Deployment Of Smart Meters Research Articles

Dealing with sparse smart metering data in techno-economic analysis of low-voltage networks

Many distribution system operators are currently working on the deployment of smart meters to ensure, among other things, a better observability of their system. However, in the context of low-voltage (LV) networks, every customer does not benefit from such a measurement device, yet. Practically, when evaluating the techno-economic potential of an investment decision (cable upgrade, installation of storage units etc.), probabilistic load flows generally based on pseudo-sequential Monte Carlo algorithms can be used. Within this framework, the quality of the consumption/generation stochastic models has a major impact on the accuracy of the collected reliability indices. However, microscopic loads are often modelled with synthetic load profiles that are rather representative of an aggregated consumption behaviour. This study aims at evaluating the benefits of a modelling strategy based on the definition of reference cumulative distribution functions by applying it to an LV feeder in Belgium. Practically, this strategy requires a pre-processing clustering step. Moreover, a sensitivity analysis on the number (per cluster) of customers having a smart metering in their installation is complementarily conducted to evaluate the robustness of this modelling process in the context of techno-economic studies.

A secure ECC-based privacy preserving data aggregation scheme for smart grids

Development of Smart Grid and deployment of smart meters in large scale has raised a lot of concerns regarding customers’ privacy. Consequently, several schemes have been proposed to overcome the above mentioned issue. These schemes mainly rely on data aggregation as a method of protecting users’ privacy from the grid operators. However, the main problem with most of these schemes is the fact that they require a large amount of processing power at the meter side. This, together with the fact that smart meters don’t usually have a powerful processor, can cause the unavailability of smart meter data at the required time for operators of the grid, and at the same time prevents smart meters from performing their other duties, such as managing the home area network. In this paper, we propose an efficient privacy preserving data aggregation scheme, based on elliptic curves. In our scheme, each smart meter signs its encrypted data and sends it to the aggregator. After receiving the data, the aggregator verifies the incoming messages and aggregates them, without the need to decrypt the data received from each smart meter. The aggregator signs the aggregated messages and sends it to the operation center. At the end, the smart meters’ data are obtained and verified by the operation center. In addition to providing efficient computation cost, for the users, our protocol also satisfies the security requirements of smart grid data aggregation schemes. We provide security analysis of the proposed scheme as well as comparisons to show the efficiency of our scheme on computation and communication overheads.

C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas toward identifying multidimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.

Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids

Internet of Things (IoT) has recently emerged as an enabling technology for context-aware and interconnected “smart things.” Those smart things along with advanced power engineering and wireless communication technologies have realized the possibility of next generation electrical grid, smart grid, which allows users to deploy smart meters, monitoring their electric condition in real time. At the same time, increased environmental consciousness is driving electric companies to replace traditional generators with renewable energy sources which are already productive in user’s homes. One of the most incentive ways is for electric companies to institute electricity buying-back schemes to encourage end users to generate more renewable energy. Different from the previous works, we consider renewable energy buying-back schemes with dynamic pricing to achieve the goal of energy efficiency for smart grids. We formulate the dynamic pricing problem as a convex optimization dual problem and propose a day-ahead time-dependent pricing scheme in a distributed manner which provides increased user privacy. The proposed framework seeks to achieve maximum benefits for both users and electric companies. To our best knowledge, this is one of the first attempts to tackle the time-dependent problem for smart grids with consideration of environmental benefits of renewable energy. Numerical results show that our proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.

Integration of PV and EVs in unbalanced residential LV networks and implications for the smart grid and advanced metering infrastructure deployment

Voltage unbalance is a relevant problem that causes a less efficient operation of the system due to higher energy losses and lower hosting capacity. Unbalance has often been neglected by distribution system operators due to the lack of monitoring data in the low voltage (LV) grid. However, the massive deployment of smart metering in recent years in many countries provides very valuable information to detect unbalance. Moreover, in the current context of increasing presence of single-phase distributed energy resources connected to LV networks, such as electric vehicles (EVs) and photovoltaic (PV) generation, unbalance is bound to increase.This article investigates the technical impact of future integration of EV and PV in LV unbalanced networks. This paper has assessed the daily energy losses and voltage problems as load unbalance gradually increases, based on load flow analysis on an hourly basis, considering residential demand and homogeneously distributed EV and PV. The analysis has been carried out for several rural and semi-rural LV networks and various scenarios of demand level and penetration degree of EV and PV. The three-phase load flow analysis is computed using the forward-backward sweep algorithm.Furthermore, this work discusses the implications for the deployment of supervision and monitoring solutions based on advanced metering infrastructure (AMI). Their implementation should be prioritized in more loaded and longer networks where high integration of distributed energy resources is expected so that unbalance can be detected and corrective actions can be applied.

Fault location method for distribution networks using smart meters

Abstract The ongoing deployment of smart meters, with data processing and communication features, has provided the opportunity to improve distribution systems performance. This paper presents a state estimation-based method for fault location in distribution networks using the measurements provided by the smart meters. During the normal operation of the system, state estimation methods can handle the errors in real or pseudo-measurements to give the best estimate of the system state and to identify large measurement errors, called bad data. This concept is extended in this paper under fault conditions where the fault is considered as an unknown and temporarily connected load which can be dealt with as bad data. The proposed method uses the changeable weighting matrix bad data identification technique to find the fault location. The method is algorithmically simple and does not require the fault type. The fault location accuracy is verified by extensive simulation tests on a real 13.8 kV, 134-node distribution network for different fault scenarios. The results indicate that the method has a good performance under measurement and load data errors and for different number and locations of the smart meters in the network.

Security and Privacy-Preserving Metering Service in the Smart Grid

The deployment of smart metering provides an immense amount of data for power grid operators and energy providers. By using this data, a more efficient and flexible power grid can be realized. However, this data also raises privacy concerns since it contains very sensitive information about customers. In this paper, we present a security and privacy-preserving metering scheme for the community customers, by utilizing the password authenticated key exchange (PAKE) protocol and Elliptic Curve Cryptosystem (ECC). The proposed scheme will protect the community network from possible malicious behavior, and security analysis is also given in the paper.

Definitions of Demand Flexibility for Aggregate Residential Loads

Nowadays, enhanced knowledge of the nature of the electricity demand is achieved through the progressively increasing deployment of smart meters and advanced data analysis techniques. One of the major challenges is to exploit this knowledge to support the introduction of strategies to modify the demand according to relevant objectives to be achieved, like users’ participation in demand response programmes. A key point for facing this challenge is to characterize the demand flexibility. In spite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. This paper starts from the analysis of time-variable patterns of aggregate residential customers, ending up with suitable definitions of expected flexibility for aggregate demand. These definitions are based on assessing positive and negative pattern variations and are identified from the analysis of the collective behavior of the aggregate users. A set of results is shown for different numbers of aggregate customers, by considering different values of the averaging time step for load pattern representation.

Distribution System Distributed Quasi-Dynamic State Estimator

With increasing deployment of smart meters and other smart grid sensors in distribution systems, the amount of available measurements has been growing. This change enables implementation of comprehensive distribution system state estimators, a basic tool that provides the real-time model and operating conditions of the distribution network. The performance of many smart grid applications is dependent upon an accurate and reliable real-time model. This paper presents the development of a distributed quasi-dynamic state estimator (DS-DQSE) for distribution systems utilizing smart meter and other sensor data, as well as traditional SCADA data. The DS-DQSE implementation described in this paper works for both radial or meshed distribution networks and variable configuration systems, as distribution systems may be reconfigured following the clearing of a fault. This paper provides the problem formulation, solution methodology, modeling issues unique to distribution systems, and test results on multiple distribution circuits.

Advanced Metering Infrastructure Deployment in the United States: The Impact of Polycentric Governance and Contextual Changes

AbstractAdvanced metering infrastructure provides the first building block in smart grids by empowering customers and utilities with real‐time information regarding energy use. It is a key element in the U.S. government's push for electric grid modernization. Using a panel dataset for 50 U.S. states and the District of Columbia over the years 2007–2012, we evaluate the impacts of a polycentric governance system and socioeconomic contexts on states' performance in smart metering deployment. We find that the advanced metering technological change in the United States has been exclusively created by the interdependencies and interactions between different layers of government. High‐tech industry is the only socioeconomic factor that has a negative impact on smart meter deployment, whereas other factors, such as pressures from energy consumers and environmental groups, and electric grid conditions, have negligible impacts.

Guest Editorial Big Data Analytics for Grid Modernization

Advanced analytics plays a vital role in the era of big data, such as managing smart cities, predicting crime activities, optimizing medicine formula based on genetic defects, detecting financial frauds, and personalizing marketing campaigns. Information extracted from the big data benefits many industries in their day-to-day operations. The deployment of phasor measurement units (PMUs), smart meters and other smart devices has offered engineers the access to a large variety of data at an unprecedented granularity and volume. However, the old data management systems and applications are not designed to handle the big data. Therefore, how to extract actionable information and values out of the big data and how to integrate the information into grid operations and planning to ensure the secure, reliable and economical supply of electricity are becoming increasingly critical.

A NOVEL APPROACH OF INTELLIGENT LOAD SCHEDULING IN SMART GRID ENVIRONMENT

The deployment of smart meters in distribution systems provides an excellent pathway for load monitoring in the future smart grid paradigm. The proposed methodology involves a novel idea of power distribution optimization from the substation level. This paper elaborates a model by identifying the dynamic intelligent load scheduling problem and the approach is simple with less complexity thereby reducing the power outage time at the end users. This solution can be easily applicable to power distribution network using smart grid concepts and develops a load scheduling architecture. The problem efficiency is explained clearly which enhances the idea when the consumption among users is centrally available and it is easy to generate bills without errors. The main focus of this paper is modelling the loads in consumer side targeting an optimal scheduling .The proposed system is implemented in a realistic scenario and the obtained results exhibits effectiveness of the model.

Modelling the Effects of User Learning on Forced Innovation Diffusion

Technology adoption theories assume that users’ acceptance of an innovative technology is on a voluntary basis. However, sometimes users are force to accept an innovation. In this case users have to learn what it is useful for and how to use it. This learning process will enable users to transit from zero knowledge about the innovation to making the best use of it. So far the effects of user learning on technology adoption have received little research attention. In this paper - for the first time - we investigate the effects of user learning on forced innovation adoption by using an agentbased simulation approach using the case of forced smart metering deployments in the city of Leeds

Regulatory and market barriers to the realization of demand response in electricity distribution networks: A European perspective

Improving electricity demand response (DR) to support the operation and planning of distribution networks is an essential component of the European Commission strategy to increase economic efficiency in electric power systems across Europe. Due to the lack of the appropriate infrastructure, end consumers have traditionally been blind to wholesale market conditions as well as from the real costs they cause on the network and the power system operation. With the recent deployment of smart metering and communication technologies, provided an appropriate regulatory environment exists, new forms of local DR involving distribution system operators and small consumers could be developed. In a context where many EU Member States are still in the process of opening their retail sectors up to competition and being any active distribution management procedures still to be defined, DR is materializing at a very slow rate. It is the role of regulators to provide a suitable regulatory framework to allow DR to become effective at distribution level. This paper revises the main regulatory barriers that slow down the successful development of demand response mechanisms addressed to small consumers and an active distribution network management across Europe. In order to illustrate this discussion, an overview of six particular national examples is provided: France, Germany, Italy, Spain, Great Britain and Sweden. Finally, recommendations for policy makers and regulators are given.

Temporal Self-Regulation of Energy Demand

The increase in the deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand–response (DR) programs. This paper offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and feasible transitions therein is introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of smart grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world smart grid project. This paper is the first one to comprehensively analyze the temporal dynamics of demands.

Probabilistic simulation framework for balanced and unbalanced low voltage networks

In Low Voltage (LV) distribution networks, the high volatility of distributed photovoltaic (PV) generation has a severe impact on the variation of operation indices, in steady state conditions. During periods with high PV injection and low demand, LV feeders are more and more subject to overvoltage events and temporary PV units’ cut-offs. As a result, the delivered power quality is affected and network operational expenses increase. Moreover, the income of the PV owner is decreased due to the loss of generated energy. For efficiently addressing such operational issues, long term observability analytics of the LV network are required. Distribution System Operators (DSOs) currently deploy such studies in a deterministic manner, focusing on “worst-case” hypothesis, without considering the uncertainty of nodal power injection and consumption. This approach can lead to over restrictive decisions and costly technical solutions. For refining DSO strategies to the variability of network states, probabilistic methods are highly recommended. In this context, this paper presents a Monte-Carlo (MC) framework that simulates the steady operation of the LV network by elaborating user-specific smart metering (SM) measurements. The presented framework integrates a complete three-phase power flow algorithm that can analyse most possible LV network configurations, balanced and unbalanced, considering nodal power injections and consumptions as random variables of each network state. Such unbalanced power flow algorithms had not up to now been linked with probabilistic analysis using network-specific SM readings. For demonstrating the interest of the proposed framework, the latter is used to simulate several configurations in an existing LV feeder with high PV integration and SM deployment.

Smart Water Networks and Cyber Security

Smart water networks are instrumented, interconnected water systems that integrate data, computation, control, and communication technologies. They aim to improve system performance and resilience as well as consumer satisfaction and engagement. Partially inspired by advances in electric grid technology, smart water networks have ushered in a new era in design, operation, and management of urban water infrastructure. Contemporary advances and increased interest in smart water network technology are evident through development of a broad array of industry solutions and adoption by numerous water/wastewater utilities. Proliferation of, and increased maturity in, advanced metering infrastructure (AMI), data analytics, and automation technologies has resulted in commercial, field-proven products such as Accenture’s Water Analytics, Arqiva’s long-range radio communication suite, and i2O’s PRV Remote Control solutions. Such technologies have been adopted by utilities worldwide—by Thames Water in the United Kingdom (Accenture 2014), which launched a 15-year program to deploy smart meters across 3.3 million properties; and Philippines Manila Water, which deployed data-driven pressure optimization to enhance services provided to over 6.2 million people. The exponential development and deployment has several compelling drivers, which include escalating droughts, aging infrastructure, increasing water demand and costs, and environmental impacts. Smart water management as an intelligent approach promises enhanced situational awareness and real-time monitoring, predictive control, crisis response/recovery, and self-healing operations. This technological promise has manifested itself through success stories like South African eThekwini Water’s reported 67% mains burst reduction and Romanian APA Nowa Water’s 20% energy consumption reduction through smart pressure management (i2O Water 2015). Merging of cyberspace with physical water assets that are accountable for such tremendous promises, nevertheless, has also exported the malignancies of cyberspace to the traditionally ingenuous infrastructure environments.

A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids

With the gradual deployment of smart meters in many cities around the world, new opportunities arise in reducing energy usage and improving consumers’ information and control on their electricity consumption. Central to the provision of these newer services is the ability to accurately forecast the electricity demand of individual households. Compared with load forecasting at the city level and larger system aggregates, load forecasting for individual households is a much harder problem as the loads are much more stochastic and volatile. In this paper, we study the use of sparse coding for modeling and forecasting these individual household electricity loads. The proposed methods are tested on a data set of 5000 households in a joint project with electric power board of Chattanooga, for the period from September 2011 to August 2013. We obtain 10% improvements in the accuracy of forecasting next-day total load and next-week total load when we add sparse code features in ridge regression in this difficult problem. We also evaluate more classical forecasting methods on this forecasting problem, including autoregressive integrated moving average and Holt-Winters smoothing.

Low-complexity energy disaggregation using appliance load modelling

Large-scale smart metering deployments and energy saving targets across the world have ignited renewed interest in residential non-intrusive appliance load monitoring (NALM), that is, disaggregating total household’s energy consumption down to individual appliances, using purely analytical tools. Despite increased research efforts, NALM techniques that can disaggregate power loads at low sampling rates are still not accurate and/or practical enough, requiring substantial customer input and long training periods. In this paper, we address these challenges via a practical low-complexity lowrate NALM, by proposing two approaches based on a combination of the following machine learning techniques: k-means clustering and Support Vector Machine, exploiting their strengths and addressing their individual weaknesses. The first proposed supervised approach is a low-complexity method that requires very short training period and is fairly accurate even in the presence of labelling errors. The second approach relies on a database of appliance signatures that we designed using publicly available datasets. The database compactly represents over 200 appliances using statistical modelling of measured active power. Experimental results on three datasets from US, Italy, Austria and UK, demonstrate the reliability and practicality.

Comparative Analysis of Smart Meters Deployment Business Models on the Example of the Russian Federation Markets

This paper presents the comparison of smart meter deployment business models to determine the most suitable option providing smart meters deployment. Authors consider 3 main business model of companies: distribution grid company, energy supplier (energosbyt) and metering company. The goal of the article is to compare the business models of power companies from massive smart metering roll out in power system of Russian Federation.