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ADAPT: A Price-Stabilizing Compliance Policy for Renewable Energy Certificates: The Case of SREC Markets

OPERATIONS RESEARCH 2017年第6期65卷 1429-1445页

作者： Khazaei, Javad Coulon, Michael Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08540 USA Univ Sussex Dept Business & Management Brighton BN1 9SL E Sussex England

Currently, most Renewable Energy Certificate (REC) markets are defined based on targets that create an artificial step demand function resembling a cliff. This target policy produces volatile prices that can make investing in renewables a risky proposition. In this paper, we propose an alternative policy called Adjustable dynamic Assignment of Penalties and Targets (ADAPT) that uses a sloped compliance penalty and a self-regulating requirement schedule, both designed to stabilize REC prices, helping to alleviate a common weakness of environmental markets. To capture market behavior, we model the market as a stochastic dynamic programming problem to understand how the market might balance the decision to use a REC now versus holding it for future periods (in the face of uncertain new supply). Then, we present and prove some of the properties of this market, and finally we show that this mechanism reduces the volatility of REC prices, which should stabilize the market and encourage long-term investment in renewables.

关键词： compliance policy renewable energy certificates stochastic dynamic programming price volatility mechanism design

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

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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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A NEW CONDITION FOR THE EXISTENCE OF OPTIMAL STATIONARY POLICIES IN AVERAGE COST MARKOV DECISION-PROCESSES

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OPERATIONS RESEARCH LETTERS 1986年第1期5卷 17-23页

作者： SENNOTT, LI Department of Mathematics Illinois State University Normal IL 61761 USA

Discrete time countable state Markov decision processes with finite decision sets and bounded costs are considered. Conditions are given under which an unbounded solution to the average cost optimality equation exists and yields an optimal stationary policy. A new form of the optimality equation is derived for the case in which every stationary policy gives rise to an ergodic Markov chain.

关键词： Markov processes stochastic dynamic programming

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Optimal energy management of a series hybrid vehicle with combined fuel economy and low-emission objectives

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第12期228卷 1424-1439页

作者： Johri, Rajit Filipi, Zoran Univ Michigan Ann Arbor MI 48109 USA Ford Motor Co Dearborn MI 48121 USA Clemson Univ Greenville SC 29607 USA

A series hybrid powertrain provides ultimate freedom in controlling the engine. The flexibility enabled with hybridization creates chances for a synergistic approach, in which the hybrid supervisory control can be augmented to address both the emissions and the efficiency. In this paper, two policy optimization techniques are proposed, namely stochastic dynamic programming and neurodynamic programming, for designing power management controllers. These controllers are then compared with a baseline rule-based controller. The intention is to investigate the additional benefits possible through application of policy optimization algorithms and a systematic framework capable of representing complex system-level effects. The power management of a series hydraulic hybrid vehicle is pursued as a sequential decision-making problem under uncertainty (stochastic control). The low energy density of the hydraulic accumulator adds to the control challenge. First, stochastic dynamic programming and neurodynamic programming are applied to design a controller based on the fuel economy objective. The problem is subsequently expanded to include minimization of transient diesel engine emissions. This poses additional challenges due to the increased state space. The problem is computationally intractable by stochastic dynamic programming and is solved using the newly proposed neurodynamic programming framework. Finally, the supervisory controllers are implemented and evaluated using simulations and an engine-in-the-loop facility. It is shown that, by designing an intelligent multi-objective controller, significant reduction in both the fuel consumption and the emissions can be achieved compared with strategies which focus solely on the fuel consumption.

关键词： Hybrid vehicle supervisory control neurodynamic programming stochastic dynamic programming emissions engine-in-the-loop system

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Two-step optimal energy management strategy for single-shaft series-parallel powertrain

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MECHATRONICS 2016年 36卷 147-158页

作者： Li, Liang Yan, Bingjie Song, Jian Zhang, Yuanbo Jiang, Guirong Li, Lei Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Yanshan Univ Coll Mech Engn Qinhuangdao 066004 Hebei Peoples R China Suzhou Green Control Transmiss Technol Co Ltd Suzhou Jiangsu Peoples R China

The series-parallel architecture of the plug-in hybrid electric powertrain has attracted wide attentions in recent years for its flexible and highly efficient operating modes. However, despite the improvement of the vehicle fuel economy, it has been gradually facing more challenges on the design of the optimal controller due to the complex structure and the fast depletion of the battery. In this paper, a two-step optimal energy management strategy is proposed for a novel single-shaft series-parallel powertrain. In the first step, an equivalent method is adopted, in which two motors are equivalently regarded as one. After detailed analysis of the operating modes, various objective functions are established to pre-optimize the power split between engine and motor or two motors. In the second step, a stochastic dynamic programming (SDP) is adopted to optimize the power split between the engine and the equivalent motor, and the optimal combination of the operating modes. Then coupled with the pre-optimized results of the first step, the optimal power split among the engine and two motors could be obtained and then constructed as simple lookup-tables, which have great potential for practical applications. Finally, the preliminary test about the real-time performance of the optimal results is developed on the hardware-in-the-loop (HIL) system. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy management strategy Plug-in hybrid electric powertrain Two step method stochastic dynamic programming HIL

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