The complex structures of distributed energy systems(DES)and uncertainties arising from renewable energy sources and user load variations pose significant operational challenges.Model predictive control(MPC)and reinfo...The complex structures of distributed energy systems(DES)and uncertainties arising from renewable energy sources and user load variations pose significant operational challenges.Model predictive control(MPC)and reinforcement learning(RL)are widely used to optimize DES by predicting future outcomes based on the current state.However,MPC’s real-time application is constrained by its computational demands,making it less suitable for complex systems with extended predictive horizons.Meanwhile,RL’s model-free approach leads to suboptimal data utilization,limiting its overall performance.To address these issues,this study proposes an improved reinforcement learning-model predictive control(RL-MPC)algorithm that combines the high-precision local optimization of MPC with the global optimization capability of RL.In this study,we enhance the existing RL-MPC algorithm by increasing the number of optimization steps performed by the MPC component.We evaluated RL,MPC,and the enhanced RL-MPC on a DES comprising a photovoltaic(PV)and battery energy storage system(BESS).The results indicate the following:(1)The twin delayed deep deterministic policy gradient(TD3)algorithm outperforms other RL algorithms in energy cost optimization,but is outperformed in all cases by RL-MPC.(2)For both MPC and RL-MPC,when the mean absolute percentage error(MAPE)of the first-step prediction is 5%,the total cost increases by∼1.2%compared to that when the MAPE is 0%.However,if the accuracy of the initial prediction data remains constant while only the error gradient of the data sequence increases,the total cost remains nearly unchanged,with an increase of only∼0.1%.(3)Within a 12 h predictive horizon,RL-MPC outperforms MPC,suggesting it as a suitable alternative to MPC when high-accuracy prediction data are limited.展开更多
Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural...Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural network(ANN)chip,including multiply-and-accumulate(MAC),memory and activation function circuits.Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors(FETs)on a wafer-scale and high-homogeneity MoS2 film,with a gate-last process to realize top gate structured FETs.A 62-level simulation program with integrated circuit emphasis(SPICE)model is utilized to design and optimize our analog ANN circuits.To demonstrate a practical application,a tactile digit sensing recognition was demonstrated based on our ANN circuits.After training,the digit recognition rate exceeds 97%.Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale inte-grated circuits,but also paves the way for its future application in AI computation.展开更多
Nanosized dispersive hexagonal magnesium hydroxide （Mg（OH）2） has been prepared using an ammonia- hydrothermal method. Citric acid and monoethanolamine （MEA） were added to the reaction system during the ammonia p...Nanosized dispersive hexagonal magnesium hydroxide （Mg（OH）2） has been prepared using an ammonia- hydrothermal method. Citric acid and monoethanolamine （MEA） were added to the reaction system during the ammonia precipitation and hydrothermal processes, respectively, to improve the crystallinity and dispersion of the （Mg（OH）2） particles. The resulting Mg（OH）2 samples obtained under the opti- mum preparation conditions were characterized by scanning electron microscopy, X-ray diffraction and thermal gravity analysis, which showed that this newly developed procedure afforded well-dispersed hexagonal nanoolates of Mg（OH）2 with a mean diameter of 246 nm.展开更多
基金supported by National Key R&D Program of China(Grant No.2023YFC3807100)State Grid Corporation of China Science and Technology Program(Grant No.5211YF24000T).
文摘The complex structures of distributed energy systems(DES)and uncertainties arising from renewable energy sources and user load variations pose significant operational challenges.Model predictive control(MPC)and reinforcement learning(RL)are widely used to optimize DES by predicting future outcomes based on the current state.However,MPC’s real-time application is constrained by its computational demands,making it less suitable for complex systems with extended predictive horizons.Meanwhile,RL’s model-free approach leads to suboptimal data utilization,limiting its overall performance.To address these issues,this study proposes an improved reinforcement learning-model predictive control(RL-MPC)algorithm that combines the high-precision local optimization of MPC with the global optimization capability of RL.In this study,we enhance the existing RL-MPC algorithm by increasing the number of optimization steps performed by the MPC component.We evaluated RL,MPC,and the enhanced RL-MPC on a DES comprising a photovoltaic(PV)and battery energy storage system(BESS).The results indicate the following:(1)The twin delayed deep deterministic policy gradient(TD3)algorithm outperforms other RL algorithms in energy cost optimization,but is outperformed in all cases by RL-MPC.(2)For both MPC and RL-MPC,when the mean absolute percentage error(MAPE)of the first-step prediction is 5%,the total cost increases by∼1.2%compared to that when the MAPE is 0%.However,if the accuracy of the initial prediction data remains constant while only the error gradient of the data sequence increases,the total cost remains nearly unchanged,with an increase of only∼0.1%.(3)Within a 12 h predictive horizon,RL-MPC outperforms MPC,suggesting it as a suitable alternative to MPC when high-accuracy prediction data are limited.
基金the National Key Research and Development Program of China(2016YFA0203900,2018YFB2202500)Innovation Program of Shanghai Municipal Education Commission(2021-01-07-00-07-E00077)+3 种基金Shanghai Municipal Science and Technology Commission(18JC1410300,21DZ1100900)Research Grant Council of Hong Kong(15205619)the National Natural Science Foundation of China(61925402,61934008,and 6210030233)the Natural Science Foundation of Shanghai(21ZR1405700)。
文摘Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural network(ANN)chip,including multiply-and-accumulate(MAC),memory and activation function circuits.Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors(FETs)on a wafer-scale and high-homogeneity MoS2 film,with a gate-last process to realize top gate structured FETs.A 62-level simulation program with integrated circuit emphasis(SPICE)model is utilized to design and optimize our analog ANN circuits.To demonstrate a practical application,a tactile digit sensing recognition was demonstrated based on our ANN circuits.After training,the digit recognition rate exceeds 97%.Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale inte-grated circuits,but also paves the way for its future application in AI computation.
文摘Nanosized dispersive hexagonal magnesium hydroxide （Mg（OH）2） has been prepared using an ammonia- hydrothermal method. Citric acid and monoethanolamine （MEA） were added to the reaction system during the ammonia precipitation and hydrothermal processes, respectively, to improve the crystallinity and dispersion of the （Mg（OH）2） particles. The resulting Mg（OH）2 samples obtained under the opti- mum preparation conditions were characterized by scanning electron microscopy, X-ray diffraction and thermal gravity analysis, which showed that this newly developed procedure afforded well-dispersed hexagonal nanoolates of Mg（OH）2 with a mean diameter of 246 nm.
