Open Journal Systems

An oppositional learning based gravitational search algorithm for short term hydrothermal scheduling

Gouthamkumar N., Veena Sharma, R. Naresh

crossmark logo side by side horizontal


This paper presents an oppositional based gravitational search algorithm (OGSA) for solving the short term hydrothermal scheduling (STHTS) problem. The STHTS problem involves the optimization of nonlinear constrained objective function by taking into consideration of multireservoir cascaded nature hydro plants, water transportation delay between cascaded reservoirs and scheduled time linkages, variable system active load balance, water discharge and reservoir storage limits, initial and final reservoir storage limits, reservoir flow balance and operating limits of hydro and thermal plants. A stochastic search algorithm known as gravitational search algorithm (GSA) inspired from the law of gravity and mass interactions is used to solve this complex constrained STHTS problem. In order to improve the convergence rate of GSA, the opposite numbers are utilized in the evolution process of GSA. Finally, the proposed oppositional gravitational search algorithm (OGSA) approach is evaluated on two test systems, one consisting of four hydro plants and an equivalent thermal plant and the other one with nine cascaded hydro and three thermal plants. The results obtained with the proposed approach give better solution in terms of less production cost, execution time and better convergence characteristics while comparing with the results of other methods reported in the literature.

Full Text:



G. Chang, M. Aganagic, J. Waight, Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Trans. Power Syst. 16(4) (2001) 743-749.

N. Petcharaks, W. Ongsakul, Hybrid enhanced Lagrangian relaxation and quadratic programming for hydrothermal scheduling. Elect. Power Comp. Syst. 35(1) (2007) 19-42.

M. Salam, K. Mohamed, Hydrothermal scheduling based Lagrangian relaxation approach to hydrothermal coordination. IEEE Trans. Power Syst. 13(1) (1998) 226-235.

S. Chang, C. Chen, I. Fung, P. B. Luh, Hydroelectric generation scheduling with an effective differential dynamic programming. IEEE Trans. Power Syst. 5(3) (1990) 737-743.

R. Naresh, J. Sharma, Two-phase neural network based solution technique for short term hydrothermal scheduling. IEE Proc. Gene. Trans. Dist. 146(6) (1999) 657-663.

S. Kumar, R. Naresh, Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem. Elect. Power Energy Syst. 29(10) (2007) 738-747.

Y. Xiaohui, B. Cao, B. Yang, Y. Yanbin, Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Conv. Manag. 49 (2008) 3627-3633.

Y. Wang, J. Zhou, C. Zhou, Y. Wang, H. Qin, Y. Lu, An improved self-adaptive PSO technique for short-term hydrothermal scheduling. Expert Syst. Appl. 39 (2012) 2288-2295.

K.R. Provas, S. Aditi, K.P. Dinesh, Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization. Engine. Appl. Artif. Intell. 26 (2013) 2516-2524.

N. Gouthamkumar, S. Veena, R. Naresh, P.K. Singhal, Quadratic migration of biogeography based optimization for short term hydrothermal scheduling. In Proc. IEEE: First Int. Conf. Netw. Soft Comp. (ICNSC-2014). (2014) 400-405.

M.P. Camargo, J.L. Rueda, I. Erlich, A. Osvaldo, Comparison of emerging metaheuristic algorithms for optimal hydrothermal system operation. Swarm Evolu. Compu. 18 (2014) 83-96.

E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A gravitational search algorithm. Info. Scie. 179(13) (2009) 2232-2248.

H. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence. In Proc. Int. Conf. Compu. Intell. Model. Cont. Auto. (2005) 695-701.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.