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An Energy Management Strategy to concurrently optimise fuel consumption & PEM fuel cell lifetime in a hybrid vehicle

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2016年第46期41卷 21503-21515页

作者： Fletcher, Toni Thring, Rob Watkinson, Martin Univ Loughborough AACME Loughborough LE11 3TU Leics England HORIBA MIRA Watling St Nuneaton CV10 0TU Warwick England

The cost and reliability of fuel cells are major obstructions preventing fuel cell hybrid electric vehicle (FCHEV) from entering the mainstream market. However, many of the degradation methods are strongly affected by the operating conditions of the fuel cell and therefore can be mitigated by optimisation of the Energy Management Strategy (EMS). The major causes of fuel cell degradation are identified from the literature and a model is produced in order to estimate the effect of the EMS on the fuel cell degradation. This is used to produce an optimal strategy for a low speed campus vehicle using stochastic dynamic programming (SDP). The SDP controller attempts to minimise the total running cost of the fuel cell, inclusive of both fuel consumption and degradation, each weighted by their respective costs. The new strategy is shown to increase the lifetime of the fuel cell by 14%, with only a 3.5% increase in fuel consumption, largely by avoiding transient loading on the fuel cell stack. (C) 2016 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC.

关键词： Energy Management Strategy stochastic dynamic programming Hybrid vehicle Fuel cell Reliability Cost

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Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization

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APPLIED ENERGY 2019年 238卷 863-875页

作者： Li, He Liu, Pan Guo, Shenglian Ming, Bo Cheng, Lei Yang, Zhikai Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Hubei Peoples R China Hubei Prov Collaborat Innovat Ctr Water Resources Wuhan 430072 Hubei Peoples R China

The complementary operation of hydro-photovoltaic (PV) hybrid power plants has become important in the modern power systems. However, the strong variations involved in the streamflow and PV generation lead to uncertainties in the complementary operations. To improve the long-term complementary operation performance of a large-scale hydro-PV hybrid power plant, this study developed long-term stochastic optimization methods that simultaneously consider the uncertainty of the streamflow and PV power output. A multi-objective optimization model was established to maximize the total energy production and guaranteed rate. The model was then solved using stochastic dynamic programming to obtain the operation decisions (carryover storage), which are functions of the initial storage, streamflow, and PV output. Finally, four schemes for deriving the operation rules were constructed: (1) stochastic optimization of the hydropower station directly plus PV output, (2) stochastic optimization of the hydro-PV hybrid power plant considering stochastic streamflow and deterministic PV output, (3) stochastic optimization of the hydro-PV hybrid power plant considering independent stochastic streamflow and PV output, and (4) stochastic optimization of the hydro-PV hybrid power plant considering correlative stochastic streamflow and PV output. The proposed methods were applied to the Longyangxia hydro-PV hybrid power plant in Qinghai province, China, which is the largest hydro-PV plant in the world. The performance of the four schemes was compared across calibration and validation periods. The results show that: (1) the complementarity of hydropower and PV power in long-term operations is highly necessary, (2) considering the uncertainty of stochastic streamfiow and PV output simultaneously improves the efficiency of complementary operations, and (3) stochastic optimization of the hydro-PV hybrid power plant considering the independent stochastic streamflow and PV output gives the best pe

关键词： Hydro-PV hybrid power plant Uncertainty Streamflow PV output stochastic dynamic programming

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Fuzzy rule-based model for hydropower reservoirs operation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第2期33卷 171-178页

作者： Moeini, R. Afshar, A. Afshar, M. H. Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Real-time hydropower reservoir operation is a continuous decision-making process of determining the water level of a reservoir or the volume of water released from it. The hydropower operation is usually based on operating policies and rules defined and decided upon in strategic planning. This paper presents a fuzzy rule-based model for the operation of hydropower reservoirs. The proposed fuzzy rule-based model presents a set of suitable operating rules for release from the reservoir based on ideal or target storage levels. The model operates on an 'if-then' principle, in which the 'if' is a vector of fuzzy premises and the 'then' is a vector of fuzzy consequences. In this paper, reservoir storage, inflow, and period are used as premises and the release as the consequence. The steps involved in the development of the model include, construction of membership functions for the inflow, storage and the release, formulation of fuzzy rules, implication, aggregation and defuzzification. The required knowledge bases for the formulation of the fuzzy rules is obtained form a stochastic dynamic programming (SDP) model with a steady state policy. The proposed model is applied to the hydropower operation of "Dez" reservoir in Iran and the results are presented and compared with those of the SDP model. The results indicate the ability of the method to solve hydropower reservoir operation problems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuzzy rules-based model stochastic dynamic programming Hydropower reservoir operation

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A finite horizon model for repairable systems with repair restrictions

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2008年第10期59卷 1321-1331页

作者： Lugtigheid, D. Jiang, X. Jardine, A. K. S. Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Louisiana State Univ Baton Rouge LA 70803 USA

In this paper, we will present a new finite horizon repair/replacement decision model and derive the structure of the optimal policy for components that have a failure intensity that is a non-decreasing function of the number of times the component has been repaired, and independent of the component's age. Furthermore, the component has physical restrictions on the number of times it can be repaired, after which the only feasible decision is to replace the component. The fundamentals of this new decision model are based on the outcomes of several case studies done by the authors. Besides presenting the model and showing the structure of the optimal policy, the model will be applied to a real industry data set, and its results discussed.

关键词： repairable system maintenance optimization stochastic dynamic programming proportional hazards model

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Quad-Q-learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2000年第2期11卷 279-294页

作者： Clausen, C Wechsler, H George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

This paper develops the theory of quad-Q-learning which is a new learning algorithm that evolved from Q-learning, Quad-Q-learning is applicable to problems that can be solved by "divide and conquer" techniques. Quad-&-learning concerns an autonomous agent that learns without supervision to act optimally to achieve specified goals. The learning agent acts in an environment that can be characterized by a state. Although Q-learning and quad-Q-learning are in many respects similar, they differ in their notion of state transitions. In the Q-learning environment, when an action is taken, a reward is received and a single new state results. The objective of Q-learning is to learn a policy function that maps states to actions so as to maximize a function of the rewards such as the sum of rewards, However, with respect to quad-Q-learning, when an action is taken from a state either an immediate reward and no new state results, or no reward is received and four new states result from taking that action. If four new states result, then each new state is treated independently as if four new environments resulted. If no new state results, no further action is taken in the associated environment. The environment in which quad-Q-learning operates can thus be viewed as a hierarchy of states where lower level states are the children of higher level states, The hierarchical aspect of quad-Q-learning leads to a bottom up view of learning that improves the efficiency of learning at higher levels in the hierarchy. The objective of quad-Q-learning is to maximize the sum of rewards obtained from each of the environments that result as actions are taken. Quad-Q-learning can be readily generalized to a family of n-Q-learning algorithms which are applicable to learning how to partition large intractable problem domains into smaller problems that can be solved independently. Tao versions of quad-Q-learning are discussed;these are discrete state and mixed discrete and continuous state quad-Q-l

关键词： fractal compression image compression Q-learning reinforcement learning stochastic dynamic programming

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The optimal timber rotation: An option value approach

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FOREST SCIENCE 1998年第2期44卷 192-202页

作者： Plantinga, AJ Univ Maine Dept Resource Econ & Policy Orono ME 04469 USA

Previous analyses of the timber rotation problem under price uncertainty find that timber owners can increase the expected values of their stands by using a reservation price policy that exploits stochastic variations in prices. This result is found when the process governing prices is stationary and with nonstationary prices when there are fixed costs. This paper highlights the role of option values in influencing the optimal timing of harvests. In this context, the option value is a premium over the expected net present value of a timber stand reflecting the opportunity cost of harvesting now and foregoing the option to delay harvest until information on future stand values is revealed. With stationary prices, option values arise from two sources of forthcoming information: the level of the stumpage price relative to the long-term mean and the stand value relative to fixed costs. With nonstationary prices, option values are associated only with the latter information. This paper demonstrates analytically that reservation price policies act as a mechanism for incorporating the option value into the calculus of the optimal rotation length, Numerical simulations provide further insights into the effects of option values on rotation lengths, expected stand values, and reservation prices.

关键词： stochastic prices stochastic dynamic programming

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Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2023年第3期35卷 797-837页

作者： Cheng, Chen-Yang Pourhejazy, Pourya Chen, Tzu-Li Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 10608 Taiwan UiT Arctic Univ Norway Dept Ind Engn Lodve Langsgate 2 N-8514 Narvik Norway

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate dynamic programming and the Rolling-Horizon-based Approximate dynamic programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to stochastic dynamic programming.

关键词： Production management Capacity planning Approximate dynamic programming stochastic dynamic programming Optimization

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Production Campaign Planning Under Learning and Decay

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2020年第3期22卷 615-632页

作者： Jahandideh, Hossein Rajaram, Kumar McCardle, Kevin Univ Calif Los Angeles Anderson Sch Management Los Angeles CA 90095 USA

Problem definition: We analyze a catalyst-activated batch-production process with uncertainty in production times, learning about catalyst-productivity characteristics and decay of catalyst performance across batches. The goal is to determine the quality level of batches and to decide when to replenish a catalyst so as to minimize average costs, consisting of inventory-holding, backlogging, and catalyst-switching costs. Academic/ practical relevance: This is an important problem in a variety of process-industry sectors, such as food processing, pharmaceuticals, and specialty chemicals, but has not been adequately studied in the academic literature. This paper also contributes to the stochastic economic lot-sizing literature. Methodology: We formulate this problem as a semi-Markov decision process (SMDP) and develop a two-level heuristic to solve this problem. The heuristic consists of a lower-level problem that plans the duration of batches within the current campaign to maximize the efficiency of the catalyst while ensuring that the target attribute level for each batch is set to meet a quality specification represented by an average attribute level across all the batches in a campaign. The higher-level problem determines when to replace the costly catalyst as its productivity decays. To evaluate our heuristic, we present a lower bound on the optimal value of the SMDP. This bound accounts for all costs, as well as the randomness and discreteness in the process. We then extend our methods to multiple-product settings, which results in an advanced stochastic economic lot-sizing problem. Results: We test our proposed solution methodology with data from a leading food-processing company and show that our methods outperform current practice with an average improvement of around 22% in costs. In addition, compared with the stochastic lower bounds, our results show that the two-level heuristic attains near-optimal performance for the intractable multidimensional SMDP. Man

关键词： campaign planning batch production stochastic economic lot sizing problem Bayesian updating production learning semi-Markov processes stochastic dynamic programming

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Genetic algorithm for optimal operating policy of a multipurpose reservoir

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WATER RESOURCES MANAGEMENT 2005年第2期19卷 145-161页

作者： Ahmed, JA Sarma, AK Indian Inst Technol Dept Civil Engn Gauhati 781039 India

This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation.

关键词： genetic algorithm multipurpose reservoir optimal operating policy stochastic dynamic programming streamflow

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A Condition-Based Maintenance Model Including Resource Constraints on the Number of Inspections

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IEEE TRANSACTIONS ON RELIABILITY 2020年第3期69卷 1165-1176页

作者： Alvarez, Claudio Lopez-Campos, Monica Stegmaier, Raul Mancilla-David, Fernando Schurch, Roger Angulo, Alejandro Univ Tecn Federico Santa Maria Dept Ind Engn Valparaiso 1680 Chile Univ Colorado Dept Elect Engn Denver CO 80217 USA Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 1680 Chile

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections;it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, age-based maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.

关键词： Inspection Degradation Numerical models Integrated circuit modeling stochastic processes Preventive maintenance Condition-based maintenance reliability resource constraints stochastic dynamic programming

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