Application of SCE-UA Approach to Economic Load Dispatch of Hydrothermal Generation System

Abstract:

The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network and water transport delay, which make the problem of finding global optimum difficult using standard optimization methods. This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using shuffled complex evolution (SCE-UA) method. The proposed method introduces the new concept of competitive evolution and complex shuffling, which ensure that the information on the parameter space gained by each of individual complexes is shared throughout the entire population. This conducts an efficient search of the parameter space. In this study, the hydrothermal scheduling is formulated as an objective problem that maximizes the social welfare. Penalty function is proposed to handle the equality, inequality constraints especially active power balance constraint and ramp rate constraints. The simulation results reveal that SCE-UA effectively overcomes the premature phenomenon and improves the global convergence and optimization searching capability. It is a relatively consistent, effective and efficient optimization method in solving the short-term hydrothermal scheduling problem.

• Periodical: Applied Mechanics and Materials (Volumes 448-453)
• Pages: 4296-4303
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.448-453.4296
• Online since: October 2013
• Authors: Jia Rui Dong, Chui Yong Zheng*, Guang Yuan Kan, Zhi Jia Li, Min Zhao
• Keywords: Ramping Rate, SCE-UA Algorithm, Short-Term Hydrothermal Scheduling

References

[1] Yang J, Chen N, Short-term hydrothermal coordination using multi-pass dynamic programming, IEEE Trans Power Syst, 4(3), 1050-6. (1989).

DOI: 10.1109/59.32598

[2] Tang J, Peter B, Hydrothermal scheduling via extended differential dynamic programming and mixed coordination, IEEE Trans Power Syst, 10(4), 2021-8. (1995).

DOI: 10.1109/59.476071

[3] Guan X, Peter B, Nonlinear approximation method in Lagrangian relaxation based algorithms for hydrothermal scheduling, IEEE Trans Power Syst, 13(1), 226-35. (1998).

DOI: 10.1109/59.387916

[4] Salam M, Mohamed K, Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination, IEEE Trans Power Syst, 13(1), 226-35. (1998).

DOI: 10.1109/59.651640

[5] Pereira MVF, Pinto LMVG , A decomposition approach to the economic dispatch of the hydrothermal systems, IEEE Trans PAS, 101(10), 3851-60. (1982).

DOI: 10.1109/tpas.1982.317035

[6] K.P. Wong Y.W. Wong, Short-term hydrothermal scheduling part 1: simulated annealing approach, IEE Proc. Gen. Transm. Distribution, 141(5), 371-376. (1994).

DOI: 10.1049/ip-gtd:19941350

[7] P.C. Yang, H.T. Yang, C.L. Huang, Scheduling short-term hydrothermal generation using evolutionary Programming techniques, IEE Proc. Gen. Transm. Distribution, 143(4), 371-376. (1996).

DOI: 10.1049/ip-gtd:19960463

[8] S.O. Orero M.R. Irving, A genetic algorithm modeling framework and solution technique for short term optimal hydrothermal scheduling, IEEE Trans. PWRS, 13 (2), 247-251. (1998).

DOI: 10.1109/59.667375

[9] E. Gil. Bustos,H. Rudnick, Short-term hydrothermal generation scheduling model using a genetic algorithm, IEEE Trans. Power Syst, 18(4), 1256-1264. (2003).

DOI: 10.1109/tpwrs.2003.819877

[10] N. Sinha, R. Chakrabarty, P.K. Chattopadhyay, Fast evolutionary programming techniques for short-term hydrothermal scheduling, Electr. Power Syst, Res. 66, 97-103. (2003).

DOI: 10.1016/s0378-7796(03)00016-6

[11] B. Yu, X. Yuan, and J. Wang, Short-term hydro-thermal scheduling using Particle Swarm Optimization method, Energy Conversion &Management, 48, 1902-1908. (2007).

DOI: 10.1016/j.enconman.2007.01.034

[12] Songfeng Lu, Chengfu Sun, Zhengding Lu, An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling, Energy Conversion and Management, 51, 561-571. (2010).

DOI: 10.1016/j.enconman.2009.10.024

[13] Qingyun Duan, Soroosh Sorooshian, Vijai K. Gupta, Optimal use of the SCE-UA global optimization method for calibrating watershed models, Journal of Hydrolog, 158, 265-284. (1994).

DOI: 10.1016/0022-1694(94)90057-4

[14] Mark Thyer and George Kuczera; Bryson C. Bates, Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms, Water Resources Research, vol35, NO. 3, pp.767-773, March. (1999).

DOI: 10.1029/1998wr900058

[15] Soroosh Sorooshian, Qingyun Duan, Vijai Kumar Gupta, Calibration of rainfall - runoff models: application of global optimization to the sacrament soil moisture accounting model, Water Resource Research, 29(4), 1185-1197. (1993).

DOI: 10.1029/92wr02617

[16] Duan D Y, Gupta V K, Sorooshian S, Shuffled comples evolution approach for effective and efficient global minimization, Journal of Optimization Theory and Applications, 158, 265-284. (1994).

DOI: 10.1007/bf00939380

[17] Duan Q, Sorooshian S, Gupta VK, Effective and efficient global optimization for conceptual rainfall- runoff models, Water Resource Research, 28 (4), 1015-1031. (1992).

DOI: 10.1029/91wr02985