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Journal ArticleDOI

Demand response and smart grids—A survey

01 Feb 2014-Renewable & Sustainable Energy Reviews (Pergamon)-Vol. 30, pp 461-478

Abstract: The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.
Topics: Smart grid (69%), Demand response (64%), Smart system (60%), Peak demand (57%), Grid (54%)
Citations
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Journal ArticleDOI
TL;DR: A holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution is proposed.
Abstract: Although Internet of Things (IoT) brings significant advantages over traditional communication technologies for smart grid and smart home applications, these implementations are still very rare. Relying on a comprehensive literature review, this paper aims to contribute towards narrowing the gap between the existing state-of-the-art smart home applications and the prospect of their integration into an IoT enabled environment. We propose a holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution. We identify a smart home management model for the proposed framework and the main tasks that should be performed at each level. We additionally discuss practical design challenges with emphasis on data processing, as well as smart home communication protocols and their interoperability. We believe that the holistic framework ascertained in this paper can be used as a solid base for the future developers of Internet of Things based smart home solutions.

737 citations


Journal ArticleDOI
TL;DR: This paper aims to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time ofFlight (RTOF), and received signal strength (RSS) based on technologies that have been proposed in the literature.
Abstract: Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

692 citations


Journal ArticleDOI
TL;DR: This paper provides a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program, and presents various optimization models for the optimal control of the DR strategies that have been proposed so far.
Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

677 citations


Cites background from "Demand response and smart grids—A s..."

  • ...Recently, a survey on DR programs is presented in [29], where authors present the enabling technologies and systems, such as smart meters, energy controllers, and communication systems that are required for the application of DR in smart grids....

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  • ...Other surveys on DR can be found in the literature [26]–[29]....

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Journal ArticleDOI
TL;DR: This survey comprehensively explores the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns and outlines the potential challenges and future research directions in the context of demand response.
Abstract: The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

617 citations


Cites background from "Demand response and smart grids—A s..."

  • ...It can be defined as the rescheduling of the users’ energy usage patterns in response to the variance of the power utility’s incentive or electricity price, which is designed to reduce the demand at peak time periods or during system contingencies [8]–[10]....

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Journal ArticleDOI
Abstract: Electrifying transportation is a promising approach to alleviate the climate change issue. The adoption of electric vehicle into market has introduced significant impacts on various fields, especially the power grid. Various policies have been implemented to foster the electric vehicle deployment and the increasing trend of electric vehicle adoption in the recent years has been satisfying. The continual development of electric vehicle power train, battery and charger technologies have further improved the electric vehicle technologies for wider uptake. Despite the environmental and economical benefits, electric vehicles charging introduce negative impacts on the existing network operation. Appropriate charging management strategies can be implemented to cater for this issue. Furthermore, electric vehicle integration in the smart grid can bring many potential opportunities, especially from the perspective of vehicle-to-grid technology and as the solution for the renewable energy intermittency issue. This paper reviews the latest development in electric vehicle technologies, impacts of electric vehicle roll out and opportunities brought by electric vehicle deployment.

444 citations


References
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Journal ArticleDOI
TL;DR: This paper presents an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid.
Abstract: Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.

2,506 citations


Journal ArticleDOI
Peter Palensky1, Dietmar Dietrich2Institutions (2)
TL;DR: An overview and a taxonomy for DSM is given, the various types of DSM are analyzed, and an outlook on the latest demonstration projects in this domain is given.
Abstract: Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

2,338 citations


Journal ArticleDOI
Vehbi Cagri Gungor1, Dilan Sahin1, Taskin Kocak1, Salih Ergut2  +3 moreInstitutions (4)
TL;DR: The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field.
Abstract: For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

2,100 citations


Book
Gilbert M. Masters1Institutions (1)
01 Jan 2004-
Abstract: Preface.1 Basic Electric and Magnetic Circuits.1.1 Introduction to Electric Circuits.1.2 Definitions of Key Electrical Quantities.1.3 Idealized Voltage and Current Sources.1.4 Electrical Resistance.1.5 Capacitance.1.6 Magnetic Circuits.1.7 Inductance.1.8 Transformers.2 Fundamentals of Electric Power.2.1 Effective Values of Voltage and Current.2.2 Idealized Components Subjected to Sinusoidal Voltages.2.3 Power Factor.2.4 The Power Triangle and Power Factor Correction.2.5 Three-Wire, Single-Phase Residential Wiring.2.6 Three-Phase Systems.2.7 Power Supplies.2.8 Power Quality.3 The Electric Power Industry.3.1 The Early Pioneers: Edison, Westinghouse, and Insull.3.2 The Electric Utility Industry Today.3.3 Polyphase Synchronous Generators.3.4 Carnot Efficiency for Heat Engines.3.5 Steam-Cycle Power Plants.3.6 Combustion Gas Turbines.3.7 Combined-Cycle Power Plants.3.8 Gas Turbines and Combined-Cycle Cogeneration.3.9 Baseload, Intermediate and Peaking Power Plants.3.10 Transmission and Distribution.3.11 The Regulatory Side of Electric Power.3.12 The Emergence of Competitive Markets.4 Distributed Generation.4.1 Electricity Generation in Transition.4.2 Distributed Generation with Fossil Fuels.4.3 Concentrating Solar Power (CSP) Technologies.4.4 Biomass for Electricity.4.5 Micro-Hydropower Systems.4.6 Fuel Cells.4.6.7 Electrical Characteristics of Real Fuel Cells.4.6.8 Types of Fuel Cells.4.6.9 Hydrogen Production.5 Economics of Distributed Resources.5.1 Distributed Resources (DR).5.2 Electric Utility Rate Structures.5.3 Energy Economics.5.4 Energy Conservation Supply Curves.5.5 Combined Heat and Power (CHP).5.6 Cooling, Heating, and Cogeneration.5.7 Distributed Benefits.5.8 Integrated Resource Planning (IRP) and Demand-Side Management (DSM).6 Wind Power Systems.6.1 Historical Development of Wind Power.6.2 Types of Wind Turbines.6.3 Power in the Wind.6.4 Impact of Tower Height.6.5 Maximum Rotor Efficiency.6.6 Wind Turbine Generators.6.7 Speed Control for Maximum Power.6.8 Average Power in the Wind.6.9 Simple Estimates of Wind Turbine Energy.6.10 Specific Wind Turbine Performance Calculations.6.11 Wind Turbine Economics.7 The Solar Resource.7.1 The Solar Spectrum.7.2 The Earth's Orbit.7.3 Altitude Angle of the Sun at Solar Noon.7.4 Solar Position at any Time of Day.7.5 Sun Path Diagrams for Shading Analysis.7.6 Solar Time and Civil (Clock) Time.7.7 Sunrise and Sunset.7.8 Clear Sky Direct-Beam Radiation.7.9 Total Clear Sky Insolation on a Collecting Surface.7.10 Monthly Clear-Sky Insolation.7.11 Solar Radiation Measurements.7.12 Average Monthly Insolation.8 Photovoltaic Materials and Electrical Characteristics.8.1 Introduction.8.2 Basic Semiconductor Physics.8.3 A Generic Photovoltaic Cell.8.4 From Cells to Modules to Arrays.8.5 The PV I -V Curve Under Standard Test Conditions (STC).8.6 Impacts of Temperature and Insolation on I -V Curves.8.7 Shading impacts on I-V curves.8.8 Crystalline Silicon Technologies.8.9 Thin-Film Photovoltaics.9 Photovoltaic Systems.9.1 Introduction to the Major Photovoltaic System Types.9.2 Current-Voltage Curves for Loads.9.3 Grid-Connected Systems.9.4 Grid-Connected PV System Economics.9.5 Stand-Alone PV Systems.9.6 PV-Powered Water Pumping.APPENDIX A: Useful Conversion Factors.APPENDIX B: Sun-Path Diagrams.APPENDIX C: Hourly Clear-Sky Insolation Tables.APPENDIX D: Monthly Clear-Sky Insolation Tables.APPENDIX E: Solar Insolation Tables byCity.APPENDIX F: Maps of Solar Insolation.Index.

1,821 citations


Journal ArticleDOI
TL;DR: Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios.
Abstract: Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

1,722 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20227
2021173
2020207
2019270
2018285
2017256