A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids

Risk-based power dispatch has been proposed as a viable alternative to Security-Constrained Dispatch to reduce power grid costs and help to better understand of prominent hazards. In contrast to classical approaches, risk-based frameworks assign different weights to different contingencies, quantifying both their likelihood occurrence and severity. This leads to an economically profitable operational schedule by exploiting the trade-off between grid risks and costs. However, relevant sources of uncertainty are often neglected due to issues related to the computational cost of the analysis. In this work, we present an efficient risk assessment frameworks for power grids. The approach is based on the Line-Outage Distribution Factors for the severity assessment of post-contingency scenarios. The proposed emulator is embedded within a generalized uncertainty quantification framework to quantify: (1) The effect of imprecision on the estimation of the risk index; (2) The effect of inherent variability, aleatory uncertainty, in environmental-operational variables. The computational cost and accuracy of the proposed risk model are discussed in comparison to traditional approaches. The applicability of the proposed framework to real size grids is exemplified by several case studies

Unified polynomial expansion for interval and random response analysis of uncertain structure–acoustic system with arbitrary probability distribution

© 2018 Elsevier B.V. For structure–acousticsystem with uncertainties, the interval model, the random model and the hybrid uncertain model have been introduced. In the interval model and the random model, the uncertain parameters are described as either the random variable with well defined probability density function (PDF) or the interval variable without any probability information, whereas in the hybrid uncertain model both interval variable and random variable exist simultaneously. For response analysis of these three uncertain models of structure–acoustic problem involving arbitrary PDFs, a unified polynomial expansion method named as the Interval and Random Arbitrary Polynomial Chaos method (IRAPCM) is proposed. In IRAPCM, the response of the structure–acoustic system is approximated by APC expansion in a unified form. Particularly, only the weight function of polynomial basis is required to be changed to construct the APC expansion for the response of different uncertain models. Through the unified APC expansion, the uncertain properties of the response of three uncertain models can be efficiently obtained. As the APC expansion can provide a free choice of the polynomial basis, the optimal polynomial basis for the random variable with arbitrary PDFs can be obtained by using the proposed IRAPCM. The IRAPCM has been employed to solve a mathematical problem and a structure–acoustic problem, and the effectiveness of the unified IRAPCM for response analysis of three uncertain models is demonstrated by fully comparing it with the hybrid first-order perturbation method and several existing polynomial chaos methods

Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks

International audienceThis paper presents a method to assess system reliability in the presence of epistemic and aleatory uncertainty. This hybrid method uses belief functions to model and manipulate uncertainty. P-boxes are used to represent basic uncertainties and acyclic directed networks to model the system reliability. These choices allow a flexible modeling of uncertainty, while limiting the computational cost of inferences. In particular, they offer convenient ways of integrating expert opinions and many kinds of uncertainty sources. It also can model complex systems. In this paper, we introduce the modeling method and apply it to a fire detection system with different kinds of input p-boxes for the sake of illustration