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 review of the literature on State Estimation (SE) in power systems While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE) The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security Both conventional and modern data-driven and probabilistic techniques have been reviewed This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency, and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring, and managing multiple domains in the SG. Despite the salient features of SMI, security, and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: 1) threats in system-level security; 2) threats and/or theft of services; and 3) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in SG metering networks.

/pdf/smart-grid-metering-networks-a-survey-on-security-privacy-ewxtsbvnrk.pdf

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

There are both economic and environmental urges for transition from the current outdated power grid to a sensor-embedded smart grid that monitors system stability, integrates distributed energy and schedules energy consumption for household users. Especially with the proliferation of intelligent measurement devices, exponential growth of data empowers this transition and brings new tools for the development of different applications in power system. Under this context, this paper presents a holistically overview on the state-of-the-art of big data technology in smart grid integration. First, the features of smart grid and the multisource of energy data are discussed. Then, this paper comprehensive summarizes the applications leveraged by big data in smart grid, which also contains some brand new applications with the latest big data technologies. Furthermore, some mainstream platforms and knowledge extraction techniques are looked to promote the big data insights. Finally, challenges and opportunities are pointed out in this paper as well.

Big data issues in smart grid – A review

Many utilities have the data quality problem with the geographical information system (GIS) records at distribution level This affects many business functions of a utility, including asset management, outage response, and workforce safety For correcting connectivity errors in the GIS representation of the distribution network topology, BC Hydro has developed an in-house algorithm The algorithm leverages smart meter interval measurements and identifies the neighboring meters by voltage profile correlation analysis It also predicts customers’ upstream and downstream location relationship by voltage magnitude comparisons The output of the algorithm is then compared with the existing GIS records to correct any errors in it This paper presents in detail the algorithm and the promising testing results within the practical BC Hydro system Challenges for underground services are demonstrated The algorithm’s potential use for phase detection by collectively leveraging smart meter and feeder meter data is explored It shows encouraging results when applied in a downstream section of a large feeder

Smart Meter Data Analytics for Distribution Network Connectivity Verification

This paper introduces an algorithm which was developed within BC Hydro with the purpose of detecting and correcting topology errors in existing GIS records of the distribution system. Starting with the distribution network topology information available in enterprise GIS, the proposed analytic approach uses smart meter data to detect wrongly connected customers in a transformer neighborhood based on the correlation factors and relative magnitude levels of hourly voltage profiles. Once a wrongly connected customer is identified, further analysis on correlation factors and relative voltage levels is done to correctly reposition this customer within the distribution network topology tree. Testing in a practical BC Hydro distribution system has achieved very encouraging results.

Distribution network topology error correction using smart meter data analytics

Measurement of the electricity consumption of major appliances in different time segments is of crucial significance to demand-side management and energy conservation. Non-intrusive load monitoring (NILM) can infer the target appliances’ power use information by only collecting and analyzing the aggregate power data at the single power entrance point. Inspired by the success of deep neural network in other fields, some researchers have applied it to NILM with promising results. However, existing studies require labeled real aggregate data to train the networks, while time-synchronized measurement of the target appliance for labeling is hard to achieve in practice. This paper proposes to train networks with only synthetic aggregate data. Furthermore, a training data generation method via background filtering is proposed, and the obtained training data is used to train the network for estimating electricity consumption. This generation method only needs unlabeled real aggregate data and the target appliance’s operation curves, which reduces the difficulty of training data acquisition. The proposed estimation method achieves higher accuracy than current methods in tests on a public dataset which also demonstrates the effectiveness of background filtering.

Estimation of Target Appliance Electricity Consumption Using Background Filtering

Monkey algorithm (MA) is one of the evolution algorithms originally developed for optimization problems with continuous variables. In this paper, a discrete monkey algorithm (DMA) was proposed for transmission network expansion planning, one discrete optimization problem. It includes the representation of solution, the modification of objective function, climb process, watch-jump process, cooperation process, somersault process, stochastic perturbation mechanism and termination criteria. Large-step and small-step climb process are designed to avoid the disordered climb direction during the MA optimization process. Cooperation process and stochastic perturbation mechanism are also introduced to improve computational efficiency. The proposed method is applied to a 18-bus system and the IEEE 24-bus system. Numerical results demonstrate that DMA has powerful computational capability and is capable of solving different dimensions of expansion planning problems efficiently with small population size.

Discrete monkey algorithm and its application in transmission network expansion planning

This paper investigates various Ethernet LAN architectures for substation automation system applications with protection, control, monitoring data flows and their performance requirement. The paper discusses conceptual architecture designs and related redundancy technologies such as RSTP, PRP and HSR protocols. The suitability of these architectures and the redundancy technologies for mission-critical, time stringent realtime tasks such as SMV and GOOSE tripping over the Ethernet LAN applications are examined.

Wenpeng Luan

Papers

Smart Meter Data Analytics for Distribution Network Connectivity Verification

Distribution network topology error correction using smart meter data analytics

Estimation of Target Appliance Electricity Consumption Using Background Filtering

Discrete monkey algorithm and its application in transmission network expansion planning

IEC 61850 based substation automation system architecture design