In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a dr...In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.展开更多
Metallic biomedical implants based on magnesium,zinc and iron alloys have emerged as bioresorbable alternatives to permanent orthopaedic implants over the last two decades.The corrosion rate of biodegradable metals pl...Metallic biomedical implants based on magnesium,zinc and iron alloys have emerged as bioresorbable alternatives to permanent orthopaedic implants over the last two decades.The corrosion rate of biodegradable metals plays a critical role in controlling the compatibility and functionality of the device in vivo.The broader adoption of biodegradable metals in orthopaedic applications depends on developing in vitro methods that accurately predict the biodegradation behaviour in vivo.However,the physiological environment is a highly complex corrosion environment to replicate in the laboratory,making the in vitro-to-in vivo translation of results very challenging.Accordingly,the results from in vitro corrosion tests fail to provide a complete schema of the biodegradation behaviour of the metal in vivo.In silico approach based on computer simulations aim to bridge the observed differences between experiments performed in vitro and vivo.A critical review of the state-of-the-art of computational modelling techniques for predicting the corrosion behaviour of magnesium alloy as a biodegradable metal is presented.展开更多
文摘In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.
基金supported by the Health Research Council of New Zealand.
文摘Metallic biomedical implants based on magnesium,zinc and iron alloys have emerged as bioresorbable alternatives to permanent orthopaedic implants over the last two decades.The corrosion rate of biodegradable metals plays a critical role in controlling the compatibility and functionality of the device in vivo.The broader adoption of biodegradable metals in orthopaedic applications depends on developing in vitro methods that accurately predict the biodegradation behaviour in vivo.However,the physiological environment is a highly complex corrosion environment to replicate in the laboratory,making the in vitro-to-in vivo translation of results very challenging.Accordingly,the results from in vitro corrosion tests fail to provide a complete schema of the biodegradation behaviour of the metal in vivo.In silico approach based on computer simulations aim to bridge the observed differences between experiments performed in vitro and vivo.A critical review of the state-of-the-art of computational modelling techniques for predicting the corrosion behaviour of magnesium alloy as a biodegradable metal is presented.
