Electrical load curve reconstruction required for demand response using compressed sensing techniques

In this paper, we consider a compressive sensing (CS) based compression method for aggregated power signals of smart meters in advanced metering infrastructure (AMI). If detailed power signals with high sampling rates are available for energy management systems, advanced services such as demand response and power disaggregation can be provided. However, typical smart meters have limited bandwidth resources and may require aggressive data compression for supporting the intensively sampled data. In order to alleviate the overhead for the detailed power signal, a CS based compression scheme is proposed in this study to take advantage of the sparsity of power signals based on the appliance load superposition modeling. Through simulations, the CS based compression schemes demonstrate improved energy efficiency while maintaining good reconstruction performance, compared to the wavelet based compression.

In this paper, we propose a unified approach for compression and authentication of smart meter reading in advanced metering infrastructure (AMI). In general, smart meters are urged to send sampled reading signals at a high rate for high-quality services. Meanwhile, power reading signals have to be authenticated to prevent impersonation attacks, which can cause serious economic loss. However, the security in smart grids faces more challenges than conventional human-type communications because of limited hardware resources of a smart meter (e.g., small memory). Motivated by these problems, we study simultaneous compression and authentication for power reading signals in multicarrier systems based on the notion of compressive sensing (CS). The CS-based compression and authentication method are applied to empirically modeled signals with a shared secret key, a measurement matrix in CS between a data concentrator unit (DCU) and a legitimate smart meter. In particular, for authentication, the residual error of a received signal at the DCU is used as a test statistic for hypothesis testing, which determines whether the signal is a legitimate signal or an intrusion signal in the proposed approach. Through the analysis and simulation results, we demonstrate that the CS-based compression approach can be applied to smart meter reading with good energy efficiency. In addition, it is shown that the proposed scheme can obtain a low authentication error probability under reasonable conditions. For example, when the number of subcarriers is 64, the DCU can distinguish legitimate and intrusion smart meters with a probability of $1 - P_{E}$ , where $P_{E} \leq 10^{-4}$ .

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).

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.

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.

As the core component of the smart grid, advanced metering infrastructure (AMI) is responsible for automated billing, demand response, load forecasting, management, etc. The jamming attack poses a serious threat to the AMI communication networks, especially the neighborhood area network where wireless technologies are widely adopted to connect a tremendous amount of smart meters. An attacker can easily build a jammer using a software-defined radio and jam the wireless communications between smart meters and local controllers, causing failures of on-line monitoring and state estimation. Accurate jammer localization is the first step for defending AMIs against jamming attacks. In this paper, we propose JamCatcher, a mobile jammer localization scheme for defending the AMI. Unlike existing jammer localization schemes, which only consider stationary jammers and usually require a high density of anchor nodes, the proposed scheme utilizes a tracker and can localize a mobile jammer with sparse anchor nodes. The time delay of data transmission is also considered, and the jammer localization process is divided into two stages, i.e., far-field chasing stage and near-field capturing stage. Different localization algorithms are developed for each stage. The proposed method has been tested with data from both simulation and real-world experiment. The results demonstrate that JamCatcher outperforms existing jammer localization algorithms with a limited number of anchor nodes in the AMI scenario.

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)

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

Smart grid is considered as next generation power system which performs automated control and monitoring operations to make it self-resilience. Advanced Metering Infrastructure is an essential part of smart grid which enables two-way communication thereby providing real-time communication link between consumer and grid operators. Emergence of Distributed Energy Resources (DERs) at end user make AMI's operation more challenging. Smart Meters (SMs) are key components in AMI that sends Power Consumption Data (PCD) of home appliances to Distributed System Operator (DSO). PCD received at DSO can be used for real time pricing applications, real time energy consumption, automatic diagnostic, daily energy metering and tasks such as billing, monitoring, planning and predicting of energy usage. In the context of selling of excess energy generated at DER's to the grid, SMs participates in Demand Response (DR) applications to perform energy trading. Revealing PCD of a home leads to privacy breach, hence it is necessary to achieve secure communication between SM and DSO. At the same time, it is essential to make energy trading transparent. To address this issue, this paper proposes a cloud based private blockchain framework to accomplish secure communication between SM and DSO without compromising privacy. Furthermore, trust between SM and DSO is achieved through smart contracts. Performance evaluation of the proposed blockchain shows its applicability to the said environment.

Public-key cryptography (PKC) is widely used in smart grid (SG) communications to reduce the overhead of key management. However, PKC comes with its own problems in terms of certificate management. Specifically, certificate revocation lists (CRLs) need to be maintained and distributed to the smart meters (SMs) in order to ensure security of the communications. The size of CRLs may grow over time and eventually may introduce additional delay, bandwidth, and storage overhead when various applications are run on SG. In this paper, we propose novel algorithms for creating customized CRLs with reduced size for IEEE 802.11s-based advanced metering infrastructure (AMI) networks. Rather than maintaining a huge-size single CRL that introduces unnecessary search time and storage, the idea is to cluster/group SMs within the AMI network and create CRLs based on these groups. The grouping is mainly done in such a way that they bring together the SMs that will be very likely to communicate so that the CRLs will be kept local to that group. To this end, we propose two novel grouping algorithms. The first algorithm is a bottom-up approach, which is based on the existing routes from the SMs to the gateway. Since the SMs will be sending their data to the gateway through the nodes on the route, this forms a natural grouping. The second approach is a top-down recursive approach, which considers the minimum spanning tree of the network and then divides it into smaller subtrees. Via grouping, the length of the CRL for each SM and the corresponding distribution overhead can be reduced significantly. Simulation results have shown that our approach can maintain a balance between the size of the CRL and the number of signatures generated by CAs while guaranteeing security of the communications.

This Paper examines, in detail, all aspects related to rollout of Advanced Metering Infrastructure (AMI) in India. It covers key issues such as smart meter standards and specification, testing of smart meters, retrofitting of existing meters, communication technology options, procurement strategy and business models for AMI rollout. Under the UDAY program, Government of India plans to deploy smart meters for all customers with monthly consumption above 200 kWh by December 2019. Distribution Companies (DISCOMs) have the responsibility of choosing the most optimum deployment strategy. While the traditional AMI approach proposes deployment of smart meters for all customers on a feeder, another option is to deploy only for customers having monthly electricity consumption greater than 200 units. Part A of this Paper presents a detailed cost-benefit analysis which strongly advocates deployment of smart meters for all customers on a feeder primarily because of the potential to reduce Aggregate Technical and Commercial (AT&C) losses and the substantially lower cost for the last mile communication network when the full feeder is covered. Part B of this Paper covers a set of recommendations for AMI roll out in India.

The integrity and authenticity of the energy usage data in Advanced Metering Infrastructure (AMI) is crucial to ensure the correct energy load to facilitate generation, distribution and customer billing. Any malicious tampering to the data must be detected immediately. This paper introduces secure end-to-end data aggregation for AMI, a security protocol that allows the concentrators to securely aggregate the data collected from the smart meters, while enabling the utility back-end that receives the aggregated data to verify the integrity and data originality. Compromise of concentrators can be detected. The aggregated data is protected using Chameleon Signatures and then forwarded to the utility back-end for verification, accounting, and analysis. Using the Trapdoor Chameleon Hash Function, the smart meters can periodically send an evidence to the utility back-end, by computing an alternative message and a random value (m′, r) such that m′ consists of all previous energy usage measurements of the smart meter in a specified period of time. By verifying that the Chameleon Hash Value of (m′, r) and that the energy usage matches those aggregated by the concentrators, the utility back-end is convinced of the integrity and authenticity of the data from the smart meters. Any data anomaly between smart meters and concentrators can be detected, thus indicating potential compromise of concentrators.

Advanced Metering Infrastructure (AMI) enables two-way communication between smart devices and utility control centers. This involves remote monitoring and control of energy consumption as well as other parameters in the electrical power network in real time. However, increasing technologies in AMI due to huge deployment of smart meters, integration of devices and application of sensors, demand a strong architectural model with the best network topology to guarantee efficient usage of network resources with minimal latency. In this work, a resource efficient multi-hop network architecture is proposed using hybrid media access protocols. The architecture combines queuing and random-access protocol to achieve optimal network performance. Numerical results show that the probability of delay incurred by an arbitrary smart meter depends on the mean and distribution of the queue switch over a period. It is also observed that for a single queued system, the throughput performance is equal to the existing hybrid method. As the number of smart meters increases to 500, the throughput of the proposed method improves by 10% compared to the existing method. Likewise, as the number of smart meters increases to 500, the delay reduced by 15% compared to the existing method. Keywords: Advanced Metering Infrastructure; hybrid media access protocols; Smart Meter; Smart Grid; Power Network.

Internet of Things connects people and things at any time and in any place. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most significant components in smart grid which collects data from smart meters and sends the collected data to the utility center to be analyzed and stored. To implement two-way communication between smart grid components, machine-to-machine communications has a great potential. In this paper, we propose a machine-to-machine architecture for advanced metering infrastructure in Internet of Things-based smart grid and focus on finding the optimum number of collectors based on two metrics: collectors' cost and packet delay. We model advanced metering infrastructure as a queuing system and derive an expression for packet delay. We also propose a newperformance metric, the product of cost and delay (CD), so that by minimizing it, we can find the optimum number of collectors. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost and delay.