NEW SWARM INTELLIGENCE BASED OPTIMIZATION ALGORITHMS FOR THE OPTIMIZATION OF MICROGRIDS

Yıl 2018, , 196 - 208, 29.12.2018

Tuba Tanyıldızı Ağır , Tuba Tanyıldızı Ağır

https://doi.org/10.36222/ejt.464197

Cited By: 2

Öz

The need for new energy sources has increased due to reasons such as
the development of technology, the increase in
electricity demand, the decrease of fossil resources, and environmental
pollution. Renewable energy sources are self-renewing,
friendly, and clean energy sources. Microgrids
are small power energy networks consisting of renewable and non-renewable
energy sources, batteries, inverters, and loads. They can be operated
connected to the network and independently from the network. Metaheuristic
methods are algorithms that can achieve optimum results in the search space. In
this study, optimization of a microgrid composed of a wind turbine, solar panel, diesel generator, inverter, and loads
has been investigated with multi-objective
hybrid metaheuristic algorithms. Optimization is aimed at reducing emissions,
increasing reliability, and optimizing energy resources.  Swallow Swarm Optimization (SSO) and Hybrid
Particle Swallow Swarm Optimization (HPSSO) with different iterations and
populations are compared for the first time.

Anahtar Kelimeler

Microgrid, Metaheuristic algorithms, SSO

Kaynakça

• References
• [1] Wang, Z., et al., Intelligent Multi-Agent Control for Integrated Building and Micro-Grid Systems. In Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES, 2011, pp.1-7.[2] Aziza, M., & Walling, F., Multi-objective Particle Swarm Optimization of Hybrid Micro-Grid System: A Case Study in Sweden, Energy, 2017, Vol.123, pp. 108-118.[3] Fossati, J. P., et al., A Method for Optimal Sizing Energy Storage Systems for Microgrids. Renewable Energy, 2015, Vol.77, pp.539-549.[4] Bevrani, H., et al., Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach. IEEE Transactions on Smart Grid, 2012, Vol. 3(4), 1935-1944.[5] Chen, Y. K., et al., Design and İmplementation of Energy Management System with Fuzzy Control for DC Microgrid Systems. IEEE Transactions on Power Electronics, 2013, Vol. 28(4), 1563-1570.[6] Seung, C., et al., Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid, In Universities Power Engineering Conference (UPEC), 2012 47th International, 2012, pp. 1-6.[7] Eilaghi, S. F., et al., Optimal Voltage Unbalance Compensation in a Microgrid Using PSO Algorithm. In Power India International Conference (PIICON), 2016 IEEE 7th, 2016, pp.1-6[8] Yazdani, M., & Jolai, F., Lion Optimization Algorithm (LOA): A Nature-inspired Metaheuristic algorithm. Journal of Computational Design and Engineering, 2016, Vol. 3(1): pp.24-36.[9] Gandomi, A. H., et al., Mixed Variable Structural Optimization Using Firefly Algorithm, Computers & Structures, 2011, Vol. 89(23-24), pp. 2325-2336.[10] Gandomi, A. H., & Alavi, A. H., Krill Herd: A New Bio-inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 2012, Vol. 17(12), pp. 4831-4845.[11] Revathi, K., & Krishnamoorthy, N., The Performance Analysis of Swallow Swarm Optimization Algorithm, 2015 2nd International Conference on IEEE, 2015, pp. 558-562.[12] Bouzidi, S., & Riffi, M. E., Discrete Swallow Swarm Optimization Algorithm for Travelling Salesman Problem, In Proceedings of the 2017 International Conference on Smart Digital Environment, 2017, pp. 80-84.[13] Neshat, M., et al., Swallow Swarm Optimization Algorithm: A New Method to Optimization, Neural Computing and Applications, 2013, Vol. 23(2), pp.429-454.[14] Kaveh, A., et al., Hybrid PSO and SSO Algorithm for Truss Layout and Size Optimization Considering Dynamic Constraints, Structural Engineering and Mechanics, 2015, Vol. 54(3), pp. 453-474.[15] Sam C., & Ali, A., A., 2015. Identification of Crack in a Cantilever Beam using Improved PSO Algorithm, International Journal for Innovative Research in Science & Technology, 2015, Vol.1(11), pp. 454- 461.