Pub Date : 2025-12-01Epub Date: 2025-09-25DOI: 10.1016/j.segan.2025.101985
Chien-Liang Liu , Shu-Rong Lu , Yong-Tai Chen , Ching-Hsien Lee
Accurate solar power forecasting plays a pivotal role in sustainable energy management. We propose a temporal attention (TA) module that leverages sequential all-sky imagery to enhance solar radiation and power generation predictions. This pluggable module distinctly captures temporal dynamics in sky images, surpassing traditional methods. Comprehensive experiments verify its adaptability and universality, showing marked forecasting improvements across state-of-the-art deep-learning models. Notably, integrating TA with the Video Swin Transformer, forming ViSiT-TA, further boosts predictive accuracy by extracting spatio-temporal features. This research underscores the importance of innovative deep-learning techniques for advancing solar energy forecasting and promoting environmentally responsible solutions.
{"title":"Temporal attention for photovoltaic power forecasting using all-sky imagery","authors":"Chien-Liang Liu , Shu-Rong Lu , Yong-Tai Chen , Ching-Hsien Lee","doi":"10.1016/j.segan.2025.101985","DOIUrl":"10.1016/j.segan.2025.101985","url":null,"abstract":"<div><div>Accurate solar power forecasting plays a pivotal role in sustainable energy management. We propose a temporal attention (TA) module that leverages sequential all-sky imagery to enhance solar radiation and power generation predictions. This pluggable module distinctly captures temporal dynamics in sky images, surpassing traditional methods. Comprehensive experiments verify its adaptability and universality, showing marked forecasting improvements across state-of-the-art deep-learning models. Notably, integrating TA with the Video Swin Transformer, forming ViSiT-TA, further boosts predictive accuracy by extracting spatio-temporal features. This research underscores the importance of innovative deep-learning techniques for advancing solar energy forecasting and promoting environmentally responsible solutions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101985"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-09-09DOI: 10.1016/j.segan.2025.101963
Francesco Liberati, Mohab M.H. Atanasious, Emanuele De Santis, Alessandro Di Giorgio
This paper focuses on a novel use of deep reinforcement learning (RL) to optimally tune in real-time a model predictive control (MPC) smart charging algorithm for plug-in electric vehicles (PEVs). The coefficients of the terminal cost function of the MPC algorithm are updated online by a neural network, which is trained offline to maximize the control performance (linked to the satisfaction of the users’ charging preferences and the tracking of a power reference profile, at PEV fleet level). This approach is different and more flexible compared to most of the other approaches in the literature, which instead use deep RL to fix offline the MPC parametrization. The proposed method allows one to select a shorter MPC control window (compared to standard MPC) and/or a shorter sampling time, while improving the control performance. Simulations are presented to validate the approach: the proposed MPC-RL controller improves control performance by an average of 4.3 % compared to classic MPC, while having a lower computing time.
{"title":"A hybrid model predictive control-deep reinforcement learning algorithm with application to plug-in electric vehicles smart charging","authors":"Francesco Liberati, Mohab M.H. Atanasious, Emanuele De Santis, Alessandro Di Giorgio","doi":"10.1016/j.segan.2025.101963","DOIUrl":"10.1016/j.segan.2025.101963","url":null,"abstract":"<div><div>This paper focuses on a novel use of deep reinforcement learning (RL) to optimally tune in real-time a model predictive control (MPC) smart charging algorithm for plug-in electric vehicles (PEVs). The coefficients of the terminal cost function of the MPC algorithm are updated online by a neural network, which is trained offline to maximize the control performance (linked to the satisfaction of the users’ charging preferences and the tracking of a power reference profile, at PEV fleet level). This approach is different and more flexible compared to most of the other approaches in the literature, which instead use deep RL to fix offline the MPC parametrization. The proposed method allows one to select a shorter MPC control window (compared to standard MPC) and/or a shorter sampling time, while improving the control performance. Simulations are presented to validate the approach: the proposed MPC-RL controller improves control performance by an average of 4.3 % compared to classic MPC, while having a lower computing time.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101963"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-10-16DOI: 10.1016/j.segan.2025.102016
Zhiyuan Wu , Guohua Fang , Jian Ye , Xianfeng Huang , Min Yan
The stability and economic efficiency of hydropower-wind-photovoltaic hybrid systems are significantly influenced by various uncertainties and risks. However, existing research lacks a systematic framework to evaluate the synergistic effects of these uncertainties, identify adverse conditions that trigger risk events, and assess the role of forecasting accuracy in risk evaluation. To address these gaps, this study proposes a multi-uncertainty risk analysis framework designed to systematically quantify the impact of uncertainties on system risks. The framework evaluates system risk distribution and employs clustering and correlation analysis to identify adverse conditions, which provide critical inputs for refining scheduling strategies. Additionally, the framework conducts an ablation study to quantify the synergistic effects of multiple uncertainties and clarify their influence on system risks. It further examines the relationship between forecasting accuracy and system risk levels. The effectiveness of the framework was validated through an annual operation case study of a hydropower-wind-photovoltaic hybrid system in the Yalong River Basin. The framework systematically evaluated power shortage and over-generation risks across subsystems under multiple uncertainties using an ablation study, risk event extraction, and correlation analysis. Based on these analyses, an improvement strategy was formulated to mitigate system risks.
{"title":"Multi-uncertainty risk analysis framework for hydropower-wind-photovoltaic hybrid systems","authors":"Zhiyuan Wu , Guohua Fang , Jian Ye , Xianfeng Huang , Min Yan","doi":"10.1016/j.segan.2025.102016","DOIUrl":"10.1016/j.segan.2025.102016","url":null,"abstract":"<div><div>The stability and economic efficiency of hydropower-wind-photovoltaic hybrid systems are significantly influenced by various uncertainties and risks. However, existing research lacks a systematic framework to evaluate the synergistic effects of these uncertainties, identify adverse conditions that trigger risk events, and assess the role of forecasting accuracy in risk evaluation. To address these gaps, this study proposes a multi-uncertainty risk analysis framework designed to systematically quantify the impact of uncertainties on system risks. The framework evaluates system risk distribution and employs clustering and correlation analysis to identify adverse conditions, which provide critical inputs for refining scheduling strategies. Additionally, the framework conducts an ablation study to quantify the synergistic effects of multiple uncertainties and clarify their influence on system risks. It further examines the relationship between forecasting accuracy and system risk levels. The effectiveness of the framework was validated through an annual operation case study of a hydropower-wind-photovoltaic hybrid system in the Yalong River Basin. The framework systematically evaluated power shortage and over-generation risks across subsystems under multiple uncertainties using an ablation study, risk event extraction, and correlation analysis. Based on these analyses, an improvement strategy was formulated to mitigate system risks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102016"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-08-23DOI: 10.1016/j.segan.2025.101931
Sebastián San Martín , Fernando García-Muñoz , Franco Quezada , Sebastián Dávila
This paper presents a user-centered, fully decentralized framework to allow an energy community (EC) to self-manage line congestion issues through peer-to-peer (P2P) energy trading and a flexibility market using the users’ distributed energy resources (DERs) assets to take an energy seller (buyer) role when they have a surplus (deficit). A three-stage optimization-based model is introduced to consider the users’ preferences and identify line congestion issues using the Distflow model to evaluate the distribution network (DN) limitations. In this regard, users maximize their benefits in the first optimization stage by optimizing their DER operation. In the second stage, the distribution system operator (DSO) solves an optimal power flow model to identify potential congestion given the users’ preferences. If congestion occurs, the third stage activates a P2P energy and flexibility market designed to resolve the issue by minimizing deviations from the users’ initial preferences. To achieve full decentralization, a two-step alternating direction method of multipliers (ADMM) algorithm is employed: the first step addresses optimal power flow, while the second manages the P2P and flexibility market. Tests were conducted on a 33-bus DN for different DER penetration levels, showing that the methodology efficiently meets energy requirements while respecting the network’s physical constraints and improving information security.
{"title":"User-centered decentralized P2P energy trading model for managing line congestion in energy communities","authors":"Sebastián San Martín , Fernando García-Muñoz , Franco Quezada , Sebastián Dávila","doi":"10.1016/j.segan.2025.101931","DOIUrl":"10.1016/j.segan.2025.101931","url":null,"abstract":"<div><div>This paper presents a user-centered, fully decentralized framework to allow an energy community (EC) to self-manage line congestion issues through peer-to-peer (P2P) energy trading and a flexibility market using the users’ distributed energy resources (DERs) assets to take an energy seller (buyer) role when they have a surplus (deficit). A three-stage optimization-based model is introduced to consider the users’ preferences and identify line congestion issues using the Distflow model to evaluate the distribution network (DN) limitations. In this regard, users maximize their benefits in the first optimization stage by optimizing their DER operation. In the second stage, the distribution system operator (DSO) solves an optimal power flow model to identify potential congestion given the users’ preferences. If congestion occurs, the third stage activates a P2P energy and flexibility market designed to resolve the issue by minimizing deviations from the users’ initial preferences. To achieve full decentralization, a two-step alternating direction method of multipliers (ADMM) algorithm is employed: the first step addresses optimal power flow, while the second manages the P2P and flexibility market. Tests were conducted on a 33-bus DN for different DER penetration levels, showing that the methodology efficiently meets energy requirements while respecting the network’s physical constraints and improving information security.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101931"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-09-01DOI: 10.1016/j.segan.2025.101954
Zahra Azimi , Ahmad Afshar
This paper addresses the critical challenge of restoring structural controllability in cyber-physical-social systems (CPSS) compromised by cyber-attacks. Unlike existing studies that focus on homogenous networks or neglect the role of interlayer couplings and intralayer topologies, this work introduces a novel approach to enhance cyber resilience in interdependent multi-layered heterogenous CPSS. We establish new necessary and sufficient conditions for achieving post-attack structural controllability and propose an optimal network reconfiguration algorithm that restores controllability with minimal intervention. This novel algorithm identifies the minimum set of edges required to reconfigure this network, ensuring structural controllability in a polynomial time. Our approach is validated through simulations on various CPSS, including IEEE 14-bus and IEEE 118-bus power networks, as well as scale-free, clustered scale-free, small-world, and random networks subjected to various attack strategies. Additionally, the approach is applied to real-world large-scale datasets, demonstrating its scalability and practical applicability. The results reveal that, among intralayer topologies, scale-free networks are highly vulnerable to structural uncontrollability. Furthermore, sparse interlayer coupling significantly reduces the resilience of the CPSS compared to mesh and peer-to-peer (P2P) coupling configurations. Notably, targeted attacks based on betweenness require about 53 % more edge additions to restore controllability compared to random attacks.
{"title":"Restoring structural controllability of interdependent multi-layered heterogenous cyber-physical-social networks via optimal network reconfiguration","authors":"Zahra Azimi , Ahmad Afshar","doi":"10.1016/j.segan.2025.101954","DOIUrl":"10.1016/j.segan.2025.101954","url":null,"abstract":"<div><div>This paper addresses the critical challenge of restoring structural controllability in cyber-physical-social systems (CPSS) compromised by cyber-attacks. Unlike existing studies that focus on homogenous networks or neglect the role of interlayer couplings and intralayer topologies, this work introduces a novel approach to enhance cyber resilience in interdependent multi-layered heterogenous CPSS. We establish new necessary and sufficient conditions for achieving post-attack structural controllability and propose an optimal network reconfiguration algorithm that restores controllability with minimal intervention. This novel algorithm identifies the minimum set of edges required to reconfigure this network, ensuring structural controllability in a polynomial time. Our approach is validated through simulations on various CPSS, including IEEE 14-bus and IEEE 118-bus power networks, as well as scale-free, clustered scale-free, small-world, and random networks subjected to various attack strategies. Additionally, the approach is applied to real-world large-scale datasets, demonstrating its scalability and practical applicability. The results reveal that, among intralayer topologies, scale-free networks are highly vulnerable to structural uncontrollability. Furthermore, sparse interlayer coupling significantly reduces the resilience of the CPSS compared to mesh and peer-to-peer (P2P) coupling configurations. Notably, targeted attacks based on betweenness require about 53 % more edge additions to restore controllability compared to random attacks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101954"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-10-14DOI: 10.1016/j.segan.2025.102000
Xianglong Qi, Jian Chen, Wen Zhang, Keyu Zhang, Xianzhuo Sun
The continuous popularization of distributed energy and the increasing energy demand have led to more severe voltage violation problems in distribution networks. To address this challenge, grid-connected microgrids with sufficient flexible voltage regulation resources can be utilized to provide effective voltage support for the distribution network. However, microgrids typically operate as independent entities, and there are barriers to collaboration between distribution networks and microgrids. Consequently, this paper proposes a strategy based on incentivizing microgrids to regulate the voltage for the distribution network. First, the willingness for microgrids to participate in voltage regulation is enhanced by establishing an incentive-based voltage regulation scheme, which includes the cost savings of voltage regulation in the distribution network, the distributed generator disconnection risk in the distribution network, and the cost of voltage-dependent loads in the microgrids. The microgrids provide voltage support for the distribution network by adjusting the operation plan and obtaining the corresponding voltage regulation incentive. Second, to optimize the operation strategy of multi-microgrids while considering voltage regulation incentives, the Shapley Q-value deep deterministic policy gradient (SQDDPG) algorithm is proposed. The Shapley Q value is incorporated into the traditional multi-agent deep deterministic policy gradient (MADDPG) algorithm for distributing the global reward to measure the contribution of different microgrids in the voltage regulation process, which allows the algorithm to converge to higher cumulative rewards. Finally, the simulation results for a modified IEEE 33-bus system show that the rate of the voltage violations of the distribution network is reduced by 51.52 %, and the operational economy of microgrids has been improved by 9.12 %. The efficiency of cooperation between distribution network and microgrids has been effectively improved.
{"title":"Incentive-oriented strategy for optimizing microgrid-enabled distribution network voltage regulation based on SQDDPG algorithm","authors":"Xianglong Qi, Jian Chen, Wen Zhang, Keyu Zhang, Xianzhuo Sun","doi":"10.1016/j.segan.2025.102000","DOIUrl":"10.1016/j.segan.2025.102000","url":null,"abstract":"<div><div>The continuous popularization of distributed energy and the increasing energy demand have led to more severe voltage violation problems in distribution networks. To address this challenge, grid-connected microgrids with sufficient flexible voltage regulation resources can be utilized to provide effective voltage support for the distribution network. However, microgrids typically operate as independent entities, and there are barriers to collaboration between distribution networks and microgrids. Consequently, this paper proposes a strategy based on incentivizing microgrids to regulate the voltage for the distribution network. First, the willingness for microgrids to participate in voltage regulation is enhanced by establishing an incentive-based voltage regulation scheme, which includes the cost savings of voltage regulation in the distribution network, the distributed generator disconnection risk in the distribution network, and the cost of voltage-dependent loads in the microgrids. The microgrids provide voltage support for the distribution network by adjusting the operation plan and obtaining the corresponding voltage regulation incentive. Second, to optimize the operation strategy of multi-microgrids while considering voltage regulation incentives, the Shapley Q-value deep deterministic policy gradient (SQDDPG) algorithm is proposed. The Shapley Q value is incorporated into the traditional multi-agent deep deterministic policy gradient (MADDPG) algorithm for distributing the global reward to measure the contribution of different microgrids in the voltage regulation process, which allows the algorithm to converge to higher cumulative rewards. Finally, the simulation results for a modified IEEE 33-bus system show that the rate of the voltage violations of the distribution network is reduced by 51.52 %, and the operational economy of microgrids has been improved by 9.12 %. The efficiency of cooperation between distribution network and microgrids has been effectively improved.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102000"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of peer-to-peer (P2P) energy trading, transactive energy markets (TEMs) face challenges such as ensuring fair access for market participants to the upstream grid and addressing limitations of distribution networks. To tackle these challenges, the distribution system operator (DSO) should incentivize market participants to contribute to their mitigation. To this end, this paper proposes a bi-level framework in which the DSO designs transactive control signals (TCSs) to influence the behavior of market participants. In this framework, fairness, security, and peak shaving signals are introduced at the upper level, while P2P energy trading is implemented at the lower level. The TCSs are calculated based on sensitivity analyses related to fair allocation and secure network operation constraints. As the results demonstrate, the goals of fairness and network security are achieved simultaneously. Moreover, while market participants effectively contribute to meeting technical objectives, their privacy is preserved.
{"title":"Framework for enhancing fairness and security in active distribution networks via transactive control signals","authors":"Hajar Abdolahinia , Morteza Aryani , Moein Moeini-Aghtaie , Mohammad Heidarizadeh , Inoccent Kamwa","doi":"10.1016/j.segan.2025.101968","DOIUrl":"10.1016/j.segan.2025.101968","url":null,"abstract":"<div><div>With the development of peer-to-peer (P2P) energy trading, transactive energy markets (TEMs) face challenges such as ensuring fair access for market participants to the upstream grid and addressing limitations of distribution networks. To tackle these challenges, the distribution system operator (DSO) should incentivize market participants to contribute to their mitigation. To this end, this paper proposes a bi-level framework in which the DSO designs transactive control signals (TCSs) to influence the behavior of market participants. In this framework, fairness, security, and peak shaving signals are introduced at the upper level, while P2P energy trading is implemented at the lower level. The TCSs are calculated based on sensitivity analyses related to fair allocation and secure network operation constraints. As the results demonstrate, the goals of fairness and network security are achieved simultaneously. Moreover, while market participants effectively contribute to meeting technical objectives, their privacy is preserved.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101968"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing demand for electric vehicles (EVs) requires large-scale deployment of fast charging stations (FCS). These FCS owners are usually private investors and focus on the growth of their businesses. This enforces FCS to design a suitable pricing mechanism to achieve their financial goals, build customer relationships, and maintain competitiveness in the market while considering distributed energy resources (DERs). Therefore, there is a need to develop a holistic approach to keep the interests of all stakeholders in mind while deciding the pricing for EV charging at FCS. Hence, this paper proposes pricing mechanisms, flat and dynamic pricing for EVs charging at FCS considering DERs against dynamic market prices. The proposed pricing mechanisms are designed to keep profit margin of FCS remains same relative to no DERs considering EVs users’ convenience, satisfaction and waiting time. Price-cum-convenience responsive models are proposed for price elasticity of demand and EV users’ satisfaction. The study reveals that both pricing mechanisms under DERs are equally promising as they produce more competitive price signals which are around 11 % lower, up to 61.81 % reduction in grid energy demand during overload periods, and up to 6 % increment in mean satisfaction of EV users while keeping the profit margin intact for FCS owners.
{"title":"Pricing mechanism for EV fast charging stations considering distributed energy resources","authors":"Shakti Vashisth , Praveen Kumar Agrawal , Nikhil Gupta , Vipin Chandra Pandey , K.R. Niazi , Anil Swarnkar","doi":"10.1016/j.segan.2025.101943","DOIUrl":"10.1016/j.segan.2025.101943","url":null,"abstract":"<div><div>The growing demand for electric vehicles (EVs) requires large-scale deployment of fast charging stations (FCS). These FCS owners are usually private investors and focus on the growth of their businesses. This enforces FCS to design a suitable pricing mechanism to achieve their financial goals, build customer relationships, and maintain competitiveness in the market while considering distributed energy resources (DERs). Therefore, there is a need to develop a holistic approach to keep the interests of all stakeholders in mind while deciding the pricing for EV charging at FCS. Hence, this paper proposes pricing mechanisms, flat and dynamic pricing for EVs charging at FCS considering DERs against dynamic market prices. The proposed pricing mechanisms are designed to keep profit margin of FCS remains same relative to no DERs considering EVs users’ convenience, satisfaction and waiting time. Price-cum-convenience responsive models are proposed for price elasticity of demand and EV users’ satisfaction. The study reveals that both pricing mechanisms under DERs are equally promising as they produce more competitive price signals which are around 11 % lower, up to 61.81 % reduction in grid energy demand during overload periods, and up to 6 % increment in mean satisfaction of EV users while keeping the profit margin intact for FCS owners.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101943"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High penetration of electric cars in urban areas may lead to concentrated and uncoordinated heavy EV charging demand in residential areas of the distribution systems. This demand may coincide with the peak demand of the system and consequently poses issues pertaining to reliability, power quality, security, and the difficult operation of stressed distribution systems. Office Parking Lots (OPLs) equipped with adequate EV charging facilities have been seen as one of the tangible solutions to cope with such irresistible situations if found financially viable.
This work proposes two charging strategies, uncoordinated EV charging (UEVC) and coordinated EV charging (CEVC), for OPLs to minimize the impact of EV charging demand on local grids by placing suitable capacity of distributed energy resources (DERs) such as solar photovoltaic (SPVs) and battery energy storage systems (BESS). The proposed CEVC strategy schedules EVs considering mean generation from the grid-connected rooftop SPV system and the state-of-charge of BESS and aligns EV charging demand with SPV generation. Further, the proposed model is tested on an 83-bus real distribution system to investigate the impact of OPL integration on the system’s technical parameters, such as peak demand, total energy losses, peak power loss, and node voltage deviation. Moreover, the techno-economic benefits of both strategies are compared, and EV parking charges are calculated while considering the profit for OPL owners. Simulation results show that the proposed CEVC strategy reduces the OPL’s annual grid energy import, OPL’s annual peak demand, total system energy losses, and parking charge by 85 %, 65 %, 20 %, and 11 %, respectively, when compared with UEVC without DERs. Moreover, despite heavy investment in DERs’ placement at OPL, the proposed charging strategies preserve the benefit of the utility and OPL owners, yet offer more competitive EV parking charges.
{"title":"EV charging strategies for office parking lots to relieve local grids","authors":"Hitesh Kumar Verma, Nikhil Gupta, Praveen Kumar Agrawal, K.R. Niazi, Anil Swarnkar","doi":"10.1016/j.segan.2025.101942","DOIUrl":"10.1016/j.segan.2025.101942","url":null,"abstract":"<div><div>High penetration of electric cars in urban areas may lead to concentrated and uncoordinated heavy EV charging demand in residential areas of the distribution systems. This demand may coincide with the peak demand of the system and consequently poses issues pertaining to reliability, power quality, security, and the difficult operation of stressed distribution systems. Office Parking Lots (OPLs) equipped with adequate EV charging facilities have been seen as one of the tangible solutions to cope with such irresistible situations if found financially viable.</div><div>This work proposes two charging strategies, uncoordinated EV charging (UEVC) and coordinated EV charging (CEVC), for OPLs to minimize the impact of EV charging demand on local grids by placing suitable capacity of distributed energy resources (DERs) such as solar photovoltaic (SPVs) and battery energy storage systems (BESS). The proposed CEVC strategy schedules EVs considering mean generation from the grid-connected rooftop SPV system and the state-of-charge of BESS and aligns EV charging demand with SPV generation. Further, the proposed model is tested on an 83-bus real distribution system to investigate the impact of OPL integration on the system’s technical parameters, such as peak demand, total energy losses, peak power loss, and node voltage deviation. Moreover, the techno-economic benefits of both strategies are compared, and EV parking charges are calculated while considering the profit for OPL owners. Simulation results show that the proposed CEVC strategy reduces the OPL’s annual grid energy import, OPL’s annual peak demand, total system energy losses, and parking charge by 85 %, 65 %, 20 %, and 11 %, respectively, when compared with UEVC without DERs. Moreover, despite heavy investment in DERs’ placement at OPL, the proposed charging strategies preserve the benefit of the utility and OPL owners, yet offer more competitive EV parking charges.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101942"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-01Epub Date: 2025-08-26DOI: 10.1016/j.segan.2025.101947
Sushobhan Chatterjee, Sijia Geng
This paper studies oscillatory instability in grid-forming inverters through Hopf bifurcation analysis. An analytical expression for the parameter sensitivity of the stability margin is derived based on the normal vector to the bifurcation hypersurface. Through comprehensive analysis, we identify the most effective control parameters in counteracting the destabilizing effect due to parameter variations. In particular, the impacts of dynamic line modeling on the stability margin are investigated. It is observed that including line dynamics introduces a generally significant reduction in the stability margin across parameters. Additionally, dynamic line models introduce new bifurcations not present in the static model case. This suggests that adopting static line models may lead to overly optimistic stability assessment results.
{"title":"Effects of line dynamics on the stability margin to Hopf bifurcation in grid-forming inverters","authors":"Sushobhan Chatterjee, Sijia Geng","doi":"10.1016/j.segan.2025.101947","DOIUrl":"10.1016/j.segan.2025.101947","url":null,"abstract":"<div><div>This paper studies oscillatory instability in grid-forming inverters through Hopf bifurcation analysis. An analytical expression for the parameter sensitivity of the stability margin is derived based on the normal vector to the bifurcation hypersurface. Through comprehensive analysis, we identify the most effective control parameters in counteracting the destabilizing effect due to parameter variations. In particular, the impacts of dynamic line modeling on the stability margin are investigated. It is observed that including line dynamics introduces a generally significant reduction in the stability margin across parameters. Additionally, dynamic line models introduce new bifurcations not present in the static model case. This suggests that adopting static line models may lead to overly optimistic stability assessment results.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101947"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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