Stochastic Optimization Of Multi Reservoir Systems With Power Plants And Spillways
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Stochastic Optimization of Multi-reservoir Systems with Power-plants and Spillways
We examine a stochastic optimization model of a multiple reservoir water resource system in which the spilled outflows may have a different routing than the turbined outflows.We extend some results about the monotonicity of optimal decision rules, which were known for particular routings, and we show their validity for arbitrary routings of spilled outflows, provided they satisfy an intuitive monotonicity condition. Special cases are when the spilled outflows are expelled from the system, or when the spilled outflows are routed to the next reservoir dowstream. The monotonicity of optimal policies and of the corresponding future value function can be exploited to develop efficient computational algorithms based on a dynamic programming methodology, especially when the rewards are given by a concave, piecewise linear function of electricity generation.
River Basin Management IV
In recent years, significant advances have been made in the overall management of riverine systems, including advances in hydraulic and hydrologic modelling, environmental protection and flood forecasting. Containing papers presented at the Fourth International Conference on River Basin Management this book addresses the latest developments in these fields. Featured topics include: Hydraulics and Hydrology; Integrated Watershed Planning; River and Watershed Management; Water Quality Modelling; Flood Risk; Ecological Perspective; MIS, GIS and Remote Sensing; Sediment Transport; Environmental Impact; Hydrological Impact and Case Studies.
Handbook of Markov Decision Processes
Author: Eugene A. Feinberg
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.