Research on distributed computing method for coordinated cooperation of distributed energy and multi-devices

With the rapid development of the distributed renewable energy technologies, there is more significant increase in the amount of data generated by the smart grids, and there are a higher delay problem in the task processing, with the traditional centralized control methods, which brings in an adverse effect on the stable operation of the smart grid. Aiming at the problem, this paper proposes a distributed computing scheme that combines edge computing and cloud computing to improve power system data processing capabilities and system response capabilities. Besides, the paper analyzes the finiteness of edge node computing ability. According to the task load balancing theory, a task performance minimization as an objective function is proposed, and a high-performance load balancing strategy for immune chaotic particle swarms algorithm (ICPSO) is proposed based on this distributed computing architecture. Finally, the feasibility and effectiveness of the system architecture and load balancing strategy are verified through the simulation results.

[1]  Dinesh C. Verma,et al.  Edge computing architecture for applying AI to IoT , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[2]  Mohammad Amin Heidari,et al.  Optimal network reconfiguration in distribution system for loss reduction and voltage-profile improvement using hybrid algorithm of PSO and ACO , 2017 .

[3]  Kaile Zhou,et al.  Multi-objective optimal dispatch of microgrid containing electric vehicles , 2017 .

[4]  Tomonobu Senjyu,et al.  Intelligent Economic Operation of Smart-Grid Facilitating Fuzzy Advanced Quantum Evolutionary Method , 2013, IEEE Transactions on Sustainable Energy.

[5]  Kong Min.  A New Particle Swarm Optimization for Solving Constrained Optimization Problems , 2007 .

[6]  Manoj Agnihotri,et al.  Execution analysis of load balancing particle swarm optimization algorithm in cloud data center , 2016, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC).

[7]  Shangguang Wang,et al.  MVR: An Architecture for Computation Offloading in Mobile Edge Computing , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[8]  Li Peng,et al.  A secure-efficient data collection algorithm based on self-adaptive sensing model in mobile Internet of vehicles , 2016 .

[9]  Yan Meng,et al.  Research on Cloud Computing , 2013 .

[10]  Rui Yang,et al.  Cloud computing task scheduling based on improved particle swarm optimization algorithm , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[11]  Zhiyuan Ren,et al.  A novel load balancing strategy of software-defined cloud/fog networking in the Internet of Vehicles , 2016, China Communications.

[12]  Tong Yu,et al.  Reactive power optimization of electric power system incorporating wind power based on Parallel Immune Particle Swarm Optimization , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[13]  Robert Elsässer,et al.  Diffusion Schemes for Load Balancing on Heterogeneous Networks , 2002, Theory of Computing Systems.

[14]  Zhidong Deng,et al.  A new approach to solve the mission assignment problem for cooperative UCAVs using immune particle swarm optimizations , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[15]  Jiang Chuanwen,et al.  A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation , 2005, Math. Comput. Simul..