This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the iss...This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.展开更多
Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging manageme...Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.展开更多
Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial...Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial for effective grid management and sustainable energy integration.However,traditional forecasting methods encounter challenges such as data privacy,centralised processing,and data sharing,particularly with dispersed data sources.This review paper thoroughly examines the necessity of forecasting models,methodologies,and data integrity,with a keen eye on the evolving landscape of Federated Learning(FL)in PV and WP forecasting.Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation,the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security.A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals,followed by in-depth discussions on specific non-Federated Learning approaches for each power source.The paper subsequently introduces FL and its variants,including Horizontal,Vertical,Transfer,Cross-Device,and Cross-Silo FL,highlighting the crucial role of encryption mechanisms and addressing associated challenges.Furthermore,drawing on extensive investigations of numerous pertinent articles,the paper outlines the innovative horizon of FL-based PV and wind power forecasting,offering insights into FL-based methodologies and concluding with observations drawn from this frontier.This review synthesises critical knowledge about PV and WP forecasting,leveraging the emerging paradigm of FL.Ultimately,this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.展开更多
文摘This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.
文摘Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.
基金supported by the Artificial Intelligence,Biomechatronics,and Collaborative Robotics research group at the Top Research Center Mechatronics(TRCM),University of Agder(UIA),Norway.
文摘Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial for effective grid management and sustainable energy integration.However,traditional forecasting methods encounter challenges such as data privacy,centralised processing,and data sharing,particularly with dispersed data sources.This review paper thoroughly examines the necessity of forecasting models,methodologies,and data integrity,with a keen eye on the evolving landscape of Federated Learning(FL)in PV and WP forecasting.Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation,the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security.A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals,followed by in-depth discussions on specific non-Federated Learning approaches for each power source.The paper subsequently introduces FL and its variants,including Horizontal,Vertical,Transfer,Cross-Device,and Cross-Silo FL,highlighting the crucial role of encryption mechanisms and addressing associated challenges.Furthermore,drawing on extensive investigations of numerous pertinent articles,the paper outlines the innovative horizon of FL-based PV and wind power forecasting,offering insights into FL-based methodologies and concluding with observations drawn from this frontier.This review synthesises critical knowledge about PV and WP forecasting,leveraging the emerging paradigm of FL.Ultimately,this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.
