zhang
This paper investigates the cost implications of the carbon emission trading scheme (CETS), a crucial tool for cost-effective carbon reduction, by analyzing its impact on the marginal abatement cost (MAC) of CO2 emissions from coal-fired power plants in China. Utilizing the pilot CETS as a quasi-natural experiment, we employ a considerate shadow price framework for MAC measurement and a staggered difference-in-differences strategy to identify the causal effect of the pilot CETS and its underlying mechanisms. The findings reveal that the pilot CETS significantly increased the MAC by an average of 120 yuan/ton, highlighting the regulatory impact of carbon pilots. The primary mechanism is identified as a cost-increasing effect, where power plants curtail CO2 emissions by decreasing energy usage and electricity production. The rise in abatement cost per unit of carbon is primarily attributed to the decrease in output caused by the reduction of energy inputs, signifying that the majority of the MAC rise stems from the escalating costs associated with energy inputs. The policy effect is more pronounced for local and low-energy-efficiency power plants, as well as those situated in regions with stringent environmental regulations and low marketization levels. Additionally, higher MAC increases are observed in carbon market pilots with the benchmarking rule, higher carbon prices, and higher trading volumes. To enhance the carbon market's effectiveness in the power sector, we recommend progressively reducing administrative controls on the power sector, policy coordination to avoid conflicts with other environmental regulations, and providing financial and policy support to improve low-carbon technologies in coal-fired power plants.
{"title":"How does the carbon market influence the marginal abatement cost? Evidence from China's coal-fired power plants","authors":"Xuhui Huang , Tao Zhou , Ning Zhang","doi":"10.1016/j.apenergy.2024.124822","DOIUrl":"10.1016/j.apenergy.2024.124822","url":null,"abstract":"<div><div>This paper investigates the cost implications of the carbon emission trading scheme (CETS), a crucial tool for cost-effective carbon reduction, by analyzing its impact on the marginal abatement cost (MAC) of CO<sub>2</sub> emissions from coal-fired power plants in China. Utilizing the pilot CETS as a quasi-natural experiment, we employ a considerate shadow price framework for MAC measurement and a staggered difference-in-differences strategy to identify the causal effect of the pilot CETS and its underlying mechanisms. The findings reveal that the pilot CETS significantly increased the MAC by an average of 120 yuan/ton, highlighting the regulatory impact of carbon pilots. The primary mechanism is identified as a cost-increasing effect, where power plants curtail CO<sub>2</sub> emissions by decreasing energy usage and electricity production. The rise in abatement cost per unit of carbon is primarily attributed to the decrease in output caused by the reduction of energy inputs, signifying that the majority of the MAC rise stems from the escalating costs associated with energy inputs. The policy effect is more pronounced for local and low-energy-efficiency power plants, as well as those situated in regions with stringent environmental regulations and low marketization levels. Additionally, higher MAC increases are observed in carbon market pilots with the benchmarking rule, higher carbon prices, and higher trading volumes. To enhance the carbon market's effectiveness in the power sector, we recommend progressively reducing administrative controls on the power sector, policy coordination to avoid conflicts with other environmental regulations, and providing financial and policy support to improve low-carbon technologies in coal-fired power plants.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124822"},"PeriodicalIF":10.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662477","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 : 2024-11-14DOI: 10.1016/j.apenergy.2024.124641
Ting Yang , Zheming Xu , Shijie Ji , Guoliang Liu , Xinhong Li , Haibo Kong
The cooperative optimization dispatch of interconnected multi-microgrid (MMG) systems present broad prospects and significant opportunities for the efficient utilization of large-scale renewable energy resources. These systems facilitate the optimal allocation of energy resources and enhance economic efficiency in operational costs. Nevertheless, divergent interests among heterogeneous microgrid (MG) entities during the cooperative optimization dispatch process lead to obstacles in data sharing and issues with privacy breaches. Additionally, the process is complicated by multi-energy coupling relationships and high-dimensional decision-making, which can result in difficulties achieving convergence and a loss of accuracy in energy management. Furthermore, the lack of operational data and dispatch experience in newly established MGs hinders the ability to rapidly “cold start” dispatch tasks. To fill the above knowledge gap, a cooperative optimization dispatch method for MMG is proposed, which based on personalized federated multi-agent reinforcement learning with clustering (C-PFMARL). This method formulates an optimal low-carbon economic dispatch strategy that incorporates electricity and carbon allowance trading within multiple MG systems. Initially, a cooperative training framework for MMG is constructed under the privacy protection of federated reinforcement learning. This framework allows MMG to train optimization dispatch models based on heterogeneous multi-agent twin delayed deep deterministic policy gradient (HMATD3). With the federated aggregation of model gradient parameters instead of transferring private data, this approach achieves a privacy protection effect of “data cooperation without leaving locality “. Secondly, a dual-ended dynamic clustering algorithm for sharing knowledge within groups is proposed, characterized by model intermediate gradient parameters. It employs a personalized federated transfer strategy based on neural network layering, which enhances the convergence speed and dispatch precision under optimal strategies of the local optimization dispatch model. Moreover, a “cold start” transfer strategy aimed at newly established MG entities is formulated, achieving precise assistance and rapid cold start in optimization dispatch experience. Finally, our case analysis validates the effectiveness and training convergence of the constructed dispatch model. The overall integrated cost of the MMG system has been reduced by 5.78 %, and carbon emissions have decreased by 8.43 %. The dispatch cold-start speed for newly established MGs has improved by 42.83 %, with the optimization results also demonstrating robust economic and low-carbon benefits.
{"title":"Cooperative optimal dispatch of multi-microgrids for low carbon economy based on personalized federated reinforcement learning","authors":"Ting Yang , Zheming Xu , Shijie Ji , Guoliang Liu , Xinhong Li , Haibo Kong","doi":"10.1016/j.apenergy.2024.124641","DOIUrl":"10.1016/j.apenergy.2024.124641","url":null,"abstract":"<div><div>The cooperative optimization dispatch of interconnected multi-microgrid (MMG) systems present broad prospects and significant opportunities for the efficient utilization of large-scale renewable energy resources. These systems facilitate the optimal allocation of energy resources and enhance economic efficiency in operational costs. Nevertheless, divergent interests among heterogeneous microgrid (MG) entities during the cooperative optimization dispatch process lead to obstacles in data sharing and issues with privacy breaches. Additionally, the process is complicated by multi-energy coupling relationships and high-dimensional decision-making, which can result in difficulties achieving convergence and a loss of accuracy in energy management. Furthermore, the lack of operational data and dispatch experience in newly established MGs hinders the ability to rapidly “cold start” dispatch tasks. To fill the above knowledge gap, a cooperative optimization dispatch method for MMG is proposed, which based on personalized federated multi-agent reinforcement learning with clustering (C-PFMARL). This method formulates an optimal low-carbon economic dispatch strategy that incorporates electricity and carbon allowance trading within multiple MG systems. Initially, a cooperative training framework for MMG is constructed under the privacy protection of federated reinforcement learning. This framework allows MMG to train optimization dispatch models based on heterogeneous multi-agent twin delayed deep deterministic policy gradient (HMATD3). With the federated aggregation of model gradient parameters instead of transferring private data, this approach achieves a privacy protection effect of “data cooperation without leaving locality “. Secondly, a dual-ended dynamic clustering algorithm for sharing knowledge within groups is proposed, characterized by model intermediate gradient parameters. It employs a personalized federated transfer strategy based on neural network layering, which enhances the convergence speed and dispatch precision under optimal strategies of the local optimization dispatch model. Moreover, a “cold start” transfer strategy aimed at newly established MG entities is formulated, achieving precise assistance and rapid cold start in optimization dispatch experience. Finally, our case analysis validates the effectiveness and training convergence of the constructed dispatch model. The overall integrated cost of the MMG system has been reduced by 5.78 %, and carbon emissions have decreased by 8.43 %. The dispatch cold-start speed for newly established MGs has improved by 42.83 %, with the optimization results also demonstrating robust economic and low-carbon benefits.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124641"},"PeriodicalIF":10.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662528","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 : 2024-11-14DOI: 10.1016/j.apenergy.2024.124785
Xiangxing Kong , Zhigang Lu , Yanlin Li , Xiaoqiang Guo , Jiangfeng Zhang , Shixing Ding
The development of cyber-physical power system (CPPS) will provide potential solutions for the high efficiency and intelligent requirements of traditional power systems. However, the uncertainty of malicious attacks brings a great threat to the normal operation of the CPPS. In order to enhance the resilience of CPPS against uncertain malicious coordinated attacks, a resilience-oriented defense strategy is proposed considering attack scenario uncertainties. Firstly, an uncertain coordinated attack strategy against generation units and transmission lines is constructed based on dynamic N-k breaking scheme to describe a more harmful attack mechanism against power systems. Secondly, considering the uncertain malicious coordinated attacks, a tri-level defense model is proposed in the framework of defender-attacker-defender. Finally, the proposed model is transformed into mixed integer linear programming model by using duality theory, and a constraint-generation and benders-cut (CG&BC) algorithm is developed to solve the defense model. The model is simulated and verified on the IEEE RTS-79 test system, and the results fully validate the effectiveness of the model and solution algorithm, and show that the resilience-oriented defense strategy can effectively reduce the total expected cost of power systems against uncertain malicious coordinated attacks.
{"title":"Resilience-oriented defense strategy for power systems against uncertain malicious coordinated attacks","authors":"Xiangxing Kong , Zhigang Lu , Yanlin Li , Xiaoqiang Guo , Jiangfeng Zhang , Shixing Ding","doi":"10.1016/j.apenergy.2024.124785","DOIUrl":"10.1016/j.apenergy.2024.124785","url":null,"abstract":"<div><div>The development of cyber-physical power system (CPPS) will provide potential solutions for the high efficiency and intelligent requirements of traditional power systems. However, the uncertainty of malicious attacks brings a great threat to the normal operation of the CPPS. In order to enhance the resilience of CPPS against uncertain malicious coordinated attacks, a resilience-oriented defense strategy is proposed considering attack scenario uncertainties. Firstly, an uncertain coordinated attack strategy against generation units and transmission lines is constructed based on dynamic N-k breaking scheme to describe a more harmful attack mechanism against power systems. Secondly, considering the uncertain malicious coordinated attacks, a tri-level defense model is proposed in the framework of defender-attacker-defender. Finally, the proposed model is transformed into mixed integer linear programming model by using duality theory, and a constraint-generation and benders-cut (CG&BC) algorithm is developed to solve the defense model. The model is simulated and verified on the IEEE RTS-79 test system, and the results fully validate the effectiveness of the model and solution algorithm, and show that the resilience-oriented defense strategy can effectively reduce the total expected cost of power systems against uncertain malicious coordinated attacks.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124785"},"PeriodicalIF":10.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662525","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