Environmental regulation and industrial upgrading are the key to achieve win-win results for both economy and the environment. After environmental regulation tools are divided into market incentive and command control...Environmental regulation and industrial upgrading are the key to achieve win-win results for both economy and the environment. After environmental regulation tools are divided into market incentive and command control types,based on the provincial-level data of 30 provinces( cities and regions) in China from 2004 to 2016,the impact of environmental regulation on industrial upgrading and its transmission paths are empirically tested through an intermediary effect model. Technological innovation,FDI and capital market development all meet intermediary conditions,and the market incentive type is more dependent on technological innovation,while the command control type is more dependent on FDI and capital market development.The impact of the two environmental regulation tools on industrial upgrading is further studied. The results show that there is an " inverted U-shaped" relationship between the command control type and industrial upgrading,while there is a " U-shaped" relationship between the market incentive type and industrial upgrading,and there are also certain regional differences in the impact of environmental regulation on industrial upgrading.展开更多
In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
文摘Environmental regulation and industrial upgrading are the key to achieve win-win results for both economy and the environment. After environmental regulation tools are divided into market incentive and command control types,based on the provincial-level data of 30 provinces( cities and regions) in China from 2004 to 2016,the impact of environmental regulation on industrial upgrading and its transmission paths are empirically tested through an intermediary effect model. Technological innovation,FDI and capital market development all meet intermediary conditions,and the market incentive type is more dependent on technological innovation,while the command control type is more dependent on FDI and capital market development.The impact of the two environmental regulation tools on industrial upgrading is further studied. The results show that there is an " inverted U-shaped" relationship between the command control type and industrial upgrading,while there is a " U-shaped" relationship between the market incentive type and industrial upgrading,and there are also certain regional differences in the impact of environmental regulation on industrial upgrading.
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
