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Accounting for the increasing uncertainties related to forecast of renewables is becoming an essential requirement while assessing the security of future power system scenarios. Project iTesla in the Seventh Framework Program (FP7) of the European Union (EU) tackles these needs and reaches several major objectives, including the development of a security platform architecture. In particular, the platform implements a stochastic dependence model to simulate a reasonable cloud of plausible 'future' states - due to renewable forecast - around the expected state, and evaluates the security on relevant states after sampling the cloud of uncertainty. The paper focuses on the proposed model for the uncertainty and its exploitation in power system security assessment process and it reports the relevant validation results.

Power system security assessment for planning (offline) and for operational (online) applications requires various analysis methods aimed to highlight different phenomena (steady-state violations, angle stability, voltage stability, etc.), overall providing an exhaustive vision of the problems. However, carrying out the different analyses in an integrated way is not an easy task. Accordingly, combining the results in a consistent framework is an issue. Moreover, risk-based methods have been recently proposed to complement traditional deterministic approaches. They are expected to investigate security more in depth by introducing the concepts of probability and impact associated to the contingencies. Probabilistic approaches are, however, more complex to manage. This paper proposes the overall architecture of a security assessment platform, integrating probabilistic and deterministic tools in a unified environment.