Pub Date : 2024-07-26DOI: 10.1016/j.adapen.2024.100184
Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.
{"title":"The potential of radiative cooling enhanced photovoltaic systems in China","authors":"","doi":"10.1016/j.adapen.2024.100184","DOIUrl":"10.1016/j.adapen.2024.100184","url":null,"abstract":"<div><p>Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":13.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000222/pdfft?md5=0a129a711e045cc4c3d88283b1dec009&pid=1-s2.0-S2666792424000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-14DOI: 10.1016/j.adapen.2024.100181
Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.
{"title":"Impact of forecasting on energy system optimization","authors":"","doi":"10.1016/j.adapen.2024.100181","DOIUrl":"10.1016/j.adapen.2024.100181","url":null,"abstract":"<div><p>Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":13.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000192/pdfft?md5=fbba7e83b4274182667c200c1582b508&pid=1-s2.0-S2666792424000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-14DOI: 10.1016/j.adapen.2024.100183
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.
{"title":"Techno–Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems","authors":"","doi":"10.1016/j.adapen.2024.100183","DOIUrl":"10.1016/j.adapen.2024.100183","url":null,"abstract":"<div><p>The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":13.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000210/pdfft?md5=cd536c4a02a001e229c11a6ef7a1a59a&pid=1-s2.0-S2666792424000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.1016/j.adapen.2024.100182
This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.
{"title":"Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability","authors":"","doi":"10.1016/j.adapen.2024.100182","DOIUrl":"10.1016/j.adapen.2024.100182","url":null,"abstract":"<div><p>This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO<sub>2</sub> emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":13.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000209/pdfft?md5=1cedc846099a911397ba821e7c77e286&pid=1-s2.0-S2666792424000209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-22DOI: 10.1016/j.adapen.2024.100180
Robert Herding , Emma Ross , Wayne R. Jones , Elizabeth Endler , Vassilis M. Charitopoulos , Lazaros G. Papageorgiou
This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the -constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.
{"title":"Risk-aware microgrid operation and participation in the day-ahead electricity market","authors":"Robert Herding , Emma Ross , Wayne R. Jones , Elizabeth Endler , Vassilis M. Charitopoulos , Lazaros G. Papageorgiou","doi":"10.1016/j.adapen.2024.100180","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100180","url":null,"abstract":"<div><p>This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the <span><math><mi>ɛ</mi></math></span>-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":13.0,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000180/pdfft?md5=96b7a1215928fbf42c89bb54f97b0164&pid=1-s2.0-S2666792424000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.1016/j.adapen.2024.100178
Hui Kong , Yueqiao Sun , Hongsheng Wang , Jian Wang , Liping Sun , Jun Shen
Ammonia, a reliable low-carbon alternative fuel with energy storage capabilities, has garnered increasing attention for its application of co-firing in coal-fired power plants as a strategy to mitigate direct carbon emissions. However, various types of ammonia production technologies result in diverse economic feasibility and emission intensities. Simultaneously, each stage, spanning from upstream processes such as raw material extraction to downstream applications, contributes to carbon emissions, which cannot be ignored. It is crucial to select the appropriate assessment method to determine the transformation pathways for co-firing systems. To this end, this review presents a comprehensive life cycle assessment of ammonia co-firing systems from a whole industrial chain perspective, encompassing the entire gamut of processes from fuel production and transportation to co-firing. Studies of the industrial chain perspective and of life cycle assessment methodology that are uniquely tailored for co-firing systems are presented. A nuanced exploration of distinct technologies across the spectrum of system processes ensues, including the advantages, limitations, and trends in advancement, based on carbon emissions and economic criteria. Considering the diverse fuel production, especially ammonia, typologies and intricate processes have undergone comprehensive review. The combustion characteristics, emissions, and economic factors associated with the co-firing process are systematically summarized, drawing upon aspects such as dynamics, experiments, simulations, and demonstration projects. This study illuminates the progression and technology selection of co-firing systems across multiple stages of the whole industry chain, thereby furnishing insights relevant to the low-carbon transformation of ammonia co-firing with coal in power plants.
{"title":"Life cycle assessment of ammonia co-firing power plants: A comprehensive review and analysis from a whole industrial chain perspective","authors":"Hui Kong , Yueqiao Sun , Hongsheng Wang , Jian Wang , Liping Sun , Jun Shen","doi":"10.1016/j.adapen.2024.100178","DOIUrl":"10.1016/j.adapen.2024.100178","url":null,"abstract":"<div><p>Ammonia, a reliable low-carbon alternative fuel with energy storage capabilities, has garnered increasing attention for its application of co-firing in coal-fired power plants as a strategy to mitigate direct carbon emissions. However, various types of ammonia production technologies result in diverse economic feasibility and emission intensities. Simultaneously, each stage, spanning from upstream processes such as raw material extraction to downstream applications, contributes to carbon emissions, which cannot be ignored. It is crucial to select the appropriate assessment method to determine the transformation pathways for co-firing systems. To this end, this review presents a comprehensive life cycle assessment of ammonia co-firing systems from a whole industrial chain perspective, encompassing the entire gamut of processes from fuel production and transportation to co-firing. Studies of the industrial chain perspective and of life cycle assessment methodology that are uniquely tailored for co-firing systems are presented. A nuanced exploration of distinct technologies across the spectrum of system processes ensues, including the advantages, limitations, and trends in advancement, based on carbon emissions and economic criteria. Considering the diverse fuel production, especially ammonia, typologies and intricate processes have undergone comprehensive review. The combustion characteristics, emissions, and economic factors associated with the co-firing process are systematically summarized, drawing upon aspects such as dynamics, experiments, simulations, and demonstration projects. This study illuminates the progression and technology selection of co-firing systems across multiple stages of the whole industry chain, thereby furnishing insights relevant to the low-carbon transformation of ammonia co-firing with coal in power plants.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000167/pdfft?md5=2eee1ae8953fb0299668fa2c01a83efe&pid=1-s2.0-S2666792424000167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-11DOI: 10.1016/j.adapen.2024.100179
Akshay Ajagekar , Fengqi You
As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.
{"title":"Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization","authors":"Akshay Ajagekar , Fengqi You","doi":"10.1016/j.adapen.2024.100179","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100179","url":null,"abstract":"<div><p>As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000179/pdfft?md5=21c93fc476ac75038664b923e8d0dd02&pid=1-s2.0-S2666792424000179-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140948357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-20DOI: 10.1016/j.adapen.2024.100177
Peng Lu , Ning Zhang , Lin Ye , Ershun Du , Chongqing Kang
Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.
{"title":"Advances in model predictive control for large-scale wind power integration in power systems","authors":"Peng Lu , Ning Zhang , Lin Ye , Ershun Du , Chongqing Kang","doi":"10.1016/j.adapen.2024.100177","DOIUrl":"10.1016/j.adapen.2024.100177","url":null,"abstract":"<div><p>Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000155/pdfft?md5=8da5ee9be84dc66a46cbd485ccbef1b0&pid=1-s2.0-S2666792424000155-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140760201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.1016/j.adapen.2024.100175
Hailin Huang , Xuejian Liu , Hongfeng Lu , Chenlu Xu , Jianzhong Zhao , Yan Li , Yuhang Gu , Zhenyuan Yin
Hydrate-based CO2 sequestration (HBCS) emerges as a promising solution to sequestrate CO2 as solid hydrates for the benefit of reducing CO2 concentration in the atmosphere. The natural conditions of high-pressure and low-temperature in marine seabed provide an ideal reservoir for CO2 hydrate, enabling long-term sequestration. A significant challenge in the application of HBCS is the identification of an environmental-friendly promoter to enhance or tune CO2 hydrate kinetics, which is intrinsically sluggish. In addition, the promoter identified should be effective in all CO2 sequestration conditions, covering CO2 injection as gas or liquid. In this study, we introduced sodium lignosulfonate (SL), a by-product from the papermaking industry, as an eco-friendly kinetic promoter for CO2 hydrate formation. The impact of SL (0–3.0 wt.%) on the kinetics of CO2 hydrate formation from gaseous and liquid CO2 was systematically investigated. CO2 hydrate morphology images were acquired for both gaseous and liquid CO2 in the presence of SL for the explanation of the observed promotion effect. The promotion effect of SL on CO2 hydrate formation is optimal at 1.0 wt.% with induction time reduced to 5.3 min and 21.1 min for gaseous and liquid CO2, respectively. Moreover, CO2 storage capacity increases by around two times at 1.0 wt.% SL, reaching 85.1 v/v and 57.1 v/v for gaseous and liquid CO2, respectively. The applicability of SL as an effective kinetic promoter for both gaseous and liquid CO2 was first demonstrated. A mechanism explaining how SL promotes CO2 hydrate formation was formulated with additional nucleation sites by SL micelles and the extended contact surface offered by generated gas bubbles or liquid droplets with SL. The study demonstrates that SL as an effective promoter for CO2 hydrate kinetics is possible for adoption in large-scale HBCS projects both nearshore and offshore.
以水合物为基础的二氧化碳封存(HBCS)是以固体水合物形式封存二氧化碳以降低大气中二氧化碳浓度的一种前景广阔的解决方案。海洋海底高压低温的自然条件为二氧化碳水合物提供了理想的储层,可实现长期封存。HBCS 应用中的一个重大挑战是找到一种环境友好型促进剂,以增强或调整二氧化碳水合物动力学,因为二氧化碳水合物动力学本质上是缓慢的。此外,确定的促进剂应在所有二氧化碳封存条件下都有效,包括以气体或液体形式注入二氧化碳。在本研究中,我们引入了造纸工业的副产品木质素磺酸钠(SL)作为二氧化碳水合物形成的环保型动力学促进剂。我们系统地研究了 SL(0-3.0 wt.%)对气态和液态 CO2 形成 CO2 水合物动力学的影响。为了解释所观察到的促进作用,在 SL 存在的情况下采集了气态和液态 CO2 的 CO2 水合物形态图像。SL 对 CO2 水合物形成的促进作用在 1.0 wt.% 时达到最佳,气态 CO2 和液态 CO2 的诱导时间分别缩短至 5.3 分钟和 21.1 分钟。此外,在 1.0 wt.% SL 条件下,二氧化碳的储存能力提高了约两倍,气态和液态二氧化碳的储存能力分别达到 85.1 v/v 和 57.1 v/v。SL 作为一种有效的动力学促进剂对气态和液态 CO2 的适用性首次得到了证实。通过 SL 胶束的额外成核位点以及生成的气泡或液滴与 SL 的扩展接触面,提出了 SL 如何促进二氧化碳水合物形成的机理。研究表明,SL 作为二氧化碳水合物动力学的有效促进剂,可用于近岸和离岸的大型 HBCS 项目。
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The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.
{"title":"Assessing the conditions for economic viability of dynamic electricity retail tariffs for households","authors":"Judith Stute , Sabine Pelka , Matthias Kühnbach , Marian Klobasa","doi":"10.1016/j.adapen.2024.100174","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100174","url":null,"abstract":"<div><p>The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242400012X/pdfft?md5=0189c869809c5ea6aa382102696e1ea8&pid=1-s2.0-S266679242400012X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}