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The growing attention to environmental issues is leading to an increasing integration of renewable energy sources into electrical grids. This integration process could contribute to power system decarbonization, supporting the diversification of primary energy sources and enhancing the security of energy supply, which is threatened by the uncertain costs of conventional energy sources. Despite these environmental and economical benefits, many technological and regulatory problems should be fixed in order to significantly increase the level of penetration of renewable power generators, which are connected to power transmission and distribution systems via power electronic interfaces. Indeed, these converter-interfaced generators (CIGs) perturb grid operation, especially those fueled by non-programmable energy sources (e.g., wind and solar generators), affecting the system stability and making power systems more vulnerable to dynamic perturbations. To face these issues, the conventional operating procedures based on pre-defined system conditions, which are currently adopted in power system operation tools, should be enhanced in order to allow the “online” solution of complex decision-making problems, providing power system operators with the necessary measures and alerts to promptly adjust the system. A comprehensive analysis of the most promising research directions and the main enabling technologies for addressing this complex issue is presented in this paper.

The paper deals with a novel method to measure inter-area oscillations, i.e., electromechanical oscillations involving groups of generators geographically distant from one another and ranging within the frequency interval from 0.1 Hz up to 1 Hz. The estimation of the parameters characterizing inter-area oscillations is a crucial objective in order to take the necessary actions to avoid the instability of the transmission electrical system. The proposed approach is a signal-based method, which uses samples of electrical signals acquired by the phasor measurement unit (PMU) and processes them to extract the individual oscillations and, for each of them, determine their characteristic parameters such as frequency and damping. The method is based on Hilbert transformations, but it is optimized through further algorithms aiming at (i) improving the ability to separate different oscillatory components, even at frequencies very close to each other, (ii) enhancing the accuracy associated with the damping estimates of each oscillation, and (iii) increasing the robustness to the noise affecting the acquired signal. Results obtained in the presence of signals involving the composition of two oscillations, whose damping and frequency have been varied, are presented. Tests were conducted with signals either synthesized in simulated experiment or generated and acquired with actual laboratory instrumentation.