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More efficient optimization of long‐term water supply portfolios
Author(s) -
Kirsch Brian R.,
Characklis Gregory W.,
Dillard Karen E. M.,
Kelley C. T.
Publication year - 2009
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 - 10.1029/2008wr007018
Subject(s) - portfolio , variance reduction , computer science , noise (video) , variance (accounting) , range (aeronautics) , work (physics) , mathematical optimization , control variates , water supply , reduction (mathematics) , portfolio optimization , term (time) , environmental science , economics , engineering , mathematics , artificial intelligence , environmental engineering , image (mathematics) , bayesian probability , financial economics , hybrid monte carlo , geometry , accounting , quantum mechanics , markov chain monte carlo , mechanical engineering , physics , aerospace engineering
The use of temporary transfers, such as options and leases, has grown as utilities attempt to meet increases in demand while reducing dependence on the expansion of costly infrastructure capacity (e.g., reservoirs). Earlier work has been done to construct optimal portfolios comprising firm capacity and transfers, using decision rules that determine the timing and volume of transfers. However, such work has only focused on the short‐term (e.g., 1‐year scenarios), which limits the utility of these planning efforts. Developing multiyear portfolios can lead to the exploration of a wider range of alternatives but also increases the computational burden. This work utilizes a coupled hydrologic‐economic model to simulate the long‐term performance of a city's water supply portfolio. This stochastic model is linked with an optimization search algorithm that is designed to handle the high‐frequency, low‐amplitude noise inherent in many simulations, particularly those involving expected values. This noise is detrimental to the accuracy and precision of the optimized solution and has traditionally been controlled by investing greater computational effort in the simulation. However, the increased computational effort can be substantial. This work describes the integration of a variance reduction technique (control variate method) within the simulation/optimization as a means of more efficiently identifying minimum cost portfolios. Random variation in model output (i.e., noise) is moderated using knowledge of random variations in stochastic input variables (e.g., reservoir inflows, demand), thereby reducing the computing time by 50% or more. Using these efficiency gains, water supply portfolios are evaluated over a 10‐year period in order to assess their ability to reduce costs and adapt to demand growth, while still meeting reliability goals. As a part of the evaluation, several multiyear option contract structures are explored and compared.