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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Thorvaldsen, Kasper Emil Bjarghov, Sigurd Farahmand, Hossein Norwegian Univ Sci & Technol NTNU Dept Elect Power Engn Trondheim Norway

ISBN: (纸本)9781728128221

Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cost curves (EFCC) for the building based on the peak power backwards for each day over a month. The uncertainty in load demand and weather are considered using a discrete Markov chain setup. The model is applied to a case study for a Norwegian building with smart control of flexible loads, and compared against methods where the MP grid tariff is not accurately represented, and where the user has perfect information of the whole month. The results showed that the SDP algorithm performs 03 % better than a scenario with no accurate way of presenting future impacts, and performs 3.6 % worse compared to a scenario where the user had perfect information.

关键词： Demand-side management Grid tariff Operational planning stochastic dynamic programming

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A combined stochastic dynamic programming-statistical disaggregation approach applied to multiple reservoir systems

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Water Resources Management 1988年第3期2卷 153-171页

作者： Perera, B.J.C. Codner, G.P. Department of Civil Engineering Monash University Clayton 3168 Victoria Australia

The stochastic dynamic programming (SDP) method faces computational difficulties when used to determine the optimal operation of multiple storages. A new approach, a combined SDP-statistical disaggregation approach is introduced to determine releases for a special situation relating to multiple reservoir systems, that is, for a system of multiple storages where operational data are available. The approach consists of defining an equivalent single reservoir which represents the system of multiple reservoirs. The optimal releases from the equivalent single reservoir are derived by the use of SDP. Disaggregation of the optimal releases from the equivalent single reservoir, to produce the releases from the individual storages is based on historical operational data. The Melbourne (Australia) water supply system is considered as the example. The releases derived from the combined SDP-statistical disaggregation approach are tested by operating a simulation model, and the conclusion is made that the approach produces satisfactory releases for a system of multiple reservoirs where operational data are available. The method cannot be applied to existing systems where insufficient or no operational data are available, or to proposed systems where operational data are not available. The method uses a small amount of computer time. © 1988 Kluwer Academic Publishers.

关键词： aggregation equivalent single reservoir multiple reservoirs optimal operation statistical disaggregation stochastic dynamic programming water supply systems

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Optimal Strategy for Integrated dynamic Inventory Control and Supplier Selection in Unknown Environment via stochastic dynamic programming

Optimal Strategy for Integrated Dynamic Inventory Control an...

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International Congress on Theoretical and Applied Mathematics, Physics and Chemistry (The Science)

作者： Sutrisno Widowati Solikhin Diponegoro Univ Dept Math Semarang Indonesia

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

关键词： Inventory control problem stochastic dynamic programming supplier selection

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Optimal Management of an Annual Weed: A stochastic dynamic programming Approach

Optimal Management of an Annual Weed: A Stochastic Dynamic P...

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International Congress on Modelling and Simulation (MODSIM07)

作者： Baxter, P. W. J. Wilcox, C. McCarthy, M. A. Possingham, H. P. Univ Queensland Ctr Ecol Appl Environm Decis Anal Ctr St Lucia Qld Australia Univ Queensland Australian Ctr Excellence Risk Anal Sch Integrat Biol St Lucia Qld Australia CSIRO Marine Res Pelag Fisheries & Ecosyst Hobart Tas Australia Univ Melbourne Sch Bot Australian Ctr Excellence Risk Anal Melbourne Vic Australia

ISBN: (纸本)9780975840047

Weeds are ecologically and economically disastrous. Invasive species in general are considered the greatest cause of global biodiversity loss after habitat destruction, and agricultural weeds cost the Australian economy $4 billion annually on average. Weed eradication is difficult. Managers must operate with limited budgets and search costs can increase as weed density decreases. Even if all plants are successfully removed, a seedbank may persist, leading to future outbreaks. Thus complete eradication is often an unrealistic target and the question becomes one of how much control is enough. The decision of what level of management resources to invest must not rely solely on management costs, but also take into account plant population dynamics in the context of a stochastic environment. Here we examine ongoing management of a contained annual weed with an established seedbank, which as a "sleeper" weed may yet escape and cause harm. Using stochastic dynamic programming, we find the optimal management effort of controlling the weed population, trading off expected costs of escape versus costs of searching and removal. The optimal removal effort increases non-linearly with the density of emerging plants until management becomes futile at high population densities. Most of the state space nevertheless recommends complete removal of emergent plants. The solution is most sensitive to population growth rate, the escape probability function and the relative costs of escape and management. Our simple model leads to valuable insights for the ongoing control of a sleeper weed. In our study we have assumed that immediate eradication is elusive. Nonetheless, continued long-term management may gradually deplete the seedbank and allow the possibility of eradication;thus the management time horizon (i.e., the length of the control program) can influence the optimal strategy. The net costs of management versus escape are also important whereas population dynamics - in this case the

关键词： Conservation efficiency invasive species management costs stochastic dynamic programming

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Quality-Optimization Algorithm Based on stochastic dynamic programming for MPEG DASH Video Streaming

Quality-Optimization Algorithm Based on Stochastic Dynamic P...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Garcia, Sergio Cabrera, Julian Garcia, Narciso Univ Politecn Madrid Grp Tratamiento Imagenes E-28040 Madrid Spain

ISBN: (纸本)9781479912919

In contrast to traditional push-based protocols, adaptive streaming techniques like dynamic Adaptive Streaming over HTTP (DASH) fix attention on the client, who dynamically requests different-quality portions of the content to cope with a limited and variable bandwidth but aiming at maximizing the quality perceived by the user. Since DASH adaptation logic at the client is not covered by the standard, we propose a solution based on stochastic dynamic programming (SDP) techniques to find the optimal request policies that guarantee the users' Quality of Experience (QoE). Our algorithm is evaluated in a simulated streaming session and is compared with other adaptation approaches. The results show that our proposal outperforms them in terms of QoE, requesting higher qualities on average.

关键词： protocols quality of experience stochastic programming video streaming DASH adaptation logic MPEG DASH video streaming QoE SDP technique dynamic adaptive streaming over HTTP optimal request policies push-based protocol quality of experience quality-optimization algorithm simulated streaming session stochastic dynamic programming Bandwidth Bit rate Consumer electronics dynamic programming Heuristic algorithms Streaming media Throughput

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Controlled approximation of the value function in stochastic dynamic programming for multi-reservoir systems

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COMPUTATIONAL MANAGEMENT SCIENCE 2015年第4期12卷 539-557页

作者： Zephyr, Luckny Lang, Pascal Lamond, Bernard F. Univ Laval Operat & Decis Syst Dept Pavillon Palasis Prince2325 Rue Terrasse Quebec City PQ G1V 0A6 Canada

We present a newapproach for adaptive approximation of the value function in stochastic dynamic programming. Under convexity assumptions, our method is based on a simplicial partition of the state space. Bounds on the value function provide guidance as to where refinement should be done, if at all. Thus, the method allows for a trade-off between solution time and accuracy. The proposed scheme is experimented in the particular context of hydroelectric production across multiple reservoirs.

关键词： Value function approximation stochastic dynamic programming Simplicial decomposition Regular grids Separable grids Reservoir networks

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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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A stochastic dynamic programming BASED APPROACH TO THE OPERATION OF A MULTI‐RESERVOIR SYSTEM

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JAWRA Journal of the American Water Resources Association 1987年第3期23卷 371-377页

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

ABSTRACT: An heuristic iterative technique based upon stochastic dynamic programming is presented for the analysis of the operation of a three reservoir ‘Y’ shaped hydroelectric system. The technique is initiated using historical inflow data for the downstream reservoir. At each iteration the optimal policies for the downstream hydroelectric generating unit are used to provide relative weightings or targets for operation of upstream reservoirs. New input inflows to the downstream reservoir are then obtained by running the historical streamflow record through the optimal policies for the upstream reservoirs. These flows are then used to develop a new operating policy for the downstream reservoir and hence new targets for the upstream reservoirs. The process is continued until the operating policies for each reservoir provide the same overall system benefit for two successive iterations. Results obtained from the procedure are compared to the results obtained by historical operation of the system. The procedure is shown to develop operating policies which give benefits which are as close to the historical benefits as can be expected given the choice of the number of storage state variables. Copyright © 1987, Wiley Blackwell. All rights reserved

关键词： hydroelectricity iterative multi‐reservoir operating policy release targets stochastic dynamic programming

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A PARAMETRIC ITERATION METHOD OF stochastic dynamic-programming FOR OPTIMAL DISPATCH OF HYDROELECTRIC PLANTS

A PARAMETRIC ITERATION METHOD OF STOCHASTIC DYNAMIC-PROGRAMM...

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2nd International Conference on Advances in Power System Control, Operation and Management (APSCOM-93)

作者： LIN, F Electric Power Bureau Fujian China

ISBN: (纸本)0852965699

In this paper the zero points of Chebyshev's polynomials are used for approximating the benefit functions of the rest stage of the stochastic dynamic programming method. It has been proved that the method is an efficient way for overcoming too many combination states of the stochastic dynamic programming and improving the conventional discrete dynamic programming algorithm because of requiring less computer memory and CPU time. The method is used to solve the optimal dispatching scheme of two cascade hydroelectric stations with pluriennial regulating reservoirs, considering the inflows' stochastic property, their mutual dependence in space and the reliability of the system operation. The results obtained are satisfactory.< >

关键词： PARAMETRIC ITERATION METHOD stochastic dynamic programming OPTIMAL DISPATCHING HYDROELECTRIC PLANTS

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Reservoir Operation Rules with Uncertainties in Reservoir Inflow and Agricultural Demand Derived with stochastic dynamic programming

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2016年第11期142卷 04016046-1-4016046-10页

作者： Soleimani, Shima Bozorg-Haddad, Omid Loaiciga, Hugo A. Univ Tehran Fac Agr Engn & Technol Dept Irrigat & Reclamat Coll Agr & Nat Resources Tehran *** Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA

A proposed stochastic dynamic programming (SDP) method with uncertainties in stream flow and water demand is developed to calculate optimal reservoir operation rules. The SDP method extends the classic SDP method that considers only one uncertainty in its operation rules. Time series of reservoir inflow and agricultural water demand for the Aydoghmoush Reservoir in eastern Azarbayejan, Iran, were obtained from meteorological data, available climate parameters, hydrologic data, and crop water demand. The application of the developed SDP with two uncertainties was evaluated with operation rules corresponding to four different scenarios, and optimal reservoir releases were determined for a drought year. Reservoir operation results were evaluated with different performance indices. This study's results demonstrate the advantage of considering uncertainties in reservoir inflow and water demand with the SDP method. The developed SDP method has general applicability under a range of climatic conditions, and the calculated operation rules cover the expected ranges of streamflow and water demand during the operating years. (C) 2016 American Society of Civil Engineers.

关键词： Optimal operation rules stochastic dynamic programming Reservoir inflow Water demand

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