This paper presents an open source tool that automatically generates the so-called deterministic equivalent in stochastic programming. The tool is based on the algebraic modeling language ampl. The user is only requir...
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This paper presents an open source tool that automatically generates the so-called deterministic equivalent in stochastic programming. The tool is based on the algebraic modeling language ampl. The user is only required to provide the deterministic version of the stochastic problem and the information on the stochastic process, either as scenarios or as a transitions-based event tree.
In wireless sensor networks, a periodic broadcast message or a beacon with strict time requirement can be continuously collided when there are multiple nodes with the same period in the same space. This type of collis...
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ISBN:
(纸本)9781424406661
In wireless sensor networks, a periodic broadcast message or a beacon with strict time requirement can be continuously collided when there are multiple nodes with the same period in the same space. This type of collision prevents a network from operating normally: a node is unable to join a network, and a network topology breaks if the periodic message contains critical information such as network and timing parameters as in IEEE 802.15.4. We show that the affected area by the collision occupies significant percentage of an original service area. This paper proposes a new algorithm to solve the permanent collision problem in periodic broadcasting or beaconing. The proposed stochastic beacon algorithm avoids the collision by incorporating randomness in beacon transmission while maintaining a fixed beacon period. The algorithm is evaluated by analysis and simulation based on IEEE 802.15.4, and compared with other 7 methods. The results show that our algorithm can avoid permanent beacon collision and reduces substantially the collision probability with very low added complexity.
We study the stochastic dynamics of three FitzHugh-Nagumo neurons with chemical coupling and electrical coupling (gap junction) respectively. For both of the coupling cases, optimal coherence resonance and weak signal...
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ISBN:
(纸本)9783540747703
We study the stochastic dynamics of three FitzHugh-Nagumo neurons with chemical coupling and electrical coupling (gap junction) respectively. For both of the coupling cases, optimal coherence resonance and weak signal propagation can be achieved with intermediate noise intensity. Through comparisons and analysis, we can make conclusions that chemical synaptic coupling is more efficient than the well known linear electrical coupling for both coherence resonance and weak signal propagation. We also find that neurons with parameters locate near the bifurcation point (canard regime) can exhibit the best response of coherence resonance and weak signal propagation.
stochastic constraint satisfaction is a framework that allows to make decisions taking into account possible futures. We study two challenging aspects of this framework: (1) variables in stochastic CSP are ordered seq...
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ISBN:
(纸本)9781595934802
stochastic constraint satisfaction is a framework that allows to make decisions taking into account possible futures. We study two challenging aspects of this framework: (1) variables in stochastic CSP are ordered sequentially, which is adequate for the representation of a number of problems, but is not a natural choice for the modeling of problems in which the future can follow different branches (2) the framework was designed to allow multi-objective decision-making, yet this issue has been treated only superficially in the literature. We bring a number of clarifications to these two aspects. In particular, we show how minor modifications allow the framework to deal with non-sequential forms, we identify a number of technicalities related to the use of the sequential ordering of variables and of the use of multiple objectives, and in addition we propose the first search algorithm that solves multi-objective stochastic problems in polynomial space.
Wind power has attracted much attention as a promising Renewable energy resource. It has potential benefits in curbing emissions and reducing the consumption of irreplaceable fuel reserves. Conventional economic dispa...
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ISBN:
(纸本)9787302161462
Wind power has attracted much attention as a promising Renewable energy resource. It has potential benefits in curbing emissions and reducing the consumption of irreplaceable fuel reserves. Conventional economic dispatch problem uses deterministic models, which can not reflect situations considering the wind power injection. Since large-scale wind farms connected to power systems have characteristics of high capacity, dynamic and stochastic performance, stochastic models are more suitable. The paper is primarily intended to investigate the injection of wind power into conventional power networks and its impact on the generation resource management due to its stochastic and nondispatchable characteristics. An economic dispatch problem considering wind power injection is formulated. The stochastic programming theory is employed to develop a power dispatch scheme which is able to satisfy the operation constraints. A genetic algorithm based on the stochastic simulation technique is adopted to calculate the stochastic programming model. A numerical application example based on a typical IEEE test power system is used to demonstrate the correctness and effectiveness of the proposed optimization method.
There has recently been considerable attention devoted to sample-based approaches to chance constraints in stochastic programming, and also multi-stage optimization formulations. In this short paper, we consider the m...
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ISBN:
(纸本)9781424414970
There has recently been considerable attention devoted to sample-based approaches to chance constraints in stochastic programming, and also multi-stage optimization formulations. In this short paper, we consider the merits of a joint approach. A specific motivation for us, is the possibility of developing techniques suitable for integer-constrained future stages. We propose a technique based on structured adaptability, and some recent sampling techniques, that results in sample complexity that is polynomial in the number of stages. Thus we circumvent a difficulty that has traditionally plagued sample-based approaches for multi-stage formulations. This allows us to provide a hierarchy of adaptability schemes, not only for continuous problems, but also for discrete problems.
In this paper we describe a stochastic local search (SLS) procedure for finding satisfying models of satisfiable propositional formulae. This new algorithm, gNovelty(+), draws on the features of two other WalkSAT fami...
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ISBN:
(纸本)9783540769262
In this paper we describe a stochastic local search (SLS) procedure for finding satisfying models of satisfiable propositional formulae. This new algorithm, gNovelty(+), draws on the features of two other WalkSAT family algorithms: R+AdaptNovelty(+) and G(2) WSAT, while also successfully employing a dynamic local search (DLS) clause weighting heuristic to further improve performance. gNovelty(+) was a Cold Medal winner in the random category of the 2007 SAT competition. In this paper we present a detailed description of the algorithm and extend the SAT competition results via an empirical study of the effects of problem structure and parameter tuning on the performance of gNovelty(+). The study also compares gNovelty(+) with two of the most representative WalkSAT-based solvers: G(2)WSAT, AdaptNovelty(+), and two of the most representative DLS solvers: RSAPS and PAWS. Our new results augment the SAT competition results and show that gNovelty(+) is also highly competitive in the domain of solving structured satisfiability problems in comparison with other SLS techniques.
We extend the traditional two-stage linear stochastic program by probabilistic constraints imposed in the second stage. This adds nonlinearity such that basic arguments for analyzing the structure of linear two-stage ...
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We extend the traditional two-stage linear stochastic program by probabilistic constraints imposed in the second stage. This adds nonlinearity such that basic arguments for analyzing the structure of linear two-stage stochastic programs have to be rethought from the very beginning. We identify assumptions under which the problem is structurally sound and behaves stably under perturbations of probability measures.
The joint operation of wind energy and traditional power plants has brought new challenges to the operation of power systems. In particular, reserve scheduling may need to be modified in order to accommodate increasin...
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ISBN:
(纸本)9781424412969
The joint operation of wind energy and traditional power plants has brought new challenges to the operation of power systems. In particular, reserve scheduling may need to be modified in order to accommodate increasing levels of intermittent and random wind power. In this panel we discuss some of the issues and possible solutions regarding reserve considering the wind power characteristics. Specifically, we win outline a methodology to include secondary reserve requirements in the day-ahead operations planning as a set of probabilistic constraints.
Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on a scenario model of future environment developments. A good approximation of t...
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ISBN:
(纸本)9780955301827
Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on a scenario model of future environment developments. A good approximation of the underlying stochastic process may involve a very large number of scenarios and their probabilities. We discuss the case when enough data paths can be generated, but due to solvability of stochastic program the scenario tree has to be constructed. The proposed strategy is to generate the multistage scenario tree from the set of individual scenarios by bundling scenarios based on cluster analysis. The K-means clustering approach is modified to capture the interstage dependencies in order to model the sequential decisions. The described scenario tree generation method is implemented on sampled data of nominal interest rate.
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