Abstract
Through the rapid development of the watersheds in Turkey with projects developed by incorporated companies, a problem has arisen of how to operate a cascade reservoir system composed of state- and private sector-owned reservoirs in terms of the volume and timing of water releases to meet downstream water demands. This study presents a catchment-based optimization model based on inflow forecast with frequent updating for the integrated operation of hydropower plants under various sales methods. The model is formulated in terms of nonlinear programming (NLP) on a monthly basis for a 1-year period to assess the production strategies of the system reservoirs for that year. This model provides the basic constraints on the reservoir volume for daily and hourly optimization procedures. Forecasted flows are generated using seasonal autoregressive integrated moving average (ARIMA) models based on historical flow values. The proposed model is tested on the Garzan Hydropower System using historical, mean, and forecasted flow values. The results show that the integrated operation plan and improvement in the accuracy of inflow forecasts yield economic benefits as a consequence of optimal reservoir operation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmed I, Lansey KE (2001) Optimal operation of multi-reservoir systems under uncertainty. In: Phelps D, Shelke G (eds) Proceedings of world water and environmental resources congress: bridging the gap, meeting the world’s water and environmental resources challenges. ASCE, Orlando, pp 1–9
Aksa (Aksa Energy Generation Incorporated Company) (2004) Kor Barajı ve HES fizibilite raporu. Aksa
Barros MTL, Tsai FT, Yang S, Lopez JEG, Yeh WW (2003) Optimization of large-scale hydropower system operations. J Water Res Pl-ASCE 129(3):178–188. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(178)
Barrow CJ (2001) A review of the global experience of integrated river basin development planning and management. In: Biswas AK, Tortajada C (eds) Integrated river basin management: the Latin American experience. Oxford University Press, New Delhi, pp 1–30
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco
Demirdizen HG (2013) Market development of renewable energy in Turkey (Master’s thesis). Middle East Technical University, Ankara
DMI (General Directorate of State Meteorological Works) (2009) Monthly total precipitation, monthly mean temperature and monthly total evaporation records. DMI, Ankara
DSI (General Directorate of State Hydraulic Works) (2007) Akım gözlem istasyonları: Aylık toplam akımlar (1954–2000). DSI, Ankara
DSI (General Directorate of State Hydraulic Works) (2014) http://www.dsi.gov.tr/. Accessed 25 May 2014
DSI (XVII th Regional Directorate of State Hydraulic Works) (1987) Bitlis-Garzan Projesi: Ayşehatun Barajı ve HES planlama raporu. DSI, Van
EIE (General Directorate of Electrical Power Resources Survey and Development Administration) (2003) Aylık ortalama akımlar (1935–2000). EIE, Ankara
Enersu (Enersu Engineering Consultancy Construction Industry and Trade Limited Company) (2008) Garzan Barajı ve HES revize fizibilite raporu. Enersu, Ankara
Fernandez C, Vega JA (2009) Streamflow drought time series forecasting: a case study in a small watershed in North West Spain. Stoch Env Res Risk A 23(8):1063–1070. https://doi.org/10.1007/s00477-008-0277-8
FPGA (Euphrates Planning Group Authority) (1968) Dicle Havzası istikşaf raporu. FPGA, Diyarbakir
Ghanbarpour MR, Abbaspour KC, Hipel KW (2009) A comparative study in long-term river flow forecasting models. Intl J River Basin Management 7(4):403–413. https://doi.org/10.1080/15715124.2009. 9635398
Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems. Elsevier, Amsterdam
Huang W, Xu B, Chan-Hilton A (2004) Forecasting flow in Apalachicola River using neural networks. Hydrol Process 18(13):2545–2564. https://doi.org/10.1002/hyp.1492
Huysentruyt J, Olason T, Bridgeman S, Allen R (1996) Hydroelectric generation scheduling for the Connecticut and Deerfield river systems. Proceedings of 5th Water Resources Operation Management Workshop. ASCE, New York, pp 209-216
IBM Corporation (2012) IBM SPSS forecasting. http://public.dhe.ibm.com/common/ssi/ecm/yt/en/ytd03018usen/YTD03018USEN.PDF. Accessed 10 January 2015
IFC (International Finance Corporation) (2015) Hydroelectric power: a guide for developers and investors. http://www.ifc.org/wps/wcm/connect/topics_ext_content/ifc_external_corporate_site/ifc+sustainability/learning+and+adapting/knowledge+products/publications/hydroelectric_power_a_guide_for_developers_and_investors. Accessed 10 March 2015
Ito K, Xu ZX, Jinno K, Kojiri T, Kawamura A (2001) Decision support system for surface water planning in river basins. J Water Res Pl-ASCE 127(4):272–276. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:4(272)
Jemas-Su (Jemas-Su Groundwater Survey and Engineering Limited Company) (2001) Bitlis-Garzan Projesi: 1987 yılında hazırlanan Ayşehatun Barajı ve HES planlama raporunun revize bölümleri. Jemas-Su, Ankara
Kameswaran S, Biegler LT (2008) Advantages of nonlinear-programming-based methodologies for inequality path-constrained optimal control problems: a numerical study. SIAM J Sci Comput 30(2):957–981. https://doi.org/10.1137/050644938
Karamouz M, Zahraie B (1996) Water resources management of Tehran metropolitan area: a decision support system (DSS). Proceedings of 5th Water Resources Operation Management Workshop. ASCE, New York, pp 348-360
Karamouz M, Zahraie B, Araghinejad S (2005) Decision support system for monthly operation of hydropower reservoirs: a case study. J Comput Civ Eng 19(2):194–207. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:2(194)
Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Res Pl-ASCE 130(2):93–111. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93)
Limak (Limak Hydropower Plant Investments Incorporated Company) (2006) Alkumru Barajı ve HES Projesi fizibilite raporu. Limak, Ankara
Maier HR, Dandy GC (2000) Neural networks for the production and forecasting of water resource variables: a review and modelling issues and application. Environ Model Softw 15(1):101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
Modarres R (2007) Streamflow drought time series forecasting. Stoch Env Res Risk A 21(3):223–233. https://doi.org/10.1007/s00477-006-0058-1
Muhamad JR, Hassan JN (2005) Khabur River flow using artificial neural networks. Al-Rafidain Eng 13(2):33–42
Murtagh BA, Saunders MA, Murray W, Gill WE (2014) MINOS solver manual http://www.gams.com/dd/docs/solvers/minos.pdf. Accessed 12 October 2014
Ouarda TBMJ, Labadie JW, Fontane DG (1997) Indexed sequential hydrologic modeling for hydropower capacity estimation. J Am Water Res Assoc 33:1337–1349. https://doi.org/10.1111/ j.1752-1688.1997.tb03557.x
Peng C (1998) Dynamic operation of a reservoir system with discontinuous and short-term data (Doctoral thesis). University of Arizona, Arizona
PMUM (Market Financial Settlement Centre)(2014) Market clearing price (MCP) and system marginal price (SMP) averages. https://rapor.pmum.gov.tr/rapor/xhtml/ptfSmfDonemlik.xhtml. Accessed 14 November 2014
Pro-sem (Pro-sem Engineering Architecture Consultancy Limited Company) (2008) Pülümür Barajı ve HES fizibilite raporu. Pro-sem, Ankara
Rani D, Moreira MM (2010) Simulation-optimization modelling: a survey and potential application in reservoir systems operation. Water Resour Manag 24:1107–1138. https://doi.org/10.1007/s11269-009-9488-0
Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrolog Sci J 57(7):1275–1293. https://doi.org/10.1080/02626667.2012.714468
Shim SB, Fontane D, Lee HS, Koh DK (1996) Decision support systems in Han River Basin, South Korea. Proceedings of 5th Water Resources Operation Management Workshop. ASCE, New York, pp 67-84
Usul N (2009) Engineering hydrology. METU Press, Ankara
Valipour M, Banihabib ME, Behbahani SME (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez Dam Reservoir. J Hydrol 476:433–441. https://doi.org/10.1016/j.jhydrol.2012.11.017
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019
Wurbs RA (1993) Reservoir-system simulation and optimization models. J Water Res Pl-ASCE 119(4):455–472. https://doi.org/10.1061/(ASCE)0733-9496(1993)119:4(455)
Yalcin E (2015) Optimisation of the Tigris River Hydropower System operations (Doctoral thesis). Middle East Technical University, Ankara
Yeh WW-G (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818
Yurekli K, Kurunc A, Simsek H (2004) Prediction of daily streamflow based on stochastic approaches. J Spat Hydrol 4(2):1–12
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
ESM 1
(PDF 553 kb).
Rights and permissions
About this article
Cite this article
Yalcin, E., Tigrek, S. Optimization of the Garzan Hydropower System operations. Arab J Geosci 10, 374 (2017). https://doi.org/10.1007/s12517-017-3166-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-017-3166-y