An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting.EPA categorizes facility carbon footprints in three scopes.Scope-2 emissions include electricity,heat or steam purchased...An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting.EPA categorizes facility carbon footprints in three scopes.Scope-2 emissions include electricity,heat or steam purchased from a utility provider.This paper evaluates the existing calculation methods for scope-2 CO2 emissions for purchased electricity.The electricity grid in US is complex and is divided spatially into states,eGRID regions,balancing authorities(BAs),and utilities.Up to hourly temporal granularity can be obtained from available datasets.A matrix is developed that categorizes different datasets based on the complexity to calculate the carbon emission factors.Spatial and temporal variations are evaluated.There are significant spatial overlap between regions in different categories and emission factors within a region show sub-regional variation.An area analysis is done using zip-code polygons to determine whether a state or balancing authority is smaller for all the overlapping cases.Temporal variations in emission factors are significant depending on the balancing authority considered.A single method to calculate scope-2 emission factors may not be accurate and efficient in every case and a nuanced assessment of emission factors is warranted.An implementation pathway for a“smart carbon calculator”—one that gives accurate carbon footprint that is the spatially and temporally most granular is suggested.展开更多
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
文摘An integral part of the effort to reduce greenhouse gas emissions is carbon footprint accounting.EPA categorizes facility carbon footprints in three scopes.Scope-2 emissions include electricity,heat or steam purchased from a utility provider.This paper evaluates the existing calculation methods for scope-2 CO2 emissions for purchased electricity.The electricity grid in US is complex and is divided spatially into states,eGRID regions,balancing authorities(BAs),and utilities.Up to hourly temporal granularity can be obtained from available datasets.A matrix is developed that categorizes different datasets based on the complexity to calculate the carbon emission factors.Spatial and temporal variations are evaluated.There are significant spatial overlap between regions in different categories and emission factors within a region show sub-regional variation.An area analysis is done using zip-code polygons to determine whether a state or balancing authority is smaller for all the overlapping cases.Temporal variations in emission factors are significant depending on the balancing authority considered.A single method to calculate scope-2 emission factors may not be accurate and efficient in every case and a nuanced assessment of emission factors is warranted.An implementation pathway for a“smart carbon calculator”—one that gives accurate carbon footprint that is the spatially and temporally most granular is suggested.
文摘In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
