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stochastic dynamic programming approach to managing power system uncertainty with distributed storage

COMPUTATIONAL MANAGEMENT SCIENCE 2018年第1期15卷 87-110页

作者： Zephyr, Luckny Anderson, C. Lindsay Cornell Univ Dept Biol & Environm Engn Riley Robb Hall Ithaca NY 14853 USA

Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.

关键词： Power grid management Energy storage stochastic dynamic programming stochastic dual dynamic programming Approximate dynamic programming Generalized linear programming

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stochastic dynamic programming for long term hydrothermal scheduling considering different streamflow models

Stochastic dynamic programming for long term hydrothermal sc...

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9th International Conference on Probabilistic Methods Applied to Power Systems

作者： Siqueira, T. G. Zambelli, M. Cicogna, M. Andrade, M. Soares, S. Univ Estadual Campinas Campinas SP Brazil Univ Estadual Campinas Elect & Comp Engn Sch Hydrothermal Power Syst Grp Campinas SP Brazil Univ Estadual Campinas UNICAMP Fac Elect & Comp Engn Campinas SP Brazil Univ Estadual Campinas Elect & Comp Engn Sch Campinas SP Brazil

ISBN: (纸本)9789171785855

This paper is concerned with the performance of stochastic dynamic programming for long term hydrothermal scheduling. Different streamflow models progressively more complex have been considered in order to identify the benefits of increasing sophistication of streamflow modeling on the performance of stochastic dynamic programming. The first and simplest model considers the inflows given by their average values;the second model represents the inflows by independent probability distribution functions;and the third model adopts a Markov chain based on a lag-one periodical auto-regressive model. The effects of using different probability distribution functions have been also addressed. Numerical results for a hydrothermal test system composed by a single hydro plant have been obtained by simulation with Brazilian inflow records.

关键词： streamflow models long term hydrothermal scheduling Markov chain stochastic dynamic programming

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stochastic dynamic programming of Air Conditioning System for Electric Vehicles 8th

Stochastic Dynamic Programming of Air Conditioning System fo...

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8th International Conference on Applied Energy (ICAE)

作者： He, Hongwen Jia, Hui Huo, Weiwei Yan, Mei Beijing Inst Technol Zhongguanclm South St Beijing 100081 Peoples R China

Air conditioning (AC) system, as a main electricity consumption unit among the Electric vehicle (EV) auxiliary devices, has significant effects on the electricity efficiency of EVs. An energy management strategy is critical to distribute the energy more reasonable and extend the driving range. In this paper, a stochastic dynamic programming (SDP) control strategy is used to optimize the electricity consumption of AC system. To study the relationship between solar radiation and electricity saving of AC system, a thermal model of the cabin and a mathematical model of AC system have been built. The electricity consumption using SDP algorithm is 10.35% less than using a rulebased controller. Additionally, the fluctuation of cabin temperature is considerably reduced with an AC system controlled by SDP. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Air Conditioning System stochastic dynamic programming Electric Vehicle Electricity Consumption Optimal Control

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A data-driven practical stabilization approach for solving stochastic dynamic programming problems

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Automatica 2025年 178卷

作者： Massimo Tipaldi Raffaele Iervolino Paolo Roberto Massenio Ali Forootani Department of Electrical and Information Engineering Polytechnic University of Bari 70126 Bari Italy Department of Electrical Engineering and Information Technology University of Naples 80125 Napoli Italy Helmholtz Centre for Environmental Research - UFZ 04318 Leipzig Germany

This paper presents a data-driven practical stabilization approach for solving stochastic dynamic programming problems with unknown Markov Decision Process models over an infinite time horizon. The Bellman operator is modeled as a discrete-time switched affine system, with each mode representing a specific stationary stochastic policy and an external bounded disturbance term to account for such modeling issue. A two-step approach is followed. First, a model-based robust practical stabilization problem is solved to derive stabilization conditions which enable the practical convergence of the resulting closed-loop system trajectories towards a chosen reference value function. Then, by exploiting recent model-to-data Linear Matrix Inequality transformation tools, these results are further developed to obtain data-driven robust stabilization conditions for addressing the case of model-free problems. Such data-driven stabilization conditions are deployed into the Value Iteration algorithm, and finally tested on the recycling robot and the parking lot management problems to demonstrate the effectiveness of the proposed method.

关键词： stochastic dynamic programming Bellman operator Switched affine systems Robust practical stabilization Data-driven control design

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A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy

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ENERGY POLICY 2009年第11期37卷 4397-4406页

作者： Zhang, Xiao-Bing Fan, Ying Wei, Yi-Ming Chinese Acad Sci Ctr Energy & Environm Policy Res Inst Policy & Management Beijing 100080 Peoples R China

China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total potential cost function of establishing SPRs to evaluate the optimal SPR policy for China. Using this model, empirical results are presented for the optimal size of China's SPR and the best acquisition and drawdown strategies for a few specific cases. The results show that with comprehensive consideration, the optimal SPR size for China is around 320 million barrels. This size is equivalent to about 90 days of net oil import amount in 2006 and should be reached in the year 2017, three years earlier than the national goal, which implies that the need for China to fill the SPR is probably more pressing;the best stockpile release action in a disruption is related to the disruption levels and expected continuation probabilities. The information provided by the results will be useful for decision makers. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： stochastic dynamic programming Strategic petroleum reserve Optimization

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Developing a stochastic dynamic programming Framework for Optical Tweezer-Based Automated Particle Transport Operations

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2010年第2期7卷 218-227页

作者： Banerjee, Ashis Gopal Pomerance, Andrew Losert, Wolfgang Gupta, Satyandra K. Univ Maryland Dept Mech Engn College Pk MD 20742 USA Univ Maryland Syst Res Inst College Pk MD 20742 USA Univ Maryland Dept Phys Inst Phys Sci & Technol College Pk MD 20742 USA Univ Maryland Inst Res Elect & Appl Phys College Pk MD 20742 USA

Automated particle transport using optical tweezers requires the use of motion planning to move the particle while avoiding collisions with randomly moving obstacles. This paper describes a stochastic dynamic programming based motion planning framework developed by modifying the discrete version of an infinite-horizon partially observable Markov decision process algorithm. Sample trajectories generated by this algorithm are presented to highlight effectiveness in crowded scenes and flexibility. The algorithm is tested using silica beads in a holographic tweezer set-up and data obtained from the physical experiments are reported to validate various aspects of the planning simulation framework. This framework is then used to evaluate the performance of the algorithm under a variety of operating conditions. Note to Practitioners-Micro and nanoscale component-based devices are revolutionizing health care, energy, communication, and computing industry. Components need to be assembled together to create useful devices. Such assembly operations remain challenging in spite of the advancements in imaging, measurement, and fabrication at the small scales. This paper deals with directed assembly using optical fields that is useful for prototyping new design concepts, repairing devices, and creating templates for self-assembly.

关键词： Automated planning microsphere optical tweezer (OT) partially observable Markov decision process simulation stochastic dynamic programming

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Capacity planning with technology replacement by stochastic dynamic programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期260卷 739-750页

作者： Wang, Kung-Jeng Phuc Hong Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan

Technology replacement is capital intensive and highly risky in fast-paced high-tech industries along lumpy demand. This article proposes a solution to decision-making related to (i) technology replacement policy and (ii) capacity plan of resources to satisfy customer demand under technological changes. The addressed problem is modeled by stochastic dynamic programming for technology replacement in conjunction with an integer programming for simultaneous capacity planning. The overall objective is to maximize the expected net present profit over a finite time horizon. The problem is solved by a pattern search-genetic algorithm. Experiment results indicate that a near optimal solution is achieved in finite time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Investment analysis Capacity planning stochastic dynamic programming Technology replacement

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Approximate stochastic dynamic programming for sensor scheduling to track multiple targets

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DIGITAL SIGNAL PROCESSING 2009年第6期19卷 978-989页

作者： Li, Y. Krakow, L. W. Chong, E. K. P. Groom, K. N. Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA Sandia Natl Labs Albuquerque NM 87185 USA

The problem of sensor scheduling is to select the number and combination of sensors to activate over time. The goal is usually to trade off tracking performance and sensor usage. We formulate a version of this problem involving multiple targets as a partially observable Markov decision process, and use this formulation to develop a nonmyopic sensor-scheduling scheme. Our scheme integrates sequential multisensor joint probabilistic data association and particle filtering for belief-state estimation, and use a simulation-based Q-value approximation method called completely observable rollout for decision making. We illustrate the effectiveness of our approach by an example with multiple sensors activated simultaneously to track multiple targets. We also explore the trade-off between tracking error and sensor cost using our nonmyopic scheme. (C) 2007 Elsevier Inc. All rights reserved.

关键词： Sensor scheduling stochastic dynamic programming Multiple target tracking Partially observable Markov decision process Particle filtering

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Hybrid Electric Vehicle Downshifting Strategy Based on stochastic dynamic programming During Regenerative Braking Process

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第6期67卷 4716-4727页

作者： Liu, Binhao Li, Liang Wang, Xiangyu Cheng, Shuo Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China

Regenerative braking is the most important way to save energy for hybrid electric vehicles (HEVs) and electric vehicles (EVs). Wherein, for HEVs/EVs equipped with stepped automatic transmissions, downshifting is an effective method to improve recovery energy by adjusting motor work points. However, it is difficult to design a proper downshifting strategy due to complicated traffic conditions and unknown drivers' intention. Therefore, this paper proposes a downshifting strategy using cluster-based stochastic dynamic programing (SDP). First, driving conditions are clustered using K-means algorithm based on a large quantity of urban traffic historical data. Then, static Markov chains are built to describe the transition of the future braking torque demand for each cluster. Next, a four-dimensional SDP optimization problem for downshifting is formulated and solved through Bellman iteration. Finally, support vector machine is adopted to identify the driving conditions online, based on which SDP results are used to give the downshifting command. Simulations and controller-inloop tests are carried out, and the results show that regenerative braking could recover more energy by SDP-based strategy than no downshifting and rule-based strategy.

关键词： Regenerative braking downshifting strategy stochastic dynamic programming automated manual transmission

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On solving a bi-level stochastic dynamic programming model for analyzing fisheries policies: Fishermen behavior and optimal fish quota

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ECOLOGICAL MODELLING 2014年 272卷 68-75页

作者： van Dijk, Diana Hendrix, Eligius M. T. Haijema, Rene Groeneveld, Rolf A. van Ierland, Ekko C. Wageningen Univ Environm Econ & Nat Resources Grp NL-6706 KN Wageningen Netherlands Swiss Fed Inst Aquat Sci & Technol EAWAG CH-8600 Dubendorf Switzerland Univ Malaga ETSI Informat Dept Arquitectura Comp Comp Architecture E-29071 Malaga Spain Wageningen Univ NL-6706 KN Wageningen Netherlands

stochastic dynamic programming (SDP) is a useful tool for analyzing policy questions in fisheries management. In order to understand and reproduce solution procedures such as value function iteration, an analytic elaboration of the problem and model characteristics is required. Because of the increased use of numerical techniques, our aim is to improve the understanding of mathematical properties of the solution procedure and to give more insight into their practical implementation by means of a specific case that uses value function iteration. We provide an analytic description of model characteristics and analyze the solution procedure of a bi-level SDP model to study fisheries policies. At the first level, a policy maker decides on the fish quota to be imposed, keeping in mind fish stock dynamics, capital stock dynamics, long-term resource rents and anticipating fishermen behavior. At the second level, fishermen reveal short-term behavior by reacting on this quota and on current states of fish stock and capital stock by deciding on their investments and fishing effort. An analysis of the behavior of the model is given and a method is elaborated to obtain optimum strategies based on value function iteration. Bi-level decision making enables us to present the model in an understandable manner, and serves as a basis for extension to more complex settings. (C) 2013 Elsevier B.V. All rights reserved.

关键词： stochastic dynamic programming Value function iteration Fisheries management Bi-level

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