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Calculating the locational marginal price and solving optimal power flow problem based on congestion management using GA-GSF algorithm

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Abstract

An important factor in reviewing the performance of generation units and calculating their profit is the calculation of their locational marginal price (LMP), and this depends on our knowledge on the capacity of transmission lines and optimal power flow (OPF) based on reality by which we aim to minimize the total cost of the generators, solve the congestion of transmission lines, and hence, reduce the price of electricity in the market. Since power flow equations are nonlinear, they should be solved using numerical and repetition-based methods. In this paper, genetic algorithm (GA) has been employed to solve these equations, and in order to improve the performance of GA in its structure, generating scaling factor (GSF) has also been used for simultaneous calculations of power passing through in transmission lines so that by gaining some knowledge on the capacity of transmission lines, in addition to optimal power flow becoming real, we could determine the electricity price by uniform market pricing or LMP methods depending on using the full capacity of lines and generating power of the units, and thus, we can calculate the profit of generators. Finally, the output of the proposed GA-GSF algorithm would include values of buses voltages, lines losses, injected power to buses, power passing through lines, total generation cost, and generators’ profits. Also, the proposed algorithm in this paper has been tested on IEEE 14-BUS, IEEE 30-BUS, IEEE 57-BUS network, and the results show improvements on the OPF problem.

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References

  1. Vasant PM (2012) Meta-heuristics optimization algorithms in engineering, business, economics, and finance. p 734. https://doi.org/10.4018/978-1-4666-2086-5

  2. Shahidehpour M, Alomoush M (2001) Restructured electrical power systems: operation, trading, and volatility. Marcel Dekker, New York

    Google Scholar 

  3. Alomoush M (2003) Derivation of UPFC DC load flow model with examples of its use restructured power systems. IEEE Trans Power Syst 18(3):1173–1180

    Article  Google Scholar 

  4. Stott B, Marinho JL (1979) Linear programming for power system network security applications. IEEE Trans Power Appar Syst PAS-98:837–848

    Article  Google Scholar 

  5. Reid GF, Hasdorf L (1973) Economic dispatch using quadratic programming. IEEE Trans Power Appar Syst PAS-92:2015–2023

    Article  Google Scholar 

  6. Dommel HW, Tinney WF (1968) Optimal power flow solutions. IEEE Trans Power Appar Syst PAS-87:1866–1876

    Article  Google Scholar 

  7. Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7(1):83–94

    Article  Google Scholar 

  8. Bouktir T, Slimani L, Belkacemi M (2004) A genetic algorithm for solving the optimal power flow problem. Leonardo J Sci 4:44–58

    Google Scholar 

  9. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks (ICNN’95). Perth, Australia, vol IV, pp 1942–1948

  10. Chen CH, Yeh SN (2006) Particle swarm optimization for economic power dispatch with valve-point effects. In: 2006 IEEE PES transmission and distribution conference and exposition Latin America, Venezuela

  11. Chaturvedi KT, Pandit M, Srivastava L (2008) Self organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Trans Power Syst 23(3):1079–1087

    Article  Google Scholar 

  12. Yoshida H, Kawata K, Fukuyama Y, Nakanishi Y (2001) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 15(4):1232–1239

    Article  Google Scholar 

  13. Krost G, Venayagamoorthy GK, Grant L (2008) Swarm intelligence and evolutionary approaches for reactive power and voltage control. In: 2008 IEEE swarm intelligence symposium

  14. Mo N, Zou ZY, Chan KW, Pong TYG (2007) Transient stability constrained optimal power flow using particle swarm optimization. IEEE Gener Transm Distrib 1(3):476–483

    Article  Google Scholar 

  15. Swarup KS (2006) Swarm intelligence approach to the solution of optimal power flow. Indian Inst Sci 86(5):439–455

    Google Scholar 

  16. Abou El-Ela AA, El-Sehiemy RAA (2007) Optimized generation costs using modified particle swarm optimization version. WSEAS Trans Power Syst 10(2):225–232

    Google Scholar 

  17. Abido MA (2002) Optimal power flow using particle swarm optimization. Electr Power Energy Syst 24(2002):563–571

    Article  Google Scholar 

  18. Park J-B, Jeong Y-W, Lee W-N, Shin J-R (2006) An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions. In: IEEE power engineering society general meeting

  19. Thanushkodi K, Muthu Vijaya Pandian S, Dhivy Apragash RS, Jothikumar M, Sriramnivas S, Vindoh K (2008) An efficient particle swarm optimization for economic dispatch problems with nonsmooth cost functions. WSEAS Trans Power Syst 3(4):257–266

    Google Scholar 

  20. Meng Ke et al (2010) Quantum-inspired particle swarm optimization for valve point economic load dispatch. IEEE Trans Power Syst 25(1):215–222

    Article  Google Scholar 

  21. Vaisakh K, Srinivas LR (2005) Differential evolution approach for optimal power flow solutions. J Theor Appl Inf Technol 2:261–268

    Google Scholar 

  22. Allaoua B, Laoufi A (2009) Optimal power flow solution using ant manners for electrical network. Adv Electr Comput Eng 9(1):34–40

    Article  Google Scholar 

  23. Haibo Z, Lizi Z, Fanling M (1998) Reactive power optimization based on genetic algorithm. In: International power conference on power system technology. pp 1448–1453

  24. Sulaiman MH, Mustafa MW, Aliman O (2009) Transmission loss and load flow allocations via genetic algorithm technique. IEEE Xplore, TENCON

  25. Hazra J, Sinha AK (2007) A study on real and reactive power optimization using particle swarm optimization. In: International conference on industrial and information systems. pp 323–328

  26. Rahul J, Sharma Y, Birla D (2012) A new attempt to optimize optimal power flow based transmission losses using genetic algorithm. In: Computational intelligence and communication networks (CICN), IEEE Xplore

  27. Vu PT, Le DL, Vo ND (2010) A novel weight-improved particle swarm optimization algorithm for optimal power flow and economic load dispatch problems. In: Transmission and distribution conference and exposition

  28. Kumar S, Chaturvedi DK, Grant L (2013) Optimal power flow solution using fuzzy evolutionary and swarm optimization. Int J Electr Power Energy Syst 47:416–423

    Article  Google Scholar 

  29. Goswamia SK, Acharjee P (2010) Multiple low voltage power flow solutions using hybrid PSO and optimal multiplier method. Expert Syst Appl 37(3):2473–2476

    Article  Google Scholar 

  30. Najafi M, Ahmadi S, Dashtdar M (2015) Simultaneous energy and reserve market clearing with consideration of interruptible loads as one of demand response resources and different reliability requirements of consumers. Int J Emerg Electr Power Syst. https://doi.org/10.1515/ijeeps-2019-0018

    Article  Google Scholar 

  31. Sinsuphan N, Leeton U, Kulworawanichpong T (2013) Optimal power flow solution using improved harmony search method. Power Syst Res Unit 13(5):2364–2374

    Google Scholar 

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Acknowledgements

This work was supported by the KIEE.

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Authors and Affiliations

  1. Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr, Islamic Republic of Iran

    Masoud Dashtdar, Mojtaba Najafi & Mostafa Esmaeilbeig

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  1. Masoud Dashtdar
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  2. Mojtaba Najafi
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  3. Mostafa Esmaeilbeig
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Correspondence to Mojtaba Najafi.

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Dashtdar, M., Najafi, M. & Esmaeilbeig, M. Calculating the locational marginal price and solving optimal power flow problem based on congestion management using GA-GSF algorithm. Electr Eng 102, 1549–1566 (2020). https://doi.org/10.1007/s00202-020-00974-z

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