With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is...With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method.展开更多
The paper analyzed characters of complicated system and discussed the reason of comprehensive evaluation, realization of flexible comprehensive evaluation was researched from prospect of dynamic measure selection of e...The paper analyzed characters of complicated system and discussed the reason of comprehensive evaluation, realization of flexible comprehensive evaluation was researched from prospect of dynamic measure selection of evaluation, balance of functionality and harmony, uncertainty factor. In the end, multistage flexible comprehensive evaluation of complicated system was applied to performance evaluation of firm.展开更多
The rapid development of robotics technology has made people’s lives and work more convenient and efficient.Theresearch and simulation of robots combined with reinforcement learning intelligent algorithms have become...The rapid development of robotics technology has made people’s lives and work more convenient and efficient.Theresearch and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in variousfields of robot applications.In view of this,this study is based on deep reinforcement learning convolutional neural networks,combined with point cloud models,proximal strategy optimization algorithms,and flexible action evaluation algorithms.A sealcutting robot based on deep reinforcement learning has been proposed.The final results show that the descent speed of the sealcutting robot with the root mean square difference as the performance standard is about 1%faster than the flexible actionevaluation algorithm.About 2%is faster than the proximal strategy optimization algorithm.It is about 4%faster than the deepdeterministic strategy gradient algorithm.This indicates that the research model has certain advantages in terms of actualaccuracy after cutting.The fluctuation of this model is about 10%smaller than the evaluation of flexible actions and about 60%smaller than the gradient of deep deterministic strategies.Therefore,the research model has the highest overall stability withoutfalling into local optima.In addition,compared to the near-end strategy optimization algorithm,it falls into local optima,resultingin a low coincidence degree of about 17%.The deep deterministic strategy gradient algorithm has a large fluctuation amplitudeduring the seal cutting process,and the overall curve is relatively slow,with a final overlap of about 70%.The overlap degree offlexible action evaluation is slightly higher by about 83%.The maximum stability of the model’s overlap is best around 90%.Through experiments,it can be found that the seal cutting robot proposed in the study based on deep reinforcement learningmaintains certain advantages in performance indicators in various types of tests.展开更多
Flexible energy storage devices are becoming indispensable new elements of wearable electronics to improve our living qualities.As the main energy storage devices,lithium-ion batteries(LIBs)are gradually approaching t...Flexible energy storage devices are becoming indispensable new elements of wearable electronics to improve our living qualities.As the main energy storage devices,lithium-ion batteries(LIBs)are gradually approaching their theoretical limit in terms of energy density.In recent years,lithium metal batteries(LMBs)with metallic Li as the anode are revived due to the extremely high energy density,and are considered to be one of the ideal alternatives for the next generation of flexible power supply.In this review,key technologies and scientific problems to be overcome for flexible LMBs are discussed.Then,the recent advances in flexible LMBs,including the design of flexible Li metal anodes,electrolytes,cathodes and interlayers,are summarized.In addition,we have summed up the research progress of flexible device configurations,and emphasized the importance of flexibility evaluation and functionality integration to ensure the wearing safety in complex environment.Finally,the challenges and future development of flexible LMBs are summarized and prospected.展开更多
基金supported by the NationalNatural Science Foundation of China(Grant No.52107129).
文摘With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method.
文摘The paper analyzed characters of complicated system and discussed the reason of comprehensive evaluation, realization of flexible comprehensive evaluation was researched from prospect of dynamic measure selection of evaluation, balance of functionality and harmony, uncertainty factor. In the end, multistage flexible comprehensive evaluation of complicated system was applied to performance evaluation of firm.
文摘The rapid development of robotics technology has made people’s lives and work more convenient and efficient.Theresearch and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in variousfields of robot applications.In view of this,this study is based on deep reinforcement learning convolutional neural networks,combined with point cloud models,proximal strategy optimization algorithms,and flexible action evaluation algorithms.A sealcutting robot based on deep reinforcement learning has been proposed.The final results show that the descent speed of the sealcutting robot with the root mean square difference as the performance standard is about 1%faster than the flexible actionevaluation algorithm.About 2%is faster than the proximal strategy optimization algorithm.It is about 4%faster than the deepdeterministic strategy gradient algorithm.This indicates that the research model has certain advantages in terms of actualaccuracy after cutting.The fluctuation of this model is about 10%smaller than the evaluation of flexible actions and about 60%smaller than the gradient of deep deterministic strategies.Therefore,the research model has the highest overall stability withoutfalling into local optima.In addition,compared to the near-end strategy optimization algorithm,it falls into local optima,resultingin a low coincidence degree of about 17%.The deep deterministic strategy gradient algorithm has a large fluctuation amplitudeduring the seal cutting process,and the overall curve is relatively slow,with a final overlap of about 70%.The overlap degree offlexible action evaluation is slightly higher by about 83%.The maximum stability of the model’s overlap is best around 90%.Through experiments,it can be found that the seal cutting robot proposed in the study based on deep reinforcement learningmaintains certain advantages in performance indicators in various types of tests.
基金financially supported by the National Natural Science Foundation of China(U1804138,U1904195,and 22104079)the Program for Science&Technology Innovative Research Team(20IRTSTHN007)+2 种基金the Innovation Talents(22HASTIT028)Key Scientific Research(22A150052)in the Universities of Henan Provincethe Key Science and Technology Research of Henan Province(212102210654)。
文摘Flexible energy storage devices are becoming indispensable new elements of wearable electronics to improve our living qualities.As the main energy storage devices,lithium-ion batteries(LIBs)are gradually approaching their theoretical limit in terms of energy density.In recent years,lithium metal batteries(LMBs)with metallic Li as the anode are revived due to the extremely high energy density,and are considered to be one of the ideal alternatives for the next generation of flexible power supply.In this review,key technologies and scientific problems to be overcome for flexible LMBs are discussed.Then,the recent advances in flexible LMBs,including the design of flexible Li metal anodes,electrolytes,cathodes and interlayers,are summarized.In addition,we have summed up the research progress of flexible device configurations,and emphasized the importance of flexibility evaluation and functionality integration to ensure the wearing safety in complex environment.Finally,the challenges and future development of flexible LMBs are summarized and prospected.
