To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's oper...To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.展开更多
This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR mod...This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR models.The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly horizons.The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects.The out-of-sample results were robust across three different methods.More importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.展开更多
文摘To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.
基金supported by the National Natural Science Foundation of China(Nos.72071166,71701176,and 72133003)。
文摘This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility.Considering day-of-the-week effects,structural breaks,or both,we propose three classes of HAR models to forecast electricity volatility based on existing HAR models.The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon,whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily,weekly,and monthly horizons.The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility,and in most cases,dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects.The out-of-sample results were robust across three different methods.More importantly,we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.
