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Comparative Study of Real-Time HEV Energy Management Strategies

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2017年第12期66卷 10875-10888页

作者： Jiang, Qi Ossart, Florence Marchand, Claude Grp Elect Engn Paris F-91192 Gif Sur Yvette France

An adequate energy management strategy is the key to optimizing hybrid electric vehicle fuel efficiency. Various real-time controls have been recently developed. As each study is performed in a specific context, a comparative analysis is critically needed to point out their pros and cons. This paper proposes a comparison between three promising real-time strategies: adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL), and stochastic dynamic programming (SDP). Two offline algorithms are used as benchmark: Pontryagin's minimum principle and dynamic programming. Implementation and parameters setting issues are discussed for each strategy. The real-time strategies robustness is then evaluated over several types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation results show that OCL needs improvement. A-ECMS reaches the best fuel saving performance when used with parameter sets adjusted to the driving environment, while SDP better respects the charge sustaining constraint.

关键词： A-ECMS hybrid electric vehicle Markov chain OCL optimal control Pontryagin minimum principle real-time energy management stochastic dynamic programming

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Water storage in a changing environment: The impact of allocation institutions on value

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WATER RESOURCES RESEARCH 2017年第1期53卷 672-687页

作者： Maas, Alexander Dozier, Andre Manning, Dale T. Goemans, Christopher Colorado State Univ Dept Agr & Resource Econ Ft Collins CO 80523 USA Colorado State Univ Dept Civil Engn Ft Collins CO 80523 USA

As populations increase in arid regions of the world, investment in water infrastructure improves resource management by increasing control over the location and timing of water allocation. Many studies have explored freer trade as a substitute for additional infrastructure investment. We instead quantify how water allocation institutions, reservoir management objectives, and storage capacity influence the value derived from a reservoir system. We develop a stochastic dynamic programming model of a reservoir system that faces within-year variation in weather-dependent water demand as well as stochastic semiannual inflows. We parameterize the model using the Colorado-Big Thompson system, which transports stored water from the West Slope of the Rocky Mountains to the East Slope. We then evaluate the performance of the system under five institutional settings. Our results suggest that rigid allocation mechanisms and inefficient management objectives result in a decrease of up to 13% in the value generated from stored water when compared to a free trade scenario, an impact on par with predicted losses associated with climate-change-induced inflow reductions. We also find that under biased management objectives, increasing storage capacity can decrease the social value obtained from stored water.

关键词： stochastic dynamic programming water infrastructure water management

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Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and dynamic Control Model (DCM)

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WATER RESOURCES MANAGEMENT 2017年第11期31卷 3543-3555页

作者： Zhou, Jianzhong Xie, Mengfei He, Zhongzhen Qin, Hui Yuan, Liu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and stochastic dynamic programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic dynamic programming with hindsight and advantages and disadvantages of the two methods are analyzed.

关键词： Medium-termhydro generation scheduling Chance constrainedmodel stochastic inflow stochastic dynamic programming dynamic control model

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Learning and Bayesian updating in long cycle made-to-order (MTO) production

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 69卷 29-42页

作者： Womer, K. Li, H. Camm, J. Osterman, C. Radhakrishnan, R. Univ Missouri St Louis MO 63121 USA Wake Forest Univ Winston Salem NC 27109 USA Naval Personnel Res Study & Technol Millington TN USA Integral Analyt Cincinnati OH USA

We model production planning for made-to-order (MTO) manufacturing by choosing production rate to minimize expected discounted cost incurred up to a promised delivery date. Products that are MTO are often unique and customized. The associated learning curve slope and other production parameters cannot be precisely estimated before production starts. In this paper, a dynamic and adaptive approach to estimate the effects of learning and to optimize next period production is developed. This approach offers a closed-loop solution through stochastic dynamic programming. Monthly production data are used to update the joint probability distributions of production parameters via Bayesian methods. Our approach is illustrated using historical earned-value data from the Black Hawk Helicopter Program. Managerial insights are obtained and discussed. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Made-to-order Project management Production planning Learning stochastic dynamic programming Nonlinear programming Incomplete information process Bayesian updating

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A reinforcement learning methodology for a human resource planning problem considering knowle dge-based promotion

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SIMULATION MODELLING PRACTICE AND THEORY 2017年 79卷 87-99页

作者： Karimi-Majd, Amir-Mohsen Mahootchi, Masoud Zakery, Amir Amirkabir Univ Technol Ind Engn & Management Syst Dept 424 Hafez Ave Tehran 158754413 Iran

This paper addresses a combined problem of human resource planning (HRP) and production-inventory control for a high-tech industry, wherein the human resource plays a critical role. The main characteristics of this resource are the levels of "knowledge" and the learning process. The learning occurs during the production process in which a worker can promote to the upper knowledge level. Workers in upper levels have more productivity in the production. The objective is to maximize the expected profit by deciding on the optimal numbers of workers in various knowledge levels to fulfill both production and training requirement. As taking an action affects next periods' decisions, the main problem is to find the optimal hiring policy of non-skilled workers in long-time horizon. Thus, we develop a reinforcement learning (RL) model to obtain the optimal decision for hiring workers under the demand uncertainty. The proposed interval-based policy of our RL model, in which for each state there are multiple choices, makes it more flexible. We also embed some managerial issues such as layoffand overtime-working hours into the model. To evaluate the proposed methodology, stochastic dynamic programming (SDP) and a conservative method implemented in a real case study are used. We study all these methods in terms of four criteria: average obtained profit, average obtained cost, the number of newhired workers, and the standard deviation of hiring policies. The numerical results confirm that our developed method end up with satisfactory results compared to two other approaches. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning Production-inventory control Human resource planning stochastic dynamic programming Knowledge-intensive

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ON THE IDENTIFICATION AND MITIGATION OF WEAKNESSES IN THE KNOWLEDGE GRADIENT POLICY FOR MULTI-ARMED BANDITS

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2017年第2期31卷 239-263页

作者： Edwards, James Fearnhead, Paul Glazebrook, Kevin Univ Lancaster STOR I Ctr Doctoral Training Lancaster LA1 4YF England Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England Univ Lancaster Dept Management Sci Lancaster LA1 4YX England

The knowledge gradient (KG) policy was originally proposed for online ranking and selection problems but has recently been adapted for use in online decision-making in general and multi-armed bandit problems (MABs) in particular. We study its use in a class of exponential family MABs and identify weaknesses, including a propensity to take actions which are dominated with respect to both exploitation and exploration. We propose variants of KG which avoid such errors. These new policies include an index heuristic, which deploys a KG approach to develop an approximation to the Gittins index. A numerical study shows this policy to perform well over a range of MABs including those for which index policies are not optimal. While KG does not take dominated actions when bandits are Gaussian, it fails to be index consistent and appears not to enjoy a performance advantage over competitor policies when arms are correlated to compensate for its greater computational demands.

关键词： stochastic dynamic programming

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Assessing hydropower operational profitability considering energy and reserve markets

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IET RENEWABLE POWER GENERATION 2017年第13期11卷 1640-1647页

作者： Helseth, Arild Fodstad, Marte Askeland, Magnus Mo, Birger Nilsen, Odd Bjarte Ignacio Perez-Diaz, Juan Chazarra, Manuel Guisandez, Ignacio SINTEF Energy Res Sem Saelands Vei 11 N-7465 Trondheim Norway Lyse Prod AS N-4069 Stavanger Norway Tech Univ Madrid UPM Dept Hydraul Energy & Environm Engn Madrid 28040 Spain

The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability.

关键词： profitability power generation economics power markets hydroelectric power stations hydroelectric power stochastic programming dynamic programming pricing hydropower operational profitability assessment energy markets reserve markets energy capacity prices reserve capacity prices competitive market setting mathematical model stochastic dynamic programming water value computation power plant configurations Norwegian hydropower producer

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Optimization methods to solve adaptive management problems

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THEORETICAL ECOLOGY 2017年第1期10卷 1-20页

作者： Chades, Iadine Nicol, Sam Rout, Tracy M. Peron, Martin Dujardin, Yann Pichancourt, Jean-Baptiste Hastings, Alan Hauser, Cindy E. CSIRO GPO Box 2583 Brisbane Qld 4001 Australia Univ Melbourne Sch BioSci Parkville Vic 3010 Australia Queensland Univ Technol Sch Math Sci Brisbane Qld 4000 Australia Univ Calif Davis Dept Environm Sci & Policy Davis CA 95616 USA

Determining the best management actions is challenging when critical information is missing. However, urgency and limited resources require that decisions must be made despite this uncertainty. The best practice method for managing uncertain systems is adaptive management, or learning by doing. Adaptive management problems can be solved optimally using decision-theoretic methods;the challenge for these methods is to represent current and future knowledge using easy-to-optimize representations. Significant methodological advances have been made since the seminal adaptive management work was published in the 1980s, but despite recent advances, guidance for implementing these approaches has been piecemeal and study-specific. There is a need to collate and summarize new work. Here, we classify methods and update the literature with the latest optimal or near-optimal approaches for solving adaptive management problems. We review three mathematical concepts required to solve adaptive management problems: Markov decision processes, sufficient statistics, and Bayes' theorem. We provide a decision tree to determine whether adaptive management is appropriate and then group adaptive management approaches based on whether they learn only from the past (passive) or anticipate future learning (active). We discuss the assumptions made when using existing models and provide solution algorithms for each approach. Finally, we propose new areas of development that could inspire future research. For a long time, limited by the efficiency of the solution methods, recent techniques to efficiently solve partially observable decision problems now allow us to solve more realistic adaptive management problems such as imperfect detection and non-stationarity in systems.

关键词： Adaptive management Markov decision process MDP Partially observable Markov decision process POMDP stochastic dynamic programming Value of information Hidden Markov models Natural resource management Conservation

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Study On Energy Consumption Of Sino - German Building In Uncertain Environment

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IFAC-PapersOnLine 2017年第2期50卷 104-109页

作者： Liang, Sun Liang Ye, Li Qi, Jia Hai Ning, Hu Jun Hang, Jin Department of Information and Control Engineering Shenyang Jianzhu University ShenyangLiaoning Province110004 China Department of Information and Control Engineering Shenyang Jianzhu University ShenyangLiaoning Province110004 China

According to the experience of developed countries, with the development of the city, the construction industry will go beyond the industrial, transportation and other industries and ultimately ranked first in social energy consumption, and air conditioning energy consumption is the main part of building energy consumption, reduce its operation energy consumption is of great significance to saving energy, improving energy utilization and implementing green building strategy. This paper studies the joint control of HVACs, lights, shading blinds and natural ventilation in the Sino - German Building. This paper has developed an effective environment for integrated control of HVACs, lights, shading blinds and natural ventilation in an uncertain environment. In this paper, an easy-to-use model suitable for optimization is established, and the joint control optimization problem is transformed into a multi-stage stochastic optimization problem. Using the improved Lagrangian relaxation method, the Lagrangian multiplier is used to loosen the constraints that are coupled together by air conditioning equipment such as a common machine. A new Lagrangian relaxation is shown, which can get better gradient direction. The optimal solution can be obtained by referring to stochastic dynamic programming and heuristic rules. Finally, the simulation results of the building simulation software DeST show that the joint optimization of the building end equipment can effectively reduce the building energy consumption. © 2017

关键词： Energy utilization Air conditioning Buildings Computer software Construction industry dynamic programming Energy conservation Green manufacturing Lagrange multipliers stochastic models stochastic systems Ventilation Air conditioning energy consumption Building energy consumption Joint optimization LaGrangian relaxation Lagrangian relaxation method stochastic dynamic programming stochastic optimization problems Uncertain environments

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Optimal dynamic pricing of mobile data plans in wireless communications

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年第PartA期66卷 91-105页

作者： Ma, Xiaoyu Deng, Tianhu Xue, Mengying Shen, Zuo-Jun Max Lan, Boxiong Beijing Foreign Studies Univ Int Business Sch Beijing 100089 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA

With smartphones sweeping the world, customers use much more data service than voice service nowadays. Because mobile data service occupies much more network capacity than voice, wireless network is frequently congested. We build a dynamic cell phone plan control model where the service provider can decide a subset of plans to open at the beginning of each period. New customers can only subscribe open plans. The dynamic plan control problem is a stochastic programming problem where the system state dimension is exponential in the number of plans and periods. In this paper, we analyze the case when the number of customers approaches positive infinity. In this limiting case, we transform the stochastic dynamic programming problem to an equivalent integer linear programming problem. In a case study, we analyze a large mobile service provider in Asia using 10,000 real individual-level data. Sensitivity analysis implies that a mobile service provider whose initial market share is low can benefit more from the dynamic data plan control. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： dynamic plan control Mobile data plans Congestion effect stochastic dynamic programming

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