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stochastic model predictive control of photovoltaic battery systems using a probabilistic forecast model

EUROPEAN JOURNAL OF CONTROL 2020年 56卷 254-264页

作者： Gross, Arne Wittwer, Christof Diehl, Moritz Fraunhofer Inst Solar Energy Syst Freiburg Freiburg Germany Albert Ludwigs Univ Freiburg Inst Mikrosyst Tech Freiburg Germany

Photovoltaic (PV) battery systems allow citizens to take part in a more sustainable energy system. Using the electric energy produced on-site usually entails a financial benefit for the consumer. Furthermore, feed-in peaks during high photovoltaic generation sometimes cause local voltage violations. Therefore, a feed-in limit applies to PV battery systems. In our study, we present a method to generate an optimal control that takes into account the forecast uncertainties. To that end, a stochastic forecast model is developed and used in a dynamic programming framework. We carry out a simulation study assuming the regulatory constraints in Germany. In this setup, our method is shown to mitigate the effects of the forecast uncertainties better than comparable methods. (c) 2020 European Control Association. Published by Elsevier Ltd. All rights reserved.

关键词： stochastic MPC stochastic dynamic programming Photovoltaic battery systems

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Tradeoffs between battery energy capacity and stochastic optimal power management in plug-in hybrid electric vehicles

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JOURNAL OF POWER SOURCES 2010年第9期195卷 2979-2988页

作者： Moura, Scott J. Callaway, Duncan S. Fathy, Hosam K. Stein, Jeffrey L. Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Univ Calif Berkeley Energy & Resources Grp Berkeley CA 94720 USA

Recent results in plug-in hybrid electric vehicle (PHEV) power management research suggest that battery energy capacity requirements may be reduced through proper power management algorithm design. Specifically, algorithms which blend fuel and electricity during the charge depletion phase using smaller batteries may perform equally to algorithms that apply electric-only operation during charge depletion using larger batteries. The implication of this result is that "blended" power management algorithms may reduce battery energy capacity requirements, thereby lowering the acquisition costs of PHEVs. This article seeks to quantify the tradeoffs between power management algorithm design and battery energy capacity, in a systematic and rigorous manner. Namely, we (1) construct dynamic PHEV models with scalable battery energy capacities, (2) optimize power management using stochastic control theory, and (3) develop simulation methods to statistically quantify the performance tradeoffs. The degree to which blending enables smaller battery energy capacities is evaluated as a function of both daily driving distance and energy (fuel and electricity) pricing. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Plug-in hybrid electric vehicles Lithium ion batteries Power management stochastic dynamic programming Optimal control Battery sizing

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A Finite Horizon dynamic programming Model for Production and Repair Decisions

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2014年第15期43卷 3302-3313页

作者： Fallahnezhad, Mohammad Saber Yazd Univ Dept Ind Engn Yazd Iran

In this research, a novel optimal singlemachine replacement policy in finite stages based on the rate of producing defective items is proposed. The primary objective of this paper is to determine the optimal decision using a Markov decision process to maximize the total profit associated with a machine maintenance policy. It is assumed that a customer order is due at the end of a finite horizon and the machine deteriorates over time when operating. Repair takes time but brings the machine to a better state. Production and repair costs are considered in the model and revenue is earned for each good item produced by the end of the horizon, there is also a cost for the machine condition at the end of the horizon. In each period, we must decide whether to produce, repair, or do nothing, with the objective of maximizing expected profit during the horizon.

关键词： Machinemaintenance Defective items stochastic dynamic programming

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Simultaneous Optimization of Speed and Buffer Times with an Application to Liner Shipping

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TRANSPORTATION SCIENCE 2019年第2期53卷 365-382页

作者： Mulder, Judith van Jaarsveld, Willem Dekker, Rommert Erasmus Univ Econometr Inst NL-3062 PA Rotterdam Netherlands Eindhoven Univ Technol Sch Ind Engn NL-5600 MB Eindhoven Netherlands

Transport companies often have a published timetable. To maintain timetable reliability despite delays, companies include buffer times during timetable development and adjust the traveling speed during timetable execution. We develop an approach that integrates timetable development and execution. We model execution of the timetable as a stochastic dynamic program (SDP). An SDP is a natural framework to model random events causing (additional) delay, propagation of delays, and real-time optimal speed adjustments. However, SDPs alone cannot incorporate the buffer allocation during timetable development, as buffer allocation requires choosing the same action in different states of the SDP. Motivated by the practical need for timetables that operate well during timetable execution, our model seeks the buffer allocation that yields the SDP that has minimal long-run average costs. We derive several analytical insights into the model. We prove that costs are joint convex in the buffer times, and we develop theory to compute subgradients. Our fast and exact algorithm for buffer time allocation is based on these results. Our case study considers container vessels sailing a round tour consisting of 14 ports based on Maersk data. The algorithm finds the optimal timetable in roughly 70 seconds for realistic problem instances. The optimal timetable yields cost reductions of about four to 10 million U.S. dollars per route per year in comparison with the current timetable. Finally, we show the robustness of our solution approach for different parameter settings using a sensitivity analysis.

关键词： robust timetabling (online) speed optimization stochastic dynamic programming

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Sponsored search advertising and dynamic pricing for perishable products under inventory-linked customer willingness to pay

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期276卷 119-132页

作者： Tunuguntla, Vaishnavi Basu, Preetam Rakshit, Krishanu Ghosh, Debabrata Indian Inst Management Calcutta Operat Management Grp Diamond Harbor Rd Kolkata 700104 India Malaysia Inst Supply Chain Innovat MIT Global Scale Network Operat Management Grp 2A Persiaran Tebar LayarSeksyen U8 Selangor 40150 Malaysia

Several online retailers provide inventory availability information on their websites in addition to leveraging sponsored search advertising to drive customer traffic to their retail websites. The increased ability of users to interact over the internet encourages retailers to shift to sponsored search advertising. In this paper, we design a decision support model to provide strategic bid and pricing decisions to a retailer selling a perishable product over a short horizon using sponsored search advertising to attract customers to his website. The retailer complements sponsored search bidding with dynamic pricing in a multi-period stochastic dynamic programming framework. Our analyses show that it is optimal for the retailer to invest heavily in bidding at low inventory levels, whereas at high levels of inventory he should use price promotions to enhance profits. We also find that the optimal bid and price increase with the increase in mean and variability of the customer's reservation price. (C) 2019 Elsevier B.V. All rights reserved.

关键词： E-Commerce Sponsored search advertising dynamic pricing Customer willingness to pay stochastic dynamic programming

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Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm

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WATER RESOURCES MANAGEMENT 2011年第10期25卷 2405-2423页

作者： Jothiprakash, V. Shanthi, Ganesan Arunkumar, R. Indian Inst Technol Dept Civil Engn Bombay 400076 Maharashtra India Shivani Engn Coll Tiruchirappalli 620009 Tamil Nadu India

A genetic algorithm (GA) and a backward moving stochastic dynamic programming (SDP) model has been developed for derivation of operational policies for a multi-reservoir system in Kodaiyar River Basin, Tamil Nadu, India. The model was developed with the objective of minimizing the annual sum of squared deviation of desired target releases. The total number of population, crossover probability and number of generations of the GA model was optimized using sensitivity analysis, and penalty function method was used to handle the constraints. The policies developed using the SDP model was evaluated using a simulation model with longer length of inflow data generated using monthly time stepped Thomas-Fiering model. The performance of the developed policies were evaluated using the performance criteria namely, the monthly frequency of irrigation deficit (MFID), Monthly average irrigation deficit (MAID), Percentage monthly irrigation deficit (PMID), Annual frequency of irrigation deficit (AFID), Annual average irrigation deficit (AAID), and Percentage annual irrigation deficit (PAID). Based on the performance, it was concluded that the robostic, probabilistic, random search GA resulted in better optimal operating policies for a multi-reservoir system than the SDP models.

关键词： Genetic algorithm stochastic dynamic programming Kodaiyar river basin Multi-reservoir Operational policies

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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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Finite horizon minimax optimal control of nonlinear continuous time systems with stochastic uncertainty

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dynamicS AND CONTROL 2000年第1期10卷 63-86页

作者： Ugrinovskii, VA Petersen, IR Australian Def Force Acad Sch Elect Engn Canberra ACT 2600 Australia

This paper develops a general continuous-time stochastic framework for robustness analysis and robust control synthesis. We consider a stochastic minimax optimization problem for general stochastic uncertain systems. A general method is presented for converting problems of performance analysis or controller synthesis into unconstrained optimization problems.

关键词： stochastic systems stochastic control robust control robust performance minimax stochastic optimization stochastic dynamic programming

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Partial Equilibrium in Risk-Based Production Decisions

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NAVAL RESEARCH LOGISTICS 2012年第1期59卷 1-17页

作者： Kamrad, Bardia Siddique, Akhtar Ernst, Ricardo Georgetown Univ McDonough Sch Business Washington DC 20057 USA Off Comptroller Currency Enterprise Risk Anal Div Washington DC 20219 USA

Characteristically, a small subset of operational problems admit risk neutrality when contingent claims methodology were used in their analysis. That is, for the majority of manufacturing and production problems, operating cash flows are not directly linked to prices of traded assets. However, to the extent that correlations can be estimated, the methodology's applicability to a broader set of operational problems is supported. Our article addresses this issue with the objective of extending the use of contingent claims techniques to a larger set of operational problems. In broad terms, this objective entails a partial equilibrium approach to the problem of valuing uncertain cash flows. To this end, we assume risk aversion and cast our approach within Merton's intertemporal capital asset pricing model. In this context, we formulate a "generic" production valuation model that is framed as an exercise in stochastic optimal control. The model is versatile in its characterization and can easily be adapted to accommodate a wide-ranging set of risk-based operational problems where the underlying sources of uncertainty are not traded. To obtain results, the model is recast as a stochastic dynamic program to be solved numerically. The article addresses a number of fundamental issues in the analysis risk based decision problems in operations. First, in the approach provided, decisions are analyzed under a properly defined risk structure. Second, the process of analysis leads to suitably adjusted probability distributions through which, appropriately discounted expectations are derived. Third, through consolidating existing concepts into a standard and adaptable framework, we extend the applicability of contingent claims methodology to a broader set of operational problems. The approach is advantageous as it obviates the need for exogenously specifying utility functions or discount rates. (C) 2011 Wiley Periodicals, Inc. Naval Research Logistics 59: 1-17, 2012

关键词： contingent claims analysis in production operations control theory Merton's intertemporal capital asset pricing model stochastic dynamic programming risk management in operations

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Operation Policies through dynamic programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses

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WATER RESOURCES MANAGEMENT 2021年第5期35卷 1573-1586页

作者： Mendoza Ramirez, Rosalva Arganis Juarez, Maritza Liliana Dominguez Mora, Ramon Padilla Morales, Luis Daniel Fuentes Mariles, Oscar Arturo Mendoza Resendiz, Alejandro Carrizosa Elizondo, Eliseo Carmona Paredes, Rafael Bernardo Univ Nacl Autonoma Mexico Inst Ingn Campus Morelia Morelia 58190 Michoacan Mexico Univ Nacl Autonoma Mexico Inst Ingn Ave Univ 3000 Mexico City 04510 DF Mexico

In this study, operation policies were obtained for a reservoir in Michoacan, Mexico, used for irrigation and domestic water supplies. The main purpose of these policies is to optimize the uses of the water, an increasingly scarce resource everywhere. Two optimization methodologies were used;stochastic dynamic programming, that provides release decisions for each stage, and genetic algorithms coupled with a reservoir operation simulation program, to achieve annual release curves. The operation of the reservoir was evaluated using historical inflow records. Monthly requirements for crop cycles, as well as the volumes of spills and deficits were examined. Both methodologies gave inverse relationships between deficits and spilled volumes. While both methodologies proved efficient in achieving the objectives, the results of the stochastic dynamic programming showed a better performance for this system.

关键词： stochastic dynamic programming Genetic algorithms Optimal management Cointzio reservoir

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