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Journal ArticleDOI

Review on Implementation and Assessment of Conservation Voltage Reduction

TLDR
In this article, the authors present an in-depth review on implementing and assessing conservation voltage reduction (CVR) for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system.
Abstract
Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies.

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Journal ArticleDOI

Load Modeling—A Review

TL;DR: A thorough survey on the academic research progress and industry practices is provided, and existing issues and new trends in load modeling are highlighted.
Journal ArticleDOI

A Review of Power System Flexibility With High Penetration of Renewables

TL;DR: This paper surveys the literature on the concepts of power system flexibility, indices of flexibility, and implementation of the concept of flexibility in power system security, and highlights the effect of renewables on these aspects, and suggests new research directions.
Journal ArticleDOI

The Smart Transformer: A solid-state transformer tailored to provide ancillary services to the distribution grid

TL;DR: In this article, the authors proposed a design procedure for the smart transformer architecture, which is composed of at least two power stages, each of which provides ancillary services to the distribution and transmission grids to optimize their performance.
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Power Quality Concerns in Implementing Smart Distribution-Grid Applications

TL;DR: This paper maps the expected and possible adverse consequences for power quality of introducing several smart distribution-grid technologies and applications and recommends recommendations based on the mapping.
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MPC-Based Voltage/Var Optimization for Distribution Circuits With Distributed Generators and Exponential Load Models

TL;DR: The MPC-based VVO problem is formulated as a mixed-integer nonlinear program with reduced scenarios and the exponential load model is used to capture the various load behaviors in this paper.
References
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Journal ArticleDOI

A New Heuristic Optimization Algorithm: Harmony Search

TL;DR: A new heuristic algorithm, mimicking the improvisation of music players, has been developed and named Harmony Search (HS), which is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
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Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey

TL;DR: New trends in power electronics for the integration of wind and photovoltaic (PV) power generators are presented and a review of the appropriate storage-system technology used for the Integration of intermittent renewable energy sources is introduced.
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Neural networks for short-term load forecasting: a review and evaluation

TL;DR: This review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting, and critically evaluating the ways in which the NNs proposed in these papers were designed and tested.
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Electric load forecasting using an artificial neural network

TL;DR: In this article, an artificial neural network (ANN) approach is presented for electric load forecasting, which is used to learn the relationship among past, current and future temperatures and loads.
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Load forecasting using support vector Machines: a study on EUNITE competition 2001

TL;DR: How SVM, a new learning technique, is successfully applied to load forecasting is discussed in detail and some important conclusions are that temperature might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.
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