POWER GRID ROBUSTENSS TO SEVERE FAILURES: TOPOLGICAL AND FLOW BASED METRICS COMPARISION

Power grids are generally regarded as very reliable systems, nevertheless outages and electricity shortfalls are common events and have the potential to produce significant social and economic consequences. It is important to reduce the likelihood of those severe accidents by assuring safe operations and robust topologies. The grid safety relies on accurate vulnerability measures, control schemes and good quality information. For instance, in power network operations, contingency analysis is used to constrain the network to secure operative states with respect to predefined failures (e.g. list of single component failures). An exhaustive failure list is often not treatable, therefore a selection or ranking is performed to help in the choice. In order to better understand the power network weakness and strengths a variety of robustness metrics have been introduced in literature, although many do not account or partially account for uncertainties which might affect the analysis. In this work power network vulnerability to failure events is analysed and single line outages (N-1 contingencies) have been ranked using different metrics (i.e. topology-based, flow-based and hybrid metrics). Sources of uncertainty such as power demand variability and lack of precise knowledge on the network parameters have been accounted for and its effect on the component ranking quantified. A modified version of the IEEE 118 bus power network has been selected as representative case study. The assumption underpinning the methodologies and the vulnerability results also accounting uncertainty are discussed

Stochastic analysis and reliability-cost optimization of distributed generators and air source heat pumps

This paper presents a framework for stochastic analysis, simulation and optimisation of electric power grids combined with heat district networks. In this framework, distributed energy sources can be integrated within the grids and their performance is modelled. The effect of uncertain weather-operational conditions on the system cost and reliability is considered. A Monte Carlo Optimal Power Flow simulator is employed and statistical indicators of the system cost and reliability are obtained. Reliability and cost expectations are used to compare 4 different investments on heat pumps and electric power generators to be installed on a real-world grid. Generators' sizes and positions are analysed to reveal the sensitivity of the cost and reliability of the grid and an optimal investment problem is tackled by using a multi-objective genetic algorithm

Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision

Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This paper proposes a framework for advanced uncertainty quantification and vulnerability assessment of power grids. The framework allows critical failure scenarios to be identified and overcomes the limitations of current approaches by explicitly considering aleatory and epistemic sources of uncertainty modelled using probability boxes. The different effects of stochastic fluctuation of the power demand, imprecision in power grid parameters and uncertainty in the selection of the vulnerability model have been quantified. Spectral graph metrics for vulnerability are computed using different weights and are compared to power-flow-based cascading indices in ranking N-1 line failures and random N-k lines attacks. A rank correlation test is proposed for further comparison of the vulnerability metrics. The IEEE 24 nodes reliability test power network is selected as a representative case study and a detailed discussion of the results and findings is presented

A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids

Risk-based power dispatch has been proposed as a viable alternative to Security-Constrained Dispatch to reduce power grid costs and help to better understand of prominent hazards. In contrast to classical approaches, risk-based frameworks assign different weights to different contingencies, quantifying both their likelihood occurrence and severity. This leads to an economically profitable operational schedule by exploiting the trade-off between grid risks and costs. However, relevant sources of uncertainty are often neglected due to issues related to the computational cost of the analysis. In this work, we present an efficient risk assessment frameworks for power grids. The approach is based on the Line-Outage Distribution Factors for the severity assessment of post-contingency scenarios. The proposed emulator is embedded within a generalized uncertainty quantification framework to quantify: (1) The effect of imprecision on the estimation of the risk index; (2) The effect of inherent variability, aleatory uncertainty, in environmental-operational variables. The computational cost and accuracy of the proposed risk model are discussed in comparison to traditional approaches. The applicability of the proposed framework to real size grids is exemplified by several case studies

A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency

International audienc

Do we have enough data? Robust reliability via uncertainty quantification

\u3cp\u3eA generalised probabilistic framework is proposed for reliability assessment and uncertainty quantification under a lack of data. The developed computational tool allows the effect of epistemic uncertainty to be quantified and has been applied to assess the reliability of an electronic circuit and a power transmission network. The strength and weakness of the proposed approach are illustrated by comparison to traditional probabilistic approaches. In the presence of both aleatory and epistemic uncertainty, classic probabilistic approaches may lead to misleading conclusions and a false sense of confidence which may not fully represent the quality of the available information. In contrast, generalised probabilistic approaches are versatile and powerful when linked to a computational tool that permits their applicability to realistic engineering problems.\u3c/p\u3

Optimal allocation and sizing of decentralized solar photovoltaic generators using unit financial impact indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty

Constructing consonant beliefs from multivariate data with scenario theory

Slideshow presented at the International Symposium on Imprecise Probabilities: Theories and Applications

Constructing Consonant Predictive Beliefs from Data with Scenario Theory

A method for constructing consonant predictive beliefs for multivariate datasets is presented. We make use of recent results in scenario theory to construct a family of enclosing sets that are associated with a predictive lower probability of new data falling in each given set. We show that the sequence of lower bounds indexed by enclosing set yields a consonant belief function. The presented method does not rely on the construction of a likelihood function, therefore possibility distributions can be obtained without the need for normalization. We present a practical example in two dimensions for the sake of visualization, to demonstrate the practical procedure of obtaining the sequence of nested sets