Pub Date : 2025-07-29DOI: 10.1109/TEMPR.2025.3581754
Weihang Ren;Álinson S. Xavier;Fengyu Wang;Yongpei Guan;Feng Qiu
Interregional transmission congestion presents significant challenges for Regional Transmission Operators (RTOs), particularly when loop flow diverts electricity from scheduled paths, occupying neighboring grids and increasing congestion costs. To mitigate this cross-regional congestion, RTOs employ a market-to-market (M2M) process through an iterative method, in which they exchange real-time security-constrained economic dispatch solutions and communicate requests for congestion relief. While this method provides economic benefits, it struggles with issues like power swings and time delays. To explore the full potential of M2M enhancements, in this paper, we first analyze the current M2M iterative method practice to better understand its efficacy and identify places for improvements. Then, we explore enhancements and develop an ADMM method for M2M coordination that optimizes congestion management. Specifically, our ADMM method can achieve a minimal cost that is the same as the cost obtained through a centralized model that optimizes multiple markets altogether. Our final case studies, across a comprehensive set of multi-area benchmark instances, demonstrate the superior performance of the proposed ADMM algorithm for the M2M process. Meanwhile, we identify scenarios where the existing M2M process fails to provide solutions as a by-product. Finally, the algorithm is implemented in an open-source package UnitCommitment.jl for easy access by a broader audience.
{"title":"An Analysis of Market-to-Market Coordination","authors":"Weihang Ren;Álinson S. Xavier;Fengyu Wang;Yongpei Guan;Feng Qiu","doi":"10.1109/TEMPR.2025.3581754","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3581754","url":null,"abstract":"Interregional transmission congestion presents significant challenges for Regional Transmission Operators (RTOs), particularly when loop flow diverts electricity from scheduled paths, occupying neighboring grids and increasing congestion costs. To mitigate this cross-regional congestion, RTOs employ a market-to-market (M2M) process through an iterative method, in which they exchange real-time security-constrained economic dispatch solutions and communicate requests for congestion relief. While this method provides economic benefits, it struggles with issues like power swings and time delays. To explore the full potential of M2M enhancements, in this paper, we first analyze the current M2M iterative method practice to better understand its efficacy and identify places for improvements. Then, we explore enhancements and develop an ADMM method for M2M coordination that optimizes congestion management. Specifically, our ADMM method can achieve a minimal cost that is the same as the cost obtained through a centralized model that optimizes multiple markets altogether. Our final case studies, across a comprehensive set of multi-area benchmark instances, demonstrate the superior performance of the proposed ADMM algorithm for the M2M process. Meanwhile, we identify scenarios where the existing M2M process fails to provide solutions as a by-product. Finally, the algorithm is implemented in an open-source package UnitCommitment.jl for easy access by a broader audience.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"287-296"},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-23DOI: 10.1109/TEMPR.2025.3591824
Puneet Chitkara;Xiaoxue Hou;Johannes Urpelainen;Benjamin F. Hobbs
Institutional barriers such as local generation preferences, purchase agreements, and regulatory restrictions on contracting costs can hamper the interregional coordination needed to maximize the benefits of variable renewables. Such nonphysical barriers prevent optimization of dispatch across multiple control areas. Previous studies either assume efficient trade subject to physical transmission and generation constraints, or simple $/MWh hurdle rates for trade profitability, without explicitly relating model parameters to specific institutional features that could be addressed by market reforms. Here, we propose a market model with explicit representation of institutional barriers. Drawing inspiration from the international trade literature, where trade across jurisdictions is distinguished based on the origin of commodities, we add institutional constraints to a standard unit commitment model. Applying this model to India, we demonstrate how barriers to interstate power flow impact operating costs, emissions, and renewable energy integration. We find that these constraints could increase India’s annual bulk power operating costs by up to 29%, raise emissions by about 3%, and lead to renewable energy curtailment of more than 10% in some states. As India aims to increase non-fossil fuel-based capacity to 500 GW by 2030, addressing these inefficiencies is crucial.
{"title":"Institutional Barriers to Renewable Power Exchange and Emissions Reductions: An India Case Study","authors":"Puneet Chitkara;Xiaoxue Hou;Johannes Urpelainen;Benjamin F. Hobbs","doi":"10.1109/TEMPR.2025.3591824","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3591824","url":null,"abstract":"Institutional barriers such as local generation preferences, purchase agreements, and regulatory restrictions on contracting costs can hamper the interregional coordination needed to maximize the benefits of variable renewables. Such nonphysical barriers prevent optimization of dispatch across multiple control areas. Previous studies either assume efficient trade subject to physical transmission and generation constraints, or simple $/MWh hurdle rates for trade profitability, without explicitly relating model parameters to specific institutional features that could be addressed by market reforms. Here, we propose a market model with explicit representation of institutional barriers. Drawing inspiration from the international trade literature, where trade across jurisdictions is distinguished based on the origin of commodities, we add institutional constraints to a standard unit commitment model. Applying this model to India, we demonstrate how barriers to interstate power flow impact operating costs, emissions, and renewable energy integration. We find that these constraints could increase India’s annual bulk power operating costs by up to 29%, raise emissions by about 3%, and lead to renewable energy curtailment of more than 10% in some states. As India aims to increase non-fossil fuel-based capacity to 500 GW by 2030, addressing these inefficiencies is crucial.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 4","pages":"424-439"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compressed Air Energy Storage (CAES) and Cryogenic Energy Storage (CES) are emerging as promising technologies for sustainable grid-scale applications. To surmount the capacity and geological limitations of traditional CAES systems, this study capitalizes on the hybridization of above-ground CAES with CES, utilizing energy conversion between compressed and liquid air. Here, we develop a comprehensive mathematical model for the operation of the hybrid CAES-CES plant, incorporating discrete constraints to manage internal energy transfers and coordination. The model is leveraged to develop the: i) look-ahead dispatch schedule over the following days to enhance adaptability in managing stored energy to maximize benefits, and ii) strategic behavior in electricity markets through unified offers/bids submission. The dispatch problem is structured as a bi-level optimization, with the lower-level addressing market-clearing processes and the upper-level handling storage profit maximization. We reformulate the bi-level setup into a mixed-integer programming model using a mathematical program with equilibrium constraints. To mitigate the computational burden associated with the large number of integer variables in the optimization, we implement a learning-assisted framework for warm-starting these variables. Numerical results show that the hybrid plant can yield up to a 9.08% profit improvement over the standalone alternative under the look-ahead strategy. Further, results demonstrate that under the bi-level setup, the warm-start strategy effectively reduces computation time by 29.30% and 13.35% in the 24- and 118-bus networks, respectively.
{"title":"Hybrid Energy Storage Dispatch: A Bi-Level Look-Ahead Learning-Assisted Model","authors":"Hooman Khaloie;Andrej Stankovski;Blazhe Gjorgiev;Giovanni Sansavini;François Vallée","doi":"10.1109/TEMPR.2025.3589133","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3589133","url":null,"abstract":"Compressed Air Energy Storage (CAES) and Cryogenic Energy Storage (CES) are emerging as promising technologies for sustainable grid-scale applications. To surmount the capacity and geological limitations of traditional CAES systems, this study capitalizes on the hybridization of above-ground CAES with CES, utilizing energy conversion between compressed and liquid air. Here, we develop a comprehensive mathematical model for the operation of the hybrid CAES-CES plant, incorporating discrete constraints to manage internal energy transfers and coordination. The model is leveraged to develop the: <italic>i)</i> look-ahead dispatch schedule over the following days to enhance adaptability in managing stored energy to maximize benefits, and <italic>ii)</i> strategic behavior in electricity markets through unified offers/bids submission. The dispatch problem is structured as a bi-level optimization, with the lower-level addressing market-clearing processes and the upper-level handling storage profit maximization. We reformulate the bi-level setup into a mixed-integer programming model using a mathematical program with equilibrium constraints. To mitigate the computational burden associated with the large number of integer variables in the optimization, we implement a learning-assisted framework for warm-starting these variables. Numerical results show that the hybrid plant can yield up to a 9.08% profit improvement over the standalone alternative under the look-ahead strategy. Further, results demonstrate that under the bi-level setup, the warm-start strategy effectively reduces computation time by 29.30% and 13.35% in the 24- and 118-bus networks, respectively.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"376-392"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-08DOI: 10.1109/TEMPR.2025.3585959
Sebastian Zwickl-Bernhard;Anne F. Neumann;Majd Olleik;Haytham M. Dbouk
In Lebanon, the publicly organized electricity grid has struggled for decades to provide a reliable electricity supply and has nearly collapsed. In response, a network of distributed diesel generators has flourished as an alternative to the unreliable central grid. However, electricity prices within these microgrids remain largely unregulated. This study has two main objectives: first, to estimate the electricity demand elasticity of residential consumers using a unique dataset from a Lebanese microgrid in Deir Kanoun al Naher; second, to analyze how the microgrid owner’s knowledge of this elasticity affects electricity pricing to maximize profits. We apply an ordinary least squares estimator to determine electricity demand elasticity and a game-theory-based optimization model to derive the microgrid owner’s profit-maximizing pricing strategy. The estimated selling price elasticity is $-0.48$, indicating relatively inelastic demand, in line with values reported for developing countries. Considering the estimated elasticity in the pricing strategy resulting in an increase of $54 %$, revenues increase by up to $15 %$, while the share of suppressed demand reaches approximately $25 %$. Sensitivity analysis suggests that higher price elasticity mitigates excessive pricing strategies. The role of the public grid and solar PV penetration presents a promising direction for future research.
在黎巴嫩，公共组织的电网几十年来一直在努力提供可靠的电力供应，几乎崩溃。作为回应，分布式柴油发电机网络蓬勃发展，成为不可靠的中央电网的替代方案。然而，这些微电网的电价在很大程度上仍然不受监管。本研究有两个主要目标：首先，使用来自Deir Kanoun al Naher的黎巴嫩微电网的独特数据集来估计住宅消费者的电力需求弹性；其次，分析微电网所有者对这种弹性的认识如何影响电价以实现利润最大化。本文采用普通最小二乘估计来确定电力需求弹性，并采用基于博弈论的优化模型来推导微电网所有者利润最大化的定价策略。估计销售价格弹性为-0.48美元，表明需求相对缺乏弹性，与发展中国家的报告值一致。考虑到定价策略的估计弹性导致了54%的增长，收入增加了15%，而被抑制的需求份额达到了大约25%。敏感性分析表明，较高的价格弹性减轻了过度的定价策略。公共电网的作用和太阳能光伏的渗透是未来研究的一个有希望的方向。
{"title":"Market Organization in Low-Income Countries’ Microgrids: Insights From Electricity Demand Elasticity and Game-Theory Optimization. Case Study: Lebanon","authors":"Sebastian Zwickl-Bernhard;Anne F. Neumann;Majd Olleik;Haytham M. Dbouk","doi":"10.1109/TEMPR.2025.3585959","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3585959","url":null,"abstract":"In Lebanon, the publicly organized electricity grid has struggled for decades to provide a reliable electricity supply and has nearly collapsed. In response, a network of distributed diesel generators has flourished as an alternative to the unreliable central grid. However, electricity prices within these microgrids remain largely unregulated. This study has two main objectives: first, to estimate the electricity demand elasticity of residential consumers using a unique dataset from a Lebanese microgrid in Deir Kanoun al Naher; second, to analyze how the microgrid owner’s knowledge of this elasticity affects electricity pricing to maximize profits. We apply an ordinary least squares estimator to determine electricity demand elasticity and a game-theory-based optimization model to derive the microgrid owner’s profit-maximizing pricing strategy. The estimated selling price elasticity is <inline-formula><tex-math>$-0.48$</tex-math></inline-formula>, indicating relatively inelastic demand, in line with values reported for developing countries. Considering the estimated elasticity in the pricing strategy resulting in an increase of <inline-formula><tex-math>$54 %$</tex-math></inline-formula>, revenues increase by up to <inline-formula><tex-math>$15 %$</tex-math></inline-formula>, while the share of suppressed demand reaches approximately <inline-formula><tex-math>$25 %$</tex-math></inline-formula>. Sensitivity analysis suggests that higher price elasticity mitigates excessive pricing strategies. The role of the public grid and solar PV penetration presents a promising direction for future research.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 4","pages":"511-520"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11074425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729466","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 : 2025-07-02DOI: 10.1109/TEMPR.2025.3584189
Amin Maghami;Evrim Ursavas;Ashish Cherukuri
The transition to renewable energy is pivotal for achieving decarbonization goals in the electricity sector. The focus on low-carbon emissions extends beyond the power sector, emphasizing the need for efficient planning for other energy carriers like green hydrogen. However, the intermittency of renewable sources necessitates solutions for operational planning and supply security, making energy storage systems vital for grid services. Battery energy storage systems (BESS) play a key role in future power networks dominated by renewables, as a short-term buffer. This study explores the conditions under which integrating large-scale BESS with hydrogen facilities in the power network enhances overall system profitability. We analyze this by formulating a joint investment-operation decision-making process, modeled as a mixed-integer chance-constrained optimization problem. To address computational challenges, we implement a scalable decomposition-based solution method that enables tractable optimization. Through the optimal solutions, we analyze different incentive strategies and technology cost scenarios to accelerate the kick-off of hydrogen production. By addressing the challenges of intermittency, pricing mechanisms, and optimizing the synergy between BESS and hydrogen systems, this research provides insights for resilient and profitable renewable energy networks, highlighting that hydrogen selling price significantly influences investment decisions and that incentives can accelerate market entry and transition.
{"title":"Joint Investment-Operation Planning for Assessing the Economic Impact of Hydrogen Integration in Power Systems","authors":"Amin Maghami;Evrim Ursavas;Ashish Cherukuri","doi":"10.1109/TEMPR.2025.3584189","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3584189","url":null,"abstract":"The transition to renewable energy is pivotal for achieving decarbonization goals in the electricity sector. The focus on low-carbon emissions extends beyond the power sector, emphasizing the need for efficient planning for other energy carriers like green hydrogen. However, the intermittency of renewable sources necessitates solutions for operational planning and supply security, making energy storage systems vital for grid services. Battery energy storage systems (BESS) play a key role in future power networks dominated by renewables, as a short-term buffer. This study explores the conditions under which integrating large-scale BESS with hydrogen facilities in the power network enhances overall system profitability. We analyze this by formulating a joint investment-operation decision-making process, modeled as a mixed-integer chance-constrained optimization problem. To address computational challenges, we implement a scalable decomposition-based solution method that enables tractable optimization. Through the optimal solutions, we analyze different incentive strategies and technology cost scenarios to accelerate the kick-off of hydrogen production. By addressing the challenges of intermittency, pricing mechanisms, and optimizing the synergy between BESS and hydrogen systems, this research provides insights for resilient and profitable renewable energy networks, highlighting that hydrogen selling price significantly influences investment decisions and that incentives can accelerate market entry and transition.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"236-250"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-23DOI: 10.1109/TEMPR.2025.3581089
Jiayi Li;Matt Motoki;Baosen Zhang
The introduction of aggregator structures has proven effective in bringing fairness to energy resource allocation by negotiating for more resources and economic surplus on behalf of users. This paper extends the fair energy resource allocation problem to a multi-agent setting, focusing on interactions among multiple aggregators in an electricity market. We consider a setting where aggregators submit quantity-only bids as in a noncooperative Cournot game. Unlike classical Cournot models, where firms optimize only for profit, our framework incorporates a bi-level decision process, in which each aggregator determines its total purchase while simultaneously optimizing the internal allocation among its users based on fairness-efficiency trade-off objectives and constraints. We prove that the strategic optimization problems faced by the aggregators form a quasi-concave game, ensuring the existence of a Nash equilibrium. This resolves complexities related to market price dependencies on total purchases and balancing fairness and efficiency in energy allocation. In addition, we design simulations to characterize the equilibrium points of the induced game, demonstrating how aggregators stabilize market outcomes, ensure fair resource distribution, and optimize user surplus. Our findings offer a robust framework for understanding strategic interactions among aggregators, contributing to more efficient and equitable energy markets.
{"title":"Strategic and Fair Aggregator Interactions in Energy Markets: Multi-Agent Dynamics and Quasiconcave Games","authors":"Jiayi Li;Matt Motoki;Baosen Zhang","doi":"10.1109/TEMPR.2025.3581089","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3581089","url":null,"abstract":"The introduction of aggregator structures has proven effective in bringing fairness to energy resource allocation by negotiating for more resources and economic surplus on behalf of users. This paper extends the fair energy resource allocation problem to a multi-agent setting, focusing on interactions among multiple aggregators in an electricity market. We consider a setting where aggregators submit quantity-only bids as in a noncooperative Cournot game. Unlike classical Cournot models, where firms optimize only for profit, our framework incorporates a bi-level decision process, in which each aggregator determines its total purchase while simultaneously optimizing the internal allocation among its users based on fairness-efficiency trade-off objectives and constraints. We prove that the strategic optimization problems faced by the aggregators form a quasi-concave game, ensuring the existence of a Nash equilibrium. This resolves complexities related to market price dependencies on total purchases and balancing fairness and efficiency in energy allocation. In addition, we design simulations to characterize the equilibrium points of the induced game, demonstrating how aggregators stabilize market outcomes, ensure fair resource distribution, and optimize user surplus. Our findings offer a robust framework for understanding strategic interactions among aggregators, contributing to more efficient and equitable energy markets.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"340-351"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-16DOI: 10.1109/TEMPR.2025.3580217
Rohit Vijay;Parul Mathuria
Distributed Energy Resources (DERs) offer great potential to provide flexibility-based services but come with operational challenges when integrated in the centralized ancillary service (AS) market operated by transmission system operator (TSO). The current market arrangement lacks sufficient resolution of distribution constraints during market clearing. To address this issue, a detailed grid prequalification process is required before DER participation in the centralized market. In this context, this paper introduces a detailed framework for distribution system operators (DSOs) to qualify DERs and adjust their operational limits, maximizing flexibility with minimal bid deviation. The framework employs network capability regions to consider distribution constraints, uses the Euclidean distance to reduce complex power deviations, and considers grid integration costs as a preference criterion for bid modifications. Results confirm that using a single network capability region allows for dynamic prequalification, which enhances transparency and reduces the need for bid adjustments. Additionally, considering grid integration costs can lead to savings on future reinforcement expenses for DSOs. The approach has been tested using IEEE standard bus systems, demonstrating enhanced flexibility in DERs, which is transferred to the TSO. This approach is suitable for larger systems having multiple DERs connected under a single DSO.
{"title":"Minimizing Bid Adjustments and Maximizing Flexibility: A Comprehensive Framework for Grid Prequalification of DERs","authors":"Rohit Vijay;Parul Mathuria","doi":"10.1109/TEMPR.2025.3580217","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3580217","url":null,"abstract":"Distributed Energy Resources (DERs) offer great potential to provide flexibility-based services but come with operational challenges when integrated in the centralized ancillary service (AS) market operated by transmission system operator (TSO). The current market arrangement lacks sufficient resolution of distribution constraints during market clearing. To address this issue, a detailed grid prequalification process is required before DER participation in the centralized market. In this context, this paper introduces a detailed framework for distribution system operators (DSOs) to qualify DERs and adjust their operational limits, maximizing flexibility with minimal bid deviation. The framework employs network capability regions to consider distribution constraints, uses the Euclidean distance to reduce complex power deviations, and considers grid integration costs as a preference criterion for bid modifications. Results confirm that using a single network capability region allows for dynamic prequalification, which enhances transparency and reduces the need for bid adjustments. Additionally, considering grid integration costs can lead to savings on future reinforcement expenses for DSOs. The approach has been tested using IEEE standard bus systems, demonstrating enhanced flexibility in DERs, which is transferred to the TSO. This approach is suitable for larger systems having multiple DERs connected under a single DSO.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"352-362"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-13DOI: 10.1109/TEMPR.2025.3573826
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Pub Date : 2025-06-13DOI: 10.1109/TEMPR.2025.3573822
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Pub Date : 2025-06-13DOI: 10.1109/TEMPR.2025.3573824
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