Pub Date : 2025-02-10DOI: 10.1016/j.ref.2025.100681
Rajdip Dey, Shabari Nath
Multifrequency microgrid (MFMG) is a unique microgrid which has more than one frequency component superimposed on the bus is examined in this paper. There are three basic ideas behind MFMG which are orthogonal power flow theory, superposition theorem, and frequency selectivity criteria. It overcomes various disadvantages of traditional AC and DC microgrids and has many new features.
In MFMG, several frequency currents and voltages are superimposed on the multifrequency (MF) bus. The customers can select any available frequency currents at the load side. In MFMG, power is absorbed in different frequencies at load side and it creates different active and reactive power imbalance situations in MFMG. In existing literature, there is no analysis of the power imbalance of MFMG and the existing power control methods of microgrids cannot solve this problem. This paper bridges the gap by analyzing different power imbalance cases due to frequency selectivity criteria and proposes new control strategies to balance different frequency active and reactive powers in islanded and grid connected modes. The power balancing strategies are verified with 7 bus primitive MFMG structures in the Matlab Simulink environment.
{"title":"Management and control strategy of multiple frequency powers in multifrequency microgrid","authors":"Rajdip Dey, Shabari Nath","doi":"10.1016/j.ref.2025.100681","DOIUrl":"10.1016/j.ref.2025.100681","url":null,"abstract":"<div><div>Multifrequency microgrid (MFMG) is a unique microgrid which has more than one frequency component superimposed on the bus is examined in this paper. There are three basic ideas behind MFMG which are orthogonal power flow theory, superposition theorem, and frequency selectivity criteria. It overcomes various disadvantages of traditional AC and DC microgrids and has many new features.</div><div>In MFMG, several frequency currents and voltages are superimposed on the multifrequency (MF) bus. The customers can select any available frequency currents at the load side. In MFMG, power is absorbed in different frequencies at load side and it creates different active and reactive power imbalance situations in MFMG. In existing literature, there is no analysis of the power imbalance of MFMG and the existing power control methods of microgrids cannot solve this problem. This paper bridges the gap by analyzing different power imbalance cases due to frequency selectivity criteria and proposes new control strategies to balance different frequency active and reactive powers in islanded and grid connected modes. The power balancing strategies are verified with 7 bus primitive MFMG structures in the Matlab Simulink environment.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100681"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387346","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 : 2025-01-23DOI: 10.1016/j.ref.2025.100684
Veronica A. Rosero-Morillo , F. Gonzalez-Longatt , Juan C. Quispe , Eduardo J. Salazar , Eduardo Orduña , Mauricio E. Samper
Electrical systems are constantly transforming to achieve global decarbonization and address the climate emergency. This process involves a substantial modernization of the distribution network that includes the integration of distributed energy resources, particularly those using inverter interfaces. Given the inevitability of faults, it is crucial to strengthen the infrastructure of protection systems so they can handle the new challenges imposed by this evolution. This article explores the challenges associated with protecting active distribution networks, caused by the incorporation of technologies such as rotary machines and power electronic converters. Special attention is given to critical issues such as changes in short-circuit currents, the bidirectional flow of currents, and the response times of protection relays. Current practical solutions are examined, and their limitations identified, highlighting the urgent need to develop more sophisticated and tailored protection schemes for the particularities of these networks. Additionally, the fault detection process is described in detail, breaking down the stages of parameter acquisition, signal processing, and fault classification, based on recent research. Finally, future trends in protection schemes are discussed, emphasizing the importance of continuously adapting and optimizing protection strategies in response to the dynamic evolution of electrical networks.
{"title":"Emerging Trends in Active Distribution Network Fault Detection","authors":"Veronica A. Rosero-Morillo , F. Gonzalez-Longatt , Juan C. Quispe , Eduardo J. Salazar , Eduardo Orduña , Mauricio E. Samper","doi":"10.1016/j.ref.2025.100684","DOIUrl":"10.1016/j.ref.2025.100684","url":null,"abstract":"<div><div>Electrical systems are constantly transforming to achieve global decarbonization and address the climate emergency. This process involves a substantial modernization of the distribution network that includes the integration of distributed energy resources, particularly those using inverter interfaces. Given the inevitability of faults, it is crucial to strengthen the infrastructure of protection systems so they can handle the new challenges imposed by this evolution. This article explores the challenges associated with protecting active distribution networks, caused by the incorporation of technologies such as rotary machines and power electronic converters. Special attention is given to critical issues such as changes in short-circuit currents, the bidirectional flow of currents, and the response times of protection relays. Current practical solutions are examined, and their limitations identified, highlighting the urgent need to develop more sophisticated and tailored protection schemes for the particularities of these networks. Additionally, the fault detection process is described in detail, breaking down the stages of parameter acquisition, signal processing, and fault classification, based on recent research. Finally, future trends in protection schemes are discussed, emphasizing the importance of continuously adapting and optimizing protection strategies in response to the dynamic evolution of electrical networks.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100684"},"PeriodicalIF":4.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181118","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 : 2025-01-21DOI: 10.1016/j.ref.2025.100683
Veronica A. Rosero-Morillo , Le Nam Hai Pham , F. Gonzalez-Longatt , Eduardo Orduña
The growing integration of Inverter-Based Distributed Generation (IIDG) in distribution networks poses significant challenges for protection systems, as it alters the usual short-circuit patterns and impacts their effectiveness. International standards such as IEEE 1547-2018 and the German network code VDE-AR-N 410 for distribution networks, along with the IEEE 2800-2021 standard for transmission systems, have set criteria for the connection of IIDGs and their behavior under fault conditions, including the injection of reactive current and current limiting. These standards have driven the development of new control models for fault response: the conventional model, according to IEEE 1547-2018, requires IIDGs to inject only balanced positive sequence currents to provide voltage support to the network, while the advanced model, in accordance with VDE-AR-N 410 and IEEE 2800-2021, demands the injection of both positive and negative sequence currents to enhance voltage support during unbalanced faults. This article explores how these fault response models affect the efficiency of traditional protection schemes, including overcurrent and directional elements, and develops a methodology for modeling the inverter’s response to faults. This approach enables the replication and application of international standards for the design of new protection schemes, facilitating their adoption by researchers in the field.
{"title":"Advanced control strategies for grid-following inverter fault response: Implementation and analysis in MATLAB for protection studies in medium voltage distribution networks","authors":"Veronica A. Rosero-Morillo , Le Nam Hai Pham , F. Gonzalez-Longatt , Eduardo Orduña","doi":"10.1016/j.ref.2025.100683","DOIUrl":"10.1016/j.ref.2025.100683","url":null,"abstract":"<div><div>The growing integration of Inverter-Based Distributed Generation (IIDG) in distribution networks poses significant challenges for protection systems, as it alters the usual short-circuit patterns and impacts their effectiveness. International standards such as IEEE 1547-2018 and the German network code VDE-AR-N 410 for distribution networks, along with the IEEE 2800-2021 standard for transmission systems, have set criteria for the connection of IIDGs and their behavior under fault conditions, including the injection of reactive current and current limiting. These standards have driven the development of new control models for fault response: the conventional model, according to IEEE 1547-2018, requires IIDGs to inject only balanced positive sequence currents to provide voltage support to the network, while the advanced model, in accordance with VDE-AR-N 410 and IEEE 2800-2021, demands the injection of both positive and negative sequence currents to enhance voltage support during unbalanced faults. This article explores how these fault response models affect the efficiency of traditional protection schemes, including overcurrent and directional elements, and develops a methodology for modeling the inverter’s response to faults. This approach enables the replication and application of international standards for the design of new protection schemes, facilitating their adoption by researchers in the field.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100683"},"PeriodicalIF":4.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181120","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 rise of distributed energy resources stems from reliance on carbon-intensive energy and climate concerns. While photovoltaic solar energy leads in modern grids, its intermittent nature and weather variability challenge reliability and efficiency. Photovoltaic power output forecasting ensures a stable power supply by mitigating weather-induced disruptions. Thus, this review paper investigates the transformative impact of Deep Learning (DL) on photovoltaic power output forecasting. Leveraging the extensive data generated by smart meters, DL has shown unprecedented potential to outperform traditional forecasting models. The primary purpose of this research is to systematically analyze and compare mainstream DL-based forecasting techniques, uncovering their respective strengths and limitations. Least explored techniques such as deep transfer learning, big data DL, federated learning, probabilistic models, deterministic models, and hybrid architectures in forecasting are explored which have distinct advantages in processing large-scale multi-source data to deliver more accuracy. Covering research from 2019 to 2023, this study aims to capture the latest developments and ensure relevance to ongoing trends. Nearly 200 journals were acquired for this review paper using a systematic protocol. Among the DL methods, Autoencoder-Long Short-Term Memory outperformed its counterparts, achieving an impressive R2 score of 99.98%. Moreover, the major conclusion underscores that DL offers a promising pathway for advancing PV forecasting, with future opportunities to address identified gaps and emerging challenges. This analysis serves as a comprehensive guide to stakeholders, illuminating the unique capabilities of DL in driving the next generation of solar power forecasting solutions.
{"title":"Exploring deep learning methods for solar photovoltaic power output forecasting: A review","authors":"Dheeraj Kumar Dhaked , V.L. Narayanan , Ram Gopal , Omveer Sharma , Sagar Bhattarai , S.K. Dwivedy","doi":"10.1016/j.ref.2025.100682","DOIUrl":"10.1016/j.ref.2025.100682","url":null,"abstract":"<div><div>The rise of distributed energy resources stems from reliance on carbon-intensive energy and climate concerns. While photovoltaic solar energy leads in modern grids, its intermittent nature and weather variability challenge reliability and efficiency. Photovoltaic power output forecasting ensures a stable power supply by mitigating weather-induced disruptions. Thus, this review paper investigates the transformative impact of Deep Learning (DL) on photovoltaic power output forecasting. Leveraging the extensive data generated by smart meters, DL has shown unprecedented potential to outperform traditional forecasting models. The primary purpose of this research is to systematically analyze and compare mainstream DL-based forecasting techniques, uncovering their respective strengths and limitations. Least explored techniques such as deep transfer learning, big data DL, federated learning, probabilistic models, deterministic models, and hybrid architectures in forecasting are explored which have distinct advantages in processing large-scale multi-source data to deliver more accuracy. Covering research from 2019 to 2023, this study aims to capture the latest developments and ensure relevance to ongoing trends. Nearly 200 journals were acquired for this review paper using a systematic protocol. Among the DL methods, Autoencoder-Long Short-Term Memory outperformed its counterparts, achieving an impressive R<sup>2</sup> score of 99.98%. Moreover, the major conclusion underscores that DL offers a promising pathway for advancing PV forecasting, with future opportunities to address identified gaps and emerging challenges. This analysis serves as a comprehensive guide to stakeholders, illuminating the unique capabilities of DL in driving the next generation of solar power forecasting solutions.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100682"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181148","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 : 2025-01-10DOI: 10.1016/j.ref.2025.100680
Zahra Moshaver Shoja , Ali Bohluli Oskouei , Morteza Nazari-Heris
Growing environmental concerns have increased interest in renewable energy-powered natural gas/hydrogen refueling (NGHR) and electric charging (EC) stations, driving the adoption of advanced energy resources like power-to-X (P2X) technologies in energy systems. This paper introduces vector-bridging systems (VBSs). In this concept, P2X technologies coupled with energy storage form a bridge across multiple energy vectors, such as electricity, gas, heat, and hydrogen, to enhance flexibility in community-integrated energy systems (CIESs). We propose a risk-based optimal energy management framework that integrates P2X-based VBSs to optimize participation in multi-energy markets while meeting power, gas, heat, and hydrogen demands from NGHR and EC stations at minimum cost. An incentive-based integrated demand response (IDR) model is also incorporated to reduce daily operation costs for power and heat demands. To manage uncertainties, a hybrid multi-objective info-gap decision theory (MOIGDT)/stochastic programming approach is used, adapting to the nature and knowledge of uncertain parameters. The multi-objective problem is solved using the augmented ε-constraint method, with the best solution selected through fuzzy decision-making and the min-max approach. Numerical results demonstrate that the combined use of P2X-based VBSs and IDR lowers daily operating costs by up to 8.36% and reduces risk levels in short-term CIES scheduling by 11.3%, underscoring the effectiveness of VBSs in achieving cost-efficient, resilient energy management.
{"title":"Risk-based optimal management of a multi-energy community integrated with P2X-based vector-bridging systems considering natural gas/hydrogen refueling and electric vehicle charging stations","authors":"Zahra Moshaver Shoja , Ali Bohluli Oskouei , Morteza Nazari-Heris","doi":"10.1016/j.ref.2025.100680","DOIUrl":"10.1016/j.ref.2025.100680","url":null,"abstract":"<div><div>Growing environmental concerns have increased interest in renewable energy-powered natural gas/hydrogen refueling (NGHR) and electric charging (EC) stations, driving the adoption of advanced energy resources like power-to-X (P2X) technologies in energy systems. This paper introduces vector-bridging systems (VBSs). In this concept, P2X technologies coupled with energy storage form a bridge across multiple energy vectors, such as electricity, gas, heat, and hydrogen, to enhance flexibility in community-integrated energy systems (CIESs). We propose a risk-based optimal energy management framework that integrates P2X-based VBSs to optimize participation in multi-energy markets while meeting power, gas, heat, and hydrogen demands from NGHR and EC stations at minimum cost. An incentive-based integrated demand response (IDR) model is also incorporated to reduce daily operation costs for power and heat demands. To manage uncertainties, a hybrid multi-objective info-gap decision theory (MOIGDT)/stochastic programming approach is used, adapting to the nature and knowledge of uncertain parameters. The multi-objective problem is solved using the augmented ε-constraint method, with the best solution selected through fuzzy decision-making and the min-max approach. Numerical results demonstrate that the combined use of P2X-based VBSs and IDR lowers daily operating costs by up to 8.36% and reduces risk levels in short-term CIES scheduling by 11.3%, underscoring the effectiveness of VBSs in achieving cost-efficient, resilient energy management.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100680"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182113","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 : 2025-01-10DOI: 10.1016/j.ref.2025.100679
Mohammadreza Gholami , A. Arefi , M.E.H. Chowdhury , L. Ben-Brahim , S.M. Muyeen , IEEE Fellow Member
Greenhouses provide controlled environments for crop cultivation, and integrating semi transparent photovoltaic (STPV) panels offers the dual benefits of generating renewable energy while facilitating natural light penetration for photosynthesis. This study conducts a feasibility analysis of integrating Battery Energy Storage Systems (BESSs) with STPV systems in greenhouse agriculture, considering the Daily Light Integral (DLI) requirement for different crops as the primary constraint. Employing an enhanced Firefly Algorithm (FA) to optimize the PV cover ratio and BESS capacity, the analysis aims to maximize the Net Present Value (NPV) over a 25-year period, serving as the primary economic parameter. By incorporating DLI requirements for various crop types, the study ensures optimal crop growth while maximizing electricity generation. To ensure realistic long-term projections, the analysis incorporates BESS degradation over the 25-year period, accounting for capacity loss and efficiency reduction in energy storage. The results reveal the significant impact of crop type, with various required DLI , and transparency factor on optimized BESS and consequently the NPV of the project. Simulation results show that for crops with high DLI requirements, the feasible range of PVR% in the greenhouse varies from 42 % to 91 %, depending on the STPV’s transmittance factor. Additionally, the study reveals that initial negative revenue is common across all cases, with the highest NPV achieved at $1,331,340 for crops with low DLI requirements and a BESS capacity of 216 kW.
{"title":"Optimizing transparent photovoltaic integration with battery energy storage systems in greenhouse: a daily light integral-constrained economic analysis considering BESS degradation","authors":"Mohammadreza Gholami , A. Arefi , M.E.H. Chowdhury , L. Ben-Brahim , S.M. Muyeen , IEEE Fellow Member","doi":"10.1016/j.ref.2025.100679","DOIUrl":"10.1016/j.ref.2025.100679","url":null,"abstract":"<div><div>Greenhouses provide controlled environments for crop cultivation, and integrating semi transparent photovoltaic (STPV) panels offers the dual benefits of generating renewable energy while facilitating natural light penetration for photosynthesis. This study conducts a feasibility analysis of integrating Battery Energy Storage Systems (BESSs) with STPV systems in greenhouse agriculture, considering the Daily Light Integral (DLI) requirement for different crops as the primary constraint. Employing an enhanced Firefly Algorithm (FA) to optimize the PV cover ratio and BESS capacity, the analysis aims to maximize the Net Present Value (NPV) over a 25-year period, serving as the primary economic parameter. By incorporating DLI requirements for various crop types, the study ensures optimal crop growth while maximizing electricity generation. To ensure realistic long-term projections, the analysis incorporates BESS degradation over the 25-year period, accounting for capacity loss and efficiency reduction in energy storage. The results reveal the significant impact of crop type, with various required DLI , and transparency factor on optimized BESS and consequently the NPV of the project. Simulation results show that for crops with high DLI requirements, the feasible range of PVR% in the greenhouse varies from 42 % to 91 %, depending on the STPV’s transmittance factor. Additionally, the study reveals that initial negative revenue is common across all cases, with the highest NPV achieved at $1,331,340 for crops with low DLI requirements and a BESS capacity of 216 kW.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100679"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1016/j.ref.2024.100674
Soroush Najafi, Hanif Livani
Managing voltage and active load in distribution networks is an increasingly challenging task with the integration of volatile distributed energy resources (DERs) and flexible demands. This paper proposes a two-stage chance-constrained co-optimization framework using a Gaussian mixture model (GMM) to address Volt-VAR optimization (VVO) and demand response programs (DRP). The utilization of GMM in chance constrained optimization CCO (GMM-CCO) approach handles non-Gaussian forecast errors, ensuring network resilience with manageable computational demands. In the first stage, flexible demands, inverters’ reactive power, capacitor bank switching, and battery states of charge are co-scheduled, focusing on minimizing energy loss, reducing grid operational costs, and managing voltage deviations over a four-hour ahead schedule with hourly intervals. The second stage involves intra-hour, near-real-time optimization for VVO to respond to real-time disturbances. Simulations on a modified unbalanced three-phase IEEE 37-node system validate the framework’s effectiveness, comparing it to traditional chance-constrained optimization methods. Additionally, the proposed framework is implemented on the IEEE 69-node system to analyze its scalability and robustness under different levels of uncertainty and varying penetration levels.
{"title":"Chance-constrained co-optimization of demand response and Volt/Var under Gaussian mixture model uncertainty","authors":"Soroush Najafi, Hanif Livani","doi":"10.1016/j.ref.2024.100674","DOIUrl":"10.1016/j.ref.2024.100674","url":null,"abstract":"<div><div>Managing voltage and active load in distribution networks is an increasingly challenging task with the integration of volatile distributed energy resources (DERs) and flexible demands. This paper proposes a two-stage chance-constrained co-optimization framework using a Gaussian mixture model (GMM) to address Volt-VAR optimization (VVO) and demand response programs (DRP). The utilization of GMM in chance constrained optimization CCO (GMM-CCO) approach handles non-Gaussian forecast errors, ensuring network resilience with manageable computational demands. In the first stage, flexible demands, inverters’ reactive power, capacitor bank switching, and battery states of charge are co-scheduled, focusing on minimizing energy loss, reducing grid operational costs, and managing voltage deviations over a four-hour ahead schedule with hourly intervals. The second stage involves intra-hour, near-real-time optimization for VVO to respond to real-time disturbances. Simulations on a modified unbalanced three-phase IEEE 37-node system validate the framework’s effectiveness, comparing it to traditional chance-constrained optimization methods. Additionally, the proposed framework is implemented on the IEEE 69-node system to analyze its scalability and robustness under different levels of uncertainty and varying penetration levels.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100674"},"PeriodicalIF":4.2,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182112","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}
Energy sufficiency and the need to reduce carbon emissions have always been at the forefront of global efforts in recent times. This is the motivation of this study which seeks to reduce carbon emissions through the integration of renewable energy sources, by comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies, using a machine learning-supported approach. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data for four outputs: total annual cost, CO2 emissions, critical excess electricity production (CEEP), and electricity import. Three machine learning (ML) algorithms— multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—were tuned using Bayesian optimization to develop models mapping the inputs to outputs. A genetic algorithm was used for optimization to determine the optimal input capacities that minimize the outputs. Results indicated that incorporating electricity storage technologies (EST) leads to a 37% increase in renewable electricity sources (RES) share, resulting in a 19.14% reduction in CO2 emissions. EST such as battery energy storage systems (BESS), vehicle-to-grid (V2G) storage, and pumped hydro storage (PHS), allow for the storage of the critical excess electricity that comes with increasing RES share. Integrating EST in Nigeria’s 2050 energy landscape is crucial for incorporating more renewable electricity sources into the energy mix – thereby reducing CO2 emissions – and managing excess electricity production. This study outlines a plan for optimal electricity production to meet Nigeria’s 2050 demand, highlighting the need for a balanced approach that combines fossil fuels, renewable energy, nuclear power, and advanced storage solutions to achieve a sustainable and efficient electricity system.
{"title":"A machine learning-supported framework for predicting Nigeria’s optimal energy storage and emission reduction potentials","authors":"Stanley Aimhanesi Eshiemogie , Peace Precious Aielumoh , Tobechukwu Okamkpa , Miracle Chinonso Jude , Lois Efe , Andrew Nosakhare Amenaghawon , Handoko Darmokoesoemo , Heri Septya Kusuma","doi":"10.1016/j.ref.2024.100677","DOIUrl":"10.1016/j.ref.2024.100677","url":null,"abstract":"<div><div>Energy sufficiency and the need to reduce carbon emissions have always been at the forefront of global efforts in recent times. This is the motivation of this study which seeks to reduce carbon emissions through the integration of renewable energy sources, by comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies, using a machine learning-supported approach. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data for four outputs: total annual cost, CO<sub>2</sub> emissions, critical excess electricity production (CEEP), and electricity import. Three machine learning (ML) algorithms— multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—were tuned using Bayesian optimization to develop models mapping the inputs to outputs. A genetic algorithm was used for optimization to determine the optimal input capacities that minimize the outputs. Results indicated that incorporating electricity storage technologies (EST) leads to a 37% increase in renewable electricity sources (RES) share, resulting in a 19.14% reduction in CO<sub>2</sub> emissions. EST such as battery energy storage systems (BESS), vehicle-to-grid (V2G) storage, and pumped hydro storage (PHS), allow for the storage of the critical excess electricity that comes with increasing RES share. Integrating EST in Nigeria’s 2050 energy landscape is crucial for incorporating more renewable electricity sources into the energy mix – thereby reducing CO<sub>2</sub> emissions – and managing excess electricity production. This study outlines a plan for optimal electricity production to meet Nigeria’s 2050 demand, highlighting the need for a balanced approach that combines fossil fuels, renewable energy, nuclear power, and advanced storage solutions to achieve a sustainable and efficient electricity system.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100677"},"PeriodicalIF":4.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182050","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 primary objective of optimal power flow (OPF) in power systems is to minimize fuel expenses while simultaneously addressing several critical factors,including reducing transmission losses, minimizing voltage variations, and enhancing overall system stability. As the energy landscape evolves, the integration of renewable energy sources (RES) into the power grid has become increasingly important. In this research article, a study of Automatic Generation Control including RES to achieve cost optimization highlighting the advantages of GZA algorithm through a comprehensive study with other two evolutionary algorithm has been done. The research focuses on a three-area system integrating renewable energy sources – specifically solar, wind, and electric vehicles (EVs) – within a deregulated environment. While these sources can significantly reduce fuel costs associated with thermal power plants, they also introduce new challenges. Specifically, the variability and unpredictability of renewable energy can lead to increased frequency deviations due to changes in load inertia. This frequency deviation can disrupt the synchronization of the power system, potentially compromising stability and reliability. Detail study has been done in the simulation results for frequency deviation to achieve LFC, emphasizing performance metrics like overshoot, undershoot, and steady-state stability. Both traditional PID and FOPID controllers were evaluated for their effectiveness in managing frequency deviations.LFC ensures that the frequency of the power system remains within acceptable limits, particularly in a multi-area system where different regions may experience varying loads and generation capabilities. Effective frequency control is essential for maintaining the balance between generation and consumption, which is vital for the smooth operation of the grid. This innovative approach aims to enhance frequency regulation by effectively managing the dynamics introduced by the incorporation of renewable energy sources alongside traditional thermal power generation. The findings aim to demonstrate the effectiveness of the evolutionary algorithm GZA in enhancing the overall performance of multi-area power systems with diverse generation sources. By providing insights into the benefits of advanced control strategies, this study has been introduced a novel approach to simultaneously minimize costs and manage frequency deviations, marking a significant advancement in the field.
{"title":"Optimal power flow and grid frequency control of conventional and renewable energy source using evolutionary algorithm based FOPID controller","authors":"Debodyuti Upadhaya , Soumen Biswas , Susanta Dutta , Anagha Bhattacharya","doi":"10.1016/j.ref.2024.100676","DOIUrl":"10.1016/j.ref.2024.100676","url":null,"abstract":"<div><div>The primary objective of optimal power flow (OPF) in power systems is to minimize fuel expenses while simultaneously addressing several critical factors,including reducing transmission losses, minimizing voltage variations, and enhancing overall system stability. As the energy landscape evolves, the integration of renewable energy sources (RES) into the power grid has become increasingly important. In this research article, a study of Automatic Generation Control including RES to achieve cost optimization highlighting the advantages of GZA algorithm through a comprehensive study with other two evolutionary algorithm has been done. The research focuses on a three-area system integrating renewable energy sources – specifically solar, wind, and electric vehicles (EVs) – within a deregulated environment. While these sources can significantly reduce fuel costs associated with thermal power plants, they also introduce new challenges. Specifically, the variability and unpredictability of renewable energy can lead to increased frequency deviations due to changes in load inertia. This frequency deviation can disrupt the synchronization of the power system, potentially compromising stability and reliability. Detail study has been done in the simulation results for frequency deviation to achieve LFC, emphasizing performance metrics like overshoot, undershoot, and steady-state stability. Both traditional PID and FOPID controllers were evaluated for their effectiveness in managing frequency deviations.LFC ensures that the frequency of the power system remains within acceptable limits, particularly in a multi-area system where different regions may experience varying loads and generation capabilities. Effective frequency control is essential for maintaining the balance between generation and consumption, which is vital for the smooth operation of the grid. This innovative approach aims to enhance frequency regulation by effectively managing the dynamics introduced by the incorporation of renewable energy sources alongside traditional thermal power generation. The findings aim to demonstrate the effectiveness of the evolutionary algorithm GZA in enhancing the overall performance of multi-area power systems with diverse generation sources. By providing insights into the benefits of advanced control strategies, this study has been introduced a novel approach to simultaneously minimize costs and manage frequency deviations, marking a significant advancement in the field.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100676"},"PeriodicalIF":4.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182049","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 : 2024-12-17DOI: 10.1016/j.ref.2024.100675
Fhazhil Wamalwa , Reagan Wafula , Charles Kagiri
Minigrids offer a promising electrification solution for rural communities beyond the grid in developing countries in Sub-Saharan Africa (SSA). However, their economic viability is hindered by low electricity demand which results in high minigrid tariffs as compared to centralized utilities. This underscores the need to explore technical and policy measures to achieve grid parity tariffs and hence energy access equity as well as accelerating rural electrification. Productive use of electricity (PUE) has potential to mitigate the low demand barrier and enhance minigrid viability. In this paper, we present an integrated modeling framework for determining the optimal subsidy needed to achieve grid parity for irrigation-anchored minigrids in SSA, with Kenya as a case study. We focus on irrigation due to the economic importance of agriculture in SSA as well as the high prevalence of farming activities in rural SSA. We estimate irrigation energy demand using projections from the Global Change Assessment Model (GCAM) for 2020–2045 and formulate the minigrid model as a constrained optimization problem to minimize daily energy costs over a year with hourly resolution. The results from our techno-economic assessments show that incorporating irrigation loads in the minigrid operation can reduce their tariffs by up to 41%, with final results dependent on geographical location and the forecasted climate future scenarios. Sensitivity analysis indicates that a 50% subsidy is required to achieve grid parity in irrigation-anchored minigrids, while communal models (without irrigation as a PUE) require an estimated 75% capital subsidy to realize grid parity tariff. Our model and its results can be used as a high-level framework of reference when planning minigrids with irrigation loads in developing countries.
{"title":"Assessing the impacts of irrigation loads and capital subsidies on minigrids: A case study of Kenya","authors":"Fhazhil Wamalwa , Reagan Wafula , Charles Kagiri","doi":"10.1016/j.ref.2024.100675","DOIUrl":"10.1016/j.ref.2024.100675","url":null,"abstract":"<div><div>Minigrids offer a promising electrification solution for rural communities beyond the grid in developing countries in Sub-Saharan Africa (SSA). However, their economic viability is hindered by low electricity demand which results in high minigrid tariffs as compared to centralized utilities. This underscores the need to explore technical and policy measures to achieve grid parity tariffs and hence energy access equity as well as accelerating rural electrification. Productive use of electricity (PUE) has potential to mitigate the low demand barrier and enhance minigrid viability. In this paper, we present an integrated modeling framework for determining the optimal subsidy needed to achieve grid parity for irrigation-anchored minigrids in SSA, with Kenya as a case study. We focus on irrigation due to the economic importance of agriculture in SSA as well as the high prevalence of farming activities in rural SSA. We estimate irrigation energy demand using projections from the Global Change Assessment Model (GCAM) for 2020–2045 and formulate the minigrid model as a constrained optimization problem to minimize daily energy costs over a year with hourly resolution. The results from our techno-economic assessments show that incorporating irrigation loads in the minigrid operation can reduce their tariffs by up to 41%, with final results dependent on geographical location and the forecasted climate future scenarios. Sensitivity analysis indicates that a 50% subsidy is required to achieve grid parity in irrigation-anchored minigrids, while communal models (without irrigation as a PUE) require an estimated 75% capital subsidy to realize grid parity tariff. Our model and its results can be used as a high-level framework of reference when planning minigrids with irrigation loads in developing countries.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"53 ","pages":"Article 100675"},"PeriodicalIF":4.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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