A privacy-preserving scheme with identity traceable property for smart grid

Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describe a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.

EPPSQ: Achieving efficient and privacy-preserving statistics queries over encrypted data in smart grids

Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper demand side management schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and proposes a personalized electricity retail plan recommender system for residential users. The proposed personalized recommender system (PRS) is based on the collaborative filtering technique. The energy consumption data of users are firstly collected from the smart meter, and then key energy consumption features of the users are extracted and stored into a user knowledge database (UKD), together with the information of their chosen electricity retail plans. For a target user, the recommender system analyzes his/her energy consumption pattern, find users having similar energy consumption patterns with him/her from the UKD, and then recommend most suitable pricing plan to the target user. Experiments are conducted based on actual smart meter data and retail plan data to verify the effectiveness of the proposed PRS.

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.

In smart grid, data aggregation is considered as an essential paradigm in assessing information about current energy usage. To achieve the privacy-preserving goal, several homomorphic-based solutions have been proposed. However, these solutions either consider one-dimensional information or use costly pairing computation in order to ensure source authentication. In fact, smart grid data are likely to be multidimensional (e.g., time, purpose, and so on) for more accurate control. In addition, the aggregation node in smart grid needs to verify data that come from several smart meters in a residential area; hence, the verification must be cost-efficient. In this paper, we propose a scheme that considers multidimensional aggregation with privacy preserving and an efficient verification of smart grid data. The proposal is based on elliptic curve cryptography along with homomorphic encryption and without pairings. The performance analysis shows the efficiency of the scheme for smart grid communications in comparison with existing schemes. For instance, we show that, when an aggregator node is responsible of 600 smart meters, it spends approximately 14 s to verify the data in pairing-based schemes, while only 0.3 s is needed for verification within the proposed scheme.

Classification for consumption data in smart grid based on forecasting time series

We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.

The sharing of energy usage data in smart grids is becoming increasingly popular because it not only allows different entities to access fine-grained energy consumption data but also improves the effectiveness of smart grid technologies. How to ensure both verifiability and privacy of the shared data is a vital issue. Most existing privacy-preserving authentication schemes greatly hinder the flexibility of sharing data among multiple parties due to vulnerability and inefficiency reasons. The customer-centric energy usage data management framework based on redactable signature (RS) technology can be seen as an effective solution. It offers customers the flexibility to remove parts of privacy-sensitive data depending on different data usage demands, and ensures data verifiability for third party service providers. However, existing RS schemes are computationally inefficient for constrained devices such as smart meters. Besides, it is said that quantum computers are expected to break all traditional public-key primitives. In this regard, almost all existing RS schemes are vulnerable to quantum attacks. To address the above concerns, in this work, we propose a hash-based RS scheme HRSS based on a variant of the Goldreich-Goldwasser-Micali tree, a length-doubling pseudorandom generator, and an underlying SPHINCS\(^+\) framework. Our HRSS is the first quantum-safe RS scheme, where the security depends only on the security of underlying hash functions. We instantiate and evaluate the performance of our design. Theoretical and experimental comparisons with recent works show that HRSS is practical for smart grids.KeywordsRedactable signaturePrivacyHash-basedSmart grids

Energy is one of the primary sources of economic and technological development in any area. Energy is a leading requirement like food, cloth, and shelter just like illumination, electrical appliances like fan, television, air-conditioning and other things. So, as the demand of this all box of tricks the planning must be there on various strategies of energy management systems. To conserve energy, the investigation of energy supplied where it uses, conserves, and wastes. The audits improve the performance of activities from generation to the receiving end. This chapter is not only to pioneer the management but also to preserve. The preservation squanders the energy where and when it will be. Sundry action is planned for organizing state of the art policies for plants and also energy security. The planning is smart energy management and the smart city, including smart town planning, smart security, smart transportation/mobility, smart governance, smart building/home, smart energy, and smart grid. The propound creep up on the impact of deputy applications and high-power rating loads in magnitude hours to energy expending expenditure of consumers. Replica results show that the propound creep up on the efficiency in reducing vertex load demand and electricity spending charges with a rising in the comfort level of patrons. In the case of the smart grid, the main application is a smart city; the features are taking under the topic, i.e. need to enhance for consumers for their benefits are developing the nature of IoT in electrical appliances, reducing the maintenance cost, improve public safety via all-time surveillance, aid economic development, enhancing consumers service and obvious conservation. IoT can play a vital role in energy management strategy in smart city and grid networks; it helps to contribute to various applications in which the consumers can benefit from economics and audit. The benefits are smart metering networks, smart city lighting, smart grid automation, ecological monitoring solutions, efficient public service tracking. The main sub-assistant benefit to smart energy is a microgrid. A microgrid is a standalone structure. The Microgrid is a widely founded research in the current curriculum that has propounded the centralized and distributed scheme application. In this research, we are concentrating on energy security. The main concept about energy security is to, before expansion and delivery; we must go through the queries that why we need security, on which ultimatum, from whom, and security on which merit. Energy security is a global confusion issue for the general public, policymakers, or legislators. Strategies must be planned to increase and improve the individual renewable type of energy to use in the industrial premises to reduce consumption. Planning is to strengthen the renewable energy source, which is the only way to reduce greenhouse emissions—a blueprint for the Microgrid at an individual level in domestic and commercial consumers. Blueprint to replace the running energy infrastructure so advance new better consumption free structures develops for consumers. Strategies to reduce fuel squander. So, it will directly reduce consumption and indirectly greenhouse emissions and air pollution. This chapter is a case study of different energy management proposals to conserve energy investments via organizing online, offline training programs and evaluating energy performances, action planning, and economics.

With the inception of smart grid technologies, electric utilities have been attempting multifarious mechanisms to transport information from data generating entities to their information control centers. One such approach for carrying application data is to lease communication network capacity from Internet service providers (ISPs). Such dedicated capacity reservations on communication networks can be a costly proposition for electric utilities. This work proposes an alternative paradigm that takes advantage of the elastic nature of smart grid data traffic flows to piggyback (with authorization) on ISP networks without impacting the QoS of the primary flows on that network. Such an opportunistic scheme is expected to reduce operational costs for utilities to carry smart grid data traffic while providing guarantees to ISPs that their primary flows will be unaffected. The proposed opportunistic network sharing scheme builds upon a software-defined networking (SDN) approach at an intermediate router to provide QoS guarantees for regular ISP traffic. To mitigate the inevitable throttling of smart grid traffic during times of high network utilization in the shared network, a data granularity management algorithm is proposed that gracefully reduces the data granularity of smart grid (SG) data traffic in preemptive fashion in an attempt to meet application QoS needs.

The need for competition between decentralized governance approaches for data exchange in smart electricity grids—Fiscal federalism vs. polycentric governance

Smart grid (SG) is an efficient electrical grid that provides opportunities to manage the energy load in a reliable and efficient way. Moreover, smart meters (SMs) are introduced, which play a vital role in communication between homes and SG. SMs manage and monitor the energy consumption of homes. SGs and SMs produce a big data, which is very hard to store and process even with cloud computing. For this purpose fog computing concept is introduced, which provides a good environment for computing and storing the data of SGs and SMs before transmitting them to cloud. The concept of fog computing, acts as a bridge between cloud and SGs. In this paper, a cloud-fog based architecture is integrated with SG for efficient energy management of buildings with SMs. To manage the energy requirements of consumers, micro grids (MGs) are available near to the buildings. Fogs are distributed over the world, overhaul the cloud via important features, including low latency and increased security for MGs. To balance the load on cloud and fogs, three load balancing algorithms are used. These algorithms are round robin (RR), throttled and greedy. Closest datacenter policy is used to compare their results. Greedy gives better results than RR and throttled.

The Internet of Things (IoT) has transpired as a fascinating technology for smart cities, smart homes, and smart grids by using a vast amount of IoT data. A smart grid is one of the core components where transport, generation, delivery, and electricity consumption are enhanced in terms of protection and reliability. The existing power grid is suffering from many problems such as outages and unpredictable power disturbances, inflexible energy rates, unnoticeable customer fraud, and many other disadvantages. These problems lead to the ever-rising demand for fossil fuel and service costs. For example, the peak hour demand needs to be overestimated and more energy generated to minimize the risk of an outage. The main problem of the smart grid is the tremendous amount of data needs to be collected from the IoT devices, and processing the data is a challenge. Using and predicting a large amount of data in smart Grid and IoT is still in its infancy. To remedy this problem, we propose a hybrid solution by using the Cloud and Edge Computing to process the data. Processing and predicting at the edge that is close to the embedded devices and homes to save in latency and storage compared to putting all the processing in the Cloud. In this paper, we define a hybrid solution where we use the edge computing for the smart grid information processing where the microgrids are located on the edge of the IoT network, and on the Cloud to use for the power grid that distributes power to the microgrids. We proposed a machine learning engine that used the decision tree to establish the communication between the edge layer, failover between edges, and the Cloud layer.

Reducing the power supply-demand gap and increasing reliability of power supply are the challenges of current energy management. Implementation of smart grid, smart meters and smart metering can be a possible solution for power demand reduction, efficient power supply management, and optimization of management resource usages. Smart meters include sophisticated measurement and calculation hardware, software, calibration and communication capabilities. For interoperability within a smart grid infrastructure, smart meters are designed to perform functions, and store and communicate data according to certain standards. In this work we discuss smart meter and various elements of smart metering, current state of the technologies related to smart grid, smart meter, advanced metering infrastructure (AMI), and meter data flow in smart grid. We also discuss standards related to smart meter, meter data format and data transmission, functions of smart meter, and functionalities of smart meters, currently deployed by utilities around the world.

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.