Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm

This paper suggests a new intelligent stochastic optimisation framework to assess the optimal energy management issue of the renewable micro-grids MGs in the reconfigurable networks. The proposed method is constructed based on the harmony search HS algorithm in addition to a novel adaptive modification method to answer the problem successfully. The suggested modification method includes three sub-modifications to help the algorithm for escaping from the local optima. Regarding the uncertainty effects, Monte Carlo simulation is used to model the uncertainties of the problem. The objective functions to be considered are minimisation of power losses, voltage deviation, total cost and total emission. The high ability and sufficient performance of the proposed stochastic framework are tested on the IEEE 33-bus distribution test system with a typical MG which includes different kinds of renewable energy sources such as photovoltaics, micro-turbine, fuel-cell, wind turbine and battery as the storage device.

[1]  Abdollah Kavousi-Fard,et al.  A novel adaptive modified harmony search algorithm to solve multi-objective environmental/economic dispatch , 2014, J. Intell. Fuzzy Syst..

[2]  Saifur Rahman,et al.  Securing critical loads in a PV-based microgrid with a multi-agent system , 2012 .

[3]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[4]  Taher Niknam,et al.  Impact of thermal recovery and hydrogen production of fuel cell power plants on distribution feeder reconfiguration , 2012 .

[5]  Taher Niknam,et al.  Stochastic framework for reliability enhancement using optimal feeder reconfiguration , 2014 .

[6]  M. Mahdavi,et al.  ARTICLE IN PRESS Available online at www.sciencedirect.com , 2007 .

[7]  Kankar Bhattacharya,et al.  Optimal planning and design of a renewable energy based supply system for microgrids , 2012 .

[8]  Halim Ceylan,et al.  Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .

[9]  Abdollah Kavousi-Fard,et al.  Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids , 2014 .

[10]  N. El Halabi,et al.  Intelligent renewable microgrid scheduling controlled by a virtual power producer: A laboratory experience , 2012 .

[11]  Reza Sedaghati,et al.  A hybrid fuzzy-PEM stochastic framework to solve the optimal operation management of distribution feeder reconfiguration considering wind turbines , 2014, J. Intell. Fuzzy Syst..

[12]  Taher Niknam,et al.  Intelligent stochastic framework to solve the reconfiguration problem from the reliability view , 2014 .

[13]  Nikos D. Hatziargyriou,et al.  Centralized Control for Optimizing Microgrids Operation , 2008 .

[14]  Marcelo Godoy Simões,et al.  Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Microgrid , 2007, IEEE Transactions on Industrial Electronics.

[15]  Saifur Rahman,et al.  Unit sizing and control of hybrid wind-solar power systems , 1997 .

[16]  Taher Niknam,et al.  Multi-Objective Stochastic Distribution Feeder Reconfiguration in Systems With Wind Power Generators and Fuel Cells Using the Point Estimate Method , 2013, IEEE Transactions on Power Systems.

[17]  Taher Niknam,et al.  Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty , 2014, IEEE Transactions on Power Delivery.

[18]  Bangyin Liu,et al.  Smart energy management system for optimal microgrid economic operation , 2011 .

[19]  Abdollah Kavousi-Fard,et al.  Reliability enhancement using optimal distribution feeder reconfiguration , 2013, Neurocomputing.

[20]  Taher Niknam,et al.  Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants , 2014 .

[21]  Taher Niknam,et al.  Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimisation , 2012 .

[22]  Clifford T. Brown,et al.  Lévy Flights in Dobe Ju/’hoansi Foraging Patterns , 2007 .