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.
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.
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.
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.
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.
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.
Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions
Currently, the demand for electricity is increasing day by day, which necessitates upgrading of the existing power grid system. The conventional power grid has already been replaced with modern ICT-based infrastructure, which is known as smart grid (SG). In SG, smart meters generate a huge amount of data, and it is a challenging task to store, process, and analyze the data, which varies with respect to volume, velocity, and variety. The data generated in an SG system is generally stored and analyzed using cloud computing (CC), which provides real-time response for various applications. However, to handle the latency issue during the data analytics in SG, fog computing (FC) has emerged as a new technology that provides most of the computing resources in proximity of the end users. It acts as a bridge between SG and CC to fill the gap between processing power of remote data centers and smart devices in SG systems. To handle the aforementioned issues, there is a requirement to set up advanced sensors and measurement systems having communication network backbones in the upcoming fifth generation (5G). In this article, we discuss the architecture of SG in the context of FC for making the decision about energy requirements by the smart devices at the fog layer. Moreover, the communication and computing aspects are also explored in the context of 5G network infrastructure. We examine the influence of FC on response time, transmission delay, and energy management costs for delay-sensitive applications.
Smart Meter Based on Real Time Pricing
Sudden, unprecedented electric blackouts and outages come as a dampener for a system that thrives on electrical energy. Reliability and efficiency are essential features for any power grid system. The conventional power grid systems are incapable of making use of real-time information from consumer side as well as the supply end for a more efficient transmission. Smart grid system, in which tremendous research has been done and which has been improvised manifold over the past decade, helps in meeting the incessant fluctuations in power demand from a huge consumer population. Smart grid brings about automation in managing the energy requirements using a two way interactive system, i.e. its ability to fetch information from the user and supply ends and utilize it to improve the overall reliability and efficiency of the transmission lines. Incorporating smart energy meters along with smart grid could come a long way in conserving electricity apart from removing manual energy meter reading from the scene. Smart meters supply the required data for the smart grid which helps the grid in providing an automated response. The paper elucidates how smart meters help in digitally tackling the issue of energy conservation in the consumer end and the utility end.
Experimental implementation of Multi-Agent System algorithm for distributed restoration of a Smart Grid System
There have been numerous simulation works done on Grid Restoration using Multi-Agent System but almost no experimental work to ascertain the duplicity of these simulation results. This work aims to experimentally perform distributed restoration of a smart power grid system. The concept used in this research is based on the distributed and intelligent multi-agent system technology where multiple smart entities are geographically spread and if equipped with two-way communication capability these entities are able to reach goals or solutions that would have been impossible to reach with non-smart entities. The technology is implemented through the use of a six-bus experimental test bed set up using Tennessee Technological University Smart Grid Laboratory which is undergoing rapid development. The experimental results obtained align with simulation results published earlier and show that the proposed system can restore power in a timely manner without violating any constraints.
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