Chance‐constrained dynamic programing and the optimization of water resource systems

Abstract

In order to limit the probability that a water resource system will fail when it is operated in accordance with its optimum operating policy the latter must be derived subject to relevant chance constraints. When a stochastic dynamic program is used, certain of these constraints can be handled in the same manner as deterministic constraints, whereas others can be applied indirectly by imposing a penalty for failure, the optimum value of which can be found by an iterative search. This search and a precise evaluation of the response of the system may be carried out within the dynamic program by means of minor additions to its basic algorithm. The application of this procedure to four systems demonstrates its power and illustrates the manner in which systems respond to the imposition of constraints on failure.

An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation

“…Considering the risk of violating uncertain constraints and the stochastic uncertainty of agricultural irrigation water availability on the right hand of constraints which are expressed as probability distributions, the CCP method is introduced into the ITSP model, thus forming the ITSCCP model, in which higher water availability risk levels represent higher violation probabilities of the CCP method. Based on the original ITSP model (Maqsood et al, 2005), the CCP method (Askew, 1974) and the methodology of agricultural irrigation water resources allocation, the ITSCCP model for agricultural irrigation water resources allocation can be expressed as follows:…”

Online System Identification for Energy Resource Optimisation

“…from the operation records available for phase I at the Central Electricity Authority for the period July 1, '1974' to June 30, 1975, the energy productioTl, heäd l.e vel from tail race and input to reservoir are available. The inputs ara highly ran--dam end do not represent the actual flew into the system as also the evaporation and seepage losses are unknOwn.…”

Reservoir operations optimization via fuzzy criterion decision processes

“…Stochastic uncertainty, using probabilistic reasoning to represent uncertainty can be found in such works as Butcher (1971) who employed stochastic dynamic programming as the preferred optimization tool. Assuming randomness of inflows, Askew (1974) employed a chance-constrained dynamic programming to find optimal operations of a single reservoir subject to some imposed limitation on the probability of failure. Another study by Rossman (1977) maximized the expected net profits by assigning some reliability levels for all concerned events during the life of the reservoir.…”