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Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts
Author(s) -
Lu Mengqian,
Lall Upmanu,
Robertson Andrew W.,
Cook Edward
Publication year - 2017
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr019552
Subject(s) - hydropower , streamflow , reliability (semiconductor) , flood myth , probabilistic logic , computer science , water resources , scale (ratio) , process (computing) , operations research , environmental science , environmental economics , economics , engineering , drainage basin , ecology , power (physics) , philosophy , physics , cartography , theology , quantum mechanics , artificial intelligence , electrical engineering , biology , geography , operating system
Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short‐term and long‐term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. The issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real‐world perspective and needs a suitable institutional environment.