The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges fr...The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges from intermittency,fluctuating loads,cost optimization,and uncertainty in real-time balancing.Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation.While stochastic and machine learning methods are used,they struggle with limited data,complex temporal patterns,and scalability.Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption.To address these issues,this paper presents a novel three-stage centralized EMS for interconnected microgrids.The first stage involves comprehensive data analysis to extract meaningful patterns.The second stage introduces a hybrid forecasting framework that integrates stochastic(Prophet)with machine learning(BiLSTM)techniques to improve prediction accuracy under uncertainty.In the third stage,a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids,with the aim of reducing operational costs,minimizing carbon emissions,and improving system stability under climate variability.The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency.Four scenarios are considered to evaluate the proposed energy management strategy.The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance.The combined methods achieve the best performance among all tested models,with an RMSE of 0.0070,MAE of 0.0043,and R^(2) of 0.9988,corresponding to improvements of ΔRMSE=−0.2122 and ΔR^(2)=+0.7126 relative to Prophet.These substantial gains in predictive accuracy translate into more precise battery scheduling,reduced grid dependency,and optimized power dispatching,thereby significantly enhancing system efficiency,reliability,and sustainability.Overall,the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS,providing a viable pathway toward high penetration of renewable energy sources in future power systems.展开更多
基金Prince Sattambin AbdulazizUniversity for funding their research work through the project number PSAU/2024/01/31821.
文摘The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges from intermittency,fluctuating loads,cost optimization,and uncertainty in real-time balancing.Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation.While stochastic and machine learning methods are used,they struggle with limited data,complex temporal patterns,and scalability.Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption.To address these issues,this paper presents a novel three-stage centralized EMS for interconnected microgrids.The first stage involves comprehensive data analysis to extract meaningful patterns.The second stage introduces a hybrid forecasting framework that integrates stochastic(Prophet)with machine learning(BiLSTM)techniques to improve prediction accuracy under uncertainty.In the third stage,a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids,with the aim of reducing operational costs,minimizing carbon emissions,and improving system stability under climate variability.The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency.Four scenarios are considered to evaluate the proposed energy management strategy.The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance.The combined methods achieve the best performance among all tested models,with an RMSE of 0.0070,MAE of 0.0043,and R^(2) of 0.9988,corresponding to improvements of ΔRMSE=−0.2122 and ΔR^(2)=+0.7126 relative to Prophet.These substantial gains in predictive accuracy translate into more precise battery scheduling,reduced grid dependency,and optimized power dispatching,thereby significantly enhancing system efficiency,reliability,and sustainability.Overall,the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS,providing a viable pathway toward high penetration of renewable energy sources in future power systems.
