Demand response and smart grids—A survey
TL;DR: In this article, a survey of demand response potentials and benefits in smart grids is presented, with reference to real industrial case studies and research projects, such as smart meters, energy controllers, communication systems, etc.
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.
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TL;DR: Simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden.
Abstract: This paper proposes a real-time price (RTP)-based demand-response (DR) algorithm for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the energy management center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A one-leader, ${N}$ -follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help in selecting the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden.
282 citations
Cites background from "Demand response and smart grids—A s..."
...It induces users to consume less energy during periods of high wholesale market prices or when the system reliability is jeopardized [1]....
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...Price-based DR programs are implemented based on contractual arrangements between users and the service provider, according to which the electricity prices vary over time to motivate users to adjust their energy consumption [1]....
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...SMART GRIDS are envisioned as novel power-grid systems that incorporate a smart metering infrastructure capable of sensing and measuring the power consumption of users by integrating advanced information and communication technologies [1]–[3]....
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TL;DR: It is argued that a mature adoption of IoT technologies in the building industry is not yet realized and, therefore, calls for more attention from researchers in the relevant fields from the application perspective.
267 citations
TL;DR: A review of common existing systems utilized in buildings for occupancy detection and experimental results from the performance evaluation of chair sensors in an office building for providing fine-grained occupancy information for demand-driven control applications are presented.
262 citations
19 Jun 2017
TL;DR: Experimental results showed that the CNN outperformed SVR while producing comparable results to the ANN and deep learning methodologies, and further testing is required to compare the performances of different deep learning architectures in load forecasting.
Abstract: Smartgrids of the future promise unprecedented flexibility in energy management. Therefore, accurate predictions/forecasts of energy demands (loads) at individual site and aggregate level of the grid is crucial. Despite extensive research, load forecasting remains to be a difficult problem. This paper presents a load forecasting methodology based on deep learning. Specifically, the work presented in this paper investigates the effectiveness of using Convolutional Neural Networks (CNN) for performing energy load forecasting at individual building level. The presented methodology uses convolutions on historical loads. The output from the convolutional operation is fed to fully connected layers together with other pertinent information. The presented methodology was implemented on a benchmark data set of electricity consumption for a single residential customer. Results obtained from the CNN were compared against results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S), Factored Restricted Boltzmann Machines (FCRBM), “shallow” Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the same dataset. Experimental results showed that the CNN outperformed SVR while producing comparable results to the ANN and deep learning methodologies. Further testing is required to compare the performances of different deep learning architectures in load forecasting.
252 citations
Cites background or methods from "Demand response and smart grids—A s..."
...7 [4, 4, 8] [[ 10 ], [ 5 ],[ 2 ]] [[ 1 ], [ 1 ], [ 2 ]] [10]...
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...Further, the grid controller should have the capability of efficiently handing the distributed generation from various sources such as renewables [4]....
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...6 [4, 8, 8] [[ 10 ],[ 10 ],[ 6 ]] [[ 2 ], [ 2 ], [ 2 ]] [20,20]...
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...Readers are referred to [17], [18] and [4] for comprehensive surveys of different techniques used for load forecasting....
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...8 [4, 8, 16, 16, 32] [[ 3 ], [ 3 ], [ 3 ], [ 3 ], [ 3 ]] [[ 2 ], [ 2 ], [ 2 ], [ 2 ], [ 2 ]] [20,20]...
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TL;DR: An overview of AI methods utilised for DR applications is provided, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects, where AI methods have been used for energy DR.
Abstract: Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.
251 citations
Cites background from "Demand response and smart grids—A s..."
...In a more general setting, Siano [8] investigated the potential benefits of DR in smart grids, along with smart technologies, control, monitoring and communication systems, while Haider et al. [9] focused on the developments in DR systems, load scheduling techniques and communication technologies for DR. O’Connell et al. [10] examined the long-term and less intuitive impacts of DR, such as its effect on electricity market prices and its impact on consumers....
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...There are numerous types of DR programmes, and their most frequently used classification is based on which party initiates the demand reduction [8]....
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...In a more general setting, Siano [8] investigated the potential benefits of DR in smart grids, along with smart technologies, control, monitoring and communication systems, while Haider et al....
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References
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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.
2,715 citations
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.
2,647 citations
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.
2,331 citations
Book•
01 Jan 2004TL;DR: In this article, the authors present an overview of the early history of the electric power industry, including the early pioneers of the electrical power industry and the development of the modern electric power system.
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.
1,884 citations
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.
1,782 citations