Economic optimization of hydropower storage projects using alternative thermal powerplant approach

Sina Raeisi, S. Jamshid Mousavi, Mahmoud Taleb Beidokhti, Bentolhoda A. Rousta, Joong Hoon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages353-363
Number of pages11
Volume382
ISBN (Print)9783662479254
DOIs
Publication statusPublished - 2016
Event2nd International Conference on Harmony Search Algorithm, ICHSA 2015 - Seoul, Korea, Republic of
Duration: 2015 Aug 192015 Aug 21

Publication series

NameAdvances in Intelligent Systems and Computing
Volume382
ISSN (Print)21945357

Other

Other2nd International Conference on Harmony Search Algorithm, ICHSA 2015
CountryKorea, Republic of
CitySeoul
Period15/8/1915/8/21

Fingerprint

Particle swarm optimization (PSO)
Economics
Dams
Hot Temperature

Keywords

  • Alternative thermal powerplant
  • Hydropower
  • Operating policy
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Raeisi, S., Mousavi, S. J., Beidokhti, M. T., Rousta, B. A., & Kim, J. H. (2016). Economic optimization of hydropower storage projects using alternative thermal powerplant approach. In Advances in Intelligent Systems and Computing (Vol. 382, pp. 353-363). (Advances in Intelligent Systems and Computing; Vol. 382). Springer Verlag. https://doi.org/10.1007/978-3-662-47926-1_34

Economic optimization of hydropower storage projects using alternative thermal powerplant approach. / Raeisi, Sina; Mousavi, S. Jamshid; Beidokhti, Mahmoud Taleb; Rousta, Bentolhoda A.; Kim, Joong Hoon.

Advances in Intelligent Systems and Computing. Vol. 382 Springer Verlag, 2016. p. 353-363 (Advances in Intelligent Systems and Computing; Vol. 382).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Raeisi, S, Mousavi, SJ, Beidokhti, MT, Rousta, BA & Kim, JH 2016, Economic optimization of hydropower storage projects using alternative thermal powerplant approach. in Advances in Intelligent Systems and Computing. vol. 382, Advances in Intelligent Systems and Computing, vol. 382, Springer Verlag, pp. 353-363, 2nd International Conference on Harmony Search Algorithm, ICHSA 2015, Seoul, Korea, Republic of, 15/8/19. https://doi.org/10.1007/978-3-662-47926-1_34
Raeisi S, Mousavi SJ, Beidokhti MT, Rousta BA, Kim JH. Economic optimization of hydropower storage projects using alternative thermal powerplant approach. In Advances in Intelligent Systems and Computing. Vol. 382. Springer Verlag. 2016. p. 353-363. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-662-47926-1_34
Raeisi, Sina ; Mousavi, S. Jamshid ; Beidokhti, Mahmoud Taleb ; Rousta, Bentolhoda A. ; Kim, Joong Hoon. / Economic optimization of hydropower storage projects using alternative thermal powerplant approach. Advances in Intelligent Systems and Computing. Vol. 382 Springer Verlag, 2016. pp. 353-363 (Advances in Intelligent Systems and Computing).
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