Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

Smart energy and electricity networks are a crucial component in building smart city architectures; their consistent and harmonized inclusion in the smart city design should be carefully considered through a detailed analysis of the impacts (environmental, energy, economic, societal) and the implementation of cost benefit analysis (CBA), not only in terms of managing the grid itself but also in a wider perspective that includes environmental, security, and social aspects. This paper first discusses the main impact that smart grid deployment has, in different respects, in smart cities and then presents a methodology for an extended CBA, able to go beyond the strictly financial aspects. It is based on previous developments at the European level. The methodology conceptually illustrated can naturally be extended to the assessment of proposals for the development of smart cities.

This letter proposes a novel cascading faults graph (CFG) based on fault chain theory to assess the transmission network vulnerability. The proposed CFG is able to effectively reveal the mechanism of fault propagation in a transmission network by transforming an electrical network with spatial information into a fault propagation graph with temporal information, thus providing a better way for transmission network vulnerability assessment. Numerical results on IEEE 39-and 118-bus systems demonstrate the effectiveness of the proposed method.

Transmission network vulnerability (TNV) assessment is a key issue in power systems to identify the vulnerable components against accidents or malicious threats. Recently, constructing the topological vulnerability indices, particularly extended topological indices, is a popular method to evaluate the network vulnerability. However, the topological vulnerability indices cannot reveal the mechanism of fault propagation. To overcome the shortcomings, this paper proposes a new method to assess the TNV through the cascading faults graph (CFG) based on fault chains, which is a statistical graph that comprehensively considers the physical, operational and structural features of electrical networks. Based on the complex network theory (CNT), the scale-free properties of the CFG are revealed through simulations on various transmission networks by corresponding the degree distribution of the CFG; then, the model constancy of the CFG is analyzed. Resorting to the CFG, a set of indices from the CNT is used to identify the vulnerable branches of transmission networks. Illustrative applications are applied to the IEEE 39-bus and 118-bus test systems to demonstrate the effectiveness of the proposed method.

Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices play a crucial role. The more accurate the prediction is, the lower the market risk is. In this paper, several machine learning algorithms (Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including Mean Absolute Error (MAE), R-index, Mean Absolute Percentage Error (MAPE), and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and Tree-based models outperform other models at different time horizons.

The worldwide spread of the COVID-19 pandemic in 2020 forced most countries to intervene with policies and actions—including lockdowns, social-distancing and smart working measures—aimed at mitigating the health system and socio-economic disruption risks. The electricity sector was impacted as well, with performance largely reflecting the changes in the industrial and commercial sectors operations and in the social behavior patterns. The most immediate consequences concerned the power demand profiles, the generation mix composition and the electricity price trends. As a matter of fact, the electricity sectors experienced a foretaste of the future, with higher renewable energy penetration and concerns for security of supply. This paper presents a systemic approach toward assessing the impacts of the COVID-19 pandemic on the power sector. This is aimed at supporting decision making—particularly for policy makers, regulators, and system operators—by quantifying shorter term effects and identifying longer term impacts of the pandemic waves on the power system. Various metrics are defined in different areas—system operation, security, and electricity markets—to quantify those impacts. The methodology is finally applied to the European power system to produce a comparative assessment of the effects of the lockdown in the European context.