Affine Arithmetic for Power and Optimal Power Flow Analyses in the Presence of Uncertainties

Optimal power system operation requires intensive numerical analyses to study and im- prove system security and reliability. To address this issue, Power Flow (PF) and Optimal Power Flow (OPF) analyses are important tools, since they are the foundation of many power engineering applications. For the most common formalization of these problems, the input data are specified using deterministic variables resulting either from a snapshot of the system or defined by the analyst based on several assumptions about the system under study. This approach provides problem solutions for a single system state, which is deemed representative of the limited set of system conditions corresponding to the data assumptions. Thus, when the input conditions are uncertain, numerous scenarios need to be evaluated. To address the aforementioned problem, this thesis proposes solution methodologies based on the use of Affine Arithmetic (AA), which is an enhanced model for self-validated numerical analysis in which the quantities of interest are represented as affine combinations of certain primitive variables representing the sources of uncertainty in the data or approximations made during computations. In particular, AA-based techniques are proposed to solve uncertain PF and OPF problems. The adoption of these approaches allows to ex- press the uncertain power system equations in a more convenient formalism compared to the traditional and widely used linearization frequently adopted in interval Newton methods. The proposed techniques allow to reliably estimate the PF and OPF solution hull by taking into account the parameter uncertainty inter-dependencies, as well as the diversity of uncertainty sources. A novel AA-based computing paradigm aimed at achieving more efficient computational processes and better enclosures of PF and OPF solution sets is conceptualized. The main idea is to formulate a generic mathematical programming problem under uncertainty by means of equivalent deterministic problems, defining a coherent set of minimization, equality and inequality operators. Compared to existing solution paradigms, this formulation presents greater flexibility, as it allows to find partial solutions and inclusion of multiple equality and inequality constraints, and reduce the approximation errors to obtain better PF and OPF solution enclosures

a markov chain based model for wind power prediction in congested electrical grids

The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with the purpose of mitigating the effects induced by the injected power profiles, which are ruled by the intermittent and not-programmable wind dynamics. Although numerous forecasting tools have been proposed in the literature to predict the generated power profiles in function of the estimated wind speed, further and more complex phenomena need to be investigated in order to take into account the effects of the forecasting uncertainty on power system operation. In order to deal with this issue, this paper proposes a probabilistic model based on Markov chains, which predicts the wind power profiles injected into the grid, considering the real generator model and the effects of the power curtailments imposed by the grid operator. Experimental results obtained on a real case study are presented and discussed in order to prove the effectiveness of the proposed method

Data-driven Models to Anticipate Critical Voltage Events in Power Systems

This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal and offers insight into the strengths and weaknesses of several widely utilized prediction models for this application.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Robust optimization of energy hubs operation based on extended Affine Arithmetic

Traditional energy systems were planned and operated independently, but the diffusion of distributed and renewable energy systems led to the development of new modeling concepts, such as the energy hub. The energy hub is an integrated paradigm, based on the challenging idea of multi-carrier energy systems, in which multiple inputs are conditioned, converted and stored in order to satisfy different types of energy demand. To solve the energy hub optimal scheduling problem, uncertainty sources, such as renewable energy production, price volatility and load demand, must be properly considered. This paper proposes a novel methodology, based on extended Affine Arithmetic, which enables the solving of the optimal scheduling problem in the presence of multiple and heterogeneous uncertainty sources. Realistic case studies are presented and discussed in order to show the effectiveness of the proposed methodology

A review of the enabling methodologies for knowledge discovery from smart grids data

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances

Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed

Vulnerability analysis of satellite-based synchronized smart grids monitoring systems

The large-scale deployment of wide-area monitoring systems could play a strategic role in supporting the evolution of traditional power systems toward smarter and self-healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by a satellite-based global positioning system. Although these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radio frequency interference. Consequently, a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for correct and safe wide-area monitoring system operation. Armed with such a vision, this article presents and discusses the results of an experimental analysis aimed at characterizing the vulnerability of global positioning system based wide-area monitoring systems to external interferences. The article outlines the potential strategies that could be adopted to protect global positioning system receivers from external cyber-attacks and proposes decentralized defense strategies based on self-organizing sensor networks aimed at assuring correct time synchronization in the presence of external attacks