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Two-Phase stochastic dynamic programming Model for Optimal Operation of Irrigation Reservoir

WATER RESOURCES MANAGEMENT 1997年第5期11卷 395-406页

作者： Umamahesh, N. V. Sreenivasulu, P. Reg Engn Coll Water & Environm Div Dept Civil Engn Warrangal India Vasavi Coll Engn Dept Civil Engn Hyderabad Andhra Pradesh India

A two-phase stochastic dynamic programming model is developed for optimal operation of irrigation reservoirs under a multicrop environment. Under a multicrop environment, the crops compete for the available water whenever the water available is less than the irrigation demands. The performance of the reservoir depends on how the deficit is allocated among the competing crops. The proposed model integrates reservoir release decisions with water allocation decisions. The water requirements of crops vary from period to period and are determined from the soil moisture balance equation taking into consideration the contribution of soil moisture and rainfall for the water requirements of the crops. The model is demonstrated over an existing reservoir and the performance of the reservoir under the operating policy derived using the model is evaluated through simulation.

关键词： irrigation reservoirs operation water deficit yield response multicrop environment stochastic dynamic programming soil moisture water allocation simulation

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A Bayesian Inference and stochastic dynamic programming Approach to Determine the Best Binomial Distribution

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2009年第14期38卷 2379-2397页

作者： Nezhad, Mohammad Saber Fallah Niaki, Seyed Taghi Akhavan Sharif Univ Technol Dept Ind Engn Tehran Iran

In this research, we employ Bayesian inference and stochastic dynamic programming approaches to select the binomial population with the largest probability of success from n independent Bernoulli populations based upon the sample information. To do this, we first define a probability measure called belief for the event of selecting the best population. Second, we explain the way to model the selection problem using Bayesian inference. Third, we clarify the model by which we improve the beliefs and prove that it converges to select the best population. In this iterative approach, we update the beliefs by taking new observations on the populations under study. This is performed using Bayesian rule and prior beliefs. Fourth, we model the problem of making the decision in a predetermined number of decision stages using the stochastic dynamic programming approach. Finally, in order to understand and to evaluate the proposed methodology, we provide two numerical examples and a comparison study by simulation. The results of the comparison study show that the proposed method performs better than that of Levin and Robbins (1981) for some values of estimated probability of making a correct selection.

关键词： Bayesian inference Best binomial distribution Beta distribution Optimum stopping problem stochastic dynamic programming

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Incorporating Penalty Function to Reduce Spill in stochastic dynamic programming Based Reservoir Operation of Hydropower Plants

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2010年第5期5卷 531-538页

作者： Jha, Deependra Kumar Yorino, Naoto Zoka, Yoshifumi Sasaki, Yutaka Hayashi, Yuki Iwata, Kajunoni Oe, Ryuji Hiroshima Univ Grad Sch Engn Artificial Complex Syst Operat Lab Higashihiroshima 7398527 Japan Chugoku Elect Power Co Inc Hiroshima Japan

This paper proposes a framework that includes a penalty function incorporated stochastic dynamic programming (SDP) model in order to derive the operation policy of the reservoir of a hydropower plant, with an aim to reduce the amount of spill during operation of the reservoir. SDP models with various inflow process assumptions (independent and Markov-I) are developed and executed in order to derive the reservoir operation policies for the case study of a storage type hydropower plant located in Japan. The policy thus determined consists of target storage levels (end-of-period storage levels) for each combination of the beginning-of-period storage levels and the inflow states of the current period. A penalty function is incorporated ill the classical SDP model with objective function that maximizes annual energy generation through operation of the reservoir. Due to the inclusion of the penalty function, operation policy of the reservoir changes in a way that ensures reduced spill. Simulations are carried out to identify reservoir storage guide curves based on the derived operation policies. Reservoir storage guide curves for different values of the coefficient of penalty function a are plotted for a study horizon of 64 years, and the corresponding average annual spill values are compared. It is observed that, with increasing values of a, the average annual spill decreases;however, the simulated average annual energy value is marginally reduced. The average annual energy generation can be checked vis-a-vis the average annual spill reduction, and the optimal value of a can be identified based on the cost functions associated with energy and spill. (C) 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： reservoir operation policy stochastic dynamic programming hydropower plants Markov chain penalty function

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Optimal Forest Management Using stochastic dynamic programming Approach - A Case Study from the Hyrcanian Forests of Iran

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JOURNAL OF SUSTAINABLE FORESTRY 2023年第1期42卷 120-134页

作者： Limaei, Soleiman Mohammadi Mohammadi, Zohreh Univ Guilan Fac Nat Resources Dept Forestry Sowmeh Sara Iran Mid Sweden Univ Dept Econ Geog Law & Tourism Sundsvall Sweden Czech Univ Life Sci Prague Fac Forestry & Wood Sci EVA Unit 4 0 Suchdol Czech Republic

The aim of this study is to estimate the optimal forest harvest considering economic and environmental objectives in an uneven-aged forest in the north of Iran. Required data were collected such as stumpage price, harvesting variable costs, growth, volume, basal area, sequestrated carbon, carbon dioxide storage, and price. The required input models for optimization were estimated using the collected data. A stochastic dynamic programming technique was used for optimization. The results indicated that the harvesting volume was higher when only the economic objective was considered instead of considering economic and environmental objectives. The results also indicated that the harvesting volume would occur at a higher level of volume per hectare if we only consider the environmental objective. The results showed that the expected present value was higher when economic and environmental objectives were considered simultaneously. The results indicated when interest rate increased, the harvesting time and the expected present value decreased. The results showed that the expected present value increases with increasing the risk. The results of this study can be a guideline for optimal forest management.

关键词： Growth model stochastic dynamic programming optimal forest management Hyrcanian forests

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A Multi-Stage Two-Machines Replacement Strategy Using Mixture Models, Bayesian Inference, and stochastic dynamic programming

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2011年第4期40卷 702-725页

作者： Nezhad, Mohammad Saber Fallah Niaki, Seyed Taghi Akhavan Yazd Univ Dept Ind Engn Yazd Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

If at least one out of two serial machines that produce a specific product in manufacturing environments malfunctions, there will be non conforming items produced. Determining the optimal time of the machines' maintenance is the one of major concerns. While a convenient common practice for this kind of problem is to fit a single probability distribution to the combined defect data, it does not adequately capture the fact that there are two different underlying causes of failures. A better approach is to view the defects as arising from a mixture population: one due to the first machine failures and the other due to the second one. In this article, a mixture model along with both Bayesian inference and stochastic dynamic programming approaches are used to find the multi-stage optimal replacement strategy. Using the posterior probability of the machines to be in state (1), (2) (the failure rates of defective items produced by machine 1 and 2, respectively), we first formulate the problem as a stochastic dynamic programming model. Then, we derive some properties for the optimal value of the objective function and propose a solution algorithm. At the end, the application of the proposed methodology is demonstrated by a numerical example and an error analysis is performed to evaluate the performances of the proposed procedure. The results of this analysis show that the proposed method performs satisfactorily when a different number of observations on the times between productions of defective products is available.

关键词： Bayesian inference Gamma distribution Mixture models Production processes Replacement strategy stochastic dynamic programming Primary 90B25 Secondary 62F15

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

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ENERGY ECONOMICS 2022年 106卷

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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