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6th International Workshop on Hydro Scheduling in Competitive Electricity Markets

作者： Seguin, Sara Cote, Pascal Univ Quebec Chicoutimi Saguenay PQ G7H 2B1 Canada Rio Tinto Power Operat Saguenay PQ G7S 4R5 Canada

ISBN: (纸本)9783030033118;9783030033101

This paper presents a novel method to treat a chance constrained formulation of the hydropower reservoir management problem. An advantage of this methodology is that it is easily understandable by the decision makers. However, when using explicit optimization methods, the optimal operating policy requires to be simulated over multiple scenarios to validate the feasibility of the constraints. A blackbox optimization framework is used to determine the parameters of the chance constraints, embedding the chance constrained optimization problem and the simulation as the blackbox. Numerical results are conducted on the Kemano hydropower system in Canada.

关键词： Hydropower reservoir management Blackbox optimization Chance constrained optimization stochastic dynamic programming

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A Method for Determining Optimal Power Management Schedules for Hybrid Electric Airplanes

A Method for Determining Optimal Power Management Schedules ...

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AIAA/IEEE Electric Aircraft Technologies Symposium (EATS) / AIAA Propulsion and Energy Forum

作者： Trawick, David Milios, Konstantinos Gladin, Jonathan C. Mavris, Dimitri N. Rolls Royce Liberty Works Indianapolis IN 46241 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30309 USA Georgia Inst Technol Sch Aerosp Engn ASDL Atlanta GA 30309 USA

ISBN: (数字)9781624105906

ISBN: (纸本)9781624105906

Hybrid-electric propulsion architectures have been proposed to meet aggressive future performance and environmental goals. However, hybrid-electric airplanes present a unique power management challenge since they have two energy sources that can compete to meet the thrust requirements. Electrical power provided by the installed battery packs and the gas engine, allow for a continuous range of power settings to produce the same required power within sizing limits. Choosing when to supplement the gas engine with electrical power will influence total fuel burn, even if the total amount of battery used remains the same. This paper reviews and compares various power management optimization methods in terms of theory, difficulty of implementation, and scope of results. The techniques considered are Optimal Control, dynamic programming, as well as three "rule-based" power schedules (constant power, power at climb, and power at cruise). Each optimization technique is applied to determine the optimal power management of a parallel hybrid-electric turbofan engine with the goal of reducing total mission fuel burn over the flight envelope. Various implementation techniques are investigated including off-line optimization using engine decks and surrogate models, and in-line approaches using a detailed gas turbine engine model running with an airplane model. Best fuel burn results are compared across all optimizer and implementation schemes. Finally, a consistent process algorithm, called the Systematic Hybrid Aircraft Power Schedule Optimizer (SHAPSO) is proposed, for choosing the best power scheduling method for hybrid-electric airplanes which was informed by the results of the comparison.

关键词： Commercial Aircraft stochastic dynamic programming Gas Turbine Engines Surrogate Model Electric Propulsion Blended Wing Body Configurations Turbofan Engines Electric Power MATLAB Hybrid Electric Vehicle

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To cross or not to cross: Comparing strategies for duo colour Qwixx

To cross or not to cross: Comparing strategies for duo colou...

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作者： Lisia Smienk Vrije Universiteit Amsterdam

学位级别：硕士

This thesis is inspired by the game Qwixx, a dice game in which players try to maximise their end game points. We analyse three different strategies for a duo colour version of Qwixx: the conser- vative, race-to-lock and restricted maximisation strategy. The conservative strategy maximises its points, regardless of the number of rolls it needs for this. The race-to-lock strategy has as goal to minimise the number of rolls needed to lock one of the two rows. The r strategy maximises its points, given that it is only allowed to play R rolls. The exact moves a player should make according to each of the three strategies will be calculated with the use of three models that can be solved with dynamic programming and the value iteration algorithm. The output of the models is used as input for the simulation programs in which the strategies play solo and duo player games to see how they perform (against each other). In the single player game the conservative strategy leads to the highest expected score, whilst in the duo player game the race-to-lock strategy has the highest chance on victory. The restricted maximisation strategy doesn't live up to the expectations and could benefit from some more in-depth research.

关键词： Simulation Optimisation Markov Decision Process stochastic dynamic programming Value Iteration Algorithm

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dynamic expediting of an urgent order with uncertain progress

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第1期267卷 78-85页

作者： Bertazzi, Luca Mogre, Riccardo Univ Brescia Dept Econ & Management Contrada Santa Chiara 50 I-25122 Brescia Italy Univ Durham Business Sch Millhill Ln Durham DH1 3LB England

A supplier manages an urgent order with uncertain progress for which her client has set a deadline for its completion. The supplier observes in real time the order progress and chooses dynamically the effort level, such as manpower level, to expedite the order. Her problem is to identify expediting policies to minimise her expected cost, given by two costs in trade-off with each other: an effort level cost and a one-time penalty cost for late completion of the order. We formulate this problem using a discrete stochastic dynamic programming framework and obtain an optimal expediting policy for it. By conducting a worst-case analysis, we show that decreasing the level of flexibility, interpreted as the supplier's ability to update effort levels more often, may lead to a large increase in the costs of managing the order. We refine the problem formulation by modelling the case in which the supplier takes into account the negative effects of late order completion on her client using a penalty cost charged every period the order is delayed. We find an optimal policy for this case, by solving two dynamic programming problems sequentially. For both problems, we show that in presence of certain assumptions, there is an optimal expediting policy for which effort levels are non-increasing in the order progress. Finally, using a simulation study based on a car seat assembly case, we compare the performance of the two policies. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Supply chain management Tracking technologies Supply chain risk management stochastic dynamic programming Worst-case analysis

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stochastic dynamic Pricing for EV Charging Stations With Renewable Integration and Energy Storage

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IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 1494-1505页

作者： Luo, Chao Huang, Yih-Fang Gupta, Vijay Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA

This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties-charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms-stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm)-are applied to derive the pricing and electricity procurement policy. A Pareto front of the multi-objective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

关键词： Electric vehicle charging station dynamic pricing energy management renewable energy energy storage multi-objective optimization stochastic dynamic programming

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Efficient control for a multi-product quasi-batch process via stochastic dynamic programming

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IIE TRANSACTIONS 2011年第3期43卷 192-206页

作者： Eren, Ezgi C. Gautam, Natarajan Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA

This article considers a quasi-batch process where items are continuously processed while they move on a conveyor belt. In addition, the products arriving into the processor require variable amounts of processing, which translate into different processor levels. Keeping the processing level constant in such a system results in severe inefficiency in terms of consumption of energy and resources with high production costs and a poor level of environmental performance. A stochastic dynamic programming model is formulated that strikes a balance between consumption of energy and material, processor performance, and product quality. The model minimizes total system-wide cost, which is essentially a unified measure across all the objectives. The structural properties of the optimal policy and value functions are analyzed taking into account high-dimensionality of the state space. Based on some of these results, efficient heuristic methodologies are developed to solve large instances of the problem. It is shown using several numerical experiments that a significant amount of energy or material resources can be saved and total costs can be reduced considerably compared to the current practices in the process industry. Insights on the sensitivity of results with respect to the cost parameters are provided.

关键词： Efficient process control energy conservation environmental impact process industry multi-product quasi-batch processing stochastic dynamic programming MDP multi-dimensional state space

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MYOPIC POLICIES FOR NON-PREEMPTIVE SCHEDULING OF JOBS WITH DECAYING VALUE

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2018年第1期32卷 1-36页

作者： Master, Neal Chan, Carri W. Bambos, Nicholas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Columbia Business Sch Decis Risk & Operat New York NY USA Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA

In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perforin in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.

关键词： operations research stochastic dynamic programming stochastic modelling

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Adaptive water infrastructure planning for nonstationary hydrology

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ADVANCES IN WATER RESOURCES 2018年 118卷 83-94页

作者： Hui, Rui Herman, Jonathan Lund, Jay Madani, Kaveh Univ Calif Davis Ctr Watershed Sci Davis CA 95616 USA Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Imperial Coll London Ctr Environm Policy London SW7 2AZ England Stockholm Univ Dept Phys Geog Stockholm Sweden

The uncertainty of a changing climate raises challenges for water infrastructure planning and design. Not accounting for nonstationarity may result in under-designed structures that fail too frequently, or over-designed structures that are economically inefficient. This concern is magnified by uncertainty in the long-term frequency and magnitude of future extreme events. Planning strategies that allow adaptations over a structure's life could improve both reliability and economic efficiency. This study develops a method to inform adaptive water infrastructure planning with uncertain hydrologic and other forms of nonstationarity, applied to levee system planning. A stochastic dynamic programming model including a Markov process is developed for infrastructure planning with uncertain nonstationarity in flood frequency. Bayes' theorem is used to update peak flow probabilities conditioned on observed past peak flows and to update expected residual flood damages over time. A levee system planning problem with a numerical example from California illustrates the approach to derive optimal levee heights over time, and economic values of adapting to uncertain nonstationary flood risk. The projected range of probabilistic hydrology scenarios affects the optimal results, particularly in later planning stages as hydrology scenarios diverge with time. Adaptive planning strategies allowing more levee upgrades over time slightly lowers the overall cost and provides better flood protection than one-time construction under nonstationary hydrology for any climate in the example. Compared to a known future nonstationary hydrology, incorporating uncertain nonstationary climate results in higher levees being planned for observed severe hydrology scenarios in later stages. The overall present value cost with uncertain nonstationary climate depends on rates of change in peak flow distribution parameters in future hydrology scenarios.

关键词： Nonstationary hydrology Uncertain nonstationarity Water infrastructure planning stochastic dynamic programming Risk-based analysis

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Pathwise dynamic programming

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MATHEMATICS OF OPERATIONS RESEARCH 2018年第3期43卷 965-995页

作者： Bender, Christian Gartner, Christian Schweizerb, Nikolaus Saarland Univ Dept Math Postfach 151150 D-66041 Saarbrucken Germany Tilburg Univ Dept Econometr & Operat Res NL-5000 LE Tilburg Netherlands

We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and upper bounds ensures that the method is applicable even when the dynamic program does not satisfy a comparison principle. We apply our method to three nonlinear option pricing problems, pricing under bilateral counterparty risk, under uncertain volatility, and under negotiated collateralization.

关键词： stochastic dynamic programming Monte Carlo confidence bounds option pricing

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Moving to additive manufacturing for spare parts supply

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COMPUTERS IN INDUSTRY 2019年 113卷 103134-000页

作者： Knofius, N. van der Heijden, M. C. Zijm, W. H. M. Univ Twente Dept Ind Engn & Business Informat Syst Enschede Netherlands

This study seeks to investigate when and how a transition to additive manufacturing (AM) becomes profitable for the low-volume spare parts business. As a starting point, we conducted a case study at an OEM of radar systems which foresaw various opportunities that become available with the transition to AM. In particular, it is the case company that can perceive the prospects of shortened lead times and the promise of tool-less manufacturing. However, scepticism regarding whether a transition will pay off amid high AM piece prices and uncertain AM technology advancements remains. We employed stochastic dynamic programming to assess the situation encountered at the company. Therefore, we regarded particularities such as a decreasing AM piece price over the course of the service horizon and determined if (and when) AM should be prepared or tooling be discarded. It turned out that an immediate investment in AM technology is the most effective strategy and leads to more than 12% cost savings. Numerical experiments further substantiate the results of the case study and indicate that long lead times, high inventories, and severe backorder costs in the classical situation are all arguments in favor of an early investment in AM technology;this occurs despite an (initially) higher AM piece price and additional setup costs. Moreover, we observed that postponing the investment in AM is often not advisable. Instead, conventional manufacturing and AM are recommended to be used in parallel before making a complete transition to AM. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Digital manufacturing 3D printing Maintenance Defense industry stochastic dynamic programming

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