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Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system

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WATER RESOURCES RESEARCH 2002年第12期38卷 40-1-40-9页

作者： Huang, WC Yuan, LC Lee, CM Natl Taiwan Ocean Univ Dept Harbor & River Engn Chilung 202 Taiwan

[1] The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse'' in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m(3)/day and 920,000 m(3)/day to Shihmen during the wet season and dry season, respectively.

关键词： genetic algorithms stochastic dynamic programming multireservoir system dimensionality water supply

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Evaluating Transition Probabilities for a stochastic dynamic programming Model Used in Water System Optimization

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2018年第2期144卷 04017090-04017090页

作者： Haguma, Didier Leconte, Robert Cote, Pascal Univ Sherbrooke Dept Civil Engn 2500 Blvd Univ Sherbrooke PQ J1K 2R1 Canada Rio Tinto Alcan 1954 Davis Jonquiere PQ G7S 4R5 Canada

stochastic dynamic programming is one of the most widely used optimization techniques for water system optimization. In this study, four methods for estimating transition probabilities have been evaluated to determine how they influence water system performance for short-term operating policies. The methods are counting, ordinary least-squares regression, robust linear model regression and multivariate conditional distribution. Two discretization schemesequal-width interval and equal-frequency and data transformationhave also been included in the study as sources of uncertainty. The study was carried out for three water systems: the Outardes River, Manicouagan River, and Lac Saint-Jean, located in Quebec, Canada. The results show that the water system configuration played a significant role in the performance of the transition probabilities. The discretization scheme and data transformation had a considerable influence on the counting and regression methods, whereas they had less of an impact on the multivariate conditional distribution. The robust linear models with equal-frequency discretization without data transformation gave satisfactory results for all the water systems. (c) 2017 American Society of Civil Engineers.

关键词： Water resources system stochastic dynamic programming Transition probabilities

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Designing an Optimum Acceptance Sampling Plan Using Bayesian Inferences and a stochastic dynamic programming Approach

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SCIENTIA IRANICA TRANSACTION E-INDUSTRIAL ENGINEERING 2009年第1期16卷 19-25页

作者： Niaki, S. T. Akhavan Nezhad, M. S. Fallah Sharif Univ Technol Dept Ind Engn Tehran Iran

In this paper, we use both stochastic dynamic programming and Bayesian inference concepts to design an optimum-acceptance-sampling-plan policy in quality control environments. To determine the optimum policy, we employ a combination of costs and risk functions in the objective function. Unlike previous studies, accepting or rejecting a batch are directly included in the action space of the proposed dynamic programming model. Using the posterior probability of the batch being in state p (the probability of non-conforming products), first, we formulate the problem into a stochastic dynamic programming model. Then, we derive some properties for the optimal value of the objective function, which enable us to search for the optimal policy that minimizes the ratio of the total discounted system cost to the discounted system correct choice probability.

关键词： Quality inspection Acceptance sampling plan Bayesian inference stochastic dynamic programming

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A Multi-Quantile Approach for Open-Loop stochastic dynamic programming Problems *

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IFAC Proceedings Volumes 2009年第2期42卷 52-57页

作者： Rodrigo T.N. Cardoso Ricardo H.C. Takahashi Frederico R.B. Cruz Carlos M. Fonseca Department of Physics and Mathematics Centro Federal de Educação Tecnológica de Minas Gerais MG Brazil Department of Mathematics Universidade Federal de Minas Gerais Department of Statistics Universidade Federal de Minas Gerais Faculty of Science and Technology Universidade do Algarve Portugal CEG-IST Instituto Superior Técnico Universidade Técnica de Lisboa Portugal

This paper proposes a multi-quantile approach for solving open-loop continuous-variable discrete-time stochastic dynamic programming problems in systems with non-standard probability distribution functions. Instead of using the expected value of the objective function for building the optimization criterion, the decision maker performs a choice on the decision variables over the objective function value quantiles. The proposed procedure relies on a Monte Carlo simulation of the unknown process input outcomes, associated with an open-loop multiobjective optimization. The optimal control comes from a trade-off analysis that considers, for instance, the risk associated with each policy versus its yield.

关键词： stochastic dynamic programming Multiobjective Optimization Model Predictive Control

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Valuation of an unexplored oilfield under uncertain oil price and reservoir condition: A stochastic dynamic programming approach with simulation-based reward function

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 223卷

作者： Pratikto, Fransiscus Indratno, Sapto Suryadi, Kadarsah Santoso, Djoko Bandung Inst Technol Ind Engn Dept Jl Ganesha 10 Bandung 40132 Indonesia Bandung Inst Technol Math Dept Jl Ganesha 10 Bandung 40132 Indonesia Bandung Inst Technol Geophys Engn Dept Jl Ganesha 10 Bandung 40132 Indonesia Parahyangan Catholic Univ Ind Engn Dept Jl Ciumbuleuit 94 Bandung 40141 Indonesia Bandung Inst Technol Univ Ctr Excellence Artificial Intelligence Vis Nat Language Proc & Big Data Analyt U CoE AI VLB Bandung 40132 West Java Indonesia

We develop an unexplored oilfield valuation model under uncertain exploration outcomes, reservoir conditions, and oil prices. The exploration outcomes follow a binomial process, while oil prices are represented using the Schwartz-Smith model. The reservoir conditions are characterized by the joint probability distribution of postdiscovery parameters estimated using a machine learning approach. The corresponding valuation model is a typical stochastic dynamic programming problem with a simulation-based reward function. The net cash flow lattice takes the form of a recombining quadrinomial derived from the discrete representation of the SchwartzSmith oil price model. We apply backward induction with embedded real-coded genetic algorithms to the net cash flow lattice to calculate the oilfield value. The model allows for field abandonment before lease expiration if the remaining reserve is uneconomical. To improve computational efficiency, we combine the Latin hypercube sampling and antithetic variates to reduce the variances. The model is implemented in an unexplored oilfield under two scenarios of oil presence probability: 100% and 75%. In the first scenario, we obtained a mean oilfield value of US$5.5 million with a CVaR of US$0.79 million, while in the second, we came up with a mean of -US $0.77 million and a CVaR of US$31.74 million.

关键词： Unexplored oilfield Valuation stochastic dynamic programming Simulation -based reward function Real -coded genetic algorithms

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An Equivalent Consumption Minimisation Strategy based on 1-Step Look-Ahead stochastic dynamic programming ∗

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IFAC-PapersOnLine 2015年第15期48卷 72-77页

作者： M. Fleuren T.C.J. Romijn M.C.F. Donkers Dept. of Elect. Eng. Eindhoven University of Technology Netherlands

In this paper, a systematic procedure to determine the equivalence factor in the equivalent consumption minimisation strategy (ECMS) is proposed. This is relevant when ECMS is not only used for controlling the power split between the internal combustion engine and the electric machine of a hybrid electric vehicle (HEV), but also for controlling several auxiliary systems. In this case, the number of controlled components and energy buffers increases, which causes the number of tunable equivalence factors to increase. The procedure to determine the equivalence factors proposed in this paper is based on the observation that ECMS can be considered as a time-invariant feedback policy and dynamic programming (DP) also yields a time-invariant feedback policy when the time horizon of the control problem approaches infinity and the disturbances are constant or absent. As the drive cycle can be considered as a (stationary stochastic) disturbance, we propose to formulate ECMS as the solution of a 1-step look-ahead stochastic dynamic program (1slSDP). This strategy results in an energy management strategy that performs close to optimal and yields similar fuel consumption results, when compared to a well-tuned ECMS. The absence of any parameters to be tuned and the fact that fuel consumption is similar to a well-tuned ECMS makes 1slSDP a useful strategy for energy management of HEVs.

关键词： Hybrid Electric Vehicle Energy Management stochastic dynamic programming Equivalent Consumption Minimisation Strategy

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On the use of Blackwell's approachability theorem for stochastic dynamic programming

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IFAC Proceedings Volumes 2004年第12期37卷 765-770页

作者： Hyeong Soo Chang Department of Computer Science and Engineering Sogang University Seoul Korea

We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a given finite set of policies whose performance is asymptotically not worse than any policy in the set by a given error. We provide an analysis for almost sure convergence with an exponentially fast convergence rate.

关键词： Approachability theorem stochastic dynamic programming Sampling Learning algorithm

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Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

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COMPUTATIONAL MANAGEMENT SCIENCE 2024年第1期21卷 31-31页

作者： Zephyr, Luckny Lamond, Bernard F. Lang, Pascal Laurentian Univ Sch Business Adm Barthi Sch Engn & Comp Sci Cross Appointment Sudbury ON P3E 2C6 Canada Univ Laval Dept Operat & Syst Decis Quebec City PQ G1V 0A6 Canada

We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method exploited the convexity properties of the value function to sample the reservoir level space based on the local curvature of the value function, which is estimated by the difference between a lower and an upper bounds (error bound). Unlike the previous approach in which the state space is exhaustively partitioned into full dimensional simplices whose vertices formed a discrete grid over which the value function was approximated, here we propose instead a new randomized approach for selecting the grid points from a small number of randomly sampled simplices from which an error bound is estimated. Results of numerical experiments on three literature test problems and simulated midterm reservoir optimization problems illustrate the advantages of the randomized approach which can solve models of higher dimensions than with the exhaustive approach.

关键词： Reservoir optimization stochastic dynamic programming Simplicial-randomized approximation Piecewise linear approximation

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Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming

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ENERGY ECONOMICS 2022年 106卷 1页

作者： Keles, Dogan Dehler-Holland, Joris Tech Univ Denmark Dept Technol Management & Econ Akademivej 358 DK-2800 Kgy Lyngby Denmark Karlsruhe Inst Technol Chair Energy Econ Hertzstr 16 D-76187 Karlsruhe Germany

The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system. In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid.

关键词： PV storage Energy markets Markov decision process ARMA process stochastic dynamic programming

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PRACTICAL IMPLICATIONS IN THE USE OF stochastic dynamic programming FOR RESERVOIR OPERATION

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JAWRA Journal of the American Water Resources Association 1985年第1期21卷 65-74页

作者： Goulter, I.C. Tai, F.‐K. Department of Civil Engineering University of Manitoba Winnipeg Manitoba R3T2N2 Canada

ABSTRACT: A stochastic dynamic programming model is applied to a small hydroelectric system. The variation in number of stage iterations and the computer time required to reach steady state conditions with changes in the number of storage states is investigated. The increase in computer time required to develop the storage probability distributions with increase in the number of storage states is reviewed. It is found that for an average of seven inflow states, the largest number of storage states for which it is computationally feasible to develop the storage probability distributions is nine. It is shown that use of the dynamic program results based on a small number of storage states results in unrealistically skewed storage probability distributions. These skewed distributions are attributed to “trapping” states at the low end of the storage range. Copyright © 1985, Wiley Blackwell. All rights reserved

关键词： computer‐time practical implications reservoir operation stochastic dynamic programming storage probabilities trapping states

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