Optimal Planning and Energy Management of Distributed Generation Sources and Battery Storage in Smart Microgrids for Operating Costs Reduction by Cuckoo Search Algorithm

Subject Areas :

Esmail Khalilzadeh ^{1 *} , Ahmad Ghalibafan ² , Aida Keshavarz ³

1 - Department of Electrical and Computer Engineering, Arsanjan Branch, Islamic Azad University, Arsanjan, Iran
2 - Department of Electrical Engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
3 - Department of Operating Room, Arsanjan Branch, Islamic Azad University, Arsanjan, Iran

Received: 2024-05-25 Accepted : 2024-08-08 Published : 2024-09-07

Keywords: Battery storage, Cost reduction, Cuckoo search algorithm, Energy management, Smart micro¬grid,

Abstract :

The optimal management distributed generation resources and storage devices in power micro­grids is done with various goals such as reducing operating costs, reducing environmental pollution, improving the quality of network power, and also improving reliability indicators. In order to achieve each of the mentioned goals, The operator of the power system must know precisely all the components of the network, such as loads and sources of power generation, as well as the topology of the network. Various innovative and ultra-innovative methods have been proposed to provide energy management program, and in recent years, the use of intelligent algorithms has been used more than other methods. High accuracy and no need to estimate the exact initial point have made smart algorithms suitable for solving the problem of micro­grid energy management. In this research, the cuckoo search algorithm is used for the energy management of renewable photovoltaic and wind resources along with non-renewable resources of fuel cell and micro­turbine along with battery storage in a standard micro­grid. The performance of the proposed method was evaluated for different load conditions and solar radiation intensity in different scenarios. The simulation results were carried out in four different operating conditions with the aim of reducing the cost and were compared with the results of genetic algorithms, particle swarm optimization, bee, modified bat, and lightning search. The proposed algorithm of this research That is, the cuckoo search algorithm has performed better in all operating conditions in reducing the objective function.

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