Two-Echelon Reservoir Inventory Management with Forecast Updates: Perspective from Operations of Multireservoir in Interbasin Water Diversion Projects

• Autorzy: Xu Y. , Wang L. , Chen Z. , Xia G. , Yang Y.

Warianty tytułu

PL

Dwu-stanowiskowe zarządzanie wielorezerwuarowym systemem wodnym z uwzględnieniem prognoz

• Języki publikacji: EN

Abstrakty

EN

We consider a finite-horizon, periodic-review inventory model with inflow forecasting updates following the martingale model of forecast evolution (MMFE) in multiresevoirs. This model introduces a new method of determining an operating policy in which the policy is based on the dynamic programming (DP) model with a physical equation and a recursive equation. It adequately considers the internal relationship among multireservoirs in inter-basin water diversion projects (IBWDP) and calculates the expected benefits from future operation. The stochastic nature of the inflow is taken into account by considering the correlation between the streamflows of each pair of consecutive time intervals based on MMFE. According to interdependence, the probability of transition from a given state or stage to its succeeding ones can be calculated. Finally, to assess the effectiveness of the policies, the model is compared with other model and is applied to the Chinese South-North Water Diversion project.

PL

Analizowano model okresowej inwentaryzacji wraz z przewidywaniem nawodnienia w systemie wielu rezerwuarów. Wprowadzono programowanie dynamiczne uwzględniające wewnętrzne relacje między rezerwuarami w dywersyjnych projektach wodnych. Model sprawdzono na przykładzie chińskiego projektu systemu wodnego północ-południe.

Słowa kluczowe

EN

reservoir; forecast; martingale

PL

zbiornik wodny; prognozowanie; system wodny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 9b
• Strony: 84--91
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Xu Y.

• School of Economics and Management, Beihang University, Beijing 100191, China
• Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

autor

Wang L.

• School of Economics and Management, Beihang University, Beijing 100191, China

autor

Chen Z.

• Business School of Hohai University, Nanjing Jiangsu 210098, China

autor

Xia G.

• Business School of Hohai University, Nanjing Jiangsu 210098, China

autor

Yang Y.

• China National Institute of Standardization, Beijing 100088, China

Bibliografia

• [1] Ampitiya, H.K. (1995). Stochastic dynamic programming based approaches for the operation of a multi-reservoir system. Master’s Thesis, Wageningen Agricultural University, Wageningen,, Netherlands.
• [2] Araujo, A.R., and Terry, L.A. (1974). Operation of a hydrothermal system (in Portuguese),. Brazil Journal of Electrical Engineering.
• [3] Archibald TW, McKinnon KIM, Thomas LC. (1997). An aggregate stochastic dynamic programming model of multireservoir systems. Water Resources Research, 33(2): 333-340.
• [4] Archibald TW, McKinnon KIM, Thomas LC. (2006). Modeling the operation of multireservoir systems using decomposition and stochastic dynamic programming. Naval Research Logistics, 53(3): 217-225.
• [5] Bogardi, J. J., Budhakooncharoen, S., Shrestha, D. L., and Nandalal, K.D.W. (1988). Effect of state space and inflow discretization on stochastic dynamic programming based reservoir operation rules and system performance. In Proceedings, 6th Congress, Asian and Pacific Regional Division, IAHR, 1: 429-436.
• [6] Butcher, W. S. (1971). Stochastic dynamic programming for optimum reservoir operation. Water Resources Bulletin, 7(1): 115-123.
• [7] Contaxis, G.C., Kavatza, S.D. (1990). Hydrothermal scheduling of a multireservoir power system with stochastic inflows. IEEE Transactions on Power Systems, 5(3): 766-773.
• [8] Dias, N.L.C., Pereira, M.V.F., and Kelman, J. (1985). Optimization of flood control and power generation requirements in a multi-purpose reservoir. In Proceedings of the IFAC Symposium on Planning and Operation of Electric Energy Systems, Rio de Janeiro, Brazil, 1: 121-124.
• [9] Goor Q, Kelman R, Tilmant A. (2011). Optimal Mmultipurpose-Mmultireservoir Ooperation Mmodel with Vvariable Pproductivity of Hhydropower Pplants. Journal of Water Resources Planning and Management, 137(3): 258-267.
• [10] Goulter, I.C., and Tai, F-K. (1985). Practical implications in the use of stochastic dynamic programming for reservoir operation. Water Resources Bulletin, 121(1):65-74.
• [11] Halliburton, TS, Sirisena, HR. (1984). Development of a stochastic optimization for multireservoir scheduling. IEEE Transactions on Automatic Control, 29(1): 82-84.
• [12] Huang, W.-C., Harboe, R., and Bogardi, J. J. (1991). Testing stochastic dynamic programming models conditioned on observed or forecasted inflows. Journal of Water Resources Planning and Management, 117(1):28–36.
• [13] Jairaj PG, Vedula S. (2000). Multireservoir system optimization using fuzzy mathematical programming. Water Resources Management, 14(6): 457-472.
• [14] Karamouz, M., and Houck, M.H. (1982). Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resources Research, 18(5): 1337-1344.
• [15] Karamouz, M,. and Houck, M.H. (1987). Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation. Water Resources Bulletin, 23(1): 1-9.
• [16] Kelman, J., Stedinger, J.R., Cooper, L.A., Hsu, E., and Yuan, Sun-Quan. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3): 447-454.
• [17] Kim YO, Hyung- IE, Lee EG. (2007). Optimizing operational policies of a Korean multireservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction. Journal of Water Resources Planning and Management, 133(1): 4-14.
• [18] Kumar DN, Baliarsingh F. (2003). Folded dynamic programming for optimal operation of multireservoir systems. Water Resources Management, 17(5): 337-353.
• [19] Li CA, Yan R, Zhou JY. (1990). Stochastic optimization of interconnected multireservoir power systems. IEEE Transactions on Power System, 5(4): 1487-1496.
• [20] Loucks, D. P., and van Beek, E. (2005). In Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications (with contributions from J.R. Stedinger, J. P.M. Dijkman, and M. T. Villars), Studies and Reports in Hydrology. Paris: UNESCO Publishing. 1: 210-315
• [21] Maidment, D.R., and Chow, V.T. (1981). Stochastic state variable dynamic programming for reservoir systems analysis. Water Resources Research, 17(6): 1578-1584.
• [22] Nandalal, K.D.W., and Ampitiya, H.K. (1997). The assessment of long term operation of multi-unit reservoir systems. Engineer, Journal of the Institution of Engineers, Sri Lanka, 26(2):16-24.
• [23] Nandalal K.D.W. Nandalal, Janos J. Bogardi JJ. (2002). Dynamic Pprogramming BBased Ooperation of Rreservoirs Aapplicability and Llimits. Cambridge University Press, 1:10-22.
• [24] Nandalal, K.D.W., and Sakthivadivel, R. (2002_. Planning and management of a complex water resources system: Case study of Samanalawewa and Udawalawe reservoirs in the Walawe river, Sri Lanka. Agricultural Water Management, 57(3): 207-221.
• [25] Nandalal, K.D.W. (1986). Operation policies for two multipurpose reservoirs of the Mahaweli Development Scheme in Sri Lanka. M.Eng. Thesis No.WA-86-9,Asian Institute of Technology, Bangkok, Thailand.
• [26] Opricovic, S., and Djordjevic, B. (1976). Optimal long-term control of a multipurpose reservoir with indirect users. Water Resources Research, 12(6): 1286-1290.
• [27] Sherkat, V.R., Campo, R., Moslehi, K., Lo, E.O. (1985). Stochastic Llong-Tterm Hhydrothermal Ooptimization for a Mmultireservoir Ssystem. IEEE Transactions on Power Apparatus and Systems, 104(8): 2040-2050.
• [28] Shrestha, D. L., Bogardi, J. J., and Paudyal, G.N. (1990). Evaluating alternative state space discretization in stochastic dynamic program ming for reservoir operation studies. In S. P. Simonovic et al. (eds.), Proceedings of the International Conference on Water Resources Systems Application, University of Manitoba, Canada, 2: 378-387.
• [29] Shrestha, D. L. (1987). Optimal hydropower system configuration considering operational aspects,. M.Eng. Thesis, Asian Institute of Technology, Bangkok, Thailand.
• [30] Stedinger, J.R., Sule, B. F., and Loucks, D. P. (1984). Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research, 20(11): 1499-1505.
• [31] Tejada-Guibert, JA, Johnson SA, Stedinger JR. (1993). Comparison of Ttwo Aapproaches for Iimplementing Mmultireservoir Ooperating Ppolicies Dderived Uusing Sstochastic Ddynamic Pprogramming, Water Resources Research, 29(12): 3969-3980.
• [32] Tejada-Guibert, JA, Johnson SA, Stedinger JR. (1995). The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System, Water Resources Research, 31(10): 2571-2579.
• [33] Tilmant A, Pinte D, Goor Q. (2008). Assessing marginal water values in multipurpose multireservoir systems via stochastic programming. Water Resources Research, 44(12): 12-31.
• [34] Tilmant, A., Vanclooster, M., Duckstein, L., and Persoons, E. (2002). Comparison of fuzzy and nonfuzzy optimal reservoir operation policies,. Journal of Water Resources Planning and Management, 128(6):390-398.
• [35] Turgeon A. (2007). Stochastic optimization of multireservoir operation: The optimal reservoir trajectory approach. Water Resources Research, 43(5): 15-20.
• [36] Tetsuo Iida T., Paul H. Zipkin PH. (2006). Approximate Solutions of a Dynamic Forecast-Inventory Model. Manufacturing & Service Operations Management, 8(4): 407-425.
• [37] Vasiliadis, H.V., and Karamouz, M. (1994). Demand-driven operation of reservoirs using uncertainty-based optimal operation policies. Journal of Water Resources Planning and Management, 120(1):101-114.
• [38] Young, G.K. (1967). Finding reservoir operation rules. Journal of the Hydraulics Division, ASCE, 93(6): 297-321.