بررسی عملکرد سیستم مخازن ذخیره منفرد با استفاده از شاخص‏های عملکرد (مطالعه موردی: سد مخزنی لار)

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه ارومیه

چکیده

سیستم مخازن ذخیره برای کنترل و تنظیم رژیم جریان رودخانه­ها جهت تأمین تقاضا برای مصارف مختلف شرب، کشاورزی و . . . طراحی و احداث می‏گردند. در این مطالعه سد مخزنی لار به‏عنوان یکی از منابع اصلی تأمین آب شرب تهران با استفاده از روش شبیه‏سازی مونت‏کارلو مورد تجزیه و تحلیل قرار گرفته ‏است. بدین منظور با به‏کارگیری مدل استوکاستیک AR(1)، داده‏های جریان سالیانه تولید و سپس مقادیر جریان سالیانه با استفاده از مدل توزیعی والنسیا-شاکی در ماه‏های مختلف سال پخش یا توزیع شده ‏است. در مرحله بعد، داده‏های ماهیانه تولیدی به‏عنوان جریانات ورودی به مخزن ذخیره برای شبیه‏سازی رفتار سد مخزنی لار با استفاده از روش 3Modified-SPA و با اعمال شاخص‏های عملکرد سیستم مخازن به‏کار گرفته شد. نتایج مطالعه نشان می‏دهد که حجم ذخیره علاوه بر تقاضا، تابعی از تلفات ناشی از تبخیر و ضرایب اعتماد زمانی و آسیب‏پذیری بوده و از یک رابطه نمایی به‏ازای تقاضا تبعیت می‏کند. علاوه بر این در هر سه گونه‏ (version) SPAهای اصلاح شده (SPA-I, SPA-II, and SPA-III) دو شاخص عملکرد مخزن، یعنی؛ اعتمادپذیری زمانی و آسیب‏پذیری قابل کنترل در تحلیل بوده و تحلیل سیستم ذخیره برای مقادیر معلوم یا مشخص شاخص‏های مذکور انجام می‏پذیرد. همچنین در روش‏های SPA-II و  SPA-III امکان استفاده از رابطه غیرخطی یا رابطه واقعی سطح-حجم در برآورد حجم تلفات ناشی از تبخیر در سیستم ذخیره است. کنترل دو شاخص عملکرد مخزن و به‏کارگیری رابطه واقعی یا غیرخطی سطح-حجم مخزن در تحلیل سیستم مخازن ذخیره به‏همراه جواب یکتا به‏عنوان مزیت‏های اساسی و بسیار مهم روش‏های مذکور نسبت به روش آنالیز رفتاری (فرض خطی بودن رابطه سطح-حجم مخزن و کنترل تنها شاخص اعتمادپذیری در محاسبات) است.

کلیدواژه‌ها


عنوان مقاله [English]

Using Reservoir Performance Indices for Evaluating the Lar Storage Dam Behavior

نویسندگان [English]

  • m ahmadian
  • M. Montaseri
چکیده [English]

Introduction: In recent decades, with increasing the world population and demand for fresh water for various applications (drinking, agriculture and industry), planning, management and optimal utilization of surface water reservoirs, especially in arid and semi-arid regions, have become the most serious challenges faced by researchers and water industry professionals in many parts of the world. In surface water reservoirs, uncontrolled flow is stored in wet periods for use in low flow periods. Therefore, surface water storage dams are created to control and regulate the flow of rivers in order to meet demand for different uses at a certain level of performance indices. During the process of storing water in the reservoirs, the uncontrolled flows of the input into the reservoir are in three ways: yield or output adjusted to meet demand for various uses, infiltration loss and evaporation from the surface of the lake and spill of excess water in a reservoir that is part of an uncontrollable flow. The proposed methods of storage-yield-performance of the storage system are classified into two main groups, simulation and optimization methods, which are widely used to analyze the reservoirs system for storing surface water. Among two final methods of simulation i.e. the behavior analysis method and the modified Sequent Peak Algorithm (SPA) method, all the actual conditions governing the system of storage reservoirs, including control of indices of reliability and vulnerability in the storage-yield-performance, are required to apply SPA. The basic SPA simulation method has been proposed as a computational method for the mass curve, and major improvements have been made to increase its functionality and efficiency at the late 20th century. The first amendments to apply the effects of evaporation losses and performance indices; time-based reliability and vulnerability, were carried out by Lele (1987). Then, Montaseri (1999) developed the SPA method for the system of multiple storage reservoirs and used non-linear or real area-volume relationship for applying losses caused by evaporation.
Materials and Methods: Stochastic models provide the possibility of generating successive hydrological time series (such as rainfall and flow) that are likely to occur in the future. On the other hand, the analysis of long-term behavior of various water resources systems, especially the storage system, depends on the availability of expected river flow time series in the years to come. Therefore, the use of stochastic models and the production of artificial data are absolutely necessary for the accurate evaluation of the design, operation and optimal management of the storage system and the elaboration of their long-term behavior. For this purpose, using a single distributed stochastic model, 1000 series of annual and monthly flows of input into the storage reservoir were generated and then the series of monthly flows generated to simulate the storage reservoir system using the SPA-I method and the reservoir performance indices (time reliability, resiliency and vulnerability) were also used for single reservoir system.
Results and Discussion: The results show that combining two stochastic AR(1) and Valencia-Schaake models had very good performance in preserving statistical data of historical data at two monthly and annual levels. This is the advantage and necessity of using the stochastic distributions model relative to other stochastic models such as Thomas-Fiering and ARMA in analyzing the storage reservoirs systems. The behavior of the reservoir system or the critical period in addition to demand, depends on system performance indices and decreases the critical period by decreasing time-based reliability or increasing the vulnerability factor. The results also indicate nonlinear (exponential) changes in the critical period and demand at a certain level of performance indices. Moreover, evaporation loss changes for demand and a certain level of performance indices have a concave shape, with a reversing point consistent with the largest within-year storage system. With a decrease/ an increase demand and volume of storage, the amount of evaporation losses increased exponentially and accounted for a considerable percentage of the reservoir's storage capacity.
Conclusion: The results revealed that volume of storage in addition to demand is a function of evapotranspiration losses and time-based reliability and vulnerability indices and follows an exponential relation for demand. In addition, in all three variants of the modified SPAs (SPA-I, SPA-II, and SPA-III), two performance indices of the reservoir, namely time-based reliability and vulnerability, are controllable in analysis, and the storage system analysis is performed for specified values or mentioned indices. Also, in the SPA-II and SPA-III methods, it is possible to use a nonlinear or a real are-volume relationship to estimate the loss of evapotranspiration in the storage system. Control of two performance indices of the reservoir and the application of real or nonlinear area-volume relationship in the analysis of reservoir system reservoir are important advantages of the above methods to the behavior analysis method.

کلیدواژه‌ها [English]

  • Data generation
  • Lar reservoir dam
  • Reservoir simulation
  • SPA method
  • Stochastic model
1- Adeloye A.J., and Montaseri M. 2001. Quantifying the water resources benefits of integrated reservoir planning. Journal of Integrated Water Resources Management 272: 229-235.
2- Adeloye A.J., and Montaseri M. 2002. Preliminary streamflow data analyses prior to water resources planning study. Journal of Hydrological Science 47(5): 679-692.
3- Adeloye A.J., and Pal S. 2010. Generalized storage-yield-reliability modelling: Independent validation of the Vogel Stedinger (V–S) model using a Monte Carlo simulation approach. Journal of Hydrology 388: 234-240.
4- Ahmad S., and Simonovic S.P. 2000. System dynamics modeling of reservoir operations for flood management. Journal of Computing in Civil Engineering 14(3): 190-198.
5- Becker L., and Yeh W.W.G. 1974. Optimization of real time operation of multiple- reservoir system. Journal of Water Resource Research 10(6): 1107- 1112.
6- Burges S.J., and Linsley R.K. 1971. Some factors influencing required reservoir storage. Journal of Hydraulic Division, ASCE 97(7): 977-991.
7- Chen L., Mcphee J., and Yeh W.W.G. 2007a. A diversified multi-objective GA for optimizing reservoir rule curves. Journal of Advances in Water Resources 30(5): 1082–1093.
8- Compos J.N.B. 2010. Modeling the yield–evaporation–spill in the reservoir storage process: The regulation triangle diagram. Journal of Water Resources Management 24: 3487–3511.
9- Fiering M.B. 1982. Estimates of resilience indices by simulation. Journal of Water Resources Research 18(1): 41-50.
10- Guo S.L., Zhang H.G., and Chen H. 2004. A reservoir flood forecasting and control System for China. Journal of Hydrological Sciences 49(6): 959-972.
11- Hashimoto T., Stedinger J.R., and Loucks D.P. 1982. Reliability, resiliency and vulnerability criteria for water resources system performance evaluation. Journal of Water Resources Research 18(1): 14-20.
12- Hosseinpour Tehrani M., Davari R., and Ghahreman B. 2008. Operation of reservoir systems using fuzzy logic. 4th National Congress of Civil Eng., 6-8May. 2008. Tehran University, Tehran. (In Persian)
13- Houck M.H., Cohon J.L., and Revelle C. 1980. Linear decision rule in reservoir design and management, 6: incorporation of economic efficiency benefits and hydroelectric energy generation. Journal of Water Resource Research 16(1): 196- 200.
14- Ismail N.A., Harun S., and Yusop Z. 2004. Synthetic simulation of streamflow and rainfall data using disaggregation models. Journal of Kejuruteraan Awam 16(2): 56-65.
15- kaharkaboudi R., and KhayyatKholghi M., Jahromi M., and Arab D. 2008. Operation of multi-reservoir system using fuzzy approach. 4th National Congress of Civil Eng., 6-8May. 2008. Tehran University, Tehran. (In Persian)
16- Karamouz M., and Karachian R. 2003. Water quality management. Amirkabir University Press. Tehran.
17- Kottegoda N.T., and Horder M.A. 1980. Daily flow model based on rainfall occurrences using pulses and a transfer function. Journal of Hydrology 47: 215-234.
18- Lars S.H., and Vogel R. 2008. The probability distribution of daily rainfall in the United States. ASCE, World Environmental and Water Resources Congress. 12-16 May. 2008. Honolulu, Hawaii.
19- Lele S.M. 1987. Improved algorithms for reservoir capacity calculation incorporating storage-dependent losses and reliability norm. Journal of Water Resource Research 23(10): 1819-1823.
20- Loucks D.P., Beek E.V., Stedinger J.R., Dijkaman J.P.M., and Villars M.T. 2005. Water resources system planning and management: An introduction to methods, models and applications (1th Ed., UNESKO). France, Paris.
21- Loucks D.P. 1997. Quantifying trends in system sustainability. Journal of Hydrological Sciences 42(4): 513-530.
22- Loucks D.P., Stedinger J.R., and Haith D.A. 1981. Water resources system planning and analysis. Prentice-Hill, Englewood Cliffs, New Jersey, USA.
23- McMahon T.A., and Mein R.G. 1986. River and reservoir yield. Journal of Water Resources Publications, Littleton, Colorado.
24- McMahon T.A., and Adeloye A.J. 2005. Water Resources Yield. Water Resources Publications. LLC, USA.
25- McMahon T.A., Adeloye A.J., and Sen-Lin Z. 2006. Understanding performance measures of reservoirs. Journal of Hydrology 324: 359-382.
26- McMahon T.A., Pegram G.G.S., Vogel R.M., and Peel M.C. 2007. Revisiting reservoir storage-yield relationships using a global streamflow database. Journal of Advanced Water Resources 30: 1858–1872.
27- Montaseri M. 1999. Stochastic investigation of the planning characteristics of within-year and over-year reservoir systems. ph.D. Thesis, Heriot-Watt University, UK.
28- Montaseri M., and Adeloye, A.J. 1999. Critical period of reservoir systems for planning purposes. Journal of Hydrology (224): 115-136.
29- Montaseri M., and Adeloye A.J. 2004. A graphical rule for volumetric evaporation loss correction in reservoir capacity-yield-performance planning in Urmia region, Iran. Journal of Water Resource Management (18): 55-74.
30- Montaseri M., and Amirataee B. 2017. Comprehensive stochastic assessment of meteorological drought indices. International Journal of Climatology 37(2): 998-1013.
31- Montaseri M., Amirataee B., and Nawaz R. 2017. A Monte Carlo simulation-based approach to evaluate the performance of three meteorological drought indices in northwest of Iran. Journal of Water Resources Management 31(4): 1323–1342.
32- Moy W.S, Cohon J.L, and Revelle C.S. 1986. A programming model for analysis of reliability, resilience, and vulnerability of a water supply reservoir. Journal of Water Resource Research 22 (4): 489-498.
33- Panu U.S., and Sharma T.C. 2002. Challenge in drought research: some perspectives and future directions.Journal of Hydrological Science 47: 19-30.
34- Peters L.A., and Chang H. 2011. Urban water demand modeling: review of concepts, methods, and organizing principles. Journal of Water Resources Research 47(5) w05401.
35- Salas J.D 1993. Analysis and modeling of hydrologic time series. Chapter 19 in D.R. Maidment Handbook of Hydrology (McGraw- Hill, Inc, New York).
36- Sandoval-Soils S., Mckinney D.C., and Loucks D.P. 2011. Sustainability index for water resources planning and management. Journal of Water Resources Planning and Management (ASCE) 137(5): 381-389.
37- Silva A.T., and Portela N.M. 2012. Stochastic assessment of reservoir storage-yield relationships in Portugal. Journal of Hydrology 18(5): 567-575.
38- Simonovic S.P. 1998. Sustainability criteria for possible use in reservoir analysis. International association of Hydrological Sciences, Wallingford, U.K., No. 251: 55–58.
39- Soundharajan B-S., Adeloye A.J., and Remesan R. 2016. Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment. Journal of Hydrology 538: 625-639.
40- Srikanthan R., and McMahon T.A. 1982. Stochastic generation of monthly streamflows. Journal of Hydraulic Division, ASCE, 108: 419-441.
41- Srinivasan K., Neelakantan T.R., Shyam Narayan P., and Nagarajukumar C. 1999. Mixed-integer programming model for reservoir performance optimization. Journal of Water Resource Planning and Management 125(5): 298–301.
42- Stedinger J.R., and Taylor M.R. 1982a. Synthetic stream flow generation, part 1. Model verification and validation. Journal of Water Resource Research 31(3): 645-654.
43- Vogel R.M. 1986. The probability plot correlation coefficient test for the normal, lognormal, and gumbel distributional hypotheses. Journal of Water Resource Research 22(4): 587-590.
44- Vogel R.M., and Kroll C.N. 1989. Low flow frequency analysis using probability plot correlation coefficients. Journal of Water Resources Planning and Management 115(3): 338-357.
45- Vogel R.M., and Bolognese R.A. 1995. Storage-reliability-resilience-yield relations for over-year water supply systems. Journal of Water Resource Research 31(3): 645–654.
46- Yeh W.G. 1985. Reservoir management and operations models: a state-of-the-art Review. Journal of Water Resources Research 21(12): 1797- 1818.
47- Zongxue X., Jinno K., Kawamura A., Takesaki S., and Ito K. 1996. Performance risk analysis for Fukuoka water supply system. Journal of Water Resources Management 12: 13-30.
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