Advanced Metering Infrastructure Data Research Articles

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Optimization of low voltage distribution network configuration using forecasts based on Advanced Metering Infrastructure data

Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector

Advanced metering infrastructure (AMI) is becoming increasingly popular as an efficient means of energy demand management. By collecting energy data through AMI, it is possible to provide users with information that can induce them to change their behavior. To ensure that AMI continues to expand and to encourage the use of energy data, it is important to increase consumer participation and analyze their preferred service attributes. This study utilized a choice experiment to analyze consumer preferences for and acceptance of smart energy services based on AMI data. The results of a mixed logit model estimation show that consumers prefer the electricity information service for individual households and the social safety-net service among convergence services. A scenario analysis confirms that monetary compensation to offset any additional charges is important to maintain the level of consumer acceptance. These empirical findings offer insights for policymakers and companies seeking to develop policies and similar services.

AMI Data-Driven Strategy for Hierarchical Estimation of Distribution Line Impedances

Monitoring, operation, and protection of distribution power grids fundamentally rely on the accurate estimation of line impedances. However, line impedance estimation is challenging due to the difficulties in modeling the dependence on line temperature and aging. This paper proposes a new data-driven strategy to estimate low-voltage (LV) and medium-voltage (MV) line impedances using an advanced metering infrastructure (AMI). In the proposed strategy, two-level optimization problems are formulated using generalized equations for voltage drops along LV and MV lines and then extended based on AMI data collected over time. Hierarchical estimation is achieved using the local and global references to the LV and MV root buses, respectively, enabling parallel estimation for individual LV grids and thus reduced computation time. Reinforcement learning is also integrated to compensate for possible measurement errors in the AMI data, ensuring robust estimation of LV and MV line impedances. The proposed strategy is tested on a three-phase unbalanced MV grid with multiple single-phase LV grids under various conditions characterized by measurement samples and errors. The results of case studies and sensitivity analyses confirm that the proposed strategy improves the accuracy and robustness of line impedance estimation at both LV and MV levels.

Blockchain-based AMI framework for data security and privacy protection

As a key component of smart grid (SG), advanced metering infrastructure (AMI) realizes two-way communication between users and power companies. Considering that AMI involves a large number of important data, which are related to the measurement of users’ electricity bills and the decision-making of power suppliers. Therefore, it is necessary to strengthen the protection of AMI data security and privacy to avoid economic losses caused by data leakage and tampering. Aiming at the problems of trust and single point of failure in the existing protection schemes, this paper proposes an AMI security framework based on blockchain. By combining the characteristics of blockchain such as decentration, tamper proof, traceability, the architecture of AMI data management system is reshaped. The technologies of peer-to-peer (P2P) transmission, asymmetric encryption, consensus algorithm and smart contract of blockchain are used to solve the problems of secure storage, transmission and application of AMI data. Through theoretical analysis and experiments, the feasibility of introducing blockchain technology into AMI is verified. The results show that the proposed framework is secure and reliable, which provides a strong guarantee for AMI data security and privacy protection.

Solar PV Inverter Reactive Power Disaggregation and Control Setting Estimation

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR.

Machine Learning Approach for Classifying Power Outage in Secondary Electric Distribution Network

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. Therefore, there is a need to develop a Machine Learning (ML) model to accurately classify power line outage. In this study, machine learning models are constructed from ensemble algorithms and tested using outage AMI data from 2012 to 2019 with 2 hours interval records. We propose the following ensemble-based machine learning approach to enhance classification; data sampling, algorithm weighting and finally ensembling. Results show that the Hybrid Stacking Ensemble Classifier (HSEC) model outperforms the others by accuracy of 0.981 G-mean, followed by Extra tree with accuracy of 0.964 G-mean. This model can be used in power line outage classification in any Secondary Electrical Distribution Network (SEDN). This study can be extended to locate power outage to household.

Using advanced metering infrastructure data to evaluate consumer compliance with water advisories during a water service interruption

Water main breaks disrupt services provided by utilities and result in Water Service Interruptions (WSIs). Water utilities can manage WSIs through water advisories, which request that consumers limit their water use. The performance of water advisories depends on consumer compliance and decisions to conserve water. This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). AMI provides high temporal and spatial resolution of water consumption data, which is analyzed to identify changes in water use behaviors. This research explores water use changes during a major water main break in Orange County, North Carolina, that caused a significant WSI, limiting water supply for more than 80,000 people. Customers were asked to reduce water use to essential purposes only and to boil water over the course of two days in November 2018. This research analyzes hourly consumption data to evaluate water consumption trends during the WSI and in response to water advisories. Statistical analysis is used to estimate the number of consumers who complied with utility notifications and to evaluate the volume of water saved. Regression analysis is applied to explore compliance across different user segments. Results provide insight about the level and variation of water conservation that can be expected during a WSI.

Clustering non-stationary advanced metering infrastructure data

Clustering non-stationary advanced metering infrastructure data

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

Short-Term Three-Phase Load Prediction With Advanced Metering Infrastructure Data in Smart Solar Microgrid Based Convolution Neural Network Bidirectional Gated Recurrent Unit

The collaboration development between the power supply, solar power, and storage batteries is a formidable commission in the smart solar microgrid. The efficient power management integrated into a smart solar microgrid is required to address these problems. The deep learning predictive tools are utilized to forecast the maximum building’s three-phase load power in the short term. In this project, historical data, from the solar module, battery module, grid data, and climate data, are collected by integrated advanced metering infrastructure in a smart solar microgrid in Taiwan. These historical data are utilized in predicting the building’s three-phase load power in the smart solar microgrid. This project proposed the hybrid model in the short-term three-phase loads forecasting based on convolution neural network bidirectional gated recurrent unit (CNN-Bi-GRU). The CNN-Bi-GRU utilizes the continuous-time sliding window, which is extracted features and reshaped into vectors by CNN layers. The hyperparameter optimization is utilized to construct the highest performance structure of the CNN-Bi-GRU model. The CNN-Bi-GRU performance is compared and evaluated with other state-of-arts models, which are the recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM), and Bi-GRU models. The experiment results prove that the forecasting accuracy could be effectively improved by a hybrid CNN-Bi-GRU model with an appropriate window size of sequential historical data. According to research knowledge, this is the first work to predict the building’s three-phase load power using multiple source data from advanced metering infrastructures (AMI) and deep learning CNN-Bi-GRU in the smart solar microgrid. The CNN-Bi-GRU model is successfully integrated into the efficient power management, which maintains the adequate battery storage level, solar power, and grid power balance under different weather conditions and working requirements of the building’s three-phase load power.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.

A Concealed Based Approach for Secure Transmission in Advanced Metering Infrastructure

The smart grid has an important subsystem known as the Advanced Metering Infrastructure (AMI), responsible for measuring customer consumption of electricity. The AMI subsystem has smart meters as one of the components and they play a vital role in enabling communication between the utility provider and consumers. Smart meters can report real-time electricity consumption readings of the consumer to the utility. Securing this communication link from attacks remain very important for the secure transmission of readings. Different methods or approaches have been developed in the past and most of the works have high computational overheads. This paper proposes a lightweight Concealed Based Security Scheme (CBSS) for secure transmission within the AMI, providing authentication, reducing the computational overheads and energy consumption during transmission. The CBSS method is compared with the AMI Data Communication Scheme (ADCS) which does not have authentication process. The network is built in the Network Simulator 2 environment, showing the communication between the nodes. Further security is provided using a simple encryption/decryption method of 2 random numbers. The contribution of this paper is the proposed lightweight method for the AMI that authenticates the transfer of data between smart meters and other components of the AMI system. The paper also contains a simulation evaluation for the chosen design parameters of the resemblance of AMI network. The simulations show an improvement of 5% in delivery ratio, 3% throughput and 4% on energy consumed when adding security to the network.

A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System

With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods.

A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data.

The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP’s markets was less than . In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

국민DR 참여 세대의 전력 사용 특성과 전력 부하 감축 성과 분석

In order to promote the national demand response program, the government tries to expand the distribution of AMI(Advanced Metering Infrastructure) in residential buildings. Analysis on the AMI data can contribute for better understanding on the electricity consumption characteristics. Since similar types of consumers have completely different electricity consumption patterns, it is necessary to analyze and understand the electricity consumption patterns and characteristics of household participating in the national demand response program using clustering algorithms and load profiling. As a result, the electricity consumption patterns were mainly clustered into 3 groups and each cluster’s electricity consumption characteristics showed different load profiles. Moreover, gradient analysis was used to analyze the load reduction patterns during 5 days of DR event. Result showed that there were load reduction occurred 3 days out of 5 days of DR event.

Data falsification attacks in advanced metering infrastructure

Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced Metering Infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is False Data Injection Attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.

A Case Study to Quantify Variability in Building Load Profiles

Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user’s comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings’ load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling.

Assessment of Measurement-Based Phase Identification Methods

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

Leverage AMI Data to Calibrate Distribution System Models

After a city in western Washington installed advanced metering infrastructure (AMI) technology, it discovered the benefits of using AMI data to calibrate its distribution system model.

Service restoration enhancement by FIs deployment in distribution system considering available AMI system

Service restoration is considered as one of the most staple functions of distribution automation, which executes the fault location, isolation and service restoration (FLISR) function in case of contingency occurrence. The first part of the FLISR function is the fault location that can be improved through fault indicators (FIs) employment in electricity distribution system (EDS). On the other hand, the advanced metering infrastructure (AMI) is widely proliferated in EDSs in order to gather the consumers' consumption data for the operational purposes. In this study, FIs are deployed in the EDS considering available AMI data, using a metaheuristic optimisation method. The economic objective function of the optimisation problem consists of the total customers' interruption cost and the total cost of FI deployment. In addition, the technical objective function of the problem is the system average interruption duration index. In order to investigate the effectiveness of the proposed method, it is implemented on bus number four of the Roy Billinton test system. The results show that the number of deployed FIs reduces when the electric utility uses the proposed method.