Pub Date : 2025-05-28DOI: 10.1016/j.ref.2025.100724
Mohammad Zain Ul Abideen, Abdulrahman Alassi, Santiago Bañales
The integration of Battery Energy Storage Systems (BESS) into grid infrastructure is revolutionizing modern electricity markets. This paper presents a novel, comprehensive methodology for optimizing the profitability and operational efficiency of grid-connected BESS systems. By integrating cyclic and calendar degradation models, service stacking strategies, and bid acceptance uncertainties, the proposed approach offers a robust framework for long-term economic evaluation. A detailed case study demonstrates the methodology’s effectiveness by assessing a 60 MW BESS’s ability to optimize revenue streams in the UK market through energy arbitrage, frequency regulation, voltage regulation, and capacity market participation. The findings highlight the potential of advanced optimization techniques and data-driven sizing approaches to enhance BESS deployment sustainability and profitability. Policy recommendations are provided to support informed decision-making in the energy sector.
{"title":"Optimizing grid-connected battery energy storage systems: a comprehensive evaluation methodology","authors":"Mohammad Zain Ul Abideen, Abdulrahman Alassi, Santiago Bañales","doi":"10.1016/j.ref.2025.100724","DOIUrl":"10.1016/j.ref.2025.100724","url":null,"abstract":"<div><div>The integration of Battery Energy Storage Systems (BESS) into grid infrastructure is revolutionizing modern electricity markets. This paper presents a novel, comprehensive methodology for optimizing the profitability and operational efficiency of grid-connected BESS systems. By integrating cyclic and calendar degradation models, service stacking strategies, and bid acceptance uncertainties, the proposed approach offers a robust framework for long-term economic evaluation. A detailed case study demonstrates the methodology’s effectiveness by assessing a 60 MW BESS’s ability to optimize revenue streams in the UK market through energy arbitrage, frequency regulation, voltage regulation, and capacity market participation. The findings highlight the potential of advanced optimization techniques and data-driven sizing approaches to enhance BESS deployment sustainability and profitability. Policy recommendations are provided to support informed decision-making in the energy sector.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100724"},"PeriodicalIF":4.2,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-24DOI: 10.1016/j.ref.2025.100718
Leonardo Bitencourt , Walquiria N. Silva , Bruno H. Dias , Tiago P. Abud , Bruno Borba , Pedro Peters
The increasing adoption of electric vehicles (EVs) emphasizes the critical need to assess their integration into the electricity grid for a sustainable energy transition. Existing literature lacks comprehensive vehicle-to-grid (V2G) impact analyses and methodologies for long-term integration, particularly in developing countries. Moreover, the absence of optimized short-term operational models for EV integration poses challenges in grid management. To bridge these gaps, this research proposes a socio-economic model to estimate EV sales based on the Bass diffusion model and macroeconomic regressions. Additionally, it integrates electricity system expansion planning using the OSeMOSYS tool with a short-term operational model based on unit commitment. In this context, this work endeavors to develop a methodology for estimating the impact of V2G technology, considering both the deployment and utilization of EVs in a Brazilian case study. Applying traditional methodologies that do not consider operational system models can lead to potential future load shedding. It may accentuate disparities between long-term and short-term outcomes, especially with EV and V2G integration. The proposed methodology corrected the overestimation of the energy injection potential of EVs by the traditional model, indicating the need to consider both the expansion and the operation of the electricity system when planning the integration of EVs.
{"title":"Comprehensive methodology for assessing the impact of vehicle-to-grid integration in power system expansion planning","authors":"Leonardo Bitencourt , Walquiria N. Silva , Bruno H. Dias , Tiago P. Abud , Bruno Borba , Pedro Peters","doi":"10.1016/j.ref.2025.100718","DOIUrl":"10.1016/j.ref.2025.100718","url":null,"abstract":"<div><div>The increasing adoption of electric vehicles (EVs) emphasizes the critical need to assess their integration into the electricity grid for a sustainable energy transition. Existing literature lacks comprehensive vehicle-to-grid (V2G) impact analyses and methodologies for long-term integration, particularly in developing countries. Moreover, the absence of optimized short-term operational models for EV integration poses challenges in grid management. To bridge these gaps, this research proposes a socio-economic model to estimate EV sales based on the Bass diffusion model and macroeconomic regressions. Additionally, it integrates electricity system expansion planning using the OSeMOSYS tool with a short-term operational model based on unit commitment. In this context, this work endeavors to develop a methodology for estimating the impact of V2G technology, considering both the deployment and utilization of EVs in a Brazilian case study. Applying traditional methodologies that do not consider operational system models can lead to potential future load shedding. It may accentuate disparities between long-term and short-term outcomes, especially with EV and V2G integration. The proposed methodology corrected the overestimation of the energy injection potential of EVs by the traditional model, indicating the need to consider both the expansion and the operation of the electricity system when planning the integration of EVs.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100718"},"PeriodicalIF":4.2,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-21DOI: 10.1016/j.ref.2025.100722
Gany Gunawan
Indonesia’s power sector is the country’s largest source of energy-related carbon emissions, with coal-based generation rising to 66% by 2020 despite national and international decarbonization targets. The Just Energy Transition Partnership (JETP) outlines an ambitious vision to reduce emissions and scale renewables, but achieving these goals requires flexible and coordinated grid planning, especially in systems with high variable renewable energy (VRE) penetration.
This study evaluates the role of energy storage systems (ESS) in supporting decarbonization in the Java-Bali power grid using a mixed-integer quadratic programming (MIQP) unit commitment model. The framework simulates hourly dispatch and regulation reserve across Moderate and Deep Decarbonization pathways from 2025 to 2050, incorporating carbon taxes, curtailment penalties, ESS operational constraints, and seasonal VRE variability.
Results show that ESS reduces curtailment by up to 20.1 TWh (Moderate) and 26.5 TWh (Deep) in 2050, with corresponding system cost savings of USD 2.14–2.22 billion under base VRE conditions. Emission reductions reach 1.9–3.2 MtCO2, however rebound due to fossil-based charging under aggressive ESS deployment scenarios can raise emissions by up to 1.25 MtCO2, highlighting the importance of strategic dispatch.
These findings confirm ESS as a critical enabler of renewable integration and cost reduction but also emphasize the need for emissions-informed dispatch and integrated planning. The analysis provides a quantitative foundation to support the JETP’s implementation and highlights policy levers needed to align ESS deployment with national decarbonization goals.
{"title":"Decarbonizing Indonesia’s power system: exploring the potential of energy storage systems for a sustainable energy transition","authors":"Gany Gunawan","doi":"10.1016/j.ref.2025.100722","DOIUrl":"10.1016/j.ref.2025.100722","url":null,"abstract":"<div><div>Indonesia’s power sector is the country’s largest source of energy-related carbon emissions, with coal-based generation rising to 66% by 2020 despite national and international decarbonization targets. The Just Energy Transition Partnership (JETP) outlines an ambitious vision to reduce emissions and scale renewables, but achieving these goals requires flexible and coordinated grid planning, especially in systems with high variable renewable energy (VRE) penetration.</div><div>This study evaluates the role of energy storage systems (ESS) in supporting decarbonization in the Java-Bali power grid using a mixed-integer quadratic programming (MIQP) unit commitment model. The framework simulates hourly dispatch and regulation reserve across Moderate and Deep Decarbonization pathways from 2025 to 2050, incorporating carbon taxes, curtailment penalties, ESS operational constraints, and seasonal VRE variability.</div><div>Results show that ESS reduces curtailment by up to 20.1 TWh (Moderate) and 26.5 TWh (Deep) in 2050, with corresponding system cost savings of USD 2.14–2.22 billion under base VRE conditions. Emission reductions reach 1.9–3.2 MtCO<sub>2</sub>, however rebound due to fossil-based charging under aggressive ESS deployment scenarios can raise emissions by up to 1.25 MtCO<sub>2</sub>, highlighting the importance of strategic dispatch.</div><div>These findings confirm ESS as a critical enabler of renewable integration and cost reduction but also emphasize the need for emissions-informed dispatch and integrated planning. The analysis provides a quantitative foundation to support the JETP’s implementation and highlights policy levers needed to align ESS deployment with national decarbonization goals.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100722"},"PeriodicalIF":4.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-15DOI: 10.1016/j.ref.2025.100719
Abhishek Kumar Singh, Ashwani Kumar
The rising popularity of Electric vehicles (EV) has resulted in a substantial increase in the amount of charging stations, which extensively affects the electrical grid, causing problems like power quality degradation, voltage fluctuations and higher losses. This paper proposes the novel application of Local Randomized Neural Networks (LRNN) for optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand. The main aim of the proposed work is to reduce both active and reactive power loss and maximize reliability. The LRNN method predicts the optimal location for the fast charging station. The proposed methods performance is excluded in the MATLAB working platform and compared with several existing techniques, with Genetic Algorithm (GA), Sea Horse Optimization (SHO) and Particle Swarm Optimization (PSO).The proposed technique demonstrates superior performance by significantly reducing power losses across all buses in the system. Compared to conventional optimization techniques, the LRNN achieves the lowest computational complexity at 1.82%, and the fastest convergence speed in just 25 iterations. In terms of execution time, it completes in 0.34 s, faster than the Genetic Algorithm at 0.44 s, Sea Horse Optimization at 0.59 s, and Particle Swarm Optimization at 0.65 s. While its efficiency is 98% it offers an excellent balance between computational speed, accuracy, and loss minimization. These results highlight its potential as a highly effective solution for modern power systems integrating renewable sources and electric vehicles.
{"title":"Optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand using local randomized neural networks","authors":"Abhishek Kumar Singh, Ashwani Kumar","doi":"10.1016/j.ref.2025.100719","DOIUrl":"10.1016/j.ref.2025.100719","url":null,"abstract":"<div><div>The rising popularity of Electric vehicles (EV) has resulted in a substantial increase in the amount of charging stations, which extensively affects the electrical grid, causing problems like power quality degradation, voltage fluctuations and higher losses. This paper proposes the novel application of Local Randomized Neural Networks (LRNN) for optimal deployment of reactive power in a renewable energy sources integrated system with EVs demand. The main aim of the proposed work is to reduce both active and reactive power loss and maximize reliability. The LRNN method predicts the optimal location for the fast charging station. The proposed methods performance is excluded in the MATLAB working platform and compared with several existing techniques, with Genetic Algorithm (GA), Sea Horse Optimization (SHO) and Particle Swarm Optimization (PSO).The proposed technique demonstrates superior performance by significantly reducing power losses across all buses in the system. Compared to conventional optimization techniques, the LRNN achieves the lowest computational complexity at 1.82%, and the fastest convergence speed in just 25 iterations. In terms of execution time, it completes in 0.34 s, faster than the Genetic Algorithm at 0.44 s, Sea Horse Optimization at 0.59 s, and Particle Swarm Optimization at 0.65 s. While its efficiency is 98% it offers an excellent balance between computational speed, accuracy, and loss minimization. These results highlight its potential as a highly effective solution for modern power systems integrating renewable sources and electric vehicles.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100719"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of distributed generation, particularly renewable energy sources, has catalyzed the emergence of microgrids as a pivotal element in contemporary power system architectures. However, the integration of these sources introduces significant complexities in protection system design due to the inherent dynamic characteristics of microgrids, bidirectional power flow, and operational mode transitions between grid-connected or islanded states. Traditional protection paradigms, predicated on static fault current magnitudes prevalent in passive radial distribution networks, exhibit limitations in microgrid environments characterized by substantial fault current variability. Notably, constrained fault current contribution of inverter-interfaced DG units operating in current-limiting mode impedes the efficacy of traditional overcurrent protection. This necessitates the development of adaptive and intelligent protection methodologies. A hybrid microgrid simulation is employed to analyze fault current variations across diverse operational scenarios, underscoring the imperative for advanced protection strategies. This study evaluates the current state of microgrid protection, identifies existing research lacunae, and proposes potential future research directions to improve resilience, reliability, and security. This review examines various microgrid types, including AC and DC systems, with a focus on their operational conditions, configurations, and the diverse fault types they encounter in relation to different protection device frameworks. The study emphasizes the critical need for advanced protection technologies that are continuously evolving to address the increasing complexity of microgrid systems effectively. By presenting a comprehensive analysis of past advancements and future directions in microgrid protection, this paper aims to guide researchers and scientists, emphasizing the significance of their contributions in shaping the development and innovation of protection strategies in this essential domain.
{"title":"Microgrids protection: A review of technologies, challenges, and future trends","authors":"Goutam Kumar Yadav, Mukesh Kumar Kirar, S.C. Gupta","doi":"10.1016/j.ref.2025.100720","DOIUrl":"10.1016/j.ref.2025.100720","url":null,"abstract":"<div><div>The proliferation of distributed generation, particularly renewable energy sources, has catalyzed the emergence of microgrids as a pivotal element in contemporary power system architectures. However, the integration of these sources introduces significant complexities in protection system design due to the inherent dynamic characteristics of microgrids, bidirectional power flow, and operational mode transitions between grid-connected or islanded states. Traditional protection paradigms, predicated on static fault current magnitudes prevalent in passive radial distribution networks, exhibit limitations in microgrid environments characterized by substantial fault current variability. Notably, constrained fault current contribution of inverter-interfaced DG units operating in current-limiting mode impedes the efficacy of traditional overcurrent protection. This necessitates the development of adaptive and intelligent protection methodologies. A hybrid microgrid simulation is employed to analyze fault current variations across diverse operational scenarios, underscoring the imperative for advanced protection strategies. This study evaluates the current state of microgrid protection, identifies existing research lacunae, and proposes potential future research directions to improve resilience, reliability, and security. This review examines various microgrid types, including AC and DC systems, with a focus on their operational conditions, configurations, and the diverse fault types they encounter in relation to different protection device frameworks. The study emphasizes the critical need for advanced protection technologies that are continuously evolving to address the increasing complexity of microgrid systems effectively. By presenting a comprehensive analysis of past advancements and future directions in microgrid protection, this paper aims to guide researchers and scientists, emphasizing the significance of their contributions in shaping the development and innovation of protection strategies in this essential domain.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"55 ","pages":"Article 100720"},"PeriodicalIF":4.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As global electric vehicle ownership continues to rise, the growing number of retired electric vehicle batteries presents a significant opportunity to extend their lifespan by repurposing them for energy storage in residential solar systems. This study investigates whether it’s financially and technically feasible to repurpose old electric vehicle batteries to be used in residential off-grid Photovoltaic systems. Using Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software, we compared two types of residential solar setups: one with new batteries and the other with retired EV batteries. The data are taken from the Jordanian market, where electric vehicle adoption is significant. Our findings indicate that using retired electric vehicle batteries resulted in a 16 % lower net present cost. Additionally, the affordability of retired batteries allowed for fewer solar panels and reduced reliance on diesel generators, leading to lower emissions.
{"title":"Techno-economic feasibility of repurposing retired electric vehicle batteries in residential off-grid photovoltaic systems","authors":"Saed Altawabeyeh, Heba Abutayeh, Kholoud Hijazi, Hussein Daoud","doi":"10.1016/j.ref.2025.100717","DOIUrl":"10.1016/j.ref.2025.100717","url":null,"abstract":"<div><div>As global electric vehicle ownership continues to rise, the growing number of retired electric vehicle batteries presents a significant opportunity to extend their lifespan by repurposing them for energy storage in residential solar systems. This study investigates whether it’s financially and technically feasible to repurpose old electric vehicle batteries to be used in residential off-grid Photovoltaic systems. Using Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software, we compared two types of residential solar setups: one with new batteries and the other with retired EV batteries. The data are taken from the Jordanian market, where electric vehicle adoption is significant. Our findings indicate that using retired electric vehicle batteries resulted in a 16 % lower net present cost. Additionally, the affordability of retired batteries allowed for fewer solar panels and reduced reliance on diesel generators, leading to lower emissions.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100717"},"PeriodicalIF":4.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Three-phase grid-connected Electric Vehicles (EVs) are critical for optimizing energy flow, managing Active Power (AP) for charging and discharging, and controlling Reactive Power (RP) to ensure voltage regulation. These features enhance grid reliability and support the seamless integration of large-scale EVs into power grids. However, the unpredictable frequency of charging sessions creates challenges such as voltage fluctuations and grid imbalances, adversely affecting power quality (PQ) and stability. To address these issues, this study proposes a hybrid approach for AP and RP control in three-phase grid-connected EVs. The novel ZOA-MASTGNN technique integrates the Zebra Optimization Algorithm (ZOA) with the Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN). The ZOA dynamically optimizes system parameters, improving power management, reducing Total Harmonic Distortion (THD), and enhancing grid stability. Meanwhile, MASTGNN predicts optimal control actions, mitigating harmonics, regulating voltage dynamically, and adapting to changing operational conditions in grid-interactive EV systems. The suggested method was implemented on the MATLAB platform and evaluated with existing approaches, including Resiliency-Guided Physics-Informed Neural Networks (RPINN), Elman Neural Networks (ENN), Multilayer Feed Forward Neural Networks (ML-FFNN), Deep Neural Networks (DNN), and Particle Swarm Optimization-Artificial Neural Networks (PSO-ANN). Results showed significant improvements, achieving 19.36% load current THD and 3.52% source current THD, while outperforming other approaches in efficiency and effectiveness. This framework addresses key challenges in large-scale EV integration, offering scalable and practical solutions for sustainable power grid operations.
{"title":"Active and Reactive Power Control in Three-Phase Grid-Connected Electric Vehicles using Zebra Optimization Algorithm and Multimodal Adaptive Spatio-Temporal Graph Neural Network","authors":"E. Shiva Prasad , S.V. Evangelin Sonia , Kokkirapati Naga Suresh , T.G. Shivapanchakshari","doi":"10.1016/j.ref.2025.100715","DOIUrl":"10.1016/j.ref.2025.100715","url":null,"abstract":"<div><div>Three-phase grid-connected Electric Vehicles (EVs) are critical for optimizing energy flow, managing Active Power (AP) for charging and discharging, and controlling Reactive Power (RP) to ensure voltage regulation. These features enhance grid reliability and support the seamless integration of large-scale EVs into power grids. However, the unpredictable frequency of charging sessions creates challenges such as voltage fluctuations and grid imbalances, adversely affecting power quality (PQ) and stability. To address these issues, this study proposes a hybrid approach for AP and RP control in three-phase grid-connected EVs. The novel ZOA-MASTGNN technique integrates the Zebra Optimization Algorithm (ZOA) with the Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN). The ZOA dynamically optimizes system parameters, improving power management, reducing Total Harmonic Distortion (THD), and enhancing grid stability. Meanwhile, MASTGNN predicts optimal control actions, mitigating harmonics, regulating voltage dynamically, and adapting to changing operational conditions in grid-interactive EV systems. The suggested method was implemented on the MATLAB platform and evaluated with existing approaches, including Resiliency-Guided Physics-Informed Neural Networks (RPINN), Elman Neural Networks (ENN), Multilayer Feed Forward Neural Networks (ML-FFNN), Deep Neural Networks (DNN), and Particle Swarm Optimization-Artificial Neural Networks (PSO-ANN). Results showed significant improvements, achieving 19.36% load current THD and 3.52% source current THD, while outperforming other approaches in efficiency and effectiveness. This framework addresses key challenges in large-scale EV integration, offering scalable and practical solutions for sustainable power grid operations.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100715"},"PeriodicalIF":4.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-01DOI: 10.1016/j.ref.2025.100716
Yixi Zhang, Heng Chen, Yue Gao, Jingjia Li, Peiyuan Pan
To address the siting and sizing of an integrated energy distribution network system incorporating PV, WT, EV, SVC, and BES, as well as the operational planning of SVC and BES, this paper proposes an improved hybrid algorithm. In the first stage, a multi-objective genetic algorithm is adopted to plan the siting and sizing of each device in the integrated energy distribution network. In the second stage, based on the siting and sizing results, an adaptive particle swarm optimization algorithm is utilized to schedule the daily energy storage dispatch and reactive power output. Through this two-stage optimization, the issues of unbalanced load distribution and voltage quality in the distribution network are resolved, while minimizing investment costs. The IEEE 69-node simulation results demonstrate that under the optimal scenario, the average voltage deviation of the distribution system remains stable at 1.0 p.u., the line loss rate decreases to 2.90 %, and the initial construction cost and operational cost reach 120,220,000 CNY and 16,923.88 CNY, respectively. Compared with similar algorithms, the proposed hybrid algorithm achieves a 34.5% improvement in loss reduction, significantly enhances voltage stability, and reduces daily operational costs by 9.91 %, demonstrating its effectiveness and superiority.
{"title":"Improved hybrid algorithm-based optimization for Integrated Energy Distribution Network System: minimizing voltage deviation, line losses, and costs","authors":"Yixi Zhang, Heng Chen, Yue Gao, Jingjia Li, Peiyuan Pan","doi":"10.1016/j.ref.2025.100716","DOIUrl":"10.1016/j.ref.2025.100716","url":null,"abstract":"<div><div>To address the siting and sizing of an integrated energy distribution network system incorporating PV, WT, EV, SVC, and BES, as well as the operational planning of SVC and BES, this paper proposes an improved hybrid algorithm. In the first stage, a multi-objective genetic algorithm is adopted to plan the siting and sizing of each device in the integrated energy distribution network. In the second stage, based on the siting and sizing results, an adaptive particle swarm optimization algorithm is utilized to schedule the daily energy storage dispatch and reactive power output. Through this two-stage optimization, the issues of unbalanced load distribution and voltage quality in the distribution network are resolved, while minimizing investment costs. The IEEE 69-node simulation results demonstrate that under the optimal scenario, the average voltage deviation of the distribution system remains stable at 1.0 p.u., the line loss rate decreases to 2.90 %, and the initial construction cost and operational cost reach 120,220,000 CNY and 16,923.88 CNY, respectively. Compared with similar algorithms, the proposed hybrid algorithm achieves a 34.5% improvement in loss reduction, significantly enhances voltage stability, and reduces daily operational costs by 9.91 %, demonstrating its effectiveness and superiority.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100716"},"PeriodicalIF":4.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-25DOI: 10.1016/j.ref.2025.100714
Muhammad Majid Gulzar , Ahlam Jameel , Salman Habib , Ali Arishi , Rasmia Irfan , Hasnain Ahmad , Huma Tehreem
Due to technological advancements, the rapid expansion of renewable energy in the power sector has led to challenges with operation, security, and management. Reduced grid inertia necessitates maintaining normal operating frequency and lowering tie-line power changes to assure stability and reliability. In this article, a framework for load frequency controller (LFC) that is based on the combinations of traditional controllers is proposed. In this study, the filtered derivative proportional controller cascaded with proportional integral, abbreviated as , is suggested for LFC applications. In addition, the optimization of the proposed controller parameters for two-area power grids is tuned using the grasshopper optimization algorithm (GOA). The contribution of aggregated model for electric vehicles (EVs) is also taken into consideration. The performance of the proposed controller is evaluated with the effectiveness of other controllers like cascaded proportional integral and derivative (PI-PD), plus proportional integral derivative (1+PID), plus proportional integral (1+PI), and fractional order proportional integral derivative (FOPID). The proposed GOA optimizer tuned controller is tested for reliability against variations in load, penetration of renewable energy sources, and parametric uncertainties of the grid for the time integral absolute error (ITAE) objective function. By employing the proposed controller, the system achieves rapid and efficient minimization of the objective function. Thus, the controller is highly suitable for applications requiring quick response and precise performance.
{"title":"A framework for load frequency regulation in multi-area grid-connected hybrid power systems with plug-in electric vehicles","authors":"Muhammad Majid Gulzar , Ahlam Jameel , Salman Habib , Ali Arishi , Rasmia Irfan , Hasnain Ahmad , Huma Tehreem","doi":"10.1016/j.ref.2025.100714","DOIUrl":"10.1016/j.ref.2025.100714","url":null,"abstract":"<div><div>Due to technological advancements, the rapid expansion of renewable energy in the power sector has led to challenges with operation, security, and management. Reduced grid inertia necessitates maintaining normal operating frequency and lowering tie-line power changes to assure stability and reliability. In this article, a framework for load frequency controller (LFC) that is based on the combinations of traditional controllers is proposed. In this study, the filtered derivative proportional controller cascaded with <span><math><mi>β</mi></math></span> proportional integral, abbreviated as <span><math><mrow><msub><mrow><mi>PD</mi></mrow><mi>F</mi></msub><mo>+</mo><mrow><mo>(</mo><mi>β</mi><mo>+</mo><mi>P</mi><mi>I</mi><mo>)</mo></mrow></mrow></math></span>, is suggested for LFC applications. In addition, the optimization of the proposed controller parameters for two-area power grids is tuned using the grasshopper optimization algorithm (GOA). The contribution of aggregated model for electric vehicles (EVs) is also taken into consideration. The performance of the proposed controller is evaluated with the effectiveness of other controllers like cascaded proportional integral and derivative (PI-PD), <span><math><mrow><mn>1</mn></mrow></math></span> plus proportional integral derivative (1+PID), <span><math><mrow><mn>1</mn></mrow></math></span> plus proportional integral (1+PI), and fractional order proportional integral derivative (FOPID). The proposed GOA optimizer tuned <span><math><mrow><msub><mrow><mi>PD</mi></mrow><mi>F</mi></msub><mo>+</mo><mrow><mo>(</mo><mi>β</mi><mo>+</mo><mi>P</mi><mi>I</mi><mo>)</mo></mrow></mrow></math></span> controller is tested for reliability against variations in load, penetration of renewable energy sources, and parametric uncertainties of the grid for the time integral absolute error (ITAE) objective function. By employing the proposed controller, the system achieves rapid and efficient minimization of the objective function. Thus, the controller is highly suitable for applications requiring quick response and precise performance.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"54 ","pages":"Article 100714"},"PeriodicalIF":4.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-21DOI: 10.1016/j.ref.2025.100712
Ibrahim Sinneh Sinneh, Yanxia Sun Yanxia
Smart nano grids face enormous challenges stemming from variations in time demand and risks from cyber threats, which lead to their inefficiency and unstable operation. To overcome these difficulties, this study presents a novel “Federated Reinforced LSTM-Crayfish Whale Optimization Detection (FRLC-WOD)” procedure. The system proposed here integrates Reinforcement-LSTM-Crayfish Optimization Technique (RL-LSTM-CAO) and Federated Graph Whale Optimization Intrusion Detection (FG-WOA-ID) to improve adaptability, efficiency, and security. The RL-LSTM-CAO methodology employs Bi-directional Long Short-Term Memory (Bi-LSTM) for accurate forecasting, Reinforcement Learning-based Power Distribution (RL-PD) for real-time adaptability, and Crayfish Optimization Algorithm (CAO) for optimal energy management. On the other hand, FG-WOA-ID employs Federated Learning for decentralized anomaly detection, Graph Neural Networks for intrusion detection, and Whale Optimization Algorithm for cybersecurity measures adaptation. The results of the experiments achieved a grid stability improvement of 95 %, an energy efficiency improvement of 92 %, a response time of 1.5 s, and a 95 % improved cyber threat resistance, outperforming existing standard methodologies such as EMS GWO-OSA, RNN, and MPPT. This will show how the proposed method significantly upgrades the delivery of reliable and optimized operations for smart nano grids.
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