Showing papers on "Smart grid published in 2018"

A Survey on the Edge Computing for the Internet of Things

Abstract: The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams

Abstract: Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties. We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.

A blockchain-based smart grid: towards sustainable local energy markets

Abstract: The increasing amount of renewable energy sources in the energy system calls for new market approaches to price and distribute the volatile and decentralized generation. Local energy markets, on which consumers and prosumers can trade locally produced renewable generation directly within their community, balance generation and consumption locally in a decentralized approach. We present a comprehensive concept, market design and simulation of a local energy market between 100 residential households. Our approach is based on a distributed information and communication technology, i.e. a private blockchain, which underlines the decentralized nature of local energy markets. Thus, we provide energy prosumers and consumers with a decentralized market platform for trading local energy generation without the need of a central intermediary. Furthermore, we present a preliminary economic evaluation of the market mechanism and a research agenda for the technological evaluation of blockchain technology as the local energy market’s main information and communication technology.

An Introduction to Deep Reinforcement Learning

Abstract: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

Abstract: In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

Abstract: Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Prosumer based energy management and sharing in smart grid

Abstract: There is huge expectation from smart power grid to provide sustainable energy services using bi-directional flow of data and power enabled by advanced information, communication and control infrastructure. An important element of such a smart grid is prosumers i.e. the consumers who also produce and share surplus energy with grid and other users. Prosumers are not only an important stakeholder of the future smart grids but also have a vital role in peak demand management. Therefore, it is needed to investigate and review the Prosumers based Energy Management and Sharing (PEMS) along with associated challenges. It will help in understanding and analyzing the impact of prosumers in future smart grids. In order to achieve these objectives, this paper presents a comprehensive review of PEMS in smart grid environment and associated impact on power system reliability and energy sustainability. The process of energy sharing among prosumers involves two key elements: information and communication technologies and optimization techniques. These two elements have been discussed in detail to cover the PEMS implementation requirements. The relevant communications technologies presented in the paper include wired, wireless, short and long range options while linear and nonlinear optimization techniques, in context of PEMS, are described. Various technologies, methodologies and mechanisms adopted for PEMS are comprehensively discussed in order to enhance readers’ intuition. Challenges and issues faced by prosumer communities and energy sharing have also been elaborated in detail.

Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment

Abstract: A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview

Abstract: This paper presents an overview of the state of the art control strategies specifically designed to coordinate distributed energy storage (ES) systems in microgrids. Power networks are undergoing a transition from the traditional model of centralised generation towards a smart decentralised network of renewable sources and ES systems, organised into autonomous microgrids. ES systems can provide a range of services, particularly when distributed throughout the power network. The introduction of distributed ES represents a fundamental change for power networks, increasing the network control problem dimensionality and adding long time-scale dynamics associated with the storage systems’ state of charge levels. Managing microgrids with many small distributed ES systems requires new scalable control strategies that are robust to power network and communication network disturbances. This paper reviews the range of services distributed ES systems can provide, and the control challenges they introduce. The focus of this paper is a presentation of the latest decentralised, centralised and distributed multi-agent control strategies designed to coordinate distributed microgrid ES systems. Finally, multi-agent control with agents satisfying Wooldridge’s definition of intelligence is proposed as a promising direction for future research.

Review on the research and practice of deep learning and reinforcement learning in smart grids

Abstract: Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Transforming Energy Networks via Peer-to-Peer Energy Trading: The Potential of Game-Theoretic Approaches

Abstract: Peer-to-peer (P2P) energy trading has emerged as a next-generation energy-management mechanism for the smart grid that enables each prosumer (i.e., an energy consumer who also produces electricity) of the network to participate in energy trading with other prosumers and the grid. This poses a significant challenge in terms of modeling the decisionmaking process of the participants' conflicting interests and motivating prosumers to participate in energy trading and cooperate, if necessary, in achieving different energy-management goals. Therefore, such a decisionmaking process needs to be built on solid mathematical and signal processing principles that can ensure an efficient operation of the electric power grid. This article provides an overview of the use of game-theoretic approaches for P2P energy trading as a feasible and effective means of energy management. Various game- and auction-theoretic approaches are discussed by following a systematic classification to provide information on the importance of game theory for smart energy research. This article also focuses on the key features of P2P energy trading and gives an introduction to an existing P2P testbed. Furthermore, the article gives specific game- and auction-theoretic models that have recently been used in P2P energy trading and discusses important findings arising from these approaches.

Design of Secure User Authenticated Key Management Protocol for Generic IoT Networks

Abstract: In recent years, the research in generic Internet of Things (IoT) attracts a lot of practical applications including smart home, smart city, smart grid, industrial Internet, connected healthcare, smart retail, smart supply chain and smart farming. The hierarchical IoT network (HIoTN) is a special kind of the generic IoT network, which is composed of the different nodes, such as the gateway node, cluster head nodes, and sensing nodes organized in a hierarchy. In HIoTN, there is a need, where a user can directly access the real-time data from the sensing nodes for a particular application in generic IoT networking environment. This paper emphasizes on the design of a new secure lightweight three-factor remote user authentication scheme for HIoTNs, called the user authenticated key management protocol (UAKMP). The three factors used in UAKMP are the user smart card, password, and personal biometrics. The security of the scheme is thoroughly analyzed under the formal security in the widely accepted real-or-random model, the informal security as well as the formal security verification using the widely accepted automated validation of Internet security protocols and applications tool. UAKMP offers several functionality features including offline sensing node registration, freely password and biometric update facility, user anonymity, and sensing node anonymity compared to other related existing schemes. In addition, UAKMP is also comparable in computation and communication costs as compared to other existing schemes.

A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

Abstract: Energy crisis and carbon emission have become two seriously concerned issues universally. As a feasible solution, Energy Internet (EI) has aroused global concern once proposed. EI is a new power generation developing a vision of evolution of smart grids into the Internet. The communication infrastructure is an essential component to the implementation of EI. A scalable and permanent communication infrastructure is crucial in both construction and operation of EI. In this paper, we present an introduction and the motivation to the evolution from smart grid to EI. We also introduce a representative EI architecture, i.e., the future renewable electric energy delivery and management system. Four critical EI features are emphasized. Then, we summarize the essential requirements that EI systems have to meet. With several key supporting technologies, EI shall realize the optimal utilization of highly scalable and distributed green energy resources, so that the situation of severe energy source crisis and carbon emission can be efficiently relieved. Since an EI system might have extensively distributed consumers and devices, the guarantee of its reliability and security is extremely significant. The further specific exploration for challenges, including reliability and security, will be stated in this paper.

Smart operations of smart grids integrated with distributed generation: A review

Abstract: In last few years, many countries in the world have shown huge interest in smart grid technology. They are facing many challenges in the process of deployment of this technology at ground level. Hence a planned research is required to meet those challenges within time. This paper provides a detailed description of progress in the field of demand side management, demand response programs, distributed generation, technical issues in the way of their progress and key advantages, which will be received after the final deployment of these programs. Renewable energy resources are also becoming a main part of distributed generation, which provides a solution for environmental problems caused by conventional power plants. Few countries are working on the deployment of the advanced metering system. Along with this, the scope of research in various programs of smart grid technology has been explored.

Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

Abstract: The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.

Privacy-Preserving and Efficient Aggregation Based on Blockchain for Power Grid Communications in Smart Communities

Abstract: Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home to smart building to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, an SM is installed at each home to collect the near-real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near-real-time data may disclose a user's private information. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this article, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups, and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonyms to hide users' identities, and each user may create multiple pseudonyms and associate his/ her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements and achieve better performance than other popular methods.

Provably Secure Authenticated Key Agreement Scheme for Smart Grid

Abstract: Due to the rapid development of wireless communication systems, authentication becomes a key security component in smart grid environments. Authentication then plays an important role in the smart grid domain by providing a variety of security services including credentials’ privacy, session-key (SK) security, and secure mutual authentication. In this paper, we analyze the security of a recent relevant work in smart grid, and it is unfortunately not able to deal with SK-security and smart meter secret credentials’ privacy under the widely accepted Canetti–Krawczyk adversary (CK-adversary) model. We then propose a new efficient provably secure authenticated key agreement scheme for smart grid. Through the rigorous formal security analysis, we show that the proposed scheme achieves the well-known security functionalities including smart meter credentials’ privacy and SK-security under the CK-adversary model. The proposed scheme reduces the computation overheads for both smart meters and service providers. Furthermore, the proposed scheme offers more security functionalities as compared to the existing related schemes.

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

Abstract: Power systems are the most complex systems that have been created by men in history To operate such systems in a stable mode, several control loops are needed Voltage frequency plays a vital role in power systems which need to be properly controlled To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance Likewise, the power exchanges between different control areas are controlled by LFC approaches In recent decades, many control approaches have been suggested for LFC in power systems This paper presents a comprehensive literature survey on the topic of LFC In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC

Integrating Renewable Energy Resources Into the Smart Grid: Recent Developments in Information and Communication Technologies

Abstract: Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes are motivating a transformation of the conventional ways of generating electricity. Globally, there is a desire to rely more on renewable energy resources (RERs) for electricity generation. RERs reduce greenhouse gas emissions and may have economic benefits, e.g., through applying demand side management with dynamic pricing so as to shift loads from fossil fuel-based generators to RERs. The electricity grid is presently evolving toward an intelligent grid, the so-called smart grid (SG). One of the major goals of the future SG is to move toward 100% electricity generation from RERs, i.e., toward a 100% renewable grid. However, the disparate, intermittent, and typically widely geographically distributed nature of RERs complicates the integration of RERs into the SG. Moreover, individual RERs have generally lower capacity than conventional fossil fuel-based plants, and these RERs are based on a wide spectrum of different technologies. In this article, we give an overview of recent efforts that aim to integrate RERs into the SG. We outline the integration of RERs into the SG along with their supporting communication networks. We also discuss ongoing projects that seek to integrate RERs into the SG around the globe. Finally, we outline future research directions on integrating RERs into the SG.

Energy Cooperation Optimization in Microgrids With Renewable Energy Integration

Abstract: Microgrids are key components of future smart grids, which integrate distributed renewable energy generators to efficiently serve the load locally. However, the intermittent nature of renewable energy generations hinders the reliable operation of microgrids. Besides the commonly adopted methods such as deploying energy storage system (ESS) and supplementary fuel generator to address the intermittency issue, energy cooperation among microgrids by enabling their energy exchange for sharing is an appealing new solution. In this paper, we consider the energy management problem for two cooperative microgrids each with individual renewable energy generator and ESS. First, by assuming that the microgrids’ renewable energy generation/load amounts are perfectly known ahead of time, we solve the off-line energy management problem optimally. Based on the obtained solution, we study the impacts of microgrids’ energy cooperation and their ESSs on the total energy cost. Next, inspired by the off-line optimization solution, we propose online algorithms for the real-time energy management of the two cooperative microgrids. It is shown via simulations that the proposed online algorithms perform well in practice, have low complexity, and are also valid under arbitrary realizations of renewable energy generations/loads. Finally, we present one method to extend our proposed online algorithms to the general case of more than two microgrids based on a clustering approach.

Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids

Abstract: This paper presents the design of a low complexity fuzzy logic controller of only 25-rules to be embedded in an energy management system for a residential grid-connected microgrid including renewable energy sources and storage capability. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design is to minimize the grid power profile fluctuations while keeping the battery state of charge within secure limits. Instead of using forecasting-based methods, the proposed approach use both the microgrid energy rate-of-change and the battery state of charge to increase, decrease, or maintain the power delivered/absorbed by the mains. The controller design parameters (membership functions and rule-base) are adjusted to optimize a pre-defined set of quality criteria of the microgrid behavior. A comparison with other proposals seeking the same goal is presented at simulation level, whereas the features of the proposed design are experimentally tested on a real residential microgrid implemented at the Public University of Navarre.

Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage

Abstract: This paper proposes a stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage. This paper is motivated by the challenges associated with intermittent renewable energy supplies and the local energy storage opportunity presented by vehicle electrification. This paper seeks to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements. First, various operating modes are defined, including vehicle-to-grid, vehicle-to-home, and grid-to-vehicle. Second, we use equivalent circuit PEV battery models and probabilistic models of trip time and trip length to formulate the PEV to smart home energy management stochastic optimization problem. Finally, based on time-varying electricity price and time-varying home power demand, we examine the performance of the three operating modes for typical weekdays.

Smart Charging Strategy for Electric Vehicle Charging Stations

Abstract: Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, in reality the market penetration of plug-in electric vehicles (PEVs) has been very low. Consumer concerns over the limited availability of charging facilities and unacceptably long charging periods are major factors behind this low penetration rate. From the perspective of the electricity grid, a longer PEV peak load period can potentially overlap with the residential peak load period, making energy management more challenging. A suitably designed charging strategy can help to address these concerns, which motivated us to conduct this research. In this paper, we present a smart charging strategy for a PEV network that offers multiple charging options, including ac level 2 charging, dc fast charging, and battery swapping facilities at charging stations. For a PEV requiring charging facilities, we model the issue of finding the optimal charging station as a multiobjective optimization problem, where the goal is to find a station that ensures the minimum charging time, travel time, and charging cost. We extend the model to a metaheuristic solution in the form of an ant colony optimization. Simulation results show that the proposed solution significantly reduces waiting time and charging cost.