Advertisement

Soft Computing

, Volume 19, Issue 10, pp 2783–2797 | Cite as

An improved gravitational search algorithm for solving short-term economic/environmental hydrothermal scheduling

  • Hao Tian
  • Xiaohui Yuan
  • Yuehua Huang
  • Xiaotao Wu
Methodologies and Application

Abstract

This paper proposes an improved gravitational search algorithm (IGSA) to find the optimum solution for short-term economic/environmental hydrothermal scheduling (SEEHTS), which considers minimizing fuel cost as well as minimizing pollutant emission. In order to improve the performance of GSA, this paper firstly uses particle memory character and population social information to update velocity. Secondly, a chaotic mutation operator is embedded into GSA and a selection-operator-based greedy rule is adopted to update population. When dealing with the constraints of the SEEHTS, a modification strategy by dividing the violation water volume into several parts and randomly selecting intervals to adjust the water discharge gradually is proposed to handle the water dynamic balance constraints. Meanwhile, a new symmetrical adjusting strategy is adopted to handle reservoir storage constraints. Furthermore, the priority index strategy based on thermal power output is applied to handle system load balance constraints. To test the performance of the proposed method, simulation results have been compared with those obtained by particle swarm optimization, evolutionary programming and differential evolution reported in literature. The results show that the proposed IGSA provides the optimum solution with less fuel cost and smaller emission. So it demonstrates that IGSA is effective for solving SEEHTS problem.

Keywords

Economic/environmental scheduling  Gravitational search algorithm Constraints handling  Chaotic mutation Priority index 

Notes

Acknowledgments

This work was supported by Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, China Three Gorges University, National Natural Science Foundation of China (No. 51379080) and Fundamental Research Funds for the Central Universities (No. 2014TS152).

References

  1. Basu M (2004) An interactive fuzzy satisfying method based on evolutionary programming technique for multi-objective short-term hydrothermal scheduling. Electr Power Syst Res 69(2–3):277–285CrossRefGoogle Scholar
  2. Blaže G, Marko C (2013) A multi-objective optimization based solution for the combined economic-environmental power dispatch problem. Eng Appl Artif Intell 26(1):417–429CrossRefGoogle Scholar
  3. Catalão J, Pousinho H, Mendes V (2010) Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain. Electr Power Syst Res 80(8):935–942CrossRefGoogle Scholar
  4. Catalão J, Pousinho H, Mendes V (2011) Hydro energy systems management in Portugal: profit-based evaluation of a mixed-integer non linear approach. Energy 36(1):500–507CrossRefGoogle Scholar
  5. Chang GW, Aganagic M, Waight JG et al (2001) Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Trans Power Syst 16(4):743–749CrossRefGoogle Scholar
  6. Cheng C, Liao S, Tang Z et al (2009) Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch. Energy Convers Manag 50(12):3007–3014CrossRefGoogle Scholar
  7. Christober ARC (2011) Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach. Electr Power Energy Syst 33(4):939–946CrossRefGoogle Scholar
  8. Duman S, Guvenc U, Sonmez Y et al (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59(1):86–95CrossRefGoogle Scholar
  9. Feroldi M (2000) An application of genetic and evolutive algorithms to unit commitment problem. Soft Comput 4(4):224–236MATHCrossRefGoogle Scholar
  10. García S, Fernández A, Luengo J et al (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977CrossRefGoogle Scholar
  11. Gil E, Bustos J, Rudnick H (2003) Short-term hydrothermal generation scheduling model using a genetic algorithm. IEEE Trans Power Syst 18(4):1256–1264CrossRefGoogle Scholar
  12. Güvenç U, Sönmez Y, Duman S (2012) Combined economic and emission dispatch solution using gravitational search algorithm. Sci Iran 19(6):1754–1762CrossRefGoogle Scholar
  13. Kulkarni S, Kothari AG, Kothari DP (2000) Combined economic and emission dispatch using improved backpropagation neural network. Electr Mach Power Syst 28(1):31–44CrossRefGoogle Scholar
  14. Li Y, Xiang R, Jiao L et al (2012) An improved cooperative quantum-behaved particle swarm optimization. Soft Comput 16(6):1061–1069CrossRefGoogle Scholar
  15. Lu S, Sun C (2011) Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling. Expert Syst Appl 38(11):13950–13960Google Scholar
  16. Mandal KK, Chakraborty N (2008) Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems. Electr Power Syst Res 78(11):1972–1979CrossRefGoogle Scholar
  17. Mandal KK, Chakraborty N (2009) Short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs using differential evolution. Energy Convers Manag 50(1):97–104CrossRefGoogle Scholar
  18. Mandal KK, Chakraborty N (2011) Short-term combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using particle swarm optimization technique. Appl Soft Comput 11(1):1295–1302CrossRefGoogle Scholar
  19. Mehdi N, Malihe MF, Hossein N (2010) A modified particle swarm optimization for economic dispatch with non-smooth cost functions. Eng Appl Artif Intell 23(7):1121–1126CrossRefGoogle Scholar
  20. Mohammad K, Mohd RT, Ahmed E (2012) A modified gravitational search algorithm for slope stability analysis. Eng Appl Artif Intell 25(8):1589–1597CrossRefGoogle Scholar
  21. Nayak NC, Rajan CCA (2010) Hydro-thermal commitment scheduling by tabu search method with cooling-banking constraints. In: Swarm, evolutionary, and memetic computing. Lecture notes in computer science, vol 6466. Springer, Heidelberg, pp 739–752Google Scholar
  22. Rashedi E, Hossein N, Saeid S (2009) GSA: a gravitational search algorithm. Inf Sci 179(3):2232–2248MATHCrossRefGoogle Scholar
  23. Rashedi E, Hossien N, Saeid S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122CrossRefGoogle Scholar
  24. Santiago C, Jesus ML, Andres R (2012) Stochastic dual dynamic programming applied to nonconvex hydrothermal models. Eur J Oper Res 218(3):687–697MATHCrossRefGoogle Scholar
  25. Senthil KV, Mohan MR (2011) A genetic algorithm solution to the optimal short-term hydrothermal scheduling. Electr Power Energy Syst 33(4):827–835Google Scholar
  26. Sharma V, Jha R, Naresh R (2007) Optimal multi-reservoir network control by augmented lagrange programming neural network. Appl Soft Comput 7(3):783–790CrossRefGoogle Scholar
  27. Sun C, Lu S (2010) Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Syst Appl 37(6):4232–4241 Google Scholar
  28. Sushil K, Naresh R (2007) Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem. Electr Power Energy Syst 29(10):738–747Google Scholar
  29. Wai KF, Angus RS, Holger RM et al (2008) Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system. Ann Oper Res 159(1):433–450MATHCrossRefGoogle Scholar
  30. Yuan X, Zhang Y, Yuan Y (2008a) Improved self-adaptive chaotic genetic algorithm for hydro generation scheduling. J Water Resour Plan Manag ASCE 134(4):319–325Google Scholar
  31. Yuan X, Wang L, Yuan Y (2008b) Application of enhanced PSO approach to optimal scheduling of hydro system. Energy Convers Manag 49(11):2966–2972Google Scholar
  32. Yuan X, Cao B, Yang B et al (2008c) Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Convers Manag 49(12):3627–3633Google Scholar
  33. Yuan X, Su A, Nie H et al (2009) Application of enhanced discrete differential evolution approach to unit commitment problem. Energy Convers Manag 50(9):2449–2456CrossRefGoogle Scholar
  34. Yuan X, Wang Y, Xie J et al (2010) Optimal self-scheduling of hydro producer in the electricity market. Energy Convers Manag 51(12):2523–2530CrossRefGoogle Scholar
  35. Yuan X, Su A, Nie H et al (2011) Unit commitment problem using enhanced particle swarm optimization algorithm. Soft Comput 15(1):139–148CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hao Tian
    • 1
  • Xiaohui Yuan
    • 1
  • Yuehua Huang
    • 2
  • Xiaotao Wu
    • 1
  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.College of Electrical Engineering and New EnergyChina Three Gorges UniversityYichangChina

Personalised recommendations