Smart meter systems are being deployed to improve grid reliability and promote energy efficiency while providing improved services to their customers. Smart metering which is installed in millions of households worldwide provides utility companies with real-time meaningful and timely data about electricity consumption and allow customers to make informed choices about energy usage. Smart meter data analytics has become an active area in research and industry. It aims to help utilities and consumers understand electricity consumption patterns. This paper provides analysis methods for load data including: analysis of daily load profiles and similarity between them, analysis of load density, and analysis of seasonal and irregular components in the load time series. We evaluate our approach by analyzing smart meter data collected from 1000 households in Poland at a 15-minute granularity over a period of one year.

In order to ensure continuous energy supply, Distribution System Operators (DSOs) have to monitor and analyze the condition of the power grid, especially checking for random events, such as breakdowns or other disturbances. Still, relatively little information is available on the operation of the Low Voltage (LV) grid. This can be improved thanks to digital tools, offering online processing of data, which ultimately increases effectiveness of the power grid. Among those tools, the use of the Advanced Metering Infrastructure (AMI) is especially conducive for improving reliability. AMI is one of the elements of the system Supervisory Control and Data Acquisition (SCADA) for the LV grid. Exact knowledge of the reliability conditions of a power grid is also indispensable for optimizing investment. AMI is also key in providing operational capacity for carrying out energy balance in virtual power plants (VPPs). This paper deals with methodology of identification and location of faults in the AMI-supervised LV grid and with calculating the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) on the basis of the recorded events. The results presented in the paper are based on data obtained from seven MV/LV transformer stations that supply over 2000 customers.

The paper offers an analysis of the distribution of the values of SAIDI and SAIFI indices in nineteen Polish companies dealing with electricity distribution. The data presented concern the assessment of the reliability of electricity supply to over 10.5 million consumers. The paper analyses the influence of the number of remotely controlled switches installed within a MV network on the values of SAIDI and SAIFI. Calculations are performed in R - the language and environment for statistical computing, using the feature nonparametric methods for data analysis.

This paper discusses the problem of reliability of the Polish medium voltage power lines. It presents the benchmarking of power lines and values of SAIDI. The probability density distribution of the medium voltage lines length as well as selected indicators of the MV power lines reliability, being operated by the distribution companies under test, are also introduced. The analysis is based on the actual MV lines and uses the nonparametric method of kernel density estimation.

The paper presents an assessment of the reliability of medium voltage networks within a power company. Reliability of power supply in medium voltage networks is one of the commonly recognized targets of Smart Grid. Novel approaches are needed for evaluating the reliability of electricity distribution and the reliability of supply in distribution network planning. This paper presents a stochastic supply reliability analysis method deploying the weighted kernel density estimation.