Comparison of risk-based optimization models for reservoir management
Risk minimization in stochastic systems is a challenging problem and this paper compares results of three different techniques in reservoir management. Two-stage stochastic programming (TSP) for maximizing expected benefits is a well-known method, Fletcher and Ponnambalam (FP) and Q-Learning are the...
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| Veröffentlicht in: | Canadian journal of civil engineering 2010-01, Vol.37 (1), p.112-124 |
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| container_title | Canadian journal of civil engineering |
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| creator | Mahootchi, M Ponnambalam, K Tizhoosh, H.R |
| description | Risk minimization in stochastic systems is a challenging problem and this paper compares results of three different techniques in reservoir management. Two-stage stochastic programming (TSP) for maximizing expected benefits is a well-known method, Fletcher and Ponnambalam (FP) and Q-Learning are the two new methods in reservoir management, all of which can include risk minimization in the objective function. The water price uncertainties caused by deregulated markets are considered in addition to random inflows in optimization and simulation is used to compare the results and to develop a risk versus return trade-off curve. One of the contributions of this paper is to consider risk in the Q-Learning algorithm. |
| doi_str_mv | 10.1139/L09-165 |
| format | Article |
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Two-stage stochastic programming (TSP) for maximizing expected benefits is a well-known method, Fletcher and Ponnambalam (FP) and Q-Learning are the two new methods in reservoir management, all of which can include risk minimization in the objective function. The water price uncertainties caused by deregulated markets are considered in addition to random inflows in optimization and simulation is used to compare the results and to develop a risk versus return trade-off curve. One of the contributions of this paper is to consider risk in the Q-Learning algorithm.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Buildings. Public works</subject><subject>Comparative analysis</subject><subject>Computation methods. Tables. Charts</subject><subject>Exact sciences and technology</subject><subject>Hydraulic constructions</subject><subject>Management</subject><subject>Mathematical optimization</subject><subject>Natural resource management</subject><subject>Objective function</subject><subject>Optimization</subject><subject>programmation dynamique stochastique</subject><subject>programmation stochastique</subject><subject>Q-Learning</subject><subject>Reservoir management</subject><subject>Reservoirs</subject><subject>risk</subject><subject>Risk assessment</subject><subject>Risk reduction</subject><subject>risque</subject><subject>réservoirs d'eau</subject><subject>stochastic dynamic programming</subject><subject>Stochastic models</subject><subject>Stochastic programming</subject><subject>Structural analysis. 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Stresses</topic><topic>water reservoirs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mahootchi, M</creatorcontrib><creatorcontrib>Ponnambalam, K</creatorcontrib><creatorcontrib>Tizhoosh, H.R</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Canadian journal of civil engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mahootchi, M</au><au>Ponnambalam, K</au><au>Tizhoosh, H.R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of risk-based optimization models for reservoir management</atitle><jtitle>Canadian journal of civil engineering</jtitle><addtitle>Revue canadienne de génie civil</addtitle><date>2010-01</date><risdate>2010</risdate><volume>37</volume><issue>1</issue><spage>112</spage><epage>124</epage><pages>112-124</pages><issn>0315-1468</issn><eissn>1208-6029</eissn><coden>CJCEB8</coden><abstract>Risk minimization in stochastic systems is a challenging problem and this paper compares results of three different techniques in reservoir management. 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| fulltext | fulltext |
| identifier | ISSN: 0315-1468 |
| ispartof | Canadian journal of civil engineering, 2010-01, Vol.37 (1), p.112-124 |
| issn | 0315-1468 1208-6029 |
| language | eng |
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| source | Alma/SFX Local Collection |
| subjects | Algorithms Applied sciences Buildings. Public works Comparative analysis Computation methods. Tables. Charts Exact sciences and technology Hydraulic constructions Management Mathematical optimization Natural resource management Objective function Optimization programmation dynamique stochastique programmation stochastique Q-Learning Reservoir management Reservoirs risk Risk assessment Risk reduction risque réservoirs d'eau stochastic dynamic programming Stochastic models Stochastic programming Structural analysis. Stresses water reservoirs |
| title | Comparison of risk-based optimization models for reservoir management |
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