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Demand response and smart grids—A survey

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TLDR
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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Mining typical load profiles in buildings to support energy management in the smart city context

TL;DR: In this article, a general framework on load profiles characterisation in buildings based on the recent scientific literature is proposed, which relies on the combination of different pattern recognition and classification algorithms in order to provide a robust insight of the energy usage patterns at different levels and at different scales.
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A stepwise power tariff model with game theory based on Monte-Carlo simulation and its applications for household, agricultural, commercial and industrial consumers

TL;DR: An algorithm of demand response using stepwise power tariff with game theory (SPT-GT) to achieve the objectives in different equilibrium states on the basis of degree of information shared by categorized consumers is proposed.
Journal ArticleDOI

End user perceptions toward smart grid technology: Acceptance, adoption, risks, and trust

TL;DR: In this paper, the authors present experimental scenarios that use signal detection theory (SDT), a well-known tool in psychology research, to capture the perceptions of end users about smart grid technology.
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Multi-objective optimization of household appliance scheduling problem considering consumer preference and peak load reduction

TL;DR: An optimization model using multi-objective mixed integer linear programming considering a time-of-use electricity tariff and coordinated peak load reduction in a multiple-household environment to minimize three objectives is proposed.
References
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Journal ArticleDOI

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

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.
Journal ArticleDOI

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

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.
Journal ArticleDOI

Smart Grid Technologies: Communication Technologies and Standards

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.
Book

Renewable and Efficient Electric Power Systems

TL;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.
Journal ArticleDOI

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

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.
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