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In restructured power systems and in a wholesale power market, a distribution company as a market player intends to maximize its profit by utilizing its options. Hence determining an optimal energy acquisition strategy for a distribution company is vital, for attaining to this goal. However an important challenge for determining these strategies is forecasting other competitors and Generation companies' strategies and competitors' incomplete information must be considered as uncertainties in the problem. In this paper, an energy acquisition model for a distribution company with considering distributed generations, interruptible loads and information's uncertainties in a day-ahead electricity market has been presented. In the proposed method, distribution company energy acquisition strategy has been modeled as a two-level multi-objective optimization problem and has been solved by using nonlinear complementarities and L-P metric methods and then, the uncertainties in the competitors' information, has been applied to the model by using Monte Carlo method. An 8-bus system is employed to illustrate the proposed model and algorithm.

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Uncertainty at experimental results usually adds to experimental data in the form of error bound. Since uncertainties at input parameters play an important part at the discrepancy between numerical and experimental results, considering uncertain parameters in comparison of numerical and experimental results would be logical. Electroosmotic flow is one of the cases which uncertainty quantification on its numerical simulation is necessary because of the presence of uncertain parameters. In this study, uncertainty quantification of electroosmotic flow in the micro T-channel has been presented. Numerical method was first validated by comparison between numerical simulation results of electroosmotic flow with certain inputs and experimental data. At the first step of uncertainty quantification, sample generation of the uncertain parameters has been performed by Latin hypercube method. At the next step, governing equation of electroosmotic flow has been solved by finite element method for every sample. Mass flow rate and velocity field have been selected as objective functions and adjoint method was employed for calculating the derivatives of them. At the final stage uncertainty quantification has been performed by enhanced Monte Carlo method. Results of the adjoint method show geometry parameters and fluid viscosity as the most effective factors on the results. While temperature and density of fluid demonstrate the least effect on the objective functions. Results of the Monte Carlo method illustrate 22.4% uncertainty for the results of mass flow rate and 12.6% on average for the results of velocities.

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