Advanced Measurement Infrastructure Research Articles

ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS

The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.

AUTHENTICATED KEY AGREEMENT SCHEME BASED ON BLOCKCHAIN FOR AMI COMMUNICATION SECURITY

Security is the basis for the normal operation of advanced measurement infrastructure (AMI). As an important part of key management scheme, key establishment is indispensable in meeting AMI communication security requirements. Most proposed key management schemes rely on a trusted third party (TTP). Once there is a problem with TTP, the security of these schemes will be greatly reduced. Furthermore, the data concentrators (DCs) in traditional AMI architectures all manage smart meters (SMs) in their respective regions, and the lack of interaction between the DCs exposes a serious single point of failure. To alleviate these problems, we propose a blockchain-based authenticated key agreement scheme to secure the communication of AMI. In this scheme, the blockchain comprises DCs as network nodes that interact with the SMs. The proposed key agreement and distributed consensus protocol ensure the authenticity and validity of the communication content without relying on TTP. We analyse the resistance of the proposed protocol to multiple known attacks and evaluate its performance. The proposed protocol has higher security or better performance than other schemes.

Double-Layer Optimization of Industrial-Park Energy System Based on Discrete Hybrid Automaton

Combing advanced measurement infrastructure (AMI) and information and communications technology (ICT), the Internet of Things (IoT) has been widely used in the energy system. The continuous state modeling method cannot effectively deal with the various operation scenarios in the park multiple energy system. This paper proposes a hybrid state system modeling method based on the discrete hybrid automaton (DHA). The DHA-based model judges the equipment mode through the logical discriminant and describes the energy transmission process of the equipment with a switched affine system. And a doublelayer optimization structure for the multiple energy system is proposed, which combine the long-term forecasting data and short-term forecasting data. Based on the advanced measurement infrastructure, the forecasting system predicts the system state and determines the scheduling plan of each equipment. This paper uses a typical industrial park case with multiple energy system to verify advantages of the DHA-based modeling method, calculates the economic benefits brought by the optimization and evaluates the maximum carbon emission reduction capacity of the industrial park.

Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components.

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids, data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves. However, owing to anomaly submergence, which shows that the usage patterns of electricity thieves may not always deviate from those of normal users, the performance of the existing usage-pattern-based method could be affected. In addition, the detection results of some unsupervised learning algorithm models are abnormal degrees rather than “0-1” to ascertain whether electricity theft has occurred. The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users. To address these issues, this study proposes a new electricity theft detection method based on load shape dictionary of users. A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft, and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.

Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

A Method for Noninvasive Remote Monitoring of Energy Meter Error Using Power Consumption Profile

In this article, a noninvasive remote monitoring method of error of electricity energy meter (EM) is introduced and its achievable precision is investigated. The aim of the method is to determine remotely the smart EM active power relative measurement error using the reference EM connected at the input of a low-voltage distribution grid segment. The meter error estimation is carried out despite other customers’ electrical load changes observable by the reference meter and power fluctuations in the distribution network. The method is based on the real-time analysis of natural customer power consumption profiles without any additional load injection for error-monitoring needs. A power step in the customer power consumption profile is detected by the meter under test and then the step magnitude is delivered for the comparison with the power step magnitude estimated synchronously by the reference meter. The analysis of the method error is conducted by applying simulation of the benchmark distribution grid which includes 26 EMs and utilizing the open data power consumption profiles. It is shown that after two to three days of averaging (including approximately 170 power steps) of the meter error estimates, it becomes possible to achieve an uncertainty of meter error estimation approximately 0.63%. The feasibility of the method implementation scenarios with the state-of-the-art EM data collection protocols in advanced measurement infrastructure (AMI) by means of occupancy index modeling is performed.

Using True RMS Current Measurements to Estimate Harmonic Impacts of Multiple Nonlinear Loads in Electric Distribution Grids

Currently, for analyzing harmonic impacts on voltage at a point of interest, due to multiple nonlinear loads, the literature recommends carrying out simultaneous and synchronized measurement campaigns in all suspicious points with the use of high cost energy quality analyzers that are usually not available at the customers’ facilities and very often also not at the electric utilities. To overcome this drawback this paper proposes a method of assessing the harmonic impact due to multiple nonlinear loads on the total voltage harmonic distortion using only the load current true RMS values which are already available in all customers’ installations. The proposed methodology is based on Regression Tree technique using the Permutation Importance indicator which is validated in two case studies using two different electrical systems. The first case study is to ratify the use of Permutation Importance to measure the impact factor of each nonlinear load in a controlled scenario, the IEEE-13 bus test system, using ATP simulation (Alternative Transient Program). The second is to apply the methodology to a real system, an Advanced Measurement Infrastructure System (AMI) implanted on a campus of a Brazilian University, using low cost meters with only true RMS current measurements. The results achieved demonstrated the feasibility of applying the proposed methodology in real electric systems without the need for additional investments in high-cost energy quality analyzers.

Pendekatan Implementasi Model Substractive Clustering Dalam Memetakan Dan Klasifikasi Data Perilaku Konsumen Listrik Tegangan Rendah Studi Kasus : Pelanggan PT PLN (Persero) UP3 Cengkareng

Electrical energy is one of the most important and vital human needs that cannot be released from daily needs. Customers also have begun to be critical of the purchase costs that must be paid every month. So by increasing electricity rates, improving efficiency in the use of electric power is a major consideration. The Advanced Measurement Infrastructure System (AMI) provides information on the use of granular energy for needs and customers. The IT system at AMI one of which uses EMS (Energy Management System) is an application to collect data from every smart meter installed in the customer, to store it in a database, and to connect the analysis and statistics of the data stored below. In this study aims to provide an analysis of electricity usage patterns by implementing AMI / Smart meters PT. PLN (Persero) by conducting a cluster of 1 phase and 3 phase electricity usage in customers of PT PLN (Persero) UP3 Cengkareng, namely the distribution booths DK60, TG70 and DK242 for 4 months, from November 2018 to February 2019. From the results of the study sought for customers 1 phase DK 60 with a radius of 0.5 produces 1 cluster that is stable every month depending on the customer at this substation is a household class customer, TG 70 requires stable and spending usage in December 2018, and DK 242 fix stable and use customers in the month December 2018 and January 2019, while for 3-phase DK 60 customers tend to be unstable because the customers of this apartment are different, starting from shops, production sites, and CV. TG 70 substations are predominantly places of worship, namely mosques and mosques, so the average use of mosques is higher, and for DK 242 3-phase customers need to be stable and use the highest in January 2019.AMI, System, EMS, Distribution Substation, Phase.

Cluster-based energy consumption forecasting in smart grids

Accurate energy saving estimates are important for development of energy efficient projects and for demonstrating their cost-effectiveness. Increasingly, commercial buildings have an advanced measurement infrastructure, which has led to the availability of high-frequency interval data. This data can be used in a number of energy efficiency tasks, including anomaly detection, control and optimization of heating, ventilation, and air cooling systems. These data sets enable the application of advanced statistical training models and therefore lead to accurate estimates of energy savings. The K-means algorithm is a powerful tool that has been used in areas with intensive data mining applications, including ecology, computer vision, biology. In this paper, we propose a method for modeling the basic energy profile based on the clustering of consumers. A recently published test procedure was used to assess the accuracy of this method, and the estimate was based on analysis of data from 288 commercial buildings. The training periods of the model varied, and some predictive accuracy indicators were used to estimate the accuracy of the model. The results showed that the use of our model improved the determination and cross-validation parameters in more than 80 percent of cases in comparison with the best model used in the industry, which is based on regression, and in comparison with the random forest, decision trees.

Project Surveys for Determining and Defining Key Performance Indicators in the Development of Smart Grids in Energy Systems

With the increasing energy needs, it has become obligatory to use technology and computer technology in electricity networks with the advancement of technology. By using the possibilities of high technology, it is possible to carry out audits with two-way data flow and intelligent systems on the sides of energy production and consumption. Many smart grid projects have been implemented around the world, such as the establishment of advanced measurement infrastructure, intelligent meter assembly, generation of electricity from renewable energy sources, energy efficiency, use of electric vehicles and smart buildings . Many developed and developing countries have set up platforms for developing Smart Grids and have set the road map for smart grid. In particular, the main objective of the European Union; By 2020, it was possible to integrate renewable energy resources into the system by 20%, increase energy efficiency by 20%, and reduce carbon emissions by 20%. The American government finances many smart grid projects in its country with a certain amount of reimbursement. Japan has launched smart city pilot applications by taking smart grid projects one step further. Europe, the US and Japan except China, South Korea, Canada and Australia has become interested in smart grids. In this study, development and project examples of smart grids worldwide have been investigated.

Metodología para el análisis de costos y beneficios de proyectos Smart Grid y su aplicación en un piloto de medición inteligente de energía (AMI)

The aim of this paper is to present a methodological approach to obtain the cost-benefit impact at environmental and socioeconomic level, related to the integration of Advanced Measurement Infrastructure into utilities and their residential customers. The methodology used to quantify the costs and benefits of implementing such technology was established by the European Union and adjusted to the local context. It is worth highlighting that this methodology can be used to evaluate any kind of Smart Grid project. The socioeconomic benefits estimated in this research were related to job creation, energy consumption decrease, reduction of non-technical losses, and the reduction of CO2 emissions. An economic analysis using the net present value is presented here for assessing the impact caused by smart metering implementation.

Design and Implementation of a Smart Meter with Demand Response Capabilities

Abstract This paper presents the design of a smart meter (SM) with demand response (DR) capabilities. The SM design is tested in a simulation that implements an advanced measurement infrastructure (AMI), which allows a bidirectional communication between the household smart meters and the distribution management system (DMS). The DMS deploys an energy management system (EMS) that runs a simple demand response program (DRP) based on time of use (TOU), consisting in peak and off-peak rates. Results from the simulation and the data collected from the SM show significant improvements in energy consumption during peak hours thanks to the load curtailment strategies.

State of Art, Channel Allocation of Cellular Network for Adavanced Metering Infrastrcture Based on Markov Chain

Increasing demand of cellular communication service and limit frequency spectrum that cellular network has, is a problem for to channel allocation. This paper provides a way to reuse the frequency spectrum in cellular network while this hasn't be used pated, for that exist much of the radio spectrum being inefficiently used in cellular network to occupy the Advanced Measurement Infrastructure (AMI) of this way achieving also optimize the infrastructure of communication getting in a spectrum two types of data to be handled by spectrum agents in charge of sharing among different networks. All this is achieved by using the cognitive network, which is able to dynamically adapt to the demand achieving meet the needs of the user according to the policies defined by the operator. The goal is to prove that it is possible to use the white spaces spectrum network in a manner reliably and securely by using a stochastic decision model and limiting its time of use.