Stochastic differential dynamic programming for multi-reservoir system control |
| |
| Authors: | F A El-Awar J W Labadie T B M J Ouarda |
| |
| Institution: | (1) Dept. of Soils, Irrig., and Mechanization, Fac. of Agric. and Food Sci., American Univ. of Beirut, Beirut, Lebanon, LB;(2) Dept. of Civil Engineering, Colorado State University, Ft. Collins, CO 80523-1372,;(3) INRS-Eau, University of Quebec, 2800 Einstein, C.P. 7500, Sainte-Foy, Quebec, G1V 4C7 Canada, CA |
| |
| Abstract: | : As with all dynamic programming formulations, differential dynamic programming (DDP) successfully exploits the sequential
decision structure of multi-reservoir optimization problems, overcomes difficulties with the nonconvexity of energy production
functions for hydropower systems, and provides optimal feedback release policies. DDP is particularly well suited to optimizing
large-scale multi-reservoir systems due to its relative insensitivity to state-space dimensionality. This advantage of DDP
encourages expansion of the state vector to include additional multi-lag hydrologic information and/or future inflow forecasts
in developing optimal reservoir release policies. Unfortunately, attempts at extending DDP to the stochastic case have not
been entirely successful. A modified stochastic DDP algorithm is presented which overcomes difficulties in previous formulations.
Application of the algorithm to a four-reservoir hydropower system demonstrates its capabilities as an efficient approach
to solving stochastic multi-reservoir optimization problems. The algorithm is also applied to a single reservoir problem with
inclusion of multi-lag hydrologic information in the state vector. Results provide evidence of significant benefits in direct
inclusion of expanded hydrologic state information in optimal feedback release policies. |
| |
| Keywords: | : Stochastic control dynamic programming reservoir systems hydrologic forecasting hydropower feedback control autoregressive models |
| 本文献已被 SpringerLink 等数据库收录！ |
|
