Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm

Abstract In hybrid renewable energy system, optimization and control are more complex and non-linearity in nature. This research presented a hybrid algorithm based on Enhance grey wolf optimization and algorithm of Dragonfly algorithm for handling OPF (optimal power flow) issues. The hybrid algorithm is proposed for solving the minimization of fuel cost, power loss and voltage deviation. The Renewable energy are incorporated with solar and wind energy. To forecast the wind and solar photovoltaic power output the weibull distribution function are modelled. The traditional method is slow and incapable to solve non-linearity problems. The proposed method is fast and effective and it is experimented on IEEE 30 bus system by comparing with recent existing methods.

[1]  Al-Attar Ali Mohamed,et al.  Multi-objective Modified Grey Wolf Optimizer for Optimal Power Flow , 2016, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[2]  S. U. Prabha,et al.  Hybrid Swarm Algorithm for Multiobjective Optimal Power Flow Problem , 2016 .

[3]  Yang Li,et al.  A two-stage multi-objective optimal power flow algorithm for hybrid AC/DC grids with VSC-HVDC , 2017, 2017 IEEE Power & Energy Society General Meeting.

[4]  Giorgio Guariso,et al.  Methods and tools to evaluate the availability of renewable energy sources , 2011 .

[5]  N. Arsic,et al.  Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm , 2015 .

[6]  Liu Shengsong,et al.  A hybrid algorithm for optimal power flow using the chaos optimization and the linear interior point algorithm , 2002, Proceedings. International Conference on Power System Technology.

[7]  M. Narimani,et al.  A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type , 2013 .

[8]  Mehdi Savaghebi,et al.  Solving Multi-objective Optimal Power Flow Using Modified GA and PSO Based on Hybrid Algorithm , 2017 .

[9]  Dinh Luong Le,et al.  Hybrid Differential Evolution and Harmony Search for Optimal Power Flow , 2015 .

[10]  Sahand Ghavidel,et al.  Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm , 2015, Inf. Sci..

[11]  C. Shilaja,et al.  Optimal Power Flow Using Hybrid DA-APSO Algorithm in Renewable Energy Resources , 2017 .

[12]  Mojtaba Ghasemi,et al.  Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm , 2014 .

[13]  Djilani Benattous,et al.  Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions , 2017, Int. J. Syst. Assur. Eng. Manag..

[14]  S. Surender Reddy,et al.  Minimum emissions optimal power flow in wind-thermal power system using Opposition based Bacterial Dynamics algorithm , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[15]  Mousumi Basu,et al.  Group Search Optimization for Solution of Different Optimal Power Flow Problems , 2016 .

[16]  Serhat Duman,et al.  Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones , 2017, Neural Computing and Applications.

[17]  H. Seifi,et al.  Application of bat optimization algorithm in optimal power flow , 2016, 2016 24th Iranian Conference on Electrical Engineering (ICEE).

[18]  Hassan Monsef,et al.  A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning , 2015, IEEE Systems Journal.

[19]  Dongmei Wu,et al.  A hybrid algorithm based on BFA and PSO for optimal reactive power problem , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[20]  Hui Xu,et al.  An improved grey wolf optimizer algorithm integrated with Cuckoo Search , 2017, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).