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The risk-based assessment is a new approach to the voltage stability assessment in power system. Under several uncertainties, the security risk of static voltage stability with considerable of wind power can be evaluated. In this paper, the Quasi-Monte Carlo (QMC) simulation is used to speed up Monte Carlo by improving the technical of sample generation. Besides, a new voltage stability index is defined, which reveal critical characteristics of static voltage stability. The local voltage stability margin (LVSM) considering the security risk at a forecasted operating point can be calculated to evaluate the voltage stability. Testing on a modified IEEE New England 39bus system, results from the proposed method are compared against the result from the conventional method. The proposed method's effectiveness and advantages are demonstrated with the test results.Index Terms-risk assessment, state voltage stability, wind power, uncertainty

Abstract-The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.Index Terms-Dictionary learning, electricity market, machine learning in power systems, power flow distributions, probabilistic price forecasting. NOMENCLATURE c(·)Real-time generation cost function. Shadow prices for max/min transmission constraints at time t.

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