Multi-source energy mixing for renewable energy microgrids by particle swarm optimization

Distributed intelligence is one of the prominent prospects of future smart grids besides distributed generation, distributed storage and demand side load management. This study illustrates utilization of particle swarm optimization (PSO) method for cost-efficient energy management in multi-source renewable energy microgrids. PSO algorithm is used to find out optimal energy mixing rates that can minimize daily energy cost of a renewable microgrids under energy balance and anti-islanding constraints. The optimal energy mixing rates can be used by multi-pulse width modulation (M-PWM) energy mixer units. In our numerical analyses, we consider a multi-source renewable energy grid scenario that includes solar energy system, wind energy system, battery system and utility grid connection. We assume that variable energy pricing is used in utility grid to control energy dispatches between microgrids. This numerical analysis shows that the proposed scheme can adjust energy mixing rates for M-PWM energy mixers to achieve the cost-efficient and energy balanced management of microgrid under varying generation, demand and price conditions. The proposed method illustrates an implementation of distributed intelligence in smart grids.

[1]  Rahul Mohanani,et al.  Demand side energy management in hybrid microgrid system using heuristic techniques , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[2]  Mohammad Hassan Moradi,et al.  Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm , 2016 .

[3]  Hamza Abunima,et al.  An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid , 2017 .

[4]  Laxmi Srivastava,et al.  Generation scheduling and micro-grid energy management using differential evolution algorithm , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[5]  Francisco Jurado,et al.  Long-term optimization based on PSO of a grid-connected renewable energy/battery/hydrogen hybrid system , 2014 .

[6]  Nadeem Javaid,et al.  Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources , 2016 .

[7]  Wadaed Uturbey,et al.  Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand , 2013 .

[8]  Azah Mohamed,et al.  Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm , 2017 .

[9]  Tarek Y. ElMekkawy,et al.  Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach , 2014 .

[10]  Zhang Chenghui,et al.  Particle Swarm Optimization for energy management fuzzy controller design in dual-source electric vehicle , 2007, 2007 IEEE Power Electronics Specialists Conference.

[11]  N. C. Sahoo,et al.  Demand side management of residential loads in a smart grid using 2D particle swarm optimization technique , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[12]  Lei Wang,et al.  Chance Constrained Optimization in a Home Energy Management System , 2018, IEEE Transactions on Smart Grid.

[13]  Yi-Hsuan Hung,et al.  Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization , 2015 .

[14]  Baris Baykant Alagoz,et al.  An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids , 2013 .

[15]  Motaz Amer,et al.  Optimization of Hybrid Renewable Energy Systems (HRES) Using PSO for Cost Reduction , 2013 .

[16]  Cemal Keles,et al.  Multi-source energy mixing by time rate multiple PWM for microgrids , 2016, 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG).

[17]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[18]  Abdulkerim Karabiber,et al.  A user-mode distributed energy management architecture for smart grid applications , 2012 .

[19]  Baris Baykant Alagoz,et al.  Dynamic energy pricing by closed-loop fractional-order PI control system and energy balancing in smart grid energy markets , 2016 .

[20]  Murat Akcin,et al.  A smart building power management concept: Smart socket applications with DC distribution , 2015 .