Big data analytics in power systems

Abstract

With the increasing penetration of advanced sensor systems in power systems, an influx of extremely large datasets presents a valuable opportunity to gain insight for improving system operation and planning in the context of the large-scale integration of intermittent energy sources. To this end, it becomes imperative to implement big data methodologies to handle such complex datasets with the challenges of volume, velocity, variety, and veracity (4Vs) for further dealing with the problems in power systems. The large-scale integration of intermittent energy sources, the introduction of shiftable load elements and the growing interconnection that characterizes electricity systems worldwide have led to a significant increase of operational uncertainty. The construction of suitable statistical models is a fundamental step towards building Monte Carlo analysis frameworks to be used for exploring the uncertainty state-space and supporting real-time decision-making. This thesis firstly proposes the novel composite modelling approaches that employ dimensionality reduction, clustering and parametric modelling techniques with a particular focus on the use of pair copula construction schemes. Large power system datasets are modelled using different combinations of the aforementioned techniques, and detailed comparisons are drawn on the basis of Kolmogorov-Smirnov tests, multivariate two-sample energy tests and visual data comparisons. The proposed methods are shown to be superior to alternative high-dimensional modelling approaches. In addition, the benefits of the proposed model are demonstrated through the applications on the calculation of a system’s PNS profile and the security assessment based on decision trees. Furthermore, this thesis presents a novel finite mixture modelling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method. In addition, in the interest of economic efficiency, design of distribution networks should be tailored to the demonstrated needs of its consumers. However, in the absence of detailed knowledge related to the characteristics of electricity consumption, planning has traditionally been carried out on the basis of empirical metrics; conservative estimates of individual peak consumption levels and of demand diversification across multiple consumers. Although such practices have served the industry well, the advent of smart metering opens up the possibility for gaining valuable insights on demand patterns, resulting in enhanced planning capabilities. This thesis is motivated by the collection of demand measurements across 2,613 households in London, as part of Low Carbon London project’s smart-metering trial. Demand diversity and other metrics of interest are quantified for the entire dataset as well as across different customer classes, investigating the degree to which occupancy level and wealth can be used to infer peak demand behavior. This thesis also presents a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units’ output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated. Finally, this thesis proposes a novel scenarios selection framework for the transmission expansion problem to obtain an accurate solution in terms of operating costs and investment decisions with a significantly reduced number of operating states. Different classification variables and clustering techniques are considered and compared to determine the most appropriate combination for this specific problem. Benders decomposition is applied to solve the TNEP problem by splitting the investment and operation problems. The superior performance of the proposed scenarios selection framework is demonstrated through a numerical case study on the modified IEEE 118-bus system.