The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

This paper presents a three-phase four-wire grid-interfacing power quality compensator for microgrid applications. The compensator is proposed for use with each individual distributed generation (DG) system in the microgrid and consists of two four-phase-leg inverters (a shunt and a series), optimally controlled to achieve an enhancement of both the quality of power within the microgrid and the quality of currents flowing between the microgrid and the utility system. During utility grid voltage unbalance, the four-phase-leg compensator can compensate for all the unwanted positive-, negative-, and zero-sequence voltage-current components found within the unbalanced utility. Specifically, the shunt four-leg inverter is controlled to ensure balanced voltages within the microgrid and to regulate power sharing among the parallel-connected DG systems. The series inverter is controlled complementarily to inject negative- and zero-sequence voltages in series to balance the line currents, while generating zero real and reactive power. During utility voltage sags, the series inverter can also be controlled using a newly proposed flux-charge current-limiting algorithm to limit the flow of large fault currents between the micro- and utility grids. The performance of the proposed compensator has been verified in simulations and experimentally using a laboratory prototype.

Microgrid power quality enhancement using a three-phase four-wire grid-interfacing compensator

In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers’ electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.

Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

This paper proposes a grid-interfacing power quality compensator for three-phase three-wire microgrid applications with consideration of both the power quality of the microgrid and the quality of currents flowing between the microgrid and utility system. It is proposed that two inverters connected in shunt and series are used for each distributed generation (DG) system in the microgrid. In each inverter, both positive- and negative-sequence components are controlled to compensate for the effects caused by the unbalanced utility grid voltages. Specifically, the shunt inverter is controlled to ensure balanced voltages within the microgrid and to regulate power dispatches among parallel-connected DG systems, while the series inverter balances the line currents by injecting appropriate voltage components. A current-limiting algorithm is also proposed and integrated within the inverter control schemes to protect the microgrid from large fault currents during utility voltage sags. The proposed compensator has been tested in simulations and experimentally using a laboratory hardware prototype.

A grid-interfacing power quality compensator for three-phase three-wire microgrid applications

Voltage sag distributions caused by faults at different voltage levels and experienced by low-voltage customers were established for four different power system areas. The shares of different fault types at each voltage level and the sag propagation throughout the power system were taken into account. The results show that the origin of sags in urban and rural areas tends to be different. These data are needed when, for example, planning measures for sag mitigation in different parts of the power system.

Voltage sag distributions caused by power system faults

The annual number and cost of voltage sags were determined for five Finnish distribution companies. The method of fault positions was applied for the calculation of voltage sag frequency. The economic consequences were assessed by multiplying the sag frequency and cost by the number of customers. The cost of a single sag was taken from a survey that had been carried out in three Nordic countries in the mid 1990s. This paper proposes a method for retrieving sag-related information from statistics originally prepared for other purposes. In addition, this paper provides an estimate of the total annual sag-related cost for each of the companies considered in this study and for each customer category. The total cost per company appeared to be much higher than had generally been assumed.

A method for estimating the frequency and cost of voltage sags

The lifetime of transmission and distribution power systems is long and thus, long-term plans are needed for their successful development. In generating long-term scenarios, the starting point is the analysis of the present electricity consumption. The data of electricity consumption will become more exact by the end of 2013, when hourly based automated meter reading (AMR) consumption data will be received from each customer in Finland. The amount of data is huge and powerful analysis methods are needed. This paper presents a method for clustering the electricity consumptions using principal component analysis (PCA) and K-means clustering. AMR data of 18 098 customers from two city districts of Helsinki, Finland is applied for a case study reported in this paper. A multiple regression analysis is also carried out on the two largest clusters to find the most important explanatory factors for the load modeling. The interpretations of the clusters and the plausibility of the regression coefficients are considered very important. Five distinct and meaningful clusters are found. The regression models give interesting insights into the explanatory factors behind electricity consumption. The models of the main customer groups assist the distribution system operator (DSO) in the long-term development of the power system.

Clustering of Connection Points and Load Modeling in Distribution Systems

Earth fault management in medium voltage networks involves assessing of customer interruptions, challenges in fault clearing processes and safety issues. The major advantage by allowing sustained earth faults is the avoidance of customer interruptions. However, the network operation with sustained earth faults does not only include welfare. The paper will generally discuss the advantages and disadvantages of operating the network with sustained earth faults. In addition, in the paper, the Helsinki medium voltage network and the developments in neutral point treatment and network operation during earth faults will be outlined. The paper presents the earth fault analysis in the Helsinki 10 kV network and compares the results to a reference analysis. Based on the analysis, the paper presents the possibilities and limitations of a sustained operation with earth faults. The paper also presents the strategy steps of managing sustained earth faults as well in the 10 kV and 20 kV areas and the required long term development steps.

Neutral point treatment and earth fault suppression

Frequency and cost of voltage sags for customers of five Finnish distribution companies were evaluated. For voltage sag frequency calculations the method of fault positions was applied. Only sags caused by short circuit faults in medium voltage distribution networks were considered. Sag frequency below considered 50% magnitude showed quite low numbers. However the cost related to this sag magnitude revealed that voltage sags are a relevant power quality issue having economic significance.

Pirjo Heine

Papers

Voltage sag distributions caused by power system faults

A method for estimating the frequency and cost of voltage sags

Clustering of Connection Points and Load Modeling in Distribution Systems

Neutral point treatment and earth fault suppression

Estimating the annual frequency and cost of voltage sags for customers of five Finnish distribution companies