A Combination of Genetic Algorithm and Particle Swarm Optimization for Power Systems Planning Subject to Energy Storage
محورهای موضوعی : Journal of Computer & Robotics
Mohsen Mohammadhosseini
1
,
Hamid Ghadiri
2
1 - Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
کلید واژه: Optimization, GAMS, PSO, MATLAB, GA, Energy storage, Energy distribution,
چکیده مقاله :
With the ever-increasing growth of electrical energy consumption in different fields of a power plant, expanding strategies in power plants is a vital, important and inevitable action. Generally, greenhouse gas emissions can be reduced by replacing wind energy instead of using fossil fuels in power plants for electricity generation. A physical system that is capable of harnessing energy for distribution and compensation electricity at a desired and determined later time is called a typical energy storage system. In this paper, a proper optimization method for expansion planning of electrical energy storage is presented. Since the meta-heuristic algorithms are a very suitable tool for optimization purposes, a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) technique are used in this research. The main objective of the optimization problem is to increase the energy storage. The implementation of the proposed method is performed using MATLAB and GAMS tools. The simulation results strongly validate the correctness and effectiveness of the proposed method.
[1] Resener, M.; Haffner, S.; Pereira, L.A., "Optimization techniques applied to planning of electric power distribution systems: a bibliographic survey", Energy Syst, vol. 9, pp. 473–509 (2018).
[2] Lin, J.; Magnago, F.; Alemany, J. M., "Chapter 1 - Optimization Methods Applied to Power Systems: Current Practices and Challenges", Eds.: Academic Press, pp. 1-18 (2018).
[3] Nazari-Heris, M.; Mohammadi-Ivatloo, B., "Chapter 2 - Application of Robust Optimization Method to Power System Problems", Eds.: Academic Press, pp. 19-32 (2018).
[4] Tamil Selvi, S.; Baskar, S.; Rajasekar, S., "Chapter 5 - An Intelligent Approach Based on Metaheuristic for Generator Maintenance Scheduling", Eds.: Academic Press, pp. 99-136 (2018).
[5] Matilainen, J.A., "Planning a power system with large-scale wind power in an electricity market environment", Licentiate thesis (2017).
[6] Sobu, A.; Wu, G., "Optimal operation planning method for isolated micro grid considering uncertainties of renewable power generations and load demand", in IEEE PES Innovative Smart Grid Technologies, pp. 1-6 (2012).
[7] Derakhshandeh, S. Y., "Reliability Evaluation of a Microgrid in Presence of Hydro, Wind and Photovoltaic Generation", Iranian-Hydropower-Association, vol. 2, no. 5, pp. 7-12 (2015). (in Persian)
[8] Parizy, E. S.; Choi, S.; Bahrami, H., "Grid-Specific Co-Optimization of Incentive for Generation Planning in Power Systems with Renewable Energy Sources", IEEE Transactions on Sustainable Energy, pp. 1-1 (2019).
[9] Dehghan, S.; Amjady, N.; Conejo, A. J., "Reliability-Constrained Robust Power System Expansion Planning", IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 2383-2392 (2016).
[10] Hong, T.; Koo, C.; Jeong, K., "A decision support model for reducing electric energy consumption in elementary school facilities", Applied Energy, vol. 95, pp. 253-266 (2012).
[11] Boffino, L.; Conejo, A. J.; Sioshansi, R.; Oggioni, G., "A Two-Stage Stochastic Optimization Planning Framework to Decarbonize Deeply Electric Power Systems", Energy Economics (2019).
[12] Taghizadegan kalantari, N.; hamzeh aghdam, F., "Energy Management in Multi-Microgrid Systems Considering Security Constraints and Demand Response Programs", Iranian Electric Industry Journal of Quality and Productivity, Research vol. 6, no. 12, pp. 86-97 (2018). (in Persian)
[13] Ashori, A. B.; Dehghan, S.; "A robust approach based on stochastic programming and minimum-maximum regression criteria for power system development planning with optimal transmission line
switching", 24th Iranian Conference on Electrical Engineering (ICEE 2016). (in Persian)
[14] Moradi, M. H.; Abedini, M., "A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems", International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66-74 (2012).
[15] Xi, Y.; "Harmonic estimation in power systems using an optimised adaptive Kalman filter based on PSO-GA", IET Generation, Transmission and Distribution, vol. 13, no. 17, pp. 3968-3979 (2019).
[16] Arora, S.; Singh, S., "An improved butterfly optimization algorithm with chaos", Journal of Intelligent & Fuzzy Systems, vol. 32, pp. 1079-1088 (2017).
[17] Arora, S.; Singh, S., "Butterfly optimization algorithm: a novel approach for global optimization", Soft Computing, vol. 23, no. 3, pp. 715-734 (2019).
[18] Arora, S.; Singh, S.; Yetilmezsoy, K., "A modified butterfly optimization algorithm for mechanical design optimization problems", Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 40, no. 1 (2018).
[19] Grigg, C.; Wong, P.; Albrecht, P.; Allan, R.; Bhavaraju, M.; Billinton,
R.; Chen, Q., "The IEEE Reliability Test System 1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee", IEEE Trans. Power Syst., vol. 14, no. 3, pp. 1010-1020 (1999).
