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Saifur Rahman

Bio: Saifur Rahman is an academic researcher from Virginia Tech. The author has contributed to research in topics: Smart grid & Demand response. The author has an hindex of 53, co-authored 257 publications receiving 14018 citations. Previous affiliations of Saifur Rahman include University of Virginia & Missouri University of Science and Technology.


Papers
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Proceedings ArticleDOI
15 Mar 2009
TL;DR: Simulation results indicate that the proposed multi-agent system can facilitate the seamless transition from grid connected to an island mode when upstream outages are detected, which denotes the capability of a multi- agent system as a technology for managing the microgrid operation.
Abstract: The objective of this paper is to discuss the design and implementation of a multi-agent system that provides intelligence to a distributed smart grid — a smart grid located at a distribution level. A multi-agent application development will be discussed that involves agent specification, application analysis, application design and application realization. The message exchange in the proposed multi-agent system is designed to be compatible with an IP-based network (IP = Internet Protocol) which is based on the IEEE standard on Foundation for Intelligent Physical Agent (FIPA). The paper demonstrates the use of multi-agent systems to control a distributed smart grid in a simulated environment. The simulation results indicate that the proposed multi-agent system can facilitate the seamless transition from grid connected to an island mode when upstream outages are detected. This denotes the capability of a multi-agent system as a technology for managing the microgrid operation.

715 citations

01 Jan 1989
TL;DR: A comparative evaluation of five short-term load forecasting techniques is presented and the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy.

654 citations

Journal ArticleDOI
TL;DR: In this article, a comparative evaluation of five short-term load forecasting techniques is presented, which are: 1. Multiple Linear Regression, 2. Stochastic Time Series, 3. General Exponential Smoothing, 4. State Space Method and 5. Knowledge-Based Approach.
Abstract: Load forecast has been a central and an integral process in the planning and operation of electric utilities. Many techniques and approaches have been investigated to tackle this problem in the last two decades. These are often different in nature and apply different engineering considerations and economic analyses. In this paper a comparative evaluation of five short-term load forecasting techniques is presented. These techniques are: 1. Multiple Linear Regression; 2. Stochastic Time Series; 3. General Exponential Smoothing; 4. State Space Method; and 5. Knowledge-Based Approach. The authors have applied these algorithms to obtain hourly load forecasts (for up to 24 hours) during the winter and summer peaking seasons. Thus the five forecasting methodologies have been applied to the same database and their performances are directly compared. The forecast error analyses are provided in Tables 1 and 2 for the winter and summer days respectively. As these results are based on forecasts of two single days, these should be used for comparative purposes only. Some interesting observations are made about the results presented in Tables 1 and 2. For example, for the peak summer day the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy. During the peak summer day the temperature profile was typical whereas during the peak winter day the profile was unseasonal.

648 citations

Journal ArticleDOI
TL;DR: An intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis is presented and a simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level.
Abstract: A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.

590 citations

Journal ArticleDOI
TL;DR: In this article, a survey of papers and reports that address various aspects of economic dispatch is presented, including optimal power flow, economic dispatch in relation to AGC, dynamic dispatch, and economic dispatch with nonconventional generation sources.
Abstract: A survey is presented of papers and reports that address various aspects of economic dispatch. The time period considered is 1977-88. Four related areas of economic dispatch are identified and papers published in the general areas of economic dispatch are classified into these. These areas are: optimal power flow, economic dispatch in relation to AGC, dynamic dispatch, and economic dispatch with nonconventional generation sources. >

587 citations


Cited by
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Journal ArticleDOI
TL;DR: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed in this paper, and at least 19 distinct methods have been introduced in the literature, with many variations on implementation.
Abstract: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.

5,022 citations

Journal ArticleDOI
TL;DR: Nanocrystals (NCs) discussed in this Review are tiny crystals of metals, semiconductors, and magnetic material consisting of hundreds to a few thousand atoms each that are among the hottest research topics of the last decades.
Abstract: Nanocrystals (NCs) discussed in this Review are tiny crystals of metals, semiconductors, and magnetic material consisting of hundreds to a few thousand atoms each. Their size ranges from 2-3 to about 20 nm. What is special about this size regime that placed NCs among the hottest research topics of the last decades? The quantum mechanical coupling * To whom correspondence should be addressed. E-mail: dvtalapin@uchicago.edu. † The University of Chicago. ‡ Argonne National Lab. Chem. Rev. 2010, 110, 389–458 389

3,720 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.

3,680 citations

Journal ArticleDOI
Shouheng Sun1, Hao Zeng1, David B. Robinson1, Simone Raoux1, Philip M. Rice1, Shan X. Wang1, Guanxiong Li1 
TL;DR: As-synthesized iron oxide nanoparticles have a cubic spinel structure as characterized by HRTEM, SAED, and XRD and can be transformed into hydrophilic ones by adding bipolar surfactants, and aqueous nanoparticle dispersion is readily made.
Abstract: High-temperature solution phase reaction of iron(III) acetylacetonate, Fe(acac)3, with 1,2-hexadecanediol in the presence of oleic acid and oleylamine leads to monodisperse magnetite (Fe3O4) nanoparticles. Similarly, reaction of Fe(acac)3 and Co(acac)2 or Mn(acac)2 with the same diol results in monodisperse CoFe2O4 or MnFe2O4 nanoparticles. Particle diameter can be tuned from 3 to 20 nm by varying reaction conditions or by seed-mediated growth. The as-synthesized iron oxide nanoparticles have a cubic spinel structure as characterized by HRTEM, SAED, and XRD. Further, Fe3O4 can be oxidized to Fe2O3, as evidenced by XRD, NEXAFS spectroscopy, and SQUID magnetometry. The hydrophobic nanoparticles can be transformed into hydrophilic ones by adding bipolar surfactants, and aqueous nanoparticle dispersion is readily made. These iron oxide nanoparticles and their dispersions in various media have great potential in magnetic nanodevice and biomagnetic applications.

3,244 citations

Journal ArticleDOI
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