This article presents a stochasticmulti-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives a...
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This article presents a stochasticmulti-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the epsilon-constraint method is used to solve the formulated problem. The N - 1 contingency analysis is also considered for the proposed TEP problem. The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm. (C) 2012 Elsevier Ltd. All rights reserved.
Electricity producers participating in electricity markets face risks pertaining to both selling prices and the availability of the production units. Among electricity derivatives, options represent an adequate instru...
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Electricity producers participating in electricity markets face risks pertaining to both selling prices and the availability of the production units. Among electricity derivatives, options represent an adequate instrument to manage these risks. In this paper, we propose a multi-stagestochastic model to determine the optimal selling strategy of a risk-averse electricity producer including options, forward contracts, and pool trading. A detailed case study highlights the advantages of an option vs. a forward contract to hedge against the financial risks related to pool prices and unexpected unit failures. (C) 2012 Elsevier B.V. All rights reserved.
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...
详细信息
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.
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