A cloud edge computing method for economic dispatch of active distribution network with multi-microgrids

[1]  Zhiyi Li,et al.  Coordinated Cloud-Edge Anomaly Identification for Active Distribution Networks , 2023, IEEE Transactions on Cloud Computing.

[2]  Chunxia Dou,et al.  Economic-Driven Hierarchical Voltage Regulation of Incremental Distribution Networks: A Cloud-Edge Collaboration Based Perspective , 2022, IEEE Transactions on Industrial Informatics.

[3]  M. E. Meral,et al.  A Coordinated Virtual Impedance Control Scheme for Three Phase Four Leg Inverters of Electric Vehicle to Grid , 2022, SSRN Electronic Journal.

[4]  Daqing Chen,et al.  ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments , 2021, Int. J. Intell. Syst..

[5]  Jinsong Wu,et al.  MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge , 2021, Applied Sciences.

[6]  Albert Y. Zomaya,et al.  Machine-Learning-Based Real-Time Economic Dispatch in Islanding Microgrids in a Cloud-Edge Computing Environment , 2021, IEEE Internet of Things Journal.

[7]  Guizhong Liu,et al.  Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks , 2021, IEEE Internet of Things Journal.

[8]  Chen Li,et al.  A Cloud-edge Cooperative Dispatching Method for Distribution Networks Considering Photovoltaic Generation Uncertainty , 2020, Journal of Modern Power Systems and Clean Energy.

[9]  Wei Du,et al.  A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications , 2020, Artificial Intelligence Review.

[10]  Xiaoqing Zhu,et al.  Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks , 2020 .

[11]  Mehmet Emin Meral,et al.  Multi‐objective control scheme for operation of parallel inverter‐based microgrids during asymmetrical grid faults , 2020, IET Renewable Power Generation.

[12]  Kezhi Wang,et al.  Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing , 2020, IEEE Transactions on Cognitive Communications and Networking.

[13]  Jingrui Zhang,et al.  Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders , 2020 .

[14]  Siyuan Wang,et al.  Cloud Computing and Local Chip-Based Dynamic Economic Dispatch for Microgrids , 2020, IEEE Transactions on Smart Grid.

[15]  Yu Peng,et al.  Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy , 2020, Comput. Commun..

[16]  Ju Ren,et al.  A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms , 2019, ACM Comput. Surv..

[17]  Yannan Li,et al.  Toward Data Security in Edge Intelligent IIoT , 2019, IEEE Network.

[18]  Yan Xu,et al.  Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search , 2019, IEEE Transactions on Power Systems.

[19]  Amjad Anvari-Moghaddam,et al.  A decentralized robust model for optimal operation of distribution companies with private microgrids , 2019, International Journal of Electrical Power & Energy Systems.

[20]  Zhen Yang,et al.  Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing , 2018, Applied Energy.

[21]  Lingfeng Wang,et al.  Robust Coordinated Optimization of Active and Reactive Power in Active Distribution Systems , 2018, IEEE Transactions on Smart Grid.

[22]  Qingshan Xu,et al.  Day-Ahead Centralized Optimal Dispatching of Active Distribution Power System with Combined Cooling, Heating and Power-Based Microgrids , 2018, 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

[23]  Xiang Ji,et al.  Autonomous optimized economic dispatch of active distribution system with multi-microgrids , 2018, Energy.

[24]  Gang Chen,et al.  Distributed Finite-Time Economic Dispatch of a Network of Energy Resources , 2017, IEEE Transactions on Smart Grid.

[25]  Athanasios V. Vasilakos,et al.  IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges , 2017, IEEE Internet of Things Journal.

[26]  Xiaochuan Luo,et al.  Cloud Computing for Power System Simulations at ISO New England—Experiences and Challenges , 2016, IEEE Transactions on Smart Grid.

[27]  Benjamin Sovacool.  Deploying Off-Grid Technology to Eradicate Energy Poverty , 2012, Science.

[28]  Hongbin Sun,et al.  Family of energy management system for smart grid , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[29]  Peng Zhang,et al.  Reliability evaluation of active distribution systems including microgrids , 2012, 2013 IEEE Power & Energy Society General Meeting.

[30]  Lieven Vandevelde,et al.  Active Load Control in Islanded Microgrids Based on the Grid Voltage , 2011, IEEE Transactions on Smart Grid.

[31]  Taher Niknam,et al.  Non-smooth economic dispatch computation by fuzzy and self adaptive particle swarm optimization , 2011, Appl. Soft Comput..

[32]  Anurag K. Srivastava,et al.  Controls for microgrids with storage: Review, challenges, and research needs , 2010 .

[33]  Sungchul Kang,et al.  Impedance Learning for Robotic Contact Tasks Using Natural Actor-Critic Algorithm , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[35]  Feng Zhang,et al.  Optimal energy management of multi-microgrids connected to distribution system based on deep reinforcement learning , 2021 .

[36]  Ping Yang,et al.  Analysis on the organization and Development of multi-microgrids , 2018 .