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Evaluation of electric power procurement strategies by stochastic dynamic programming

ELECTRICAL ENGINEERING IN JAPAN 2007年第2期160卷 20-29页

作者： Saisho, Yuichi Hayashi, Taketo Fujii, Yasumasa Yamaji, Kenji Univ Tokyo Tokyo Japan

In deregulated electricity markets, the role of a distribution company is to purchase electricity from the wholesale electricity market at randomly fluctuating prices and to provide it to its customers at a given fixed price. Therefore, the company has to take risk stemming from the uncertainties of electricity prices and/or demand fluctuation instead of the customers. The way to avoid the risk is to make a bilateral contract with generating companies or install its own power generation facility. This entails the necessity to develop a certain method to make an optimal strategy for electric power procurement. In such a circumstance, this research proposes a mathematical method based on stochastic dynamic programming and considers the characteristics of the start-up cost of an electric power generation facility to evaluate strategies of combination of the bilateral contract and power auto-generation with its own facility for procuring electric power in a deregulated electricity market. In the beginning we proposed two approaches to solve the stochastic dynamic programming, and they are a Monte Carlo simulation method and a finite difference method to derive the solution of a partial differential equation of the total procurement cost of electric power. Finally we discussed the influences of the prime uncertainty on optimal strategies of power procurement. (c) 2007 Wiley Periodicals, Inc.

关键词： electricity market uncertainty stochastic dynamic programming startup cost procurement cost

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A stochastic dynamic programming approach for delay management of a single train line

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期271卷 501-518页

作者： Schoen, Cornelia Koenig, Eva Univ Mannheim Business Sch D-68131 Mannheim Germany

Railway delay management considers the question of whether a train should wait for a delayed feeder train. Several works in the literature analyze these so-called wait-depart decisions. The underlying models range from rules of thumb to complete network optimizations. Almost none of them account for uncertainties regarding future delays. In this paper, we present a multi-stage stochastic dynamic programming (SDP) model to make wait-depart decisions in the presence of uncertain future delays. The SDP approach explicitly accounts for potential recourse actions at later stations in a look-ahead manner when making the decision in the current stage. The objective is to minimize the total delay experienced by passengers at their final station by recursively solving Bellman equations. We focus on a single train line but consider the effects on direct feeder and connecting trains. In an extensive numerical study, we compare the solution quality and computational effort of the SDP to other optimization approaches and simple heuristic decision rules that are frequently used in delay management. The SDP approach outperforms the other approaches in almost every scenario with regard to solution quality in reasonable time and seems to be a promising starting point for stochastic dynamic delay management with interesting future research opportunities. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Transportation Railway delay management stochastic dynamic programming

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A stochastic dynamic programming model for valuing exclusivity from encroachment in franchising

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2012年第2期63卷 151-159页

作者： Tikoo, S. Liu, S. Nair, S. K. SUNY Coll New Paltz Sch Business New Paltz NY 12561 USA Univ Connecticut Storrs CT USA

Valuing territorial exclusivity in franchising is difficult because of the uncertainty associated with variables such as future franchise sales and brand strength. We present a stochastic dynamic programming model to value the exclusivity option from the perspective of both the franchisor and the franchisee. When there is positive value to the franchisor of including the exclusivity option in the contract, and to the franchisee of purchasing this option, the likelihood of franchisor-franchisee encroachment-related conflict is reduced. We also discuss structural results and explain our results using a numerical example. Journal of the Operational Research Society (2012) 63, 151-159. doi: 10.1057/jors.2010.184 Published online 8 June 2011

关键词： franchising territorial encroachment exclusivity stochastic dynamic programming

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Comparison of Policies Derived from stochastic dynamic programming and Genetic Algorithm Models

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WATER RESOURCES MANAGEMENT 2009年第8期23卷 1563-1580页

作者： Jothiprakash, V. Shanthi, Ganesan Indian Inst Technol Dept Civil Engn Mumbai 400076 Maharashtra India JJ Coll Engn & Technol Dept Civil Engn Tiruchirappalli 620009 Tamil Nadu India

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed stochastic dynamic programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas-Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

关键词： stochastic dynamic programming Genetic algorithm Multi-purpose reservoir operation Simulation Synthetic stream flow generation

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Optimized Speed Trajectories for Cyclists, Based on Personal Preferences and Traffic Light Information-A stochastic dynamic programming Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第2期23卷 777-793页

作者： Dabiri, Azita Hegyi, Andreas Hoogendoorn, Serge Delft Univ Technol Dept Transport & Planning NL-2628 Delft Netherlands

The literature on green mobility and eco-driving in urban areas has burgeoned in recent years, with special attention to using infrastructure to vehicle (I2V) communications to obtain optimal speed trajectory which minimize the economic and environmental costs. This article shares the concept with these studies but turns the spotlight on cyclists. It examines the problem of finding optimal speed trajectory for a cyclist in signalised urban areas. Unlike the available studies on motorised vehicles which predominantly designed for pre-defined, fixed traffic lights timing, this article uses an algorithm based on stochastic dynamic programming to explicitly address uncertainty in traffic light timing. Moreover, through a comprehensive set of simulation experiments, the article examines the impact of the speed advice's starting point as well as the cyclist's willingness for changing his/her speed on enhancing the performance. The proposed approach targets various performance metrics such as minimising the total travel time, energy consumption, or the probability of stopping at a red light. Hence, the resulting speed advice can be tailored according to the personal preferences of each cyclist. In a simulation case study, the results of the proposed approach is also compared with an existing approach in the literature.

关键词： Timing Urban areas dynamic programming Trajectory Fuels Heuristic algorithms Acceleration Speed advice cycling energy consumption travel time stochastic dynamic programming

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Planning water resources systems with interval stochastic dynamic programming

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WATER RESOURCES MANAGEMENT 2007年第6期21卷 997-1014页

作者： Luo, B. Maqsood, I. Huang, G. H. Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada

This study addresses water resources system planning problems with capacity expansion in an uncertain environment. An interval stochastic dynamic programming (SDP) model is presented, which is a hybrid of interval-number optimization and SDP. Besides the dynamic features of the model, it can incorporate and reflect uncertainties expressed as probability distribution functions and discrete intervals. The solution method for the proposed model is computationally effective, which makes it applicable to practical problems. The results acquired through a case study indicate that reasonable solutions have been obtained. They are further analyzed and interpreted for identifying significant factors that affect the system's performance. The information obtained through these post-optimality analyses can provide useful decision support for water authorities.

关键词： interval stochastic dynamic programming water resources planning uncertainty

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Role of hydrologic information in stochastic dynamic programming: a case study of the Kemano hydropower system in British Columbia

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2014年第9期41卷 839-844页

作者： Desreumaux, Quentin Cote, Pascal Leconte, Robert Univ Sherbrooke Dept Civil Engn Fac Engn Sherbrooke PQ J1K 2R1 Canada Rio Tinto Alcan Quebec Power Operat Jonquiere PQ G7S 4R5 Canada

This paper presents a study describing the effect of various hydrological variables in stochastic dynamic programming (SDP) for solving the optimization problem of managing a hydropower system. We will show how choosing the best hydrological variables can strongly affect management policies. This is especially true for the system studied here, namely the Kemano hydroelectric system located in British Columbia, Canada, which is subject to large streamflow volumes due to significant snow cover during winter. Real-time snow water equivalent (SWE) data can be used directly as a variable in SDP management policies. Results indicate that for the system in this study, the maximum SWE (i.e., highest level of SWE observed from the start of winter to the current decision period) is the best among the methods investigated for effective, safe management, compared with Markov or order p autoregressive models when forecasts are not available.

关键词： stochastic dynamic programming hydrologic information hydropower reservoir management

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Reservoir operation using grey fuzzy stochastic dynamic programming

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HYDROLOGICAL PROCESSES 2002年第12期16卷 2395-2408页

作者： Chang, FJ Hui, SC Chen, YC Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei Taiwan

This paper presents an optimal regulation programme, grey fuzzy stochastic dynamic programming (GFSDP), for reservoir operation. It is composed of a grey system, fuzzy theory and dynamic programming. The grey system represents data by covering the whole range without loss of generality, and the fuzzy arithmetic takes charge of the rules of reservoir operation. The GFSDP deals with the multipurpose decision-making problem by fuzzy optimization theorem. The practicability and effectiveness of the proposed approach is tested on the operation of the Shiman reservoir in Taiwan. The current M5 operating rule curves of this reservoir also are evaluated. The simulation results demonstrate that this new approach, in comparison with the M5 rule curves, has superior performance with regard to the total water deficit and number of monthly deficits. Copyright (C) 2002 John Wiley Sons, Ltd.

关键词： fuzzy arithmetic grey system reservoir operation stochastic dynamic programming

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Modeling the operation of multireservoir systems using decomposition and stochastic dynamic programming

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NAVAL RESEARCH LOGISTICS 2006年第3期53卷 217-225页

作者： Archibald, TW McKinnon, KIM Thomas, LC Univ Edinburgh Sch Management Edinburgh EH8 9YL Midlothian Scotland Univ Edinburgh Sch Math Edinburgh EH8 9YL Midlothian Scotland Univ Southampton Sch Management Southampton SO9 5NH Hants England

stochastic dynamic programming models are attractive for multireservoir control problems because they allow nonlinear features to be incorporated and changes in hydrological conditions to be modeled as Markov processes. However, with the exception of the simplest cases, these models are computationally intractable because of the high dimension of the state and action spaces involved. This paper proposes a new method of determining an operating policy for a multireservoir control problem that uses stochastic dynamic programming, but is practical for systems with many reservoirs. Decomposition is first used to reduce the problem to a number of independent subproblems. Each subproblem is formulated as a low-dimensional stochastic dynamic program and solved to determine the operating policy for one of the reservoirs in the system. (c) 2006 Wiley Periodicals, Inc.

关键词： multireservoir systems stochastic dynamic programming decomposition principal components

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On stochastic dynamic programming for solving large-scale planning problems under uncertainty

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COMPUTERS & OPERATIONS RESEARCH 2009年第8期36卷 2418-2428页

作者： Pilar Cristobal, M. Escudero, Laureano F. Monge, Juan F. Univ Politecn Madrid ETSI Montes Dpto Matemt Aplicada E-28040 Madrid Spain Univ Rey Juan Carlos Dpto Estadist & Invest Operat Madrid 28933 Spain Univ Miguel Hernandez Ctr Invest Operat Alicante 03203 Spain

The stochastic dynamic programming approach outlined here, makes use of the scenario tree in a back-to-front scheme. The multi-period stochastic problems, related to the subtrees whose root nodes are the starting nodes (i.e., scenario groups), are solved at each given stage along the time horizon. Each subproblem considers the effect of the stochasticity of the uncertain parameters from the periods of the given stage, by using curves that estimate the expected future value (EFV) of the objective function. Each subproblem is solved for a set of reference levels of the variables that also have nonzero elements in any of the previous stages besides the given stage. An appropriate sensitivity analysis of the objective function for each reference level of the linking variables allows us to estimate the EFV curves applicable to the scenario groups from the previous stages, until the curves for the first stage have been computed. An application of the scheme to the problem of production planning with logical constraints is presented. The aim of the problem consists of obtaining the planning of tactical production over the scenarios along the time horizon. The expected total cost is minimized to satisfy the product demand. Some computational experience is reported. The proposed approach compares favorably with a state-of-the-art optimization engine in instances on a very large scale.

关键词： stochastic dynamic programming Scenario tree Mixed 0-1 model Tactical production planning

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