A Combination of Genetic Algorithm and Particle Swarm Optimization for Power Systems Planning Subject to Energy Storage

Document Type : Original Research (Full Papers)

Authors

Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

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.

Keywords


[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).