Blockchain Based Security and Privacy Scheme for Smart Meter Communication

The demand for enhanced energy management systems is being driven by the desire for sustainable development and the rapid evolution of energy consumption patterns. This study examines the potential for advanced metering infrastructure (AMI) and smart meters to enhance the efficacy of energy transmission and consumption. Smart meters enable customers to monitor their energy consumption in real time, thereby increasing their awareness of their actions and motivating them to adopt more environmentally favorable behaviors. These technologies facilitate two-way communication between utilities and end consumers when integrated with AMI. These features include demand response, automatic invoicing, dynamic pricing, and power outage monitoring. The integration of data analytics and the Internet of Things by AMI enhances the environmental sustainability, operational efficiency, and reliability of the grid. This paper investigates the design, advantages, disadvantages, and potential future improvements of smart energy systems that are powered by AMI and smart meters. We are of the opinion that these technologies will be beneficial in the creation of an intelligent and resilient energy system

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs).In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation(SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

Providing Feedback of power consumption by using Advanced Metering Infrastructure (AMI) and smart meters is considered the best method to lower consumers' power consumption. Automatic analysis of power consumption data that provides information on user activity is essential in order to provide effective feedback in time, and advanced services such as automatic power load control based on context information. The activity should be matched with user's behavior in order to make it understandable for the user and readable for the machine. In this paper, a new method for extracting activities from power consumption data is proposed. By using the concept of activities in daily living (ADLs), the method extracts the activities as tasks from context information such as identification and location of electric appliances and temporal power consumption from the AMI and the smart meters. Using ontology, the tasks are tagged by semantic meta-information in order to be used for sophisticated user friendly services.

This paper studies the Okumura- Hat propagation model on CDMA technology applied in AMI (Advanced Metering Infrastructure). The aim of this proposal is to get information from the intelligent meter such us: outage management system, status and ratings reading. Prepaid services are oriented to the specific characteristics of rural dwellers. Furthermore, it allows to the energy distribution companies to know the energy consumption in real time. The Okumura - Hata propagation model helps to evaluate a communication system due to its good practices and results obtained with this method using cellular technology. The use of CDMA technology in electrical measurement systems helps to reduce the costs of non-technical losses, resulting in: better quality in the electrical systems and obtaining actual data readings that improves communication between the consumer and the distributor.

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

As an important branch of the Internet of Things, the smart grid has become a crucial field of modern information technology. It realizes the two-way information flow and power flow by integrating the advanced metering infrastructure (AMI) and distributed energy resources, which greatly improves users’ participation. However, owing to smart meters, the most critical components of AMI, are deployed in an open network environment, AMI is a potential target for data integrity attacks. Among various attack types, the scaling attack is the most typical one, because it can be used as a general expression for most of other ones. By launching a scaling attack, adversaries can randomly reduce hourly reported values in smart meters, thereby causing economic losses. A number of research efforts have been devoted to detecting data integrity attacks. Nonetheless, most of the existing investigations focus on all attack types, and little attention has been paid to a detection strategy specially designed for scaling attacks. Our contribution addresses this issue in this paper and hence, developing a detection model of scaling attacks considering consumption pattern diversity (SA2CPD), to ensure that scaling attacks can be effectively detected when users have multiple consumption patterns. To be specific, we leverage Kmeans to distinguish different consumption patterns, and then the consumption intervals can be extracted to binarize the data. We divide time periods in every day into two categories based on the binarization values, and use one of them with the greatest information gain to construct a decision tree for judgment. Both theoretical and simulation results based on the GEFCom2012 dataset show that our SA2CPD model has a higher F1 score than the decision tree model without considering consumption pattern diversity, the KNN model and the Naive Bayes model.

Nowadays, legacy electrical distribution systems are migrating to a new modern electric grid with the capability of supporting different applications such as advanced metering infrastructure (AMI), distributed energy resources, and electric vehicles. Among these applications, AMI is playing an important role in delivering data from customers to power utilities, supporting reliable real-time monitoring, and remote operation of power quality data and voltage profile. The AMI consists of smart meters, data aggregation points (DAPs), the utility control center, and communication networks. Appropriate network planning plays an essential role in facilitating the exchange of data between consumers and power utilities as well as accommodating new smart grid applications and future growth. This work proposes an optimal placement of DAPs for AMI based on machine learning clustering techniques in residential grids. Network partitioning is introduced to create sub-networks and graph algorithms generate a deployment topology given optimization constraints. A new measurement metric called coverage density is considered to indicate neighborhood area networks (NAN) zones with the appropriate coverage. Three real scenarios of NAN are considered: urban, suburban, and rural. The proposed algorithm is evaluated and compared with conventional heuristic optimization methods with respect to average and maximum distance between smart meters and DAPs, coverage density, and execution time.

Technical and governance considerations for advanced metering infrastructure/smart meters: Technology, security, uncertainty, costs, benefits, and risks

A framework for participation of prosumers in peer-to-peer energy trading and flexibility markets

Advanced metering infrastructure (AMI) is an enabling technology improving the quality of metering and transferring the energy consumption data in the electric energy network. Data with such quality acquired from a bidirectional infrastructure is a highly instrumental in increasing energy awareness in smart cities. The bidirectional communication infrastructure provided by AMI increases the customers' awareness about their consumption behavior, too. One of the issues affected by considering AMI in distribution network is demand side management (DSM). The accurate customer characterization using AMI data is a practical point in increasing energy awareness in the smart cities. The role of AMI in DSM programs is embossed when customers are regarded as influencing participants in these programs. This paper proposes a procedure investigating the role of AMI in improving demand response (DR) by costumer characterization in distribution network. Authors believe that DR is suitably implemented in distribution network if the analysis of customers' consumption behavior is conducted accurately. Besides, customers' capacities for participating in DR are different which is unfold by analyzing their consumption behaviors. Customers' consumption that are recorded by smart meters in different time slots compose time series on which pattern recognition procedures are applied. Different time series are clustered on the basis of their similarities and DR is implemented on each cluster separately. In This study, DR is implemented in two years in one of which capacities of each cluster for participation in DR is estimated and in another one these capacities are compared with the observed capacities. The results of this study confirm the accuracy of the estimated capacities and the proposed method. This knowledge helps the networks' operators to get a more accurate overview on the total capacity of the distribution network for implementing DR. The proposed method is implemented on a real dataset provided by Irish social science data archive (ISSDA).

Smart meters are key components of the smart grid infrastructure, connecting households to utility providers. With Internet connectivity to such devices, the smart grid Advanced Metering Infrastructure (AMI) is exposed to attacks by the adversary class. Any compromise of a smart meter may jeopardize the security of not just the household in question, but also the entire neighborhood network and possibly the utility provider's core network. One attack category that may pose a serious threat to the AMI is a meter compromise attack, wherein a smart meter is controlled by the adversary, with the intent of committing energy fraud. In this paper, we present a detection model based on a cumulative analysis of smart meter readings of clustered smart meters. The clustering of smart meters is done based on the physical deployment points of individual smart meters within a neighbourhood area network. Results obtained through simulation of the proposed scheme are promising.

With the substantial increase in implementation of green technology across all sectors worldwide to overcome energy loss, attributes such as reliability, efficiency, productivity, and security become very crucial for a system. Especially with the integration of IoT and smart features into a system alongside the evolution of Internet, cybersecurity and privacy of an individual component should be taken into important consideration. A smart grid comprises wireless network components exposed to cyber-attacks, smart meters being one the most vulnerable part of an advanced metering infrastructure. An advanced metering infrastructure enables two-way communication for automatic meter readings (AMR) and transmits data through wireless network. To counter cyber-attacks afflicting the network of AMI and functioning of a smart meter, additional intrusion detection methods are implemented into the grid network. The review includes cybersecurity concerns associated with smart meters along with an in-depth insight to some major cyber-attacks and their aftermath.KeywordsCybersecurityCyber-attacks advanced metering infrastructure (AMI)Smart metersDenial-of-service (DoS)False data injection (FDI)Man-in-the-middle (MitM)

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11–17 secs of execution time, depending on detection accuracy.

The delivery of energy and water meter data, management and control information on separate networks is expensive and defeats the gains of the Advanced Metering Infrastructure (AMI) Smart Grid (SG). In most cases, energy, gas and water services are offered by the same organizational entity, hence the use of different infrastructure for data, service delivery, control and management is expensive and highly illogical. There is a need for a combined energy and water infrastructure to reap the benefits of the AMI SG. Furthermore, combined metering will result in accurate billing, potential cost savings, and improved resource management. This work therefore develops and investigates a combined energy and water AMI smart metering framework. This is possible through a thorough understanding of the AMI technological standards. The implementation of such a system is not trivial, as it depends on many factors: environmental, geographical, technological, economical, regulatory and the existing legacy infrastructure. Optimal technological implementation choices are developed towards an integrated AMI infrastructure. An experimental test bed is developed for delivering energy and water metering data to the utility. The optimal placement results favor the system of separating energy and water actuators at the home area network of the SG while using an integrated communication system. Such a system is feasible, given the different evolution of electricity and water meters and their placement at the home area network, and enables water metering to benefit from the more advanced electrical metering infrastructure.

A computer worm is malicious software that can spread rapidly between interconnected devices and perform malicious actions. In an Advanced Metering Infrastructure (AMI), smart meters may communicate in an ad-hoc fashion to perform many functionalities like remote metering and demand response. However, the AMI is susceptible to cyber-physical threats caused by malware like worms. The speed at which worms propagate in the AMI affects the physical consequences of cyber attacks. In this paper, worm propagation in the AMI is simulated and a probabilistic model is derived. This model can be used to estimate the time required to infect N meters in the AMI and accordingly predict possible physical consequences.