China’s wind power has gone through an exploring course of 20 years. At present, it is on the fast track under the support of the state’s preferential policies concerned. The various wind power development areas in ...China’s wind power has gone through an exploring course of 20 years. At present, it is on the fast track under the support of the state’s preferential policies concerned. The various wind power development areas in China have made rapid progress and put forward their own thinking of developing wind energy.展开更多
The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar po...The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar power, are summarized in the article. The experiences and lessons of implementation of these price policies are analyzed. It is concluded that reasonable price policy is quite effective for promoting re- newable power development. According to the requirement of China's renewable power development, the suggestions for improving renewable power pricing mechanism and price incentive policies are proposed.展开更多
The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of ...The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.展开更多
文摘China’s wind power has gone through an exploring course of 20 years. At present, it is on the fast track under the support of the state’s preferential policies concerned. The various wind power development areas in China have made rapid progress and put forward their own thinking of developing wind energy.
文摘The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar power, are summarized in the article. The experiences and lessons of implementation of these price policies are analyzed. It is concluded that reasonable price policy is quite effective for promoting re- newable power development. According to the requirement of China's renewable power development, the suggestions for improving renewable power pricing mechanism and price incentive policies are proposed.
文摘The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.
