Showing papers on "Smart grid published in 2017"

Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

Abstract: We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party A localized P 2P E lectricity T rading system with CO nsortium blockchai N (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading Security analysis shows that our proposed PETCON improves transaction security and privacy protection Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs

Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

Abstract: According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy.

Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism

Abstract: Application of computing and communications intelligence effectively improves the quality of monitoring and control of smart grids However, the dependence on information technology also increases vulnerability to malicious attacks False data injection (FDI), that attack on the integrity of data, is emerging as a severe threat to the supervisory control and data acquisition system In this paper, we exploit deep learning techniques to recognize the behavior features of FDI attacks with the historical measurement data and employ the captured features to detect the FDI attacks in real-time By doing so, our proposed detection mechanism effectively relaxes the assumptions on the potential attack scenarios and achieves high accuracy Furthermore, we propose an optimization model to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft We illustrate the performance of the proposed strategy through the simulation by using IEEE 118-bus test system We also evaluate the scalability of our proposed detection mechanism by using IEEE 300-bus test system

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Abstract: Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

DC Microgrid Technology: System Architectures, AC Grid Interfaces, Grounding Schemes, Power Quality, Communication Networks, Applications, and Standardizations Aspects

Abstract: To meet the fast-growing energy demand and, at the same time, tackle environmental concerns resulting from conventional energy sources, renewable energy sources are getting integrated in power networks to ensure reliable and affordable energy for the public and industrial sectors However, the integration of renewable energy in the ageing electrical grids can result in new risks/challenges, such as security of supply, base load energy capacity, seasonal effects, and so on Recent research and development in microgrids have proved that microgrids, which are fueled by renewable energy sources and managed by smart grids (use of smart sensors and smart energy management system), can offer higher reliability and more efficient energy systems in a cost-effective manner Further improvement in the reliability and efficiency of electrical grids can be achieved by utilizing dc distribution in microgrid systems DC microgrid is an attractive technology in the modern electrical grid system because of its natural interface with renewable energy sources, electric loads, and energy storage systems In the recent past, an increase in research work has been observed in the area of dc microgrid, which brings this technology closer to practical implementation This paper presents the state-of-the-art dc microgrid technology that covers ac interfaces, architectures, possible grounding schemes, power quality issues, and communication systems The advantages of dc grids can be harvested in many applications to improve their reliability and efficiency This paper also discusses benefits and challenges of using dc grid systems in several applications This paper highlights the urgent need of standardizations for dc microgrid technology and presents recent updates in this area

A survey of network lifetime maximization techniques in wireless sensor networks

Abstract: Emerging technologies, such as the Internet of Things, smart applications, smart grids, and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints, and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems

Abstract: The Internet of Things (IoT) is entering the daily operation of many industries; applications include but are not limited to smart cities, smart grids, smart homes, physical security, e-health, asset management, and logistics. For example, the concept of smart cities is emerging in multiple continents, where enhanced street lighting controls, infrastructure monitoring, public safety and surveillance, physical security, gunshot detection, meter reading, and transportation analysis and optimization systems are being deployed on a city-wide scale. A related and cost-effective user-level IoT application is the support of IoT-enabled smart buildings. Commercial space has substantial requirements in terms of comfort, usability, security, and energy management. IoT-based systems can support these requirements in an organic manner. In particular, power over Ethernet, as part of an IoT-based solution, offers disruptive opportunities in revolutionizing the in-building connectivity of a large swath of devices. However, a number of deployment-limiting issues currently impact the scope of IoT utilization, including lack of comprehensive end-to-end standards, fragmented cybersecurity solutions, and a relative dearth of fully-developed vertical applications. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the smart building arena.

Classification and assessment of energy storage systems

Abstract: The increasing electricity generation from renewable resources has side effects on power grid systems, because of daily and seasonally intermittent nature of these sources. Additionally, there are fluctuations in the electricity demand during the day, so energy storage system (ESS) can play a vital role to compensate these troubles and seems to be a crucial part of smart grids in the future. This study comparatively presents a widespread and comprehensive description of energy storage systems with detailed classification, features, advantages, environmental impacts, and implementation possibilities with application variations.

Battling the Extreme: A Study on the Power System Resilience

Abstract: The electricity infrastructure is a critical lifeline system and of utmost importance to our daily lives. Power system resilience characterizes the ability to resist, adapt to, and timely recover from disruptions. The resilient power system is intended to cope with low probability, high risk extreme events including extreme natural disasters and man-made attacks. With an increasing awareness of such threats, the resilience of power systems has become a top priority for many countries. Facing the pressing urgency for resilience studies, the objective of this paper is to investigate the resilience of power systems. It summarizes practices taken by governments, utilities, and researchers to increase power system resilience. Based on a thorough review on the existing metrics system and evaluation methodologies, we present the concept, metrics, and a quantitative framework for power system resilience evaluation. Then, system hardening strategies and smart grid technologies as means to increase system resilience are discussed, with an emphasis on the new technologies such as topology reconfiguration, microgrids, and distribution automation; to illustrate how to increase system resilience against extreme events, we propose a load restoration framework based on smart distribution technology. The proposed method is applied on two test systems to validify its effectiveness. In the end, challenges to the power system resilience are discussed, including extreme event modeling, practical barriers, interdependence with other critical infrastructures, etc.

Distributed and Decentralized Voltage Control of Smart Distribution Networks: Models, Methods, and Future Research

Abstract: The future grid is evolving into a smart distribution network that integrates multiple distributed energy resources ensuring at the same time reliable operation and increased power quality. In recent years, many research papers have addressed the voltage violation problems that arise from the high penetration of distributed generation. In view of the transition to active network management and the increase in the quantity of collected data, distributed control schemes have been proposed that use pervasive communications to deal with the complexity of smart grid. This paper reviews the recent publications on distributed and decentralized voltage control of smart distribution networks, summarizes their control models, and classifies the solution methodologies. Moreover, it comments on issues that should be addressed in the future and the perspectives of industry applications.

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Abstract: Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid

Abstract: Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.

Enhancing smart grid with microgrids: Challenges and opportunities

Abstract: The modern electric power systems are going through a revolutionary change because of increasing demand of electric power worldwide, developing political pressure and public awareness of reducing carbon emission, incorporating large scale renewable power penetration, and blending information and communication technologies with power system operation. These issues initiated in establishing microgrid concept which has gone through major development and changes in last decade, and recently got a boost in its growth after being blessed by smart grid technologies. The objective of this paper is to presents a detailed technical overview of microgrid and smart grid in light of present development and future trend. First, it discusses microgrid architecture and functions. Then, smart features are added to the microgrid to demonstrate the recent architecture of smart grid. Finally, existing technical challenges, communication features, policies and regulation, etc. are discussed from where the future smart grid architecture can be visualized.

Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery

Abstract: This paper proposes an energy management and control system for laboratory scale microgrid based on hybrid energy resources such as wind, solar, and battery. Power converters and control algorithms have been used along with dedicated energy resources for the efficient operation of the microgrid. The control algorithms are developed to provide power compatibility and energy management between different resources in the microgrid. It provides stable operation of the control in all microgrid subsystems under various power generation and load conditions. The proposed microgrid, based on hybrid energy resources, operates in autonomous mode and has an open architecture platform for testing multiple different control configurations. A real-time control system has been used to operate and validate the hybrid resources in the microgrid experimentally. The proposed laboratory scale microgrid can be used as a benchmark for future research in smart grid applications.

A Survey on Smart Grid Cyber-Physical System Testbeds

Abstract: An increasing interest is emerging on the development of smart grid cyber-physical system testbeds. As new communication and information technologies emerge, innovative cyber-physical system testbeds need to leverage realistic and scalable platforms. Indeed, the interdisciplinary structure of the smart grid concept compels heterogeneous testbeds with different capabilities. There is a significant need to evaluate new concepts and vulnerabilities as opposed to counting on solely simulation studies especially using hardware-in-the-loop test platforms. In this paper, we present a comprehensive survey on cyber-physical smart grid testbeds aiming to provide a taxonomy and insightful guidelines for the development as well as to identify the key features and design decisions while developing future smart grid testbeds. First, this survey provides a four step taxonomy based on smart grid domains, research goals, test platforms, and communication infrastructure. Then, we introduce an overview with a detailed discussion and an evaluation on existing testbeds from the literature. Finally, we conclude this paper with a look on future trends and developments in cyber-physical smart grid testbed research.

On heat pumps in smart grids: A review

Abstract: This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literatu ...

Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies

Abstract: Power systems have typically been designed to be reliable to expected, low-impact high-frequency outages. In contrast, extreme events, driven for instance by extreme weather and natural disasters, happen with low-probability, but can have a high impact. The need for power systems, possibly the most critical infrastructures in the world, to become resilient to such events is becoming compelling. However, there is still little clarity as to this relatively new concept. On these premises, this paper provides an introduction to the fundamental concepts of power systems resilience and to the use of hardening and smart operational strategies to improve it. More specifically, first the resilience trapezoid is introduced as visual tool to reflect the behavior of a power system during a catastrophic event. Building on this, the key resilience features that a power system should boast are then defined, along with a discussion on different possible hardening and smart, operational resilience enhancement strategies. Further, the so-called $\Phi \Lambda {E}\Pi $ resilience assessment framework is presented, which includes a set of resilience metrics capable of modeling and quantifying the resilience performance of a power system subject to catastrophic events. A case study application with a 29-bus test version of the Great Britain transmission network is carried out to investigate the impacts of extreme windstorms. The effects of different hardening and smart resilience enhancement strategies are also explored, thus demonstrating the practicality of the different concepts presented.

Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security

Abstract: Blockchain may help solve several complex problems related to securing the integrity and trustworthiness of rapid, distributed, complex energy transactions and data exchanges. In a move towards grid resilience, blockchain commoditizes trust and enables automated smart contracts to support auditable multiparty transactions based on predefined rules between distributed energy providers and customers. Blockchain based smart contracts also help remove the need to interact with third-parties, facilitating the adoption and monetization of distributed energy transactions and exchanges, both energy flows as well as financial transactions. This may help reduce transactive energy costs and increase the security and sustainability of distributed energy resource (DER) integration, helping to remove barriers to a more decentralized and resilient power grid. This paper explores the application of blockchain and smart contracts to improve smart grid cyber resiliency and secure transactive energy applications.

Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Abstract: Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers’ energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home’s power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RA ${k}$ EL), and a bespoke multi-label classification algorithm (ML ${k}$ NN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain

Abstract: The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach.

A cascaded life cycle: reuse of electric vehicle lithium-ion battery packs in energy storage systems

Abstract: Purpose Lithium-ion (Li-ion) battery packs recovered from end-of-life electric vehicles (EV) present potential technological, economic and environmental opportunities for improving energy systems and material efficiency. Battery packs can be reused in stationary applications as part of a “smart grid”, for example to provide energy storage systems (ESS) for load leveling, residential or commercial power. Previous work on EV battery reuse has demonstrated technical viability and shown energy efficiency benefits in energy storage systems modeled under commercial scenarios. The current analysis performs a life cycle assessment (LCA) study on a Li-ion battery pack used in an EV and then reused in a stationary ESS.

Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication

Abstract: This paper investigates the problem of distributed energy management for both generation and demand side in smart grid. Different from existing works, we formulate a social welfare maximization problem for a more practical scenario by taking transmission losses into account. The formulated problem is non-convex due to the non-convexity of the power balance equality constraint caused by the transmission losses. To solve the problem, we first transform the equality constraint into an inequality constraint and obtain a new convex optimization problem. We then derive a sufficient condition to guarantee that the new problem has the same solution as the original one. Because of the coupling in the constraint, Lagrange duality method is adopted to decompose the problem. Considering the general communication topology among generators and demands, i.e., directed connected topology, we design a consensus-based algorithm to solve the problem in a distributed way. We also prove the convergence and optimality of the proposed algorithm, under which the social welfare maximization is achieved. Extensive simulations validate the theoretical results and demonstrate the effectiveness of the proposed algorithm.

A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

Abstract: Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in high-dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis and compares the findings with RMT predictions to conduct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this paper is the first attempt to apply big data technology into smart grids.

A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station

Abstract: Demand response (DR) is a vital part in smart grid to restore the balance between electricity demand and supply. In this paper, a real-time charging scheme is proposed to coordinate the electric vehicle (EV) charging and accommodate DR programs in the parking station. The charging scheduling is formulated as a binary optimization problem because the on–off strategy is leveraged to achieve faster charging speed of EV. Given the computationally expensive nature of exhaustive search in solving the optimal solution of binary optimization problem, a convex relaxation method is developed as an alternative to compute the near-optimal charging schedules. Extensive simulation results show that the proposed work is able to satisfy EV charging demand while accommodating both types of DR programs in the parking station. The proposed work is also able to simultaneously maximize the number of EVs for charging and minimize the monetary expenses.

Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Smart Grid

Abstract: 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.