Diego Cirio

Hybrid AC/DC grids with High Voltage Direct Current (HVDC) systems enhance grid resilience and enable efficient long-distance power transfer, asynchronous network interconnection, and seamless integration of offshore renewable energy sources. However, ensuring secure and reliable operation of these complex hybrid systems, particularly under contingency scenarios, presents significant challenges. This paper proposes a novel and computationally efficient two-stage linearized decoupled formulation for security-constrained redispatch in hybrid AC/DC grids. The methodology explicitly addresses N-1 security criterion, incorporating constraints from both the AC and DC subsystems, as well as the DC/AC converters. Simulation results on a test power system demonstrate the effectiveness of the proposed approach in mitigating the impact of both transmission line and generator outages, validating its applicability for enhancing grid resilience.

In response to the increasing frequency of extreme natural events, Terna (the Italian TSO) and Ricerca sul Sistema Energetico—RSE, an Italian research centre in the energy sector, developed a risk-based methodology for assessing power system resilience. This methodology evaluates the benefits of grid hardening measures and supports Cost–Benefit Analyses (CBA) for resilience plans, as required by the Italian regulatory authority. A key innovation is the inclusion of climate change models to assess the evolving probability of natural threats, alongside traditional factors like supply security and renewable energy integration. The methodology primarily addresses wet snow and strong wind, which are major causes of grid disruptions in Italy, with plans to extend the framework to hydrogeological risks. The methodology uses analytical models based on international and national design standards to simulate grid component vulnerability. Unlike statistical approaches, this framework quantifies the effectiveness of countermeasures, such as adding anti-torsional devices to overhead lines. An advanced model is adopted to assess the vulnerability of lines to indirect effects of wet snow and wind, such as vegetation interference. By combining probabilistic models of extreme events with component vulnerability models, the methodology calculates failure return periods (RP) of grid components, subsequently substation outages and cascading failures are simulated. The key outputs are the return periods of substation outages and the Expected Energy Not Served (EENS) due to contingencies. A case study demonstrates the methodology’s effectiveness in prioritizing resilience interventions, showing good alignment between predicted failure RPs and actual fault data from the Italian transmission grid.

The selection of multiple contingency scenarios is a key task to perform resilience-oriented long-term planning analyses. However, the identification of relevant multiple contingencies may easily lead to combinatorial explosion issues, even for relatively small systems. This paper proposes an effective methodology for the identification of relevant multiple contingencies and their probabilities, suitable for the long-term resilience analysis of large power systems. The methodology is composed of two main pillars: (1) the clustering of lines that are more likely to fail together, to reduce the computational complexity of the analysis exploiting historical weather data and (2) the probability-based identification of multiple contingencies within each cluster, where the contingency probability is computed applying the copula theory. Tests performed on a portion of the Italian EHV transmission system confirm the validity of the clustering results compared against historical failure events. Moreover, the copula-based algorithm for contingency probability estimation passes the tests carried out on relatively large clusters with very low error tolerance. The method successfully pinpoints critical multiple contingency scenarios and their likelihoods, making it valuable for assessing power system resilience over long-term horizons in support of resilience-oriented planning activities.

This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This allows the control to be adaptive, i.e., able to adjust its decisions to the system characteristics, which vary over time due to the daily distribution of users hot water consumption. To answer the demand response requirements, aggregated power variations are realized by modifying the temperature set-points of the water heaters without compromising the users comfort. The developed approach allows tracking a regulation signal and mitigating the so-called rebound, i.e., the recovery of energy needed by the aggregate at the end of the service to return to the baseline thermal state. Analyses in a simulation environment allow the validation of the potentialities of the proposed method.

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.

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.

The continuous increase in generation from renewable energy sources, marked by correlated forecast uncertainties, requires specific methodologies to support power system operators in security management. This paper proposes a probabilistic preventive control to ensure N-1 security in presence of correlated uncertainties of renewable sources and loads. By adopting a decoupled linear formulation of the AC load flow equations, the preventive control is decomposed into two subsequent linear programming problems, the former concerning the active power and the latter the voltage/reactive power-related issues. In particular, in the active control problem, the algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method, and Cornish–Fisher expansion to model the forecast uncertainties and characterize the chance constraints in the problem. The goal is to find the optimal phase shifting transformer tap setting, conventional generation redispatching, and renewable curtailment at the minimum cost to assure the probabilistic fulfillment of N and N-1 security constraints on branch active power flows. The second stage solves another linear programming problem, which aims to minimize the adjustments to generators’ set-point voltages to avoid violations at node voltages and branch-rated limits due to reactive power flows while meeting generator reactive power constraints. Simulations performed on an IEEE test system demonstrate the effectiveness of the proposed security control method in limiting the probability of violating security limits in N and N-1 state, including voltage/reactive power constraints, in presence of correlated uncertainties.

Although the control of modular multi-level converters (MMCs) in high-voltage direct-current (HVDC) networks has become a mature subject these days, the potential for adverse interactions between different converter controls remains an under-researched challenge attracting the attention from both academia and industry. Even for point-to-point HVDC links (i.e., simple HVDC systems), converter control interactions may result in the shifting of system operating voltages, increased power losses, and unintended power imbalances at converter stations. To bridge this research gap, the risk of multiple cross-over of control characteristics of MMCs is assessed in this paper through mathematical analysis, computational simulation, and experimental validation. Specifically, the following point-to-point HVDC link configurations are examined: (1) one MMC station equipped with a current versus voltage droop control and the other station equipped with a constant power control; and (2) one MMC station equipped with a power versus voltage droop control and the other station equipped with a constant current control. Design guidelines for droop coefficients are provided to prevent adverse control interactions. A 60-kW MMC test-rig is used to experimentally verify the impact of multiple crossing of control characteristics of the DC system configurations, with results verified through software simulation in MATLAB/Simulink using an open access toolbox. Results show that in operating conditions of 650 V and 50 A (DC voltage and DC current), drifts of 7.7% in the DC voltage and of 10% in the DC current occur due to adverse control interactions under the current versus voltage droop and power control scheme. Similarly, drifts of 7.7% both in the DC voltage and power occur under the power versus voltage droop and current control scheme.

Nowadays, distribution network operators are urged by regulatory authorities to reduce the load disruptions due to extreme weather events, i.e., to enhance network resilience: in particular, in Italy they are required to present a yearly plan (called “resilience plans”) describing the interventions aimed to improve network resilience. To this purpose, they need new methodologies and tools to assess the network resilience and to quantify the benefits of countermeasures. This paper proposes the application of a risk-based framework and tool to assess the impacts of extreme weather events in T&D grids, which anticipate critical network situations in presence of incumbent weather threats. To do this, the forecasting of weather events is combined with the component vulnerability models in order to predict which components are more prone to fail. Based on this set of components, the set of most risky contingencies is identified and their impacts on the distribution network in terms of unsupplied load are quantified. The major advantage of the applied methodology is its generality: in fact, it is applicable to both distribution and transmission systems as well as integrated transmission and distribution (T&D) systems, considering the peculiarities of each type of grid, in terms of operation, maintenance and component vulnerabilities. In particular, the application refers to a distribution network connected to a portion of high voltage transmission system in a mountainous zone, with focus on two major threats in the area, i.e., wet snow and fall of trees induced by combined wind and snow. The methodology also quantifies the benefits brought to the system resilience by countermeasures such as reconductoring, optimal reconfiguration or new right-of-way maintenance procedures. Simulations demonstrate the ability of the methodology to support T&D operators in an operational planning context in case of different incumbent threats.