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Application of Bio-inspired Social Spider Algorithm in Valve-Point and Prohibited Operating Zones Constrained Optimal Load Flow of Combined Heat and Power System

  • Pradosh K. AdhvaryyuEmail author
  • Shreya Adhvaryyu
  • Santosh Prabhakar
  • Sudip Bid
Conference paper
  • 83 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

Combined heat and power (CHP) is the fuel unbiased method of production of heat energy and electrical power. Increased efficiency to the tune of 80% and above and reduced emission are two key attributes of this method as the production is obtained in a single process. This dissertation considered prohibited operating zones (POZ) and valve point effect (VPE) of generators in the study of Static Optimal load flow of CHP with the objective to reduce production cost while voltage level of buses are maintained within specified limit in order to satisfy the electrical power and heat demands. Optimization of load flow through such a power system is done using bio-inspired Social Spider algorithm. The distance of spider from food is defined as objective function. Two test systems having six generators in each have been used in IEEE 30 bus framework to demonstrate the efficacy of this algorithm. Results show that this method is competent enough to solve problem of multi objective optimization.

Keywords

Cogeneration unit (CGU) Combined heat and power (CHP) Feasible operation region (FOR) Optimal load flow (OLF) Social spider algorithm (SSA) 

References

  1. 1.
  2. 2.
    Chen, L., Suzuki, H., Katou, K.: Mean field theory for optimal power flow. IEEE Trans. Power Syst. 12, 1481–1486 (1997)CrossRefGoogle Scholar
  3. 3.
    Abido, M.A.: Optimal power flow using particle swarm optimization. Proc. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)CrossRefGoogle Scholar
  4. 4.
    Lai, L.L., Ma, J.T., Yokohoma, R., Zhao, M.: Improved genetic algorithm for optimal power flow under both normal and contingent operation states. Electr. Power Energy Syst. 19, 287–291 (1997)CrossRefGoogle Scholar
  5. 5.
    Cai, H.R., Chung, C.Y., Wong, K.P.: Application of differential evolutionary algorithm for transient stability constrained optimal power flow. IEEE Trans. Power Syst. 23(2), 719–728 (2008)CrossRefGoogle Scholar
  6. 6.
    Yuryevich, J., Wong, K.P.: Evolutionary programming based optimal power flow algorithm. IEEE Trans. Power Syst. 14(4), 1245–1250 (1999)CrossRefGoogle Scholar
  7. 7.
    Bhattacharjya, A., Chattopadhyay, P.K.: Application of biogeography based optimization to solve different optimal power flow problems. IET Gener. Transm. Distrib. 5(1), 70–80 (2011)CrossRefGoogle Scholar
  8. 8.
    Nimnam, T., et al.: A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect. Energy Convers. Manage. 58, 197–206 (2012)CrossRefGoogle Scholar
  9. 9.
    Ongsakul, W., Tantimaporn, T.: Optimal power flow by improved evolutionary programming. Electr. Power Compon. Syst. 34(1), 79–95 (2006)CrossRefGoogle Scholar
  10. 10.
    James, J.Q., Li, V.O.K.: A social spider algorithm for global optimization. Appl. Soft Comput. 30, 614–627 (2015)CrossRefGoogle Scholar
  11. 11.
    Lee, F.N., Breipohl, A.M.: Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans. Power Syst. 8, 246–254 (1993)CrossRefGoogle Scholar
  12. 12.
    Daneshi, H., Shahidehpour, M.: Impact of prohibited operating zones of generating units on locational marginal prices. In: Proceedings of 16th PSCC, Glasgow, Scotland, 14–18 July, pp. 1–6 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pradosh K. Adhvaryyu
    • 1
    Email author
  • Shreya Adhvaryyu
    • 2
  • Santosh Prabhakar
    • 3
  • Sudip Bid
    • 4
  1. 1.Siliguri Institute of TechnologySiliguriIndia
  2. 2.NSHM Faculty of EngineeringDurgapurIndia
  3. 3.KK College of Engineering and ManagementDhanbadIndia
  4. 4.Sanaka Educational Trust’s Group of InstitutionDurgapurIndia

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