Abstract
This paper presents a simulation-optimization model integrating particle swarm optimization (PSO) algorithm and sequential streamflow routing (SSR) method to maximize the net present value (NPV) of a hydropower storage development project. In the PSO-SSR model, the SSR method simulates the operation of reservoir and its powerplant on a monthly basis over long term for each set of controllable design and operational variables, which includes dam reservoir and powerplant capacities as well as reservoir rule curve parameters, being searched for by the PSO algorithm. To evaluate the project NPV for each set of the controllable variables, the “alternative thermal powerplant (ATP)” approach is employed to determine the benefit term of the project NPV. The PSO-SSR model has been used in the problem of optimal design and operation of Garsha hydropower development project in Iran. Results show that the model with a simple, hydropower standard operating policy results in an NPV comparable to another model optimizing operating policies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Oliveira, R., Loucks, D.P.: Operating Rules for Multireservoir Systems. Water Resour. Res. 33(4), 839–852 (1997)
Le Ngo, L., Madsen, H., Rosbjerg, D.: Simulation and Optimisation modeling approach for operation of the Hoa Binh reservoir. Vietnam. J. Hydro. 336, 269–281 (2007)
Jalali, M.R., Afshar, A., Marino, M.A.: Reservoir Operation by Ant Colony Optimization Algorithms. Iran J. Sci. Technol. Trans. B-Eng. 30(B1), 107–117 (2006)
Haddad, O.B., Afshar, A., Marino, M.A.: Honey-Bees Mating Optimization (HBMO) Algorithm: a New Heuristic Approach for Water Resources Optimization. Water Resour. Manag. 20(5), 661–680 (2006)
Meraji, S.H., Afshar, M.H., Afshar, A.: Reservoir operation by particle swarm optimization algorithm. In: Proceedings of the 7th International Conference of Civil Engineering (Icce7th), Tehran, Iran, vol. 9, pp. 8–10 (2005)
Kumar, D.N., Reddy, M.J.: Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resour. Manag. 20(6), 879–898 (2006)
Kumar, D.N., Reddy, M.J.: Multipurpose Reservoir Operation Using Particle Swarm Optimization. J. Water Resour. Plan. Manage. ASCE 133(3), 192–201 (2007)
Baltar, A.M., Fontane, D.G.: A multiobjective particle swarm optimization model for reservoir operations and planning. In: Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montréal, Canada (2006)
Mousavi, S.J., Shourian, M.: Capacity optimization of hydropower storage projects using particle swarm optimization algorithm. J. Hydroinform. 12(3), 275–291 (2010)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceeding of the Sixth Symposium on Micro Machine and Human Science IEEE Service Centre, Piscataway, NJ, pp. 39–43 (1995)
Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Stretching technique for obtaining global minimizers through particle swarm optimization. In: Proc. Particle Swarm Optimization Workshop, Indianapolis, IN, USA, pp. 22–29 (2001)
Iran Water and Power Resources Development Company: Garsha Economic Report, Tehran, Iran (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raeisi, S., Jamshid Mousavi, S., Beidokhti, M.T., Rousta, B.A., Kim, J.H. (2016). Economic Optimization of Hydropower Storage Projects Using Alternative Thermal Powerplant Approach. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-662-47926-1_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47925-4
Online ISBN: 978-3-662-47926-1
eBook Packages: EngineeringEngineering (R0)