The integration of Renewable Energy Sources (RES) into the power system requires an upgrade of the adopted management and control solutions. Focusing on frequency regulation, the same RES, as well as loads, both possibly coupled with energy storage systems, are called to provide a contribution through suitable regulation services. In this context, this paper proposes a control strategy for enabling a unit composed by a Wind Farm and Battery Energy Storage Systems (BESSs) to provide a fast frequency regulation service. The control is based on Model Predictive Control technique, whereas the regulation service is defined according to the technical requirements of the Italian Transmission System Operator (TSO). The approach is also tested via Hardware-In-the-Loop simulations. More specifically, tests are carried out by implementing the control algorithm on a Raspberry Pi board that communicates with a real BESS and with a real-time simulator implementing a benchmark power system. The validation results prove the practical effectiveness of the proposed control method since they demonstrate that the designed algorithms can be implemented on a low-cost hardware and applied, without computational and communication issues, in a real-field framework.

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation.