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The massive diffusion of renewable power generators in existing power grids introduces large uncertainties in power system operation, hindering their hosting capacity, and introducing several critical issues in network management. To address these challenging issues, weather-based optimal power flow has been recognized as one of the most promising enabling methodology for increasing the system flexibility by exploiting the real power components loadability. Anyway, the deployment of this technique in a real operation scenario could be seriously compromised due to the effects of data uncertainty, which could sensibly affect both the generated/demanded power profiles, and the components thermal modeling. In this context, the research for reliable techniques aimed at representing and managing these uncertainties represents one of the most relevant problem to solve. Armed with such a vision, this paper advocates the role of Affine Arithmetic in reliable solving weather-based OPF problems in the presence of multiple and correlated uncertainties. Experimental results obtained on a real case study, which is based on a congested portion of a transmission system characterized by a massive pervasion of wind generators, will be presented and discussed in order to assess the benefits deriving by the application of the proposed method.

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Large-scale integration of variable and unpredictable renewable-based generation systems poses significant challenges to the secure and reliable operation of transmission networks. Application of dynamic thermal rating (DTR) allows for a higher utilisation of transmission lines and effectively avoids high-cost upgrading and/or reinforcing of transmission system infrastructure. In order to efficiently handle ranges of uncertainties introduced by the variations of both wind energy sources and system loads, this paper introduces a novel optimization model, which combines affine arithmetic (AA) and probabilistic optimal power flow (P-OPF) for DTR-based analysis of transmission networks. The proposed method allows for the improved analysis of underlying uncertainties on the supply, transmission and demand sides, which are expressed in the form of probability distributions (e.g. for wind speeds, wind directions, wind power generation and demand variations) and related interval values. The paper presents a combined AA-P-OPF method, which can provide important information to transmission system operators for evaluating the trade-off between security and costs at a planning stage, as well as for selecting optimal controls at operational stage. The AA-P-OPF methodology is illustrated for a day-ahead planning, using a case study of a real transmission network and a medium size test distribution network.. .

Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios.

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