More customers are tending to install batteries with photovoltaic(PV), so they can better control their electricity bills. In this context, customers may be tempted to go offgrid at a substantial up-front cost, leadin...More customers are tending to install batteries with photovoltaic(PV), so they can better control their electricity bills. In this context, customers may be tempted to go offgrid at a substantial up-front cost, leading electricity companies into a death spiral, thereby raising electricity price further on those remaining on grid. Neighborhood energy markets can promote the sharing of locally generated renewable energy and encourage prosumers to stay on grid with financial incentives. A novel neighborhood energy trading(NET) mechanism is developed using the topology of existing radial distribution network to encourage sustainable energy sharing in neighborhood and encourage prosumers to stay on grid. This mechanism considers loss, congestion management, and voltage regulation, and it is scalable with low computation and communication overhead.An IEEE test system is used to validate the NET mechanism.The simulation shows that the price and flow results are obtained with fast computation speed(within 10 iterations) and with loss reflected, flow limit reinforced, and voltage regulated.This study proves that the economic demand-supply-based pricing mechanism can be applied effectively in distribution networks to help encourage more renewable energy sharing in sustainable neighborhood and avoid energy network death spiral.展开更多
Spatial information remains to be an important topic in geographic information system and in remote sensing fields,and spatial relationships have been increasingly incorporated into the image classification processes....Spatial information remains to be an important topic in geographic information system and in remote sensing fields,and spatial relationships have been increasingly incorporated into the image classification processes.Previous studies have employed multiple occurrences of spatial features(shape,texture,etc.,)to improve classification results.However,less attention has been focused on using higher-level spatial relationships for image classification.In this study,two novel spatial relationships,namely,maximum spatial adjacency(MSA)and directional spatial adjacency(DSA),were proposed to assist in image classification.The proposed methods were implemented to extract buildings,beach,and emergent vegetation land-cover classes according to their spatial relationships with their corresponding reference classes.The promising results obtained from this study suggest that the proposed MSA and DSA spatial relationships can be valuable information in defining rule sets for a more reasonable and accurate classification.展开更多
基金supported in part by the Australian Research Council Discovery Project (No. 160102570)。
文摘More customers are tending to install batteries with photovoltaic(PV), so they can better control their electricity bills. In this context, customers may be tempted to go offgrid at a substantial up-front cost, leading electricity companies into a death spiral, thereby raising electricity price further on those remaining on grid. Neighborhood energy markets can promote the sharing of locally generated renewable energy and encourage prosumers to stay on grid with financial incentives. A novel neighborhood energy trading(NET) mechanism is developed using the topology of existing radial distribution network to encourage sustainable energy sharing in neighborhood and encourage prosumers to stay on grid. This mechanism considers loss, congestion management, and voltage regulation, and it is scalable with low computation and communication overhead.An IEEE test system is used to validate the NET mechanism.The simulation shows that the price and flow results are obtained with fast computation speed(within 10 iterations) and with loss reflected, flow limit reinforced, and voltage regulated.This study proves that the economic demand-supply-based pricing mechanism can be applied effectively in distribution networks to help encourage more renewable energy sharing in sustainable neighborhood and avoid energy network death spiral.
基金This research is partially supported by a NSERC Discovery Grant awarded to Dr.Jinfei Wang,University of Western Ontario.
文摘Spatial information remains to be an important topic in geographic information system and in remote sensing fields,and spatial relationships have been increasingly incorporated into the image classification processes.Previous studies have employed multiple occurrences of spatial features(shape,texture,etc.,)to improve classification results.However,less attention has been focused on using higher-level spatial relationships for image classification.In this study,two novel spatial relationships,namely,maximum spatial adjacency(MSA)and directional spatial adjacency(DSA),were proposed to assist in image classification.The proposed methods were implemented to extract buildings,beach,and emergent vegetation land-cover classes according to their spatial relationships with their corresponding reference classes.The promising results obtained from this study suggest that the proposed MSA and DSA spatial relationships can be valuable information in defining rule sets for a more reasonable and accurate classification.
