Open AccessEnergy management in smart grids for the integration of hybrid wind–PV–FC–battery renewable energy resources using multi‐objective particle swarm optimisation (MOPSO)
Author(s) -
Elgammal Adel,
ElNaggar Mohamed
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.5036
Subject(s) - microgrid , renewable energy , computer science , photovoltaic system , particle swarm optimization , wind power , automotive engineering , distributed generation , energy management , reliability engineering , engineering , electrical engineering , energy (signal processing) , mathematics , statistics , machine learning
This study introduces an efficient energy management system (EMS) for a wind–photovoltaic (PV)–fuel cell (FC)–battery energy scheme with an effective control strategy to integrate with the utility grid. The suggested technique utilises the multi‐objective particle swarm optimisation (MOPSO) to minimise the operation cost of the microgrid (MG) and maximise the generated power by each source. The presented MOPSO has the ability to minimise the operation cost of the MG with respect to the renewable penetration, the fluctuation in the generated power, uncertainty in power demand, and continuous change of the utility market price. The proposed optimisation strategy makes the exact choice of sources in right planning and chooses the power that must be created by each source and the power required by the utility network. The price fluctuation data of power and the variance of the renewable energy produced by each unit will be sent to the EMS by a communication network using global positioning system. The dynamic performance and the operation cost of the MG have been tested under different weather conditions and the variance of the power demand through a whole simulation period of 24 h. The stability of the MG, power quality, and voltage regulation is verified by Matlab simulation and experimental results.