A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges
A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.
Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile
This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications
The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyberphysical systems. In this context, this article is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: microgrid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game-theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control.
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.
The Progressive Smart Grid System from Both Power and Communications Aspects
The present electric power system structure has lasted for decades; it is still partially proprietary, energy-inefficient, physically and virtually (or cyber) insecure, as well as prone to power transmission congestion and consequent failures. Recent efforts in building a smart grid system have focused on addressing the problems of global warming effects, rising energy-hungry demands, and risks of peak loads. One of the major goals of the new system is to effectively regulate energy usage by utilizing the backbone of the prospectively deployed Automatic Meter Reading (AMR), Advanced Meter Infrastructure (AMI), and Demand Response (DR) programs via the advanced distribution automation and dynamic pricing models. The function of the power grid is no longer a system that only supplies energy to end users, but also allows consumers to contribute their clean energy back to the grid in the future. In the meantime, communications networks in the electric power infrastructure enact critical roles. Intelligent automation proposed in smart grid projects include the Supervisory Control And Data Acquisition/Energy Management Systems (SCADA/EMS) and Phasor Management Units (PMU) in transmission networks, as well as the AMR/AMI associated with field/neighborhood area networks (FAN/NAN) and home area networks (HAN) at the distribution and end-use levels. This article provides an overview of the essentials of the progressive smart grid paradigm and integration of different communications technologies for the legacy power system. Additionally, foreseeable issues and challenges in designing communications networks for the smart grid system are also rigorously deliberated in this paper.
The internet of energy: a web-enabled smart grid system
The quest for sustainable energy models is the main factor driving research on smart grid technology. SGs represent the bridging paradigm to enable highly efficient energy production, transport, and consumption along the whole chain, from the source to the user. Although this concept promises to be very fruitful, the research on how to deploy it in the real world has just begun. A discussion on the enabling technologies for SGs and a possible roadmap for the profitable evolution thereof is the focus of this article. After introducing the recent trends that are pushing the SG paradigm, we will discuss various key scenarios for the SG, and briefly introduce some of its key requirements. We will then provide an analysis of how current and future standard solutions in the areas of communications and networking can be engineered into a system that fulfills the needs of the SG vision. We advocate the use of small, cheap, and resource-constrained devices with pervasive computing capabilities as the key component to deploy a ubiquitous energy control system. To this end, the recent efforts carried out by Internet standardization bodies such as the IETF and W3C toward the vision of the Internet of Things (IoT) are especially relevant. The various components of the proposed solution have been successfully showcased in real-world implementations, and relevant actors such as ETSI, ZigBee, and IPSO are already evaluating their potential for future IoT applications, making the Internet-based smart grid vision considered in this article practically achievable in the not too distant future.
Demand Response Management via Real-Time Electricity Price Control in Smart Grids
This paper proposes a real-time pricing scheme that reduces the peak-to-average load ratio through demand response management in smart grid systems. The proposed scheme solves a two-stage optimization problem. On one hand, each user reacts to prices announced by the retailer and maximizes its payoff, which is the difference between its quality-of-usage and the payment to the retailer. On the other hand, the retailer designs the real-time prices in response to the forecasted user reactions to maximize its profit. In particular, each user computes its optimal energy consumption either in closed forms or through an efficient iterative algorithm as a function of the prices. At the retailer side, we develop a Simulated-Annealing-based Price Control (SAPC) algorithm to solve the non-convex price optimization problem. In terms of practical implementation, the users and the retailer interact with each other via a limited number of message exchanges to find the optimal prices. By doing so, the retailer can overcome the uncertainty of users' responses, and users can determine their energy usage based on the actual prices to be used. Our simulation results show that the proposed real-time pricing scheme can effectively shave the energy usage peaks, reduce the retailer's cost, and improve the payoffs of the users.
Role of smart grid in renewable energy: An overview
Smart grid engineering is the key for a beneficial use of widespread energy resources, it is a modernized electrical grid that uses analog or digital information and communications technology. Renewable energy itself a thrust area of research due to its availability, applicability and environmental friendly nature and the application of smart grid in renewable energy makes it vast and more promising. This fusion enables the efficient use of renewable energies which is a key challenge for now. The present review paper attempts to investigate the role of smart grid in the renewable energy. The introductory section sets the role of renewable energy and distributed power in a smart grid system. Subsections cover the concept and availability of renewable energies, renewable energy power calculation formulae, smart grid concepts and its feasibility, case studied as performed by different researchers around the World, discussion and future recommendations and finally the conclusions from the study. To achieve this, articles from different sources such as internet, reports, conferences and journals of Elsevier, Springer, Tailor and Franacis, Wiley and many more have been collected and reviewed. This paper concludes that renewable energies can be used efficiently and in a smart way by using the smart grids. However, the smart grid technology is not mature enough and needs more research on the same.
Distributed Economic Dispatch for Smart Grids With Random Wind Power
In this paper, we present a distributed economic dispatch (ED) strategy based on projected gradient and finite-time average consensus algorithms for smart grid systems. Both conventional thermal generators and wind turbines are taken into account in the ED model. By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner with limited communication among neighbors. It is theoretically shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. This scheme also brings some advantages, such as plug-and-play property. Different from most existing distributed methods, the private confidential information, such as gradient or incremental cost of each generator, is not required for the information exchange, which makes more sense in real applications. Besides, the proposed method not only handles quadratic, but also nonquadratic convex cost functions with arbitrary initial values. Several case studies implemented on six-bus power system, as well as the IEEE 30-bus power system, are discussed and tested to validate the proposed method.
Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Smart Grid
Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.
Game Theoretic Methods for the Smart Grid
The future smart grid is envisioned as a large-scale cyber-physical system encompassing advanced power, communications, control, and computing technologies. In order to accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyber-physical systems. In this context, this paper is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: micro-grid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control.
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
With the modern advanced information and communication technologies in smart grid systems, demand response (DR) has become an effective method for improving grid reliability and reducing energy costs due to the ability to react quickly to supply-demand mismatches by adjusting flexible loads on the demand side. This paper proposes a dynamic pricing DR algorithm for energy management in a hierarchical electricity market that considers both service provider’s (SP) profit and customers’ (CUs) costs. Reinforcement learning (RL) is used to illustrate the hierarchical decision-making framework, in which the dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning is adopted to solve this decision-making problem. Using RL, the SP can adaptively decide the retail electricity price during the on-line learning process where the uncertainty of CUs’ load demand profiles and the flexibility of wholesale electricity prices are addressed. Simulation results show that this proposed DR algorithm, can promote SP profitability, reduce energy costs for CUs, balance energy supply and demand in the electricity market, and improve the reliability of electric power systems, which can be regarded as a win-win strategy for both SP and CUs.
Solid-State-Transformers: Key Components of Future Traction and Smart Grid Systems
The efficient supply of electric power relies strongly on the selection of suitable voltage levels for different sections of the energy distribution system. When higher levels of power are required, a medium-voltage level in the tens of kilovolts range is typically selected. In accordance to current trends in energy conversion, the supply of power must fulfil several functionality requirements among which high power-quality and access to a low-voltage DC interface can be highlighted. Moreover, low energy losses, high power-density, low failure rate and low total cost of ownership remain as major research challenges. Solid-state-transformers (SSTs) comply with these functionality requirements as well as with the demanded high performance levels while directly connecting to medium-voltage. This paper reviews the implementation of SST technology for transportation and Smart-Grid applications. The envisioned architectures for locomotive systems, remotely-operated-vehicles and large scale ships, which benefit from the compactness and high performance of SST are shown. In addition, the possible arrangement of micro-grid systems comprising SST concepts for integration of renewable energy and implementation of DCmicrogrids is detailed. The different SST concepts proposed for these applications can be grouped into distinctive categories, leading to a comprehensive classification of, first, general isolated AC-AC conversion systems and later to a specific classification of SST concepts based on the different levels of modularity. Finally, a detailed review of the numerous previously reported and functional SST concepts is presented and a comparison to systems employing low-frequency transformers is given. Keywords—Converter Topology Classification, Multicell and Multilevel Converter Topologis, Medium-Frequency Isolation, Performance Evaluation.
A survey on electric vehicle transportation within smart grid system
The electrification of hybrid electric vehicle reduces the reliance of transportation on fossil fuels and reduces Green House Gas emissions. The economic and environmental benefits of the hybrid electric vehicles are greatly reshaping the modern transportation sector. The transportation electrification (TE) brings various challenges to the Smart Grid (SG), such as power quality, reliability, and control. Thus, there is a need to explore and reveal the key enabling technologies for TE. Moreover, the intermittent nature of Renewable Energy Resources (RER) based generation demands for efficient, reliable, flexible, dynamic, and distributed energy storage technologies. The Electrical Vehicles (EVs) storage battery is the promising solution in accommodating RER based generation within SG. The most efficient feature of transportation sector is Vehicle to Grid (V2G) concept that will help in storing the surplus energy and feeding back this energy to the main grid during period of high demands. The storage technology is an integral part of the SG that helps in attaining the proper utilization of RER. In this paper, our goal is to explore the TE sector and its impact on economy, reliability and eco-friendly system. We reviewed the V2G technology and their implementation challenges. We further reviewed various energy storage technologies deployed in EVs within SG, considering attention to their influence on the environment. Moreover, this paper presented a detailed overview of the on board and off board charging infrastructure and communication necessities for EV. The paper also investigated the current issues and challenges of energy storage technologies in EVs. The technical and economic benefits of storage technologies are also considered. Our analysis reviews the role of EVs in decarbonizing the atmosphere. Lastly, the survey explains the current regulation, Standard, and interfacing issues within SG.
Priority-Based Traffic Scheduling and Utility Optimization for Cognitive Radio Communication Infrastructure-Based Smart Grid
Smart grid can be visualized as an intelligent control system over sensors and communication platforms. Recently, wireless multimedia sensor networks (WMSNs) have shown its advantages for smart grid by providing rich surveillance information for grid failure detection and recovery, energy source monitoring, asset management, security, etc. On the other hand, cognitive radio (CR) networks have been identified as a key wireless technology to reduce the communication interferences and improve the bandwidth efficiency for smart grid communication. There is an essential need to use the CR communication platform to support large-size and time-sensitive multimedia delivery for future smart grid system. In this paper, we consider the heterogeneous characteristics of smart grid traffic including multimedia and propose a priority-based traffic scheduling approach for CR communication infrastructure based smart grid system according to the various traffic types of smart grid such as control commands, multimedia sensing data and meter readings. Specifically, we develop CR channel allocation and traffic scheduling schemes taking into consideration of channel switch and spectrum sensing errors, and solve a system utility optimization problem for smart grid communication system. Our solutions are demonstrated through both analyzes and simulations. This research opens a new vista of future smart grid communications.
Opportunities and Challenges of Integrating Renewable Energy in Smart Grid System
Smart grid technology is the key for an efficient use of distributed energy resources. Noting the climate change becomes an important issue the whole world is currently facing, the ever increasing price of petroleum products and the reduction in cost of renewable energy power systems, opportunities for renewable energy systems to address electricity generation seems to be increasing. However, to achieve commercialization and widespread use, an efficient energy management strategy of system needs to be addressed. Recently, the concept of smart grid has been successfully applied to the electric power systems. This paper presents the study of integrating renewable energy in smart grid system. The introductory sections provide the role of renewable energy and distributed generation in smart grid system. Subsequent sections cover the concept of smart grid as well as benefits and barrier of smart grid renewable energy system. Pricing is a significant variable in success of renewable energy promotion. Thus, it is important to gain insight to renewable energy pricing by considering unique characteristics associated with renewable energy alternatives. A review of work done in renewable smart grid systems in recent years indicates the promising potential of such research characteristics in the future. This would be useful to developers and practitioners of renewable energy systems and to policy makers.
Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid
Advanced metering infrastructure (AMI) is an important component for a smart grid system to measure, collect, store, analyze, and operate users consumption data. The need of communication and data transmission between consumers (smart meters) and utilities make AMI vulnerable to various attacks. In this paper, we focus on distributed denial of service attack in the AMI network. We introduce honeypots into the AMI network as a decoy system to detect and gather attack information. We analyze the interactions between the attackers and the defenders, and derive optimal strategies for both sides. We further prove the existence of several Bayesian-Nash equilibriums in the honeypot game. Finally, we evaluate our proposals on an AMI testbed in the smart grid, and the results show that our proposed strategy is effective in improving the efficiency of defense with the deployment of honeypots.
Smart Grid security technology
The security of the United States and the way of life of its citizens is dependant on the availability of the North American power grid. Much of the technology currently in use by the grid is outdated and in many cases unreliable. There have been three major blackouts in the past nine years. Further, the reliance on old technology leads to inefficient systems, costing the utilities and taxpayers unnecessary sums. There is virtually universal agreement that it is imperative to upgrade the electric grid to increase overall system efficiently and reliability. Such upgrades will require significant dependence on distributed intelligence and broadband communication capabilities. The access and communications capabilities require the latest in proven security technology for extremely large, wide area communications networks. This paper discusses the key components for a secure Smart Grid system.
Simulating a future smart city: An integrated land use-energy model
Designing a future smart city (FSC) that copes with the reduction of CO2 has become one of the urgent tasks of the next 20years. One promising approach to achieve FSC is to combine appropriate land use (compact city with energy efficient buildings and photovoltaic panels (PVs)), transportation (electric vehicles (EVs) and public transportation system) and energy systems (smart grid systems), because of the interaction between these elements. However, there are few models which simulate these elements in an integrated manner. This paper presents the concept of the integrated model, and shows the land use-energy part of the model created for the Tokyo metropolitan area, which is the largest Mega city in the world. Firstly, a spatially explicit land use model (urban economic model) is constructed for the study area, and the model is calibrated using existing statistical data. Secondly, possible future compact/dispersion city scenarios for the year 2050 are created using the model. Thirdly, intra-day dynamics (hourly) of electricity demand and supply from PVs, which is assumed to be installed to the roofs of all detached houses in the study area, under two urban scenarios is simulated. The obtained results suggest that [1] “compact” urban form may contribute to the reduction of electricity demand from the residential sector, but [2] PV-supply under the scenario may also be reduced because of the decreased share of detached houses. Hence in the compact city scenario, it is important to discuss the effective use of vacant areas in suburbs, which may be used for large PV installations, or be re-vegetated to mitigate urban heat island effects.
A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids
Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.
neural network power system internet of things electric vehicle data analysi renewable energy smart grid learning algorithm power grid image compression hyperspectral image matrix factorization source separation cyber-physical system energy management system sparse representation deep convolutional cloud storage blind source separation demand response blind source gradient method renewable energy system grid system dictionary learning hyperspectral datum latent semantic spectral clustering nonnegative matrix nonnegative matrix factorization hyperspectral imagery low rank image representation image inpainting public cloud matrix completion spectral datum smart grid system smart grid technology remote datum smart grid communication tensor factorization data matrix latent factor future smart grid factorization method spectral unmixing grid communication hyperspectral unmixing international system future smart smart power grid nonnegative matrice power grid system dictionary learning algorithm matrix factorization method data possession projected gradient graph regularized factorization based nonnegative tensor provable data possession system of units image inpainting method smart grid security provable datum public cloud storage matrix factorization technique projected gradient method factorization technique nonnegative tensor factorization nmf algorithm low-rank matrix factorization exemplar-based image inpainting image inpainting technique emerging smart grid matrix factorization problem multiplicative update based image inpainting regularized nonnegative matrix constrained nonnegative matrix sparse nonnegative kernel k-means clustering regularized nonnegative sparse nonnegative matrix matrix and tensor sparse nmf constrained nonnegative high-dimensional vector nmf method orthogonal nonnegative matrix graph regularized nonnegative nonnegative datum multi-way datum nonnegative tucker decomposition lee and seung weighted nonnegative matrix weighted nonnegative robust nonnegative matrix projective nonnegative matrix als algorithm robust nonnegative input data matrix projective nonnegative semantic image inpainting fast nonnegative wind power