Pub Date : 2025-10-13DOI: 10.1016/j.segan.2025.101999
Ysaël Desage , François Bouffard , Benoit Boulet
NeuraFlux is an open-source, adaptive multi-agent reinforcement learning platform designed to optimize energy management in complex, dynamic environments. It addresses key challenges in coordinating distributed energy resources, including scalability limitations, difficulties in managing competing objectives, and lack of real-time adaptability. This paper presents two primary contributions: the theoretical foundations of NeuraFlux and its significance in modern power systems infrastructure and control, along with a novel training algorithm optimized for real-world deployment performance. Through three case studies—energy storage market arbitrage, heating, ventilation, and air conditioning (HVAC) system control, and electric vehicle grid integration—NeuraFlux’s effectiveness in managing intricate, multi-agent, and multi-objective optimization challenges is demonstrated. The modularity and scalability demonstrated in these examples, combined with the framework’s technical robustness for edge deployment, establish NeuraFlux as a powerful and practical tool for deploying advanced control systems in modern power and energy systems.
{"title":"NeuraFlux: A scalable and adaptive framework for autonomous data-driven multi-agent power optimization","authors":"Ysaël Desage , François Bouffard , Benoit Boulet","doi":"10.1016/j.segan.2025.101999","DOIUrl":"10.1016/j.segan.2025.101999","url":null,"abstract":"<div><div>NeuraFlux is an open-source, adaptive multi-agent reinforcement learning platform designed to optimize energy management in complex, dynamic environments. It addresses key challenges in coordinating distributed energy resources, including scalability limitations, difficulties in managing competing objectives, and lack of real-time adaptability. This paper presents two primary contributions: the theoretical foundations of NeuraFlux and its significance in modern power systems infrastructure and control, along with a novel training algorithm optimized for real-world deployment performance. Through three case studies—energy storage market arbitrage, heating, ventilation, and air conditioning (HVAC) system control, and electric vehicle grid integration—NeuraFlux’s effectiveness in managing intricate, multi-agent, and multi-objective optimization challenges is demonstrated. The modularity and scalability demonstrated in these examples, combined with the framework’s technical robustness for edge deployment, establish NeuraFlux as a powerful and practical tool for deploying advanced control systems in modern power and energy systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101999"},"PeriodicalIF":5.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323698","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 : 2025-10-11DOI: 10.1016/j.segan.2025.101997
Ziying Cai , Jingmin Yang , Yifeng Zheng , Wenjie Zhang , Liwei Yang
In smart grids, peer-to-peer trading enables small-scale electricity suppliers (SESs) and electricity consumers (ECs) to trade directly, leading to an improvement in energy efficiency and market flexibility. However, existing research mainly focuses on the interaction between the two parties, while the dynamic competition among SESs and the evolutionary behaviour of ECs have been largely ignored. In this paper, we propose a peer-to-peer electricity trading model based on two-layer game to study the complex interaction behaviour between SESs and ECs. First, we use evolutionary game theory to simulate the dynamic evolutionary behaviour of ECs, using both deterministic and stochastic models for analysis. Then, we use noncooperative game theory to study the competitive behaviour among SESs. We further prove the existence of evolutionary equilibrium and Nash equilibrium theoretically, and propose iterative algorithms based on incomplete information to make the game converge to equilibrium solutions. The experimental results verify that our method raises SES and EC utility relative to other methods and converges to the evolutionary equilibrium and Nash equilibrium.
{"title":"Dynamic trading strategy for peer-to-peer electricity markets based on two-layer game model","authors":"Ziying Cai , Jingmin Yang , Yifeng Zheng , Wenjie Zhang , Liwei Yang","doi":"10.1016/j.segan.2025.101997","DOIUrl":"10.1016/j.segan.2025.101997","url":null,"abstract":"<div><div>In smart grids, peer-to-peer trading enables small-scale electricity suppliers (SESs) and electricity consumers (ECs) to trade directly, leading to an improvement in energy efficiency and market flexibility. However, existing research mainly focuses on the interaction between the two parties, while the dynamic competition among SESs and the evolutionary behaviour of ECs have been largely ignored. In this paper, we propose a peer-to-peer electricity trading model based on two-layer game to study the complex interaction behaviour between SESs and ECs. First, we use evolutionary game theory to simulate the dynamic evolutionary behaviour of ECs, using both deterministic and stochastic models for analysis. Then, we use noncooperative game theory to study the competitive behaviour among SESs. We further prove the existence of evolutionary equilibrium and Nash equilibrium theoretically, and propose iterative algorithms based on incomplete information to make the game converge to equilibrium solutions. The experimental results verify that our method raises SES and EC utility relative to other methods and converges to the evolutionary equilibrium and Nash equilibrium.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101997"},"PeriodicalIF":5.6,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323700","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 : 2025-10-10DOI: 10.1016/j.segan.2025.101998
Yang Chen , Jeremiah Gbadegoye , Xudong Wang , Olufemi A. Omitaomu , Xueping Li
The rapid adoption of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), combined with global efforts to reduce carbon emissions, has accelerated the development of EV charging and hydrogen refueling stations. In response to this demand, this paper introduces the concept of Multi-Functional Charging Station (MFCS) that integrates power generation, EV charging, battery swapping, and hydrogen refueling. A comprehensive operational model is developed for the MFCS that couples electricity and hydrogen conversion and storage technologies to enhance infrastructure utilization and improve overall system efficiency. The model also considers multiple revenue streams, including participation in energy and ancillary markets. To validate the effectiveness of the proposed model and evaluate its performance, a series of numerical experiments are conducted with different charger numbers, different electricity purchase limits, and different charger allocations. Numerical results demonstrate that shared charger configurations can lead to 8.11 % improvement in operational profit by improving resource utilization and reducing the number of depleted batteries at the end of operations compared to allocated charger setups. By varying the number of chargers, sensitivity analysis identifies diminishing marginal returns beyond about 45 chargers, suggesting it as an optimal sizing point under current settings. The integration of electricity and hydrogen conversion is also explored under scenarios with limited external electricity purchases. These findings indicate that optimizing charger allocation and energy management can significantly enhance station productivity and profitability, ultimately supporting the broader adoption of electrified and hydrogen-based transportation solutions.
{"title":"Operational optimization for multi-functional charging station with electric and hydrogen-powered vehicles","authors":"Yang Chen , Jeremiah Gbadegoye , Xudong Wang , Olufemi A. Omitaomu , Xueping Li","doi":"10.1016/j.segan.2025.101998","DOIUrl":"10.1016/j.segan.2025.101998","url":null,"abstract":"<div><div>The rapid adoption of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), combined with global efforts to reduce carbon emissions, has accelerated the development of EV charging and hydrogen refueling stations. In response to this demand, this paper introduces the concept of Multi-Functional Charging Station (MFCS) that integrates power generation, EV charging, battery swapping, and hydrogen refueling. A comprehensive operational model is developed for the MFCS that couples electricity and hydrogen conversion and storage technologies to enhance infrastructure utilization and improve overall system efficiency. The model also considers multiple revenue streams, including participation in energy and ancillary markets. To validate the effectiveness of the proposed model and evaluate its performance, a series of numerical experiments are conducted with different charger numbers, different electricity purchase limits, and different charger allocations. Numerical results demonstrate that shared charger configurations can lead to 8.11 % improvement in operational profit by improving resource utilization and reducing the number of depleted batteries at the end of operations compared to allocated charger setups. By varying the number of chargers, sensitivity analysis identifies diminishing marginal returns beyond about 45 chargers, suggesting it as an optimal sizing point under current settings. The integration of electricity and hydrogen conversion is also explored under scenarios with limited external electricity purchases. These findings indicate that optimizing charger allocation and energy management can significantly enhance station productivity and profitability, ultimately supporting the broader adoption of electrified and hydrogen-based transportation solutions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101998"},"PeriodicalIF":5.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323697","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 : 2025-10-06DOI: 10.1016/j.segan.2025.101992
D.S. Green , M.M. Kratzer , A. Chapman , Y. Lu , A.Y. Klimenko
It is broadly expected that electricity price volatility will increase as solar and wind generation come to dominate electricity markets, but the mechanisms through which this trend occurs have yet to be clearly modelled. This makes it difficult for decision-makers to estimate future levels of price variations, or predict the form that such variations might take, including duration and frequency of prices at different levels of extremity, which are very important for planning purposes. This work begins by noticing that electricity prices have a strong visual similarity with measurements made in turbulent fluids. This motivates the subsequent investigation of whether the understanding of intermittency developed for turbulent flows may be useful when studying electricity prices. Linked to this understanding is a suite of tools for studying complex systems in general, and this paper applies these tools to electricity prices. Specifically, we introduce models of binary cascade, geometric Brownian motion, geometric Ornstein-Uhlenbeck process, an exponential model and an autoregressive (GARCH) model, and observe the features they share with electricity prices. We then analyse the price data and realisations drawn from each model using three methods: probability density function, multifractal analysis, and a simulated response of a flexible market participant. It is found that over the last decade, two major trends are observed in South Australia: electricity prices increasingly resemble intermittent models; and the model parameters evolve towards greater intermittency. These results suggest some similarity in the underlying mechanisms driving electricity prices in renewable-dominated electricity systems and the physical systems from which turbulence classically arises. Moreover, the techniques described in this paper allow for new measures of price volatility and the possibility of making predictions that more accurately capture the intermittent behaviour of prices.
{"title":"Emergence of intermittency in electricity prices and its modelling in the context of energy transition: A comparison to turbulent cascades","authors":"D.S. Green , M.M. Kratzer , A. Chapman , Y. Lu , A.Y. Klimenko","doi":"10.1016/j.segan.2025.101992","DOIUrl":"10.1016/j.segan.2025.101992","url":null,"abstract":"<div><div>It is broadly expected that electricity price volatility will increase as solar and wind generation come to dominate electricity markets, but the mechanisms through which this trend occurs have yet to be clearly modelled. This makes it difficult for decision-makers to estimate future levels of price variations, or predict the form that such variations might take, including duration and frequency of prices at different levels of extremity, which are very important for planning purposes. This work begins by noticing that electricity prices have a strong visual similarity with measurements made in turbulent fluids. This motivates the subsequent investigation of whether the understanding of <em>intermittency</em> developed for turbulent flows may be useful when studying electricity prices. Linked to this understanding is a suite of tools for studying complex systems in general, and this paper applies these tools to electricity prices. Specifically, we introduce models of binary cascade, geometric Brownian motion, geometric Ornstein-Uhlenbeck process, an exponential model and an autoregressive (GARCH) model, and observe the features they share with electricity prices. We then analyse the price data and realisations drawn from each model using three methods: probability density function, multifractal analysis, and a simulated response of a flexible market participant. It is found that over the last decade, two major trends are observed in South Australia: electricity prices increasingly resemble intermittent models; and the model parameters evolve towards greater intermittency. These results suggest some similarity in the underlying mechanisms driving electricity prices in renewable-dominated electricity systems and the physical systems from which turbulence classically arises. Moreover, the techniques described in this paper allow for new measures of price volatility and the possibility of making predictions that more accurately capture the intermittent behaviour of prices.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101992"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324435","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 : 2025-10-06DOI: 10.1016/j.segan.2025.101990
Fareed Ahmad , Tousif Khan Nizami , Atif Iqbal
The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.
电动汽车（ev）的迅速普及带来了生态和经济效益，但也给配电网络带来了挑战，包括能源损失增加、电压波动、可靠性降低和峰值需求增加。充电站的不协调部署可能会进一步恶化电网的性能。虽然现有的研究分别考察了EVCS选址或可再生能源整合，但很少有研究提供一个整体框架，同时考虑EVCS规划、可再生能源发电、基于储能的能源管理和不确定性下的用户行为。本研究的目的是建立一个综合规划模型，确定电动汽车储能系统的最佳位置和规模，以最小化能源损失、投资成本和驾驶员出行成本，同时减少峰值需求，最大限度地提高可再生能源利用率。为了实现这一目标，采用灰狼优化-粒子群优化（GWO-PSO）混合算法进行多目标优化，该算法有效地平衡了全局探索和局部开发。光伏（PV）系统被纳入选定的配电节点，能源管理策略（ems）被设计来协调储能系统（ESS）的运行。利用蒙特卡罗仿真（Monte Carlo Simulation， MCS）对光伏发电和电动汽车充电需求的不确定性进行了求解。该方法在IEEE 33总线配电系统上进行了24小时仿真验证。结果表明，将EMS与最佳位置的evcs集成，可将平均能量损失降低15%，将峰值功率需求降低20%。这些发现表明，所提出的方法为EVCS基础设施规划提供了一个强大的、具有成本效益的和可持续的途径。
{"title":"Electric vehicle charging infrastructure planning with integrated energy management and parking behavior analysis","authors":"Fareed Ahmad , Tousif Khan Nizami , Atif Iqbal","doi":"10.1016/j.segan.2025.101990","DOIUrl":"10.1016/j.segan.2025.101990","url":null,"abstract":"<div><div>The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101990"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266128","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 : 2025-10-01DOI: 10.1016/j.segan.2025.101994
S. Poorani , P. Josephin Shermila , R. Niruban , T. Maris Murugan
The widespread adoption of electric vehicles (EVs) is a pivotal step toward achieving sustainable transportation and energy systems. However, several technological and infrastructural challenges hinder their scalability and efficiency. Despite advancements, current EV technologies are constrained by battery energy density, charging rates, and thermal management, limiting vehicle range and performance. Manufacturing limitations, supply chain issues, and charging infrastructure prevent large-scale implementation. The increasing demand for EVs also poses challenges to power system stability, particularly with the integration of intermittent renewable energy sources. This paper proposes a Multi-Faceted Method (M-FM) to address these challenges by integrating next-generation solid-state batteries with 40 % higher energy density, advanced battery management systems for optimal performance, and AI-driven predictive maintenance. This study aims to develop and assess a scalable, AI-augmented EV infrastructure model leveraging solid-state battery technologies for enhanced grid integration and sustainability. The proposed solutions include modular battery designs, automated gigafactories, and circular economy strategies for battery recycling to enable scalability. A smart grid integration architecture with bidirectional charging, dynamic load balancing algorithms, and blockchain-enabled energy trading platforms is introduced to transform EVs into grid-stabilizing assets. Experimental results show that the solid-state battery design achieves 500 Wh/kg energy density and 99.8 % faster charging. Vehicle-to-grid (V2G) integration can potentially fulfill to 96.3 % of a city's frequency control needs. Economic analyses indicate that these innovations could reduce the overall cost of EV ownership by 28 % compared to technologies. The study also emphasizes the need for legislative interventions, standardized billing, tariff reforms, and public-private partnerships, to accelerate implementation.
{"title":"A multi-faceted strategy for scalable, efficient, and grid-integrated electric vehicle systems using solid-state batteries and AI technologies","authors":"S. Poorani , P. Josephin Shermila , R. Niruban , T. Maris Murugan","doi":"10.1016/j.segan.2025.101994","DOIUrl":"10.1016/j.segan.2025.101994","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) is a pivotal step toward achieving sustainable transportation and energy systems. However, several technological and infrastructural challenges hinder their scalability and efficiency. Despite advancements, current EV technologies are constrained by battery energy density, charging rates, and thermal management, limiting vehicle range and performance. Manufacturing limitations, supply chain issues, and charging infrastructure prevent large-scale implementation. The increasing demand for EVs also poses challenges to power system stability, particularly with the integration of intermittent renewable energy sources. This paper proposes a Multi-Faceted Method (M-FM) to address these challenges by integrating next-generation solid-state batteries with 40 % higher energy density, advanced battery management systems for optimal performance, and AI-driven predictive maintenance. This study aims to develop and assess a scalable, AI-augmented EV infrastructure model leveraging solid-state battery technologies for enhanced grid integration and sustainability. The proposed solutions include modular battery designs, automated gigafactories, and circular economy strategies for battery recycling to enable scalability. A smart grid integration architecture with bidirectional charging, dynamic load balancing algorithms, and blockchain-enabled energy trading platforms is introduced to transform EVs into grid-stabilizing assets. Experimental results show that the solid-state battery design achieves 500 Wh/kg energy density and 99.8 % faster charging. Vehicle-to-grid (V2G) integration can potentially fulfill to 96.3 % of a city's frequency control needs. Economic analyses indicate that these innovations could reduce the overall cost of EV ownership by 28 % compared to technologies. The study also emphasizes the need for legislative interventions, standardized billing, tariff reforms, and public-private partnerships, to accelerate implementation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101994"},"PeriodicalIF":5.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219718","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 : 2025-09-30DOI: 10.1016/j.segan.2025.101996
J. Vergara-Zambrano, Parth Brahmbhatt, Styliani Avraamidou
The transition to low-carbon energy systems is crucial for mitigating climate change. However, it remains challenging due to the intermittency of renewable energy sources and increasing energy demands. This study introduces a multi-scale optimization framework for the infrastructure planning of urban energy systems, considering the complex interplay between heating and electricity systems, and, unlike existing approaches, simulating a planning horizon of multiple years at an hourly resolution, without relying on representative-day approaches. It links short-term operational decisions with long-term sustainability goals, providing a realistic representation of energy system performance. It is applied to a case study considering the energy transition of a university campus, with the model solved at an hourly resolution over a 25-year horizon. The proposed framework includes weather data forecasting and preprocessing to generate hourly energy production profiles and reduce computational complexity. The results show that by 2030, 50 %–95 % of electricity can be supplied from low-carbon sources, achieving a 50 %–88 % reduction in annual CO2 emissions compared to 2025, though this requires high upfront investments, highlighting the trade-offs between emissions reduction and costs. Energy storage will be crucial for mitigating renewable intermittency, potentially accounting for 40 % of the system costs. The electrical grid decarbonization pathway strongly influences infrastructure requirements but is insufficient alone to achieve net-zero targets, as heating and cooling systems must also be decarbonized. Overall, the analysis highlights the importance of temporal granularity: hourly modeling captures peak loads, seasonal mismatches, and variability across timescales, enabling more accurate technology sizing and assessment of operational flexibility.
{"title":"A multi-scale optimization framework for energy transition planning in urban areas: Insights from a university campus case study","authors":"J. Vergara-Zambrano, Parth Brahmbhatt, Styliani Avraamidou","doi":"10.1016/j.segan.2025.101996","DOIUrl":"10.1016/j.segan.2025.101996","url":null,"abstract":"<div><div>The transition to low-carbon energy systems is crucial for mitigating climate change. However, it remains challenging due to the intermittency of renewable energy sources and increasing energy demands. This study introduces a multi-scale optimization framework for the infrastructure planning of urban energy systems, considering the complex interplay between heating and electricity systems, and, unlike existing approaches, simulating a planning horizon of multiple years at an hourly resolution, without relying on representative-day approaches. It links short-term operational decisions with long-term sustainability goals, providing a realistic representation of energy system performance. It is applied to a case study considering the energy transition of a university campus, with the model solved at an hourly resolution over a 25-year horizon. The proposed framework includes weather data forecasting and preprocessing to generate hourly energy production profiles and reduce computational complexity. The results show that by 2030, 50 %–95 % of electricity can be supplied from low-carbon sources, achieving a 50 %–88 % reduction in annual CO<sub>2</sub> emissions compared to 2025, though this requires high upfront investments, highlighting the trade-offs between emissions reduction and costs. Energy storage will be crucial for mitigating renewable intermittency, potentially accounting for 40 % of the system costs. The electrical grid decarbonization pathway strongly influences infrastructure requirements but is insufficient alone to achieve net-zero targets, as heating and cooling systems must also be decarbonized. Overall, the analysis highlights the importance of temporal granularity: hourly modeling captures peak loads, seasonal mismatches, and variability across timescales, enabling more accurate technology sizing and assessment of operational flexibility.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101996"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266125","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 : 2025-09-30DOI: 10.1016/j.segan.2025.101988
Kutikuppala Nareshkumar, Debapriya Das
Electric vehicles (EVs) offer a sustainable path for decarbonizing transportation, and solar rooftop parking lots (SRPLs) enable their integration with solar energy. The growing adoption of EVs poses challenges for power grid integration, including peak demand spikes, voltage instability, network congestion, and uncertain charging behaviour. Addressing these issues requires coordinated planning and operation that meet both transportation and distribution network goals. A multi-stage approach effectively handles multiple objectives. This study introduces strategic two-stage planning and operation of SRPLs in a coupled transportation (TN)-distribution network (DN). In the first stage, a sensitivity analysis is conducted to identify the ideal locations and sizes of SRPLs by integrating a novel EV user satisfaction cost index. The objectives in this stage focus on enhancing the operational performance of both the transportation and distribution networks. In the second stage, the identified locations and sizes are used to determine the optimal operation of SRPLs, taking into account seasonal variations in solar generation and load demand. The objectives aim to maximize SRPL operator profit while minimizing EV user payments, additional DN operator costs, and grid emissions. Fuzzy max-min composition is used to determine the optimal solution by simultaneously satisfying all objectives to the highest possible extent. The proposed technique, validated on real (28-node TN, 69-bus DN) and test (35-node TN, 85-bus DN) systems, effectively identifies SRPL locations, ratings, and operation strategies. Vehicle-to-grid mode of EVs at SRPLs increases profit by 26.19 %, reduces EV user costs by 6.55 %, and cuts grid emissions by 4.86 %.
{"title":"A novel planning and operation strategy of solar rooftop EV parking lots in a coupled transportation-distribution network considering uncertainties","authors":"Kutikuppala Nareshkumar, Debapriya Das","doi":"10.1016/j.segan.2025.101988","DOIUrl":"10.1016/j.segan.2025.101988","url":null,"abstract":"<div><div>Electric vehicles (EVs) offer a sustainable path for decarbonizing transportation, and solar rooftop parking lots (SRPLs) enable their integration with solar energy. The growing adoption of EVs poses challenges for power grid integration, including peak demand spikes, voltage instability, network congestion, and uncertain charging behaviour. Addressing these issues requires coordinated planning and operation that meet both transportation and distribution network goals. A multi-stage approach effectively handles multiple objectives. This study introduces strategic two-stage planning and operation of SRPLs in a coupled transportation (TN)-distribution network (DN). In the first stage, a sensitivity analysis is conducted to identify the ideal locations and sizes of SRPLs by integrating a novel EV user satisfaction cost index. The objectives in this stage focus on enhancing the operational performance of both the transportation and distribution networks. In the second stage, the identified locations and sizes are used to determine the optimal operation of SRPLs, taking into account seasonal variations in solar generation and load demand. The objectives aim to maximize SRPL operator profit while minimizing EV user payments, additional DN operator costs, and grid emissions. Fuzzy max-min composition is used to determine the optimal solution by simultaneously satisfying all objectives to the highest possible extent. The proposed technique, validated on real (28-node TN, 69-bus DN) and test (35-node TN, 85-bus DN) systems, effectively identifies SRPL locations, ratings, and operation strategies. Vehicle-to-grid mode of EVs at SRPLs increases profit by 26.19 %, reduces EV user costs by 6.55 %, and cuts grid emissions by 4.86 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101988"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266130","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 : 2025-09-29DOI: 10.1016/j.segan.2025.101991
Guangxue WANG , Hongchun SHU , Botao SHI , Haixin MA , Liuqing ZHU
When wind farms participate in primary frequency regulation (PFR) of power grids, most existing methods adopt single-machine multiplication approaches, making wind power frequency regulation struggle to meet practical requirements. To enable more accurate system frequency dynamic analysis and research, it is imperative to establish equivalent models for wind power frequency regulation and optimize wind turbine control strategies. From the perspective of "wind turbine clusters", this paper proposes a Principal Component Analysis (PCA) based clustering criteria selection method, employs an improved Kernel Fuzzy C-Means (Kernel-FCM) clustering algorithm to classify wind turbine clusters, and achieves dynamic aggregation equivalence for large-scale wind farms. Based on aggregation results, a wind-storage coordinated frequency regulation control strategy for full wind speed scenarios is developed: the Energy Storage Systems (ESSs) adopts adaptive virtual droop control; turbines implement pitch angle de-loading control in constant power zones and adaptive virtual inertia control in maximum power point tracking (MPPT) zones. A determination mechanism is established upon the conclusion of inertial support and the initiation of rotor speed recovery, accompanied by corresponding power compensation schemes. The three-machine, nine-node model with a wind-storage system was established using RT-LAB, validating the advantages of the proposed frequency regulation control strategy.
{"title":"Dynamic cluster and wind-storage collaborative frequency regulation control strategy for large scale wind farms","authors":"Guangxue WANG , Hongchun SHU , Botao SHI , Haixin MA , Liuqing ZHU","doi":"10.1016/j.segan.2025.101991","DOIUrl":"10.1016/j.segan.2025.101991","url":null,"abstract":"<div><div>When wind farms participate in primary frequency regulation (PFR) of power grids, most existing methods adopt single-machine multiplication approaches, making wind power frequency regulation struggle to meet practical requirements. To enable more accurate system frequency dynamic analysis and research, it is imperative to establish equivalent models for wind power frequency regulation and optimize wind turbine control strategies. From the perspective of \"wind turbine clusters\", this paper proposes a Principal Component Analysis (PCA) based clustering criteria selection method, employs an improved Kernel Fuzzy C-Means (Kernel-FCM) clustering algorithm to classify wind turbine clusters, and achieves dynamic aggregation equivalence for large-scale wind farms. Based on aggregation results, a wind-storage coordinated frequency regulation control strategy for full wind speed scenarios is developed: the Energy Storage Systems (ESSs) adopts adaptive virtual droop control; turbines implement pitch angle de-loading control in constant power zones and adaptive virtual inertia control in maximum power point tracking (MPPT) zones. A determination mechanism is established upon the conclusion of inertial support and the initiation of rotor speed recovery, accompanied by corresponding power compensation schemes. The three-machine, nine-node model with a wind-storage system was established using RT-LAB, validating the advantages of the proposed frequency regulation control strategy.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101991"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266126","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 : 2025-09-29DOI: 10.1016/j.segan.2025.101989
Ann Mary Toms, Xingpeng Li, Kaushik Rajashekara
The global energy landscape is undergoing a transformative shift towards renewable energy and advanced storage solutions, driven by the urgent need for sustainable and resilient power systems. Isolated offshore communities, such as islands and offshore platforms, which traditionally rely on mainland grids or diesel generators, stand to gain significantly from renewable energy integration. Promising offshore renewable technologies include wind turbines, wave and tidal energy converters, and floating photovoltaic systems, paired with a storage solution like battery energy storage systems. This paper introduces a renewable energy microgrid optimizer (REMO), a tool designed to identify the optimal sizes of renewable generation and storage resources for offshore microgrids. A key challenge in such models is accurately accounting for battery degradation costs. To address this, the REMO model integrates a deep neural network-based battery degradation (DNN-BD) module, which factors in variables like ambient temperature, charge/discharge rates, state of charge, depth of discharge and battery health. Simulations on six test regions demonstrate that the REMO-DNN-BD approach minimizes lifetime energy costs while maintaining high reliability and sustainability, making it a viable design solution for offshore microgrid systems.
{"title":"Optimal microgrid sizing of offshore renewable energy sources for offshore platforms and coastal communities","authors":"Ann Mary Toms, Xingpeng Li, Kaushik Rajashekara","doi":"10.1016/j.segan.2025.101989","DOIUrl":"10.1016/j.segan.2025.101989","url":null,"abstract":"<div><div>The global energy landscape is undergoing a transformative shift towards renewable energy and advanced storage solutions, driven by the urgent need for sustainable and resilient power systems. Isolated offshore communities, such as islands and offshore platforms, which traditionally rely on mainland grids or diesel generators, stand to gain significantly from renewable energy integration. Promising offshore renewable technologies include wind turbines, wave and tidal energy converters, and floating photovoltaic systems, paired with a storage solution like battery energy storage systems. This paper introduces a renewable energy microgrid optimizer (REMO), a tool designed to identify the optimal sizes of renewable generation and storage resources for offshore microgrids. A key challenge in such models is accurately accounting for battery degradation costs. To address this, the REMO model integrates a deep neural network-based battery degradation (DNN-BD) module, which factors in variables like ambient temperature, charge/discharge rates, state of charge, depth of discharge and battery health. Simulations on six test regions demonstrate that the REMO-DNN-BD approach minimizes lifetime energy costs while maintaining high reliability and sustainability, making it a viable design solution for offshore microgrid systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101989"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266123","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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