This paper presents a multi-stagestochastic model for short-term transmission expansion planning considering the available transfer capability (ATC). The ATC can have a huge impact on the power market outcomes and th...
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This paper presents a multi-stagestochastic model for short-term transmission expansion planning considering the available transfer capability (ATC). The ATC can have a huge impact on the power market outcomes and the power system reliability. The transmission expansion planning (TEP) studies deal with many uncertainties, such as system load uncertainties that are considered in this paper. The Monte Carlo simulation method has been applied for generating different scenarios. A scenario reduction technique is used for reducing the number of scenarios. The objective is to minimize the sum of investment costs (IC) and the expected operation costs (OC). The solution technique is based on the benders decomposition algorithm. The N-1 contingency analysis is also done for the TEP problem. The proposed model is applied to the IEEE 24 bus reliability test system and the results are efficient and promising. (C) 2011 Elsevier Ltd. All rights reserved.
The advent of competitive markets confronts each producer with the problem of optimally allocating his energy/capacity so as to maximize his profits. The multiplicity of auctions in electricity markets and the nontriv...
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The advent of competitive markets confronts each producer with the problem of optimally allocating his energy/capacity so as to maximize his profits. The multiplicity of auctions in electricity markets and the nontrivial constraints imposed by technical and bidding rules make the problem of crucial importance and difficult to model and solve. Further difficulties are represented by the dynamic and stochastic natures that characterize the decision process. We formulate the problem as a multi-stage mixed-integer stochastic optimization model under the assumption that the seller is a price taker. We validate the effectiveness of the proposed model on a representative test problem. (C) 2003 Elsevier Ltd. All rights reserved.
Operating in a changing and uncertain environment, firms must make strategic and operational decisions while trying to satisfy many conflicting goals. For example, in order to maximize expected profit and minimize ris...
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Operating in a changing and uncertain environment, firms must make strategic and operational decisions while trying to satisfy many conflicting goals. For example, in order to maximize expected profit and minimize risk, they must periodically decide when and by how much to expand capacity and even more often how much to produce, all in the face of unknown future demands, available technology, and so on. We refer to this class of problem as multi-objective decision processes under uncertainty. We investigate two major methodologies from different research streams that formulate and solve this class of problem: optimal control and stochasticprogramming. We introduce an example problem of coordinated capacity planning and production-inventory control to illustrate the issues on formulations and solutions of these two methodological approaches. We show that two methodologies are equivalent in that the decision prescribed by the optimal policy found by optimal control is the same as the corresponding optimal decision found by stochasticprogramming. Both solution approaches suffer from the "curse of dimensionality" but in different ways: the former approach has an immense state space while the latter a large sample space. They possess distinctive advantages and disadvantages for specific problems, which determine that one approach may be preferably used. We discuss and compare two methods on the example problem in terms of their model formulations, computation efficiency, and handling of multiple objectives. We propose an approximation architecture that combines different approaches to solve large-scale problems. We finally present the numerical results obtained from the example problem to demonstrate that one should tailor solution strategies to specific problems. (C) 2004 Elsevier Ltd. All rights reserved.
Operating in a changing and uncertain environment, firms must make strategic and operational decisions while trying to satisfy many conflicting goals. For example, in order to maximize expected profit and minimize ris...
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Operating in a changing and uncertain environment, firms must make strategic and operational decisions while trying to satisfy many conflicting goals. For example, in order to maximize expected profit and minimize risk, they must periodically decide when and by how much to expand capacity and even more often how much to produce, all in the face of unknown future demands, available technology, and so on. We refer to this class of problem as multi-objective decision processes under uncertainty. We investigate two major methodologies from different research streams that formulate and solve this class of problem: optimal control and stochasticprogramming. We introduce an example problem of coordinated capacity planning and production-inventory control to illustrate the issues on formulations and solutions of these two methodological approaches. We show that two methodologies are equivalent in that the decision prescribed by the optimal policy found by optimal control is the same as the corresponding optimal decision found by stochasticprogramming. Both solution approaches suffer from the "curse of dimensionality" but in different ways: the former approach has an immense state space while the latter a large sample space. They possess distinctive advantages and disadvantages for specific problems, which determine that one approach may be preferably used. We discuss and compare two methods on the example problem in terms of their model formulations, computation efficiency, and handling of multiple objectives. We propose an approximation architecture that combines different approaches to solve large-scale problems. We finally present the numerical results obtained from the example problem to demonstrate that one should tailor solution strategies to specific problems. (C) 2004 Elsevier Ltd. All rights reserved.
Certain multistage decision problems that arise frequently in operations management planning and control allow a natural formulation as multistagestochastic programs. In job shop scheduling, for example, the first st...
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Certain multistage decision problems that arise frequently in operations management planning and control allow a natural formulation as multistagestochastic programs. In job shop scheduling, for example, the first stage could correspond to the acquisition of resources subject to probabilistic information about the jobs to be processed, and the second stage to the actual allocation of the resources to the jobs given deterministic information about their processing requirements. For two simple versions of this two-stage hierarchical scheduling problem, we describe heuristic solution methods and show that their performance is asymptotically optimal both in expectation and in probability.
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