The variability and uncertainty of wind and solar generation create major challenges for power system dispatch. To reduce the regulation burden on coal-fired thermal power units (TPUs) and enhance renewable integration, this paper proposes a distributionally robust optimization (DRO) strategy for electricity–heat complementary systems that jointly coordinates TPUs and pumped-storage hydro units (PSUs). Detailed operational models for TPUs and PSUs are developed, together with a hybrid energy storage framework that integrates electrochemical batteries and concentrated solar power (CSP) with thermal tanks to support both electricity and heating demands. A two-stage min–max–min DRO model is formulated, with uncertainty captured by a dual-norm ambiguity set and solved using the column-and-constraint generation algorithm. Simulation results show that the proposed method improves flexibility and renewable utilization, reducing operating costs by 22.7 % and carbon emissions by 6.2 % compared with benchmark cases. Sensitivity analyses further confirm robustness under variations in key parameters, demonstrating the model’s engineering applicability.
{"title":"A distributionally robust optimization strategy for electric-thermal complementary systems considering joint peaking of thermal power units and pumped-storage hydroelectric units","authors":"Zhifan Zhang , Zhe Yin , Jiacuo Yixi , Yifan Zhang , Ruijin Zhu","doi":"10.1016/j.segan.2025.102015","DOIUrl":"10.1016/j.segan.2025.102015","url":null,"abstract":"<div><div>The variability and uncertainty of wind and solar generation create major challenges for power system dispatch. To reduce the regulation burden on coal-fired thermal power units (TPUs) and enhance renewable integration, this paper proposes a distributionally robust optimization (DRO) strategy for electricity–heat complementary systems that jointly coordinates TPUs and pumped-storage hydro units (PSUs). Detailed operational models for TPUs and PSUs are developed, together with a hybrid energy storage framework that integrates electrochemical batteries and concentrated solar power (CSP) with thermal tanks to support both electricity and heating demands. A two-stage min–max–min DRO model is formulated, with uncertainty captured by a dual-norm ambiguity set and solved using the column-and-constraint generation algorithm. Simulation results show that the proposed method improves flexibility and renewable utilization, reducing operating costs by 22.7 % and carbon emissions by 6.2 % compared with benchmark cases. Sensitivity analyses further confirm robustness under variations in key parameters, demonstrating the model’s engineering applicability.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102015"},"PeriodicalIF":5.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362243","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 : 2025-10-17DOI: 10.1016/j.segan.2025.102013
Dawn Virginillo , Asja Derviškadić , Marc Hohmann
Recent trends in market liberalization and increasing congestion in the European interconnected grid have motivated studies of parameters provided to power markets, especially in the intraday and balancing timeframes. In this paper, we present a method using single-box inner polytope approximation to co-optimize the Available Transfer Capacities (ATCs) between multiple bidding zones. The feasible region is bounded by linear flow constraints consisting of AC PTDFs, computed for N1 contingency cases. Thanks to its formulation as a linear programming problem, the method provides efficient capacity computation, well-suited for applications close to real-time. The method is validated using the IEEE 39-Bus model and the behaviour of the algorithm is demonstrated using real case studies on the Swiss transmission system. Results demonstrate the formulation’s computational efficiency and enable analysis of the linearization accuracy. The proposed method is implemented in a near-real time decision support tool used to compute N1 secure ATCs, which is in operation in the control room of Swissgrid, the Swiss Transmission System Operator.
{"title":"Fast power market cross-zonal capacity co-optimization using inner approximations: Method, validation, and application","authors":"Dawn Virginillo , Asja Derviškadić , Marc Hohmann","doi":"10.1016/j.segan.2025.102013","DOIUrl":"10.1016/j.segan.2025.102013","url":null,"abstract":"<div><div>Recent trends in market liberalization and increasing congestion in the European interconnected grid have motivated studies of parameters provided to power markets, especially in the intraday and balancing timeframes. In this paper, we present a method using single-box inner polytope approximation to co-optimize the Available Transfer Capacities (ATCs) between multiple bidding zones. The feasible region is bounded by linear flow constraints consisting of AC PTDFs, computed for N<span><math><mo>−</mo></math></span>1 contingency cases. Thanks to its formulation as a linear programming problem, the method provides efficient capacity computation, well-suited for applications close to real-time. The method is validated using the IEEE 39-Bus model and the behaviour of the algorithm is demonstrated using real case studies on the Swiss transmission system. Results demonstrate the formulation’s computational efficiency and enable analysis of the linearization accuracy. The proposed method is implemented in a near-real time decision support tool used to compute N<span><math><mo>−</mo></math></span>1 secure ATCs, which is in operation in the control room of Swissgrid, the Swiss Transmission System Operator.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102013"},"PeriodicalIF":5.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362245","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}
This study introduces a novel Bilevel Optimization (BLO) model for optimizing Time-and-Level-of-Use (TLOU) electricity pricing in smart grids. The model overcomes limitations of prior studies by enabling dynamic tier adjustments and incorporating Levelized Cost of Electricity (LCOE), market competition, and consumer behavioral responses under spatio-temporal coupling constraints. Two methods simplify the BLO into a single-layer problem: the dual theory approach stands out for its streamlined linearization and reduced computational complexity, with mathematical proofs confirming transformation equivalence. Simulations across four real-world scenarios validate the model’s effectiveness in enhancing Load Shifting Ratio (LSR) and reducing Supply Flattening Ratio (SFR), while balancing supplier profits and consumer costs. Key findings reveal -tiered (low/medium/high) pricing yields $900 annualized benefits per 50-consumer cohort compared to -tiered (low/high) pricing. This work advances precision demand management for high-renewable grids, with future extensions to 500+ dynamic profiles and stochastic intermittency modeling.
{"title":"Bilevel optimization model for optimal time-and-level-of-use electricity pricing in smart grids","authors":"Huihui Huang , Chaoyang Zheng , Bowen Xu , Qiang Wei , Ruilong Deng , Yangyang Geng","doi":"10.1016/j.segan.2025.102005","DOIUrl":"10.1016/j.segan.2025.102005","url":null,"abstract":"<div><div>This study introduces a novel Bilevel Optimization (BLO) model for optimizing Time-and-Level-of-Use (TLOU) electricity pricing in smart grids. The model overcomes limitations of prior studies by enabling dynamic tier adjustments and incorporating Levelized Cost of Electricity (LCOE), market competition, and consumer behavioral responses under spatio-temporal coupling constraints. Two methods simplify the BLO into a single-layer problem: the dual theory approach stands out for its streamlined linearization and reduced computational complexity, with mathematical proofs confirming transformation equivalence. Simulations across four real-world scenarios validate the model’s effectiveness in enhancing Load Shifting Ratio (LSR) and reducing Supply Flattening Ratio (SFR), while balancing supplier profits and consumer costs. Key findings reveal <span><math><msub><mi>L</mi><mn>3</mn></msub></math></span>-tiered (low/medium/high) pricing yields $900 annualized benefits per 50-consumer cohort compared to <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span>-tiered (low/high) pricing. This work advances precision demand management for high-renewable grids, with future extensions to 500+ dynamic profiles and stochastic intermittency modeling.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102005"},"PeriodicalIF":5.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361531","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 : 2025-10-16DOI: 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":"2025-10-16","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 : 2025-10-16DOI: 10.1016/j.segan.2025.102011
Hejun Yang , Yangxu Yue , Yuan Gao , Yue Liu , Dabo Zhang , Yuming Shen
The penetration rate of distributed generation (DG) has been increasing year by year, exacerbating the volatility and uncertainty of the distribution system. In order to improve the ability of anti-interference of distribution system and evaluate its operation risk, this paper proposes a two-stage operation reliability evaluation method for distribution system considering dynamic scheduling of flexible resources. The scientific aim of the work is to improve the ability of the distribution system to anti interference and evaluate the operation risks of the system. This study extends the existing research on scheduling of flexible resources and operation reliability evaluation method. Two stages of causing power loss load are presented to describe flexible resource’s power supply and power-ramping balance process (i.e., load shedding before the fault because of the insufficient power-ramping and unrecovered power loss load after the fault). Through dynamic scheduling of flexible resources in two stages, the operation reliability of distribution system is optimized. Firstly, For responding the output fluctuation and power prediction error of distributed sources and considering the ramping ability and output constraints of flexible resources, an optimization scheduling model for flexible resources is established to dynamically adjust the output of flexible resources in the first stage; Secondly, based on the output data of flexible resources, dynamic scheduling of flexible resources in the second stage after the system fault is carried out, and the fault recovery strategy is formulated for collaborative distributed sources and tie lines. An operation reliability evaluation method for distribution system is proposed based on the two-stage scheduling strategy to quantify the operation risk of the system; Finally, the second-order cone method was used to transform the model into a mixed integer second-order cone programming problem, which can be solved directly by using the solver. The effectiveness of the model was verified using the improved IEEE 33 distribution system, and under the condition of designed cases, the system average interruption duration index is increased by 31.89 % and the average energy not supplied is increased by 24.34 % compared to traditional evaluation methods.
{"title":"A two-stage operation reliability evaluation method of distribution system considering dynamic scheduling of flexible resources","authors":"Hejun Yang , Yangxu Yue , Yuan Gao , Yue Liu , Dabo Zhang , Yuming Shen","doi":"10.1016/j.segan.2025.102011","DOIUrl":"10.1016/j.segan.2025.102011","url":null,"abstract":"<div><div>The penetration rate of distributed generation (DG) has been increasing year by year, exacerbating the volatility and uncertainty of the distribution system. In order to improve the ability of anti-interference of distribution system and evaluate its operation risk, this paper proposes a two-stage operation reliability evaluation method for distribution system considering dynamic scheduling of flexible resources. The scientific aim of the work is to improve the ability of the distribution system to anti interference and evaluate the operation risks of the system. This study extends the existing research on scheduling of flexible resources and operation reliability evaluation method. Two stages of causing power loss load are presented to describe flexible resource’s power supply and power-ramping balance process (i.e., load shedding before the fault because of the insufficient power-ramping and unrecovered power loss load after the fault). Through dynamic scheduling of flexible resources in two stages, the operation reliability of distribution system is optimized. Firstly, For responding the output fluctuation and power prediction error of distributed sources and considering the ramping ability and output constraints of flexible resources, an optimization scheduling model for flexible resources is established to dynamically adjust the output of flexible resources in the first stage; Secondly, based on the output data of flexible resources, dynamic scheduling of flexible resources in the second stage after the system fault is carried out, and the fault recovery strategy is formulated for collaborative distributed sources and tie lines. An operation reliability evaluation method for distribution system is proposed based on the two-stage scheduling strategy to quantify the operation risk of the system; Finally, the second-order cone method was used to transform the model into a mixed integer second-order cone programming problem, which can be solved directly by using the solver. The effectiveness of the model was verified using the improved IEEE 33 distribution system, and under the condition of designed cases, the system average interruption duration index is increased by 31.89 % and the average energy not supplied is increased by 24.34 % compared to traditional evaluation methods.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102011"},"PeriodicalIF":5.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362239","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 : 2025-10-16DOI: 10.1016/j.segan.2025.102004
Marcos Tostado-Véliz , Pablo Horrillo-Quintero , Pablo García-Triviño , Luis M. Fernández-Ramírez , Francisco Jurado
Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange energy in a peer-to-peer fashion, improving the overall efficiency and economy of the system. This paper proposes a novel methodology for optimal expansion planning of microgrid clusters, explicitly considering resource sharing. The model preserves the privacy of each microgrid by exchanging only boundary information. A three-level formulation is presented, incorporating uncertainties in renewable generation and demand through polyhedral uncertainty sets, whose bounds are determined using a novel clustering strategy. The resulting model is solved with a tailored algorithm based on robust optimization and a column-and-constraint generation scheme. The methodology is tested on a three-microgrid cluster, demonstrating its ability to manage uncertainty robustly and adapt to different levels of risk and budget constraints. In the case study, increasing robustness leads to higher costs (+31 %), lower renewable generation (-13 %), and increased unserved energy (+60 %). Finally, sensitivity analyses on fuel costs and the number of microgrids show that the proposed approach scales well with system size.
{"title":"Optimal expansion planning of microgrids clusters: A robust collaborative approach","authors":"Marcos Tostado-Véliz , Pablo Horrillo-Quintero , Pablo García-Triviño , Luis M. Fernández-Ramírez , Francisco Jurado","doi":"10.1016/j.segan.2025.102004","DOIUrl":"10.1016/j.segan.2025.102004","url":null,"abstract":"<div><div>Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange energy in a peer-to-peer fashion, improving the overall efficiency and economy of the system. This paper proposes a novel methodology for optimal expansion planning of microgrid clusters, explicitly considering resource sharing. The model preserves the privacy of each microgrid by exchanging only boundary information. A three-level formulation is presented, incorporating uncertainties in renewable generation and demand through polyhedral uncertainty sets, whose bounds are determined using a novel clustering strategy. The resulting model is solved with a tailored algorithm based on robust optimization and a column-and-constraint generation scheme. The methodology is tested on a three-microgrid cluster, demonstrating its ability to manage uncertainty robustly and adapt to different levels of risk and budget constraints. In the case study, increasing robustness leads to higher costs (+31 %), lower renewable generation (-13 %), and increased unserved energy (+60 %). Finally, sensitivity analyses on fuel costs and the number of microgrids show that the proposed approach scales well with system size.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102004"},"PeriodicalIF":5.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362240","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 : 2025-10-16DOI: 10.1016/j.segan.2025.102002
Vandana Kumari, Sanjib Ganguly
In recent years, the frequency and intensity of extreme events have increased significantly across the globe. Power distribution networks (PDNs) and transportation networks (TNs) are highly vulnerable to extreme events, leading to widespread disruption of essential services in daily life. The majority of existing research has examined the resilience of PDN and TN separately, without considering their interdependence. Therefore, to address post-disaster restoration challenges, this study develops an integrated optimization model that couples the PDN with the TN. The proposed model simultaneously optimizes the routing and scheduling of mobile emergency generators (MEGs) to supply power to islanded sections of the PDN, the deployment of repair crews to repair damaged distribution lines, and the allocation of repair crews to clear the blocked roads in the TN. Furthermore, dynamic network reconfiguration within the PDN is incorporated to speed up the overall load restoration process. The proposed approach is formulated as a mixed integer linear programming model and validated using a 33-bus test system coupled with a 12-node transportation network and a modified IEEE 123 bus system coupled with a 24 node Sioux-Falls transportation network. To demonstrate its effectiveness, seven case studies are conducted. Among them, the proposed method achieves the lowest level of energy not supplied, highlighting its effectiveness in enhancing system resilience.
{"title":"Mixed integer optimization model for resilience enhancement of power distribution networks coupled with transportation networks","authors":"Vandana Kumari, Sanjib Ganguly","doi":"10.1016/j.segan.2025.102002","DOIUrl":"10.1016/j.segan.2025.102002","url":null,"abstract":"<div><div>In recent years, the frequency and intensity of extreme events have increased significantly across the globe. Power distribution networks (PDNs) and transportation networks (TNs) are highly vulnerable to extreme events, leading to widespread disruption of essential services in daily life. The majority of existing research has examined the resilience of PDN and TN separately, without considering their interdependence. Therefore, to address post-disaster restoration challenges, this study develops an integrated optimization model that couples the PDN with the TN. The proposed model simultaneously optimizes the routing and scheduling of mobile emergency generators (MEGs) to supply power to islanded sections of the PDN, the deployment of repair crews to repair damaged distribution lines, and the allocation of repair crews to clear the blocked roads in the TN. Furthermore, dynamic network reconfiguration within the PDN is incorporated to speed up the overall load restoration process. The proposed approach is formulated as a mixed integer linear programming model and validated using a 33-bus test system coupled with a 12-node transportation network and a modified IEEE 123 bus system coupled with a 24 node Sioux-Falls transportation network. To demonstrate its effectiveness, seven case studies are conducted. Among them, the proposed method achieves the lowest level of energy not supplied, highlighting its effectiveness in enhancing system resilience.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102002"},"PeriodicalIF":5.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362241","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 : 2025-10-16DOI: 10.1016/j.segan.2025.102014
Emir Nukic , Victor Levi , Dragan Cetenovic , Nikola Vojnovic
This paper proposes a probabilistic model for the flexibility assessment of shared charging stations for electric vehicles. Flexibility is modelled and evaluated in terms of the potential to reduce demand during the specified flexibility service window. Model is developed within the probabilistic framework to ensure that the randomness in modelled quantities is addressed. Main factors, which affect demand and available flexibility of charging stations, are identified and modelled in terms of the usage patterns of shared chargers, EV charging characteristics and customers’ charging preferences. Based on the proposed links between input, internal and output quantities, probability distributions of SoC value while charging, temporary charging duration at specified time, as well as the maximum aggregate charging power are calculated and presented. Finally, limits of available flexibility [kW] are quantified from the developed model for distinctive combinations of charging power rating, chargers’ location and flexibility service window. Flexible capacity is modelled and evaluated in line with the standardised active power services in the markets. Developed model is expected to be of particular interest in the distribution network planning.
{"title":"Probabilistic flexibility assessment of shared charging stations for electric vehicles","authors":"Emir Nukic , Victor Levi , Dragan Cetenovic , Nikola Vojnovic","doi":"10.1016/j.segan.2025.102014","DOIUrl":"10.1016/j.segan.2025.102014","url":null,"abstract":"<div><div>This paper proposes a probabilistic model for the flexibility assessment of shared charging stations for electric vehicles. Flexibility is modelled and evaluated in terms of the potential to reduce demand during the specified flexibility service window. Model is developed within the probabilistic framework to ensure that the randomness in modelled quantities is addressed. Main factors, which affect demand and available flexibility of charging stations, are identified and modelled in terms of the usage patterns of shared chargers, EV charging characteristics and customers’ charging preferences. Based on the proposed links between input, internal and output quantities, probability distributions of SoC value while charging, temporary charging duration at specified time, as well as the maximum aggregate charging power are calculated and presented. Finally, limits of available flexibility [kW] are quantified from the developed model for distinctive combinations of charging power rating, chargers’ location and flexibility service window. Flexible capacity is modelled and evaluated in line with the standardised active power services in the markets. Developed model is expected to be of particular interest in the distribution network planning.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102014"},"PeriodicalIF":5.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323701","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 : 2025-10-16DOI: 10.1016/j.segan.2025.102001
Uygar Durgunay , Nader Javani
To understand the impact of operational age on availability, the statistical tools are used in the current study to analyze a dataset, leading to the parametrization of availability distribution across different turbine operational ages. The availability distribution, a bimodal distribution, is modeled with a mixed distribution. The study demonstrates that the best-fitting mixed distribution is a combination of two Beta distributions. Fitting the data into a statistical distribution enables parametric investigation of various causal factors, such as long-term and short-term contributors to unavailability. The research examines the contributions of long-term downtime and short-term downtime to overall unavailability. The mixed distribution is formed by combining two Beta distributions—one skewed toward higher availability values to represent short-term downtime, and another mirrored toward lower values to capture short-term downtime effects. This mirroring is achieved through the transformation y = 1 - x, which flips the shape of the Beta distribution around the midpoint (0.5), allowing it to peak near 0 while preserving its statistical properties. Together, these two components form a flexible yet stable structure that captures the distinct influences of both short- and long-duration downtime events across all operational years. To understand the impact of operational age on availability, the article uses statistical tools to analyze a large-scale dataset, resulting in the parametrization of availability distribution across different turbine operational ages. The availability distribution, a bimodal distribution, is modeled with a mixed beta distribution. The study demonstrates that the best-fitting mixed distribution is a combination of two Beta distributions. Fitting the data into a statistical distribution enables parametric investigation of various causal factors, such as long-term and short-term contributors to unavailability. The research examines the contributions of long-term downtime and short-term downtime to overall unavailability. The mixed distribution is formed by combining two Beta distributions—one skewed toward higher availability values to represent short-term downtime, and another mirrored toward lower values to capture long-term downtime effects. This mirroring is achieved through the transformation y = 1 - x, which flips the shape of the Beta distribution around the midpoint (0.5), allowing it to peak near 0 while preserving its statistical properties. Together, these two components form a flexible yet stable structure that captures the distinct influences of both short- and long-duration downtime events across all operational years.
{"title":"Onshore wind turbine availability: A statistical assessment","authors":"Uygar Durgunay , Nader Javani","doi":"10.1016/j.segan.2025.102001","DOIUrl":"10.1016/j.segan.2025.102001","url":null,"abstract":"<div><div>To understand the impact of operational age on availability, the statistical tools are used in the current study to analyze a dataset, leading to the parametrization of availability distribution across different turbine operational ages. The availability distribution, a bimodal distribution, is modeled with a mixed distribution. The study demonstrates that the best-fitting mixed distribution is a combination of two Beta distributions. Fitting the data into a statistical distribution enables parametric investigation of various causal factors, such as long-term and short-term contributors to unavailability. The research examines the contributions of long-term downtime and short-term downtime to overall unavailability. The mixed distribution is formed by combining two Beta distributions—one skewed toward higher availability values to represent short-term downtime, and another mirrored toward lower values to capture short-term downtime effects. This mirroring is achieved through the transformation y = 1 - x, which flips the shape of the Beta distribution around the midpoint (0.5), allowing it to peak near 0 while preserving its statistical properties. Together, these two components form a flexible yet stable structure that captures the distinct influences of both short- and long-duration downtime events across all operational years. To understand the impact of operational age on availability, the article uses statistical tools to analyze a large-scale dataset, <strong>resulting in</strong> the parametrization of availability distribution across different turbine operational ages. The availability distribution, a bimodal distribution, is modeled with a mixed <strong>beta</strong> distribution. The study demonstrates that the best-fitting mixed distribution is a combination of two Beta distributions. Fitting the data into a statistical distribution enables parametric investigation of various causal factors, such as long-term and short-term contributors to unavailability. The research examines the contributions of long-term downtime and short-term downtime to overall unavailability. The mixed distribution is formed by combining two Beta distributions—one skewed toward higher availability values to represent short-term downtime, and another mirrored toward lower values to capture <strong>long-term</strong> downtime effects. This mirroring is achieved through the transformation y = 1 - x, which flips the shape of the Beta distribution around the midpoint (0.5), allowing it to peak near 0 while preserving its statistical properties. Together, these two components form a flexible yet stable structure that captures the distinct influences of both short- and long-duration downtime events across all operational years.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102001"},"PeriodicalIF":5.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362244","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 : 2025-10-14DOI: 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.
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