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[Online] Smart Grid

Guest Editors-in-Chief 
Yu, Yixin, Tianjin University, China
Jenkins, Nick, Cardiff University, UK
 
Executive Associate Editors
Wang, Chengshan, Tianjin University, China
Wu, Jianzhong, Cardiff University, UK
 
Consultant
Han, Yingduo, Tsinghua University, China
 
Members
Deconinck, Geert, Catholic University of Leuven, Belgium
Hatziargyriou, Nikos, National Technical University of Athens, Greece
Shahidehpour, Mohammad, Illinois Institute of Technology, USA
Wu, Felix, University of California, Berkeley, USA
Yokoyama, Ryuichi, Waseda University, Japan 
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Basic Ideas of the Smart Grid
Yixin Yu, Yanli Liu, Chao Qin
Engineering    2015, 1 (4): 405-408.   https://doi.org/10.15302/J-ENG-2015120
Abstract   HTML   PDF (434KB)
 
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An Overview of the Smart Grid in Great Britain
Nick Jenkins,Chao Long,Jianzhong Wu
Engineering    2015, 1 (4): 413-421.   https://doi.org/10.15302/J-ENG-2015112
Abstract   HTML   PDF (1468KB)

This paper presents an overview of the current status of the development of the smart grid in Great Britain (GB). The definition, policy and technical drivers, incentive mechanisms, technological focus, and the industry's progress in developing the smart grid are described. In particular, the Low Carbon Networks Fund and Electricity Network Innovation Competition projects, together with the rollout of smart metering, are detailed. A more observable, controllable, automated, and integrated electricity network will be supported by these investments in conjunction with smart meter installation. It is found that the focus has mainly been on distribution networks as well as on real-time flows of information and interaction between suppliers and consumers facilitated by improved information and communications technology, active power flow management, demand management, and energy storage. The learning from the GB smart grid initiatives will provide valuable guidelines for future smart grid development in GB and other countries.

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Smart Grids with Intelligent Periphery: An Architecture for the Energy Internet
Felix F. Wu,Pravin P. Varaiya,Ron S. Y. Hui
Engineering    2015, 1 (4): 436-446.   https://doi.org/10.15302/J-ENG-2015111
Abstract   HTML   PDF (1067KB)

A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energy-sharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system (EMS)-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of ① dispatch, ② smoothing, and ③ mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.

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Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies
David P. Chassin,Sahand Behboodi,Curran Crawford,Ned Djilali
Engineering    2015, 1 (4): 422-435.   https://doi.org/10.15302/J-ENG-2015109
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This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnection-scale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

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Smart Grid Wide-Area Transmission System Visualization
Thomas J. Overbye,James Weber
Engineering    2015, 1 (4): 466-474.   https://doi.org/10.15302/J-ENG-2015098
Abstract   HTML   PDF (10310KB)

The installation of vast quantities of additional new sensing and communication equipment, in conjunction with building the computing infrastructure to store and manage data gathered by this equipment, has been the first step in the creation of what is generically referred to as the “smart grid” for the electric transmission system. With this enormous capital investment in equipment having been made, attention is now focused on developing methods to analyze and visualize this large data set. The most direct use of this large set of new data will be in data visualization. This paper presents a survey of some visualization techniques that have been deployed by the electric power industry for visualizing data over the past several years. These techniques include pie charts, animation, contouring, time-varying graphs, geographic-based displays, image blending, and data aggregation techniques. The paper then emphasizes a newer concept of using word-sized graphics called sparklines as an extremely effective method of showing large amounts of time-varying data.

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Combining Market-Based Control with Distribution Grid Constraints when Coordinating Electric Vehicle Charging
Geert Deconinck,Klaas De Craemer,Bert Claessens
Engineering    2015, 1 (4): 453-465.   https://doi.org/10.15302/J-ENG-2015095
Abstract   HTML   PDF (2785KB)

The charging of electric vehicles (EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid (e.g., under-voltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with event-driven control at the operational level. Different case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.

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An Approach for Cost-Efficient Grid Integration of Distributed Renewable Energy Sources
Till Luhmann,Enno Wieben,Riccardo Treydel,Michael Stadler,Thomas Kumm
Engineering    2015, 1 (4): 447-452.   https://doi.org/10.15302/J-ENG-2015099
Abstract   HTML   PDF (1902KB)

We describe a specific approach to capacity management for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and first results of a field test for validating the approach in a rural distribution grid in northern Germany.

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