This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MD...This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.展开更多
The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to ma...The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 51277015,51677007 and 51977012.
文摘This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.
文摘The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.
