STOCHASTIC DIFFERENTIAL DYNAMIC-PROGRAMMING FOR MULTI-RESERVOIR SYSTEM CONTROL

As with all dynamic programming formulations, differential dynamic pro gramming (DDP) successfully exploits the sequential decision structure of multi-reservoir optimization problems, overcomes difficulties with the nonconvexity of energy production functions for hydropower system s, 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 advanta ge of DDP encourages expansion of the state vector to include addition al 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 succes sful. A modified stochastic DDP algorithm is presented which overcomes difficulties in previous formulations. Application of the algorithm t o a four-reservoir hydropower system demonstrates its capabilities as an efficient approach to solving stochastic multi-reservoir optimizati on problems. The algorithm is also applied to a single reservoir probl em with inclusion of multi-lag hydrologic information in the state vec tor. Results provide evidence of significant benefits in direct inclus ion of expanded hydrologic state information in optimal feedback relea se policies.