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IEEE/PES Power Systems Conference and Exposition

作者： Jha, Deependra Kumar Yorino, Naoto Zoka, Yoshifumi Sasaki, Yutaka Hayashi, Yuki Iwata, Kazunoni Oe, Ryuji Hiroshima Univ Grad Sch Engn Dept Artificial Complex Syst Engn Hiroshima 730 Japan Chugoku Elect Power Co Inc Hiroshima Japan

ISBN: (纸本)9781424438105

This paper proposes a backward search approach to incorporate excess stream inflows, in stochastic dynamic programming (SDP) based reservoir scheduling, of hydropower plants. The aim is to reduce the amount of spill during operation of the reservoir. Energy maximization type stochastic dynamic programming (SDP) model, with inflow process assumption considered as independent, is developed for a hydropower plant located in Japan. Reservoir operating policy consists of target storage levels at the end of a period, for each combination of beginning of the period storage levels and possible average inflow states during the period. The backward search approach has been incorporated in the SDP model, during the simulation, in order to identify the annual reservoir operation curve, with reduced spill. The storage guide curves identified with incorporation of backward search approach are compared with that from the normal SDP model. It has been found that the proposed approach serves better from the view point of spill reduction.

关键词： Backward search Reservoir operation policy Storage guide curves stochastic dynamic programming Markov chain

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Temporal Difference Learning Waveform Selection

Temporal Difference Learning Waveform Selection

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Wang, Bin Wang, Jinkuan Song, Xin Han, Yinghua Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ Engn Optimizat & Smart Antenna Inst Qingdao Peoples R China

ISBN: (纸本)9781424442461

The largest difference between cognitive radar and other adaptive radar is the adaptivity of transmitter in cognitive radar. How to optimally decide or select the radar waveform for next transmission based on the observation of past radar returns is one of the important issues. In this paper, with the stochastic dynamic programming model of waveform selection, we use the method of temporal difference learning to solve this problem and realize the adaptivity of waveform selection. The simulation results show that the uncertainty of state estimation using temporal difference learning is less than that using fixed waveform.

关键词： cognitive radar waveform selection stochastic dynamic programming temporal difference learning

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A Predictive Control Approach for Long Term Hydrothermal Scheduling

A Predictive Control Approach for Long Term Hydrothermal Sch...

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IEEE/PES Power Systems Conference and Exposition

作者： Zambelli, Monica S. Soares, Secundino Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil

ISBN: (纸本)9781424438105

This paper presents a predictive control approach for long-term generation scheduling of hydro-thermal power systems. The approach is based on an open-loop feedback control scheme that uses a neural fuzzy network forecasting model, for handling the stochastic nature of inflows, and a deterministic nonlinear optimization model, to determine the discharge decisions to be implemented. As a consequence, inflow correlations on time can be represented by nonlinear relationships, and hydropower generation and thermal fuel cost can be handled by nonlinear functions, allowing a more precise modeling of the problem. A simulation model was also developed for performance assessment of the proposed approach. A comparison with the classical stochastic dynamic programming approach, in the case of single reservoir systems, revealed that the latter and the proposed approach perform similarly. The approach was also applied to a multi-reservoir system composed of 19 hydro plants and 10 reservoirs corresponding to a major cascade of the Brazilian power system. The results show that the proposed approach performs as well as in the single reservoir case study.

关键词： fuzzy neural networks hydrothermal scheduling optimization forecasting model stochastic dynamic programming

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Knowledge gain or system benefit in environmental decision making? 18

Knowledge gain or system benefit in environmental decision m...

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IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th MODSIM09 Biennial Conference on Modelling and Simulation

作者： Probert, W. J. M. McDonald-Madden, E. Hauser, C. E. Baxter, P. W. J. Possingham, H. P. Univ Queensland Dept Math St Lucia Qld 4072 Australia

ISBN: (纸本)9780975840078

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.

关键词： adaptive management conservation biology decision theory optimal monitoring optimization stochastic dynamic programming

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Conservation decision-making in large state spaces 18

Conservation decision-making in large state spaces

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IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th MODSIM09 Biennial Conference on Modelling and Simulation

作者： Nicol, S. C. Chades, I. Linke, S. Possingham, H. P. Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9780975840078

For metapopulation management problems with small state spaces, it is typically possible to model the problem as a Markov decision process (MDP), and find an optimal control policy using stochastic dynamic programming (SDP). SDP is an iterative procedure that seeks to optimise a value function at each timestep by trying each of the actions defined in the MDP. Although SDP gives the optimal solution to conservation management questions in a stochastic world, its applicability has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. A large state space makes optimal SDP solutions computationally expensive to compute because optimal SDP techniques require the value function to be updated for the entire state space for every time step. The high computational requirements of large SDP problems means that only simple population management problems can be analysed. In this paper we present an application of the on-line sparse sampling algorithm proposed by Kearns, Mansour & Ng (2002), which can be used to approximate the optimal solution of a MDP for a given starting state. The algorithm is particularly attractive for problems with large state spaces as it has a running time that is independent of the size of the state space of the problem. We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation where we define the management objective to be to maximise the number of occupied patches during the management horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that the approximation algorithm out

关键词： Markov decision processes stochastic dynamic programming Metapopulation Optimal management On-line sparse sampling algorithm

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Wind Generation Scheduling with Pump Storage Unit by Collocation Method

Wind Generation Scheduling with Pump Storage Unit by Colloca...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Gao, Feng Hallam, Arne Yu, Chien-Ning ABB Inc Santa Clara CA 95050 USA Iowa State Univ Dept Econom Ames IA 50010 USA

ISBN: (纸本)9781424442409

Wind generation is currently undergoing the fastest rate of growth of any form of electricity generation in the world. Integration of wind power into systems could be problematic, however, due to availability and variability of wind supply. As a proven storage technology, pump storage may provide/absorb additional capacity so as to hedge against adverse situations. A scheduling software is needed to coordinate wind generation and pump storage units, given existing price and wind forecasting modules. This paper first implements a "Collocation Method" to solve a dispatching problem of wind and pump storage units, given the forecasting of future price and wind generation. Second, given that a pumping decision is discrete while generation dispatching is continuous, this paper uses a two-step strategy to solve a mixed-integer problem. Transition constraints are modeled based on the property of collocation method. Third, this paper uses real wind generation and real-time energy price data to simulate scheduling activities by the proposed model. A comparison of expected profits between wind only and wind plus pump storage unit is given.

关键词： Collocation Method Pump Storage Hydro Real Time Market stochastic dynamic programming Wind Generation

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Production and changeover control policies of failure prone buffered flow-shops

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IFAC Proceedings Volumes 2009年第4期42卷 1108-1113页

作者： A. Hajji A. Gharbi J-P. Kenne Department of Mathematics and Industrial Engineering École Polytechnique de Montréal CA Department of Automated Production Engineering École de Technologie Supérieure CA Mechanical Engineering Department École de Technologie Supérieure CA

This paper deals with a stochastic optimal control problem for a class of failure prone buffered multi parts flow-shops manufacturing system. The flow shop under consideration requires setup time and cost in order to switch the production from a part type to another. Our objective is to find the production plan and sequence of setups that minimize the cost function which penalizes inventory/backlog and setup costs. A continuous dynamic programming formulation of the problem is presented and solved numerically for a two buffered serial machines two parts case. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy.

关键词： Production and Setup control buffered flow shops stochastic dynamic programming numerical methods

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Economic Growth with Political Lobbying and Wage Bargaining *

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IFAC Proceedings Volumes 2009年第2期42卷 58-63页

作者： Tapio Palokangas

This paper examines an economy with a large number of industries, each producing a different good. Technological change follows a Poisson process where firms improve their productivity through investment in R&D. The less there are firms in the economy or the more they can coordinate their actions, the higher their profits. Labor is used in production or R&D. All workers are unionized and their wages depend on relative union bargaining power. If this power is high enough, then there is involuntary unemployment. Both workers and firms lobby the central planner of the economy which affects firms’ and unions’ market power. The main findings of the paper can be summarized the follows. Unions’ and firms’ market power decreases the level of income at each moment of time. On the other hand, the greater the firm's share of value added or the higher union wages, more incentives the firm has to increase the productivity of labor through R&D. In this respect, there can be an optimal amount of unions’ and firms’ market power. Concerning the regulation of relative union bargaining power, the central planner can increase its welfare either (a) by increasing the level of income or (b) by speeding up economic growth. If (a) is more effective than (b), then the central planner eliminates union power altogether to have full employment. On the other hand, if (b) is more effective than (a), then the central planner supports labor unions to promote cost-escaping R&D.

关键词： extensive game maximum principle stochastic dynamic programming alternative offers game common agency

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Temporal Difference Learning Waveform Selection

Temporal Difference Learning Waveform Selection

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Bin Wang College of Information Science and Engineering Northeastern University Shenyang,China Jinkuan Wang,Xin Song,Yinghua Han Engineering Optimization and Smart Antenna Institute Northeastern University at Qinhuangdao Qinhuangdao,China

The largest difference between cognitive radar and other adaptive radar is the adaptivity of transmitter in cognitive radar. How to optimally decide or select the radar waveform for next transmission based on the observation of past radar returns is one of the important issues. In this paper,with the stochastic dynamic programming model of waveform selection,we use the method of temporal difference learning to solve this problem and realize the adaptivity of waveform selection. The simulation results show that the uncertainty of state estimation using temporal difference learning is less than that using fixed waveform.

关键词： cognitive radar waveform selection stochastic dynamic programming temporal difference learning

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stochastic Optimization Based on Principal-agent Problem

Stochastic Optimization Based on Principal-agent Problem

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Xiaoyu Ren Department of Mathematics Zhejiang University Hangzhou 310027,Zhejiang,P.R.China Xinping Shao Department of Mathematics Zhejiang University Hangzhou 310027,Zhejiang,P.R.China Shenghong Li Department of Mathematics Zhejiang University Hangzhou 310027,Zhejiang,P.R.China

By the theory of stochastic dynamic program- ming,we provide the methods for deriving the optimal *** this paper,we make two models in dynamic state process to maximize the expected utility of the agent and then obtain the famous Hamilton-Jacobi-Bellman equation. Furthermore,we derive explicit form solution and closed- form solution of the optimal equations for given utility functions.

关键词： principal-agent problem stochastic dynamic programming stochastic differential equation stochastic optimal control HJB equation

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