Load Reduction, Demand Response and Energy Efficient Technologies and Strategies
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Electric Vehicle Charging Impact on Load Profile
Electric Vehicle Charging Impact on Load Profile PIA GRAHN Licentiate Thesis Stockholm, Sweden 2013 School of Electrical Engineerging TRITA-EE 2012:065 Royal Institute of Technology ISSN 1653-5146 SE-100 44 Stockholm ISBN 978-91-7501-592-7 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av Teknologie licentiatexamen i elektrotekniska system torsdagen den 17 januari 2013 klockan 10.00 i i sal E3, Kungliga Tekniska Högskolan, Osquars backe 14, Stockholm. © Pia Grahn, January 2013 Tryck: Eprint AB 2012 iii Abstract One barrier to sustainable development is considered to be greenhouse gas emissions and pollution caused by transport, why climate targets are set around the globe to reduce these emissions. Electric vehicles (EVs), may be a sustainable alternative to internal combustion engine vehicles since having EVs in the car park creates an opportunity to reduce greenhouse gas emissions. This is due to the efficiency of the electric motor. For EVs with rechargeable batteries the opportunity to reduce emissions is also dependent on the genera- tion mix in the power system. EVs with the possibility to recharge the battery from the power grid are denoted plug-in electric vehicles (PEVs) or plug-in- hybrid electric vehicles (PHEVs). Hybrid electric vehicles (HEVs), without external recharging possibility, are not studied, hence the abbreviation EV further covers PHEV and PEV. With an electricity-driven private vehicle fleet, the power system will ex- perience an increased amount of variable electricity consumption that is de- pendent on the charging patterns of EVs. Depending on the penetration level of EVs and the charging patterns, EV integration creates new quantities in the overall load profile that may increase the load peaks. -
Generation of Customer Load Profiles Based on Smart-Metering Time
Dipartimento di Ingegneria Industriale DII Dipartimento di Ingegneria dell'Informazione DEI TESI DI LAUREA MAGISTRALE IN INGEGNERIA DELL'ENERGIA ELETTRICA Generation of customer load profiles based on smart-metering time series, building-level data and aggregated measurements RELATORI: Prof. Andrea Alberto Pietracaprina Dipartimento di Ingegneria dell'Informazione (DEI) Universit`adegli Studi di Padova Dr. Andreas Ulbig Adaptricity AG, Power Systems Laboratory Swiss Federal Institute of Technology (ETH), Svizzera Prof. Pierre Pinson Department of Electrical Engineering Technical University of Denmark (DTU), Danimarca LAUREANDO: Damiano Toffanin ANNO ACCADEMICO 2015/2016 Abstract Many countries are rolling out new devices to measure household electricity consumption, the so-called \smart meters". These devices can be remotely read and are able to measure consumption multiple times per day, usually every 15 or 30 minutes in European countries. Such abundance of data allows to achieve very detailed insight on consumption patterns and power flows in distribution networks. Nevertheless, smart meters have not been rolled out everywhere yet: some areas of the distribution grid are only partially covered, others are not covered at all. The latter are \blind grid areas" from the perspective of the grid operator. This is likely to be the typical European situation, at least for the next decade. Therefore, it becomes important to provide an estimation on how these \blind grid areas" behave. This can be done by a proper exploitation of the previously unseen abundance of data. The research focus is to generate load profiles estimating a realistic consumption for a specific blind area. This thesis illustrates a model based on machine learning techniques and Markov chains for generating realistic Synthesized Load Profiles (SLPs) to model consumption of buildings not equipped with smart meters. -
Load Profile Analysis of Medium Voltage Regulating Transformers
Atlantis Highlights in Engineering, volume 6 14th International Renewable Energy Storage Conference 2020 (IRES 2020) Load Profile Analysis of Medium Voltage Regulating Transformers on Battery Energy Storage Systems (BESS) Robert Beckmann Ewald Roben¨ Jens Clemens Energy Systems Technology swb Erzeugung AG & Co. KG swb Services AG & Co. KG German Aerospace Center Bremen, Germany Bremen, Germany Institute of Networked Energy Systems [email protected] [email protected] Oldenburg, Germany [email protected] Frank Schuldt Karsten von Maydell Energy Systems Technology Energy Systems Technology German Aerospace Center German Aerospace Center Institute of Networked Energy Systems Institute of Networked Energy Systems Oldenburg, Germany Oldenburg, Germany [email protected] [email protected] Abstract—Battery Energy Storage Systems (BESS) already after completion of the prequalification procedures up to 63 % cover a large part of the Frequency Containment Reserve (FCR) of the FCR will be provided by BESS. in Germany. If these are built at locations of conventional power In 2018, swb Erzeugung AG & Co. KG constructed and plants, the infrastructure available there may be utilized. In this paper we investigate how the secondary voltages of two commissioned the hybrid control power plant (Hybridregel- medium voltage regulating transformers differ by means of kraftwerk) HyReK in Bremen Hastedt for the provision of a model-based analysis. The first transformer is the auxiliary 18 MW FCR power. The HyReK combines a 14.244 MWh power transformer of a coal-fired power plant. The second, BESS with a power-to-heat option. This hybrid approach identical transformer connects the hybrid power plant HyReK allows negative control power to be fed from the power grid to the medium voltage grid to provide FCR. -
The Demand-Side Grid (Dsgrid) Model Documentation
The Demand-side Grid (dsgrid) Model Documentation Electrification Futures Study The Demand-Side Grid (dsgrid) Model Documentation Elaine Hale,1 Henry Horsey,1 Brandon Johnson,2 Matteo Muratori,1 Eric Wilson,1 Brennan Borlaug,1 Craig Christensen,1 Amanda Farthing,1 Dylan Hettinger,1 Andrew Parker,1 Joseph Robertson,1 Michael Rossol,1 1 1 2 Gord Stephen, Eric Wood, and Baskar Vairamohan 1 National Renewable Energy Laboratory 2 Electric Power Research Institute Suggested Citation Hale, Elaine, Henry Horsey, Brandon Johnson, Matteo Muratori, Eric Wilson, et al. 2018. The Demand-side Grid (dsgrid) Model Documentation. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-71492. https://www.nrel.gov/docs/fy18osti/71492.pdf. NOTICE This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36- 08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Office of Strategic Programs. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov. Cover image from iStock 452033401. NREL prints on paper that contains recycled content. Preface This report is one in a series of Electrification Futures Study (EFS) publications. The EFS is a multi-year research project to explore widespread electrification in the future energy system of the United States. -
UNIVERSITY of CALIFORNIA Los Angeles Data Driven State
UNIVERSITY OF CALIFORNIA Los Angeles Data Driven State Estimation in Distribution Systems A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Mechanical Engineering by Zhiyuan Cao 2020 © Copyright by Zhiyuan Cao 2020 ABSTRACT OF THE DISSERTATION Data Driven State Estimation in Distribution Systems by Zhiyuan Cao Doctor of Philosophy in Mechanical Engineering University of California, Los Angeles, 2020 Professor Rajit Gadh, Chair Increasing penetration of distributed energy resources (DERs) and electric vehicle (EV) charging load has brought intermittency into the distribution systems. Specially, power generation from the onsite renewable energy sources such as solar photo-voltaic is known to be intermittent. Together with variable EV charging loads that are highly dependent on user behavior, bi-directional power flows can be introduced into the distribution networks that results in unclear power flow direction, which may violate grid operating constraints such as voltage constraints and current magnitude constraints. Hence, it is necessary to study and implement distribution system state estimation (DSSE) as it provides voltage, current, and power information of every single element in the distribution system, and further enables examination of the operating constraints that help maintain the system in a normal operating status. ii Unlike transmission systems that are well-monitored with measurement redundancy, distribution systems are usually characterized by measurement scarcity and rely on pseudo-measurements modeled from historical data. Traditional pseudo-measurement methods assume static load profiles and fail to address the impact of DERs and EV charging penetration as a dynamic process. This dissertation proposed a pseudo-measurement modeling method at phase-level using user- level data to model the change of load profiles due to this penetration. -
Controllable Load Management Approaches in Smart Grids
Energies 2015, 8, 11187-11202; doi:10.3390/en81011187 OPEN ACCESS energies ISSN 1996-1073 www.mdpi.com/journal/energies Review Controllable Load Management Approaches in Smart Grids Jingshuang Shen, Chuanwen Jiang * and Bosong Li Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; E-Mails: [email protected] (J.S.); [email protected] (B.L.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel./Fax: +86-21-5187-9516. Academic Editor: Hossam A. Gabbar (Gaber) Received: 2 June 2015 / Accepted: 29 September 2015 / Published: 9 October 2015 Abstract: With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The “broad controllable loads” management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed. Keywords: controllable load; load management; renewable energy; microgrid; active distribution system; demand response 1. -
SMART GRIDS and RENEWABLES: a Guide for Effective Deployment TABLE of CONTENTS
IRENA International Renewable Energy Agency SMART GRIDS AND RENEWABLES A Guide for Effective Deployment Working Paper Working November 2013 Copyright © IRENA 2013 Unless otherwise indicated, material in this publication may be used freely, shared or reprinted, so long as IRENA is acknowledged as the source. About IRENA The International Renewable Energy Agency (IRENA) is an intergovernmental organisation that supports countries in their transition to a sustainable energy future, and serves as the principal platform for international cooperation, a centre of excel- lence, and a repository of policy, technology, resource and financial knowledge on renewable energy. IRENA promotes the widespread adoption and sustainable use of all forms of renewable energy, including bioenergy, geothermal, hydropower, ocean, solar and wind energy in the pursuit of sustainable development, energy access, energy security and low-carbon eco- nomic growth and prosperity. www.irena.org Acknowledgements The work for the preparation of this paper was led by Paul Komor and Anderson Hoke, University of Colorado, Boulder, Colo- rado, USA. The paper benefitted from very valuable comments from Rainer Bacher (Bacher Energie, Switzerland), Melissa Chan (Navigant Consulting, USA), Kazuyuki Takada (New Energy and Industrial Technology Development Organization, Japan), Matt Wakefield (EPRI, USA). Authors: Ruud Kempener (IRENA), Paul Komor and Anderson Hoke (University of Colorado) For further information or to provide feedback, please contact: Ruud Kempener, IRENA Innovation and Technology Centre. E-mail: [email protected] or [email protected]. Disclaimer The designations employed and the presentation of materials herein do not imply the e xpression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area, or concerning their authorities or the delimitation of their frontiers or boundaries. -
Generating Realistic Building Electrical Load Profiles Through the Generative Adversarial Network (GAN)
Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN) Zhe Wang, Tianzhen Hong* Building Technology and Urban Systems Division Lawrence Berkeley National Laboratory One Cyclotron Road, Berkeley, CA 94720, USA * Corresponding author: [email protected], (+1) 510-486-7082 ABSTRACT Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time- consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. The proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings. -
V2G a Comprehensive Analysis of Vehicle-To-Grid Technology Worldwide
The A to Z of V2G A comprehensive analysis of vehicle-to-grid technology worldwide Laura Jones, Kathryn Lucas-Healey, Björn Sturmberg, Hugo Temby and Monirul Islam January 2021 This report has been created for knowledge sharing as part of the Realising Electric Vehicle to Grid Services project. This Project received funding from ARENA as part of ARENA’s Advancing Renewables Program. The views expressed herein are not necessarily the views of the Australian Government. Contents Contents ............................................................................................................................... 2 Executive Summary .............................................................................................................. 6 Recommendations............................................................................................................. 9 Standards and rules ....................................................................................................... 9 Customer value proposition ......................................................................................... 10 Open value transfer ..................................................................................................... 11 Fostering an industry ................................................................................................... 12 1 Introduction .................................................................................................................. 14 1.1 Purpose ............................................................................................................... -
Load Modeling in Synthetic Electric Grids
Load Modeling in Synthetic Electric Grids Hanyue Li, Student Member, IEEE, Ashly L. Bornsheuer, Student Member, IEEE, Ti Xu, Member, IEEE, Adam B. Birchfield, Student Member, IEEE, and Thomas J. Overbye, Fellow, IEEE To add complexity to the synthetic load profile, a location- Abstract— Synthetic electric grids provide full availability of dependent ratio of RCI load compositions is assigned to each large-scale realistic test cases to power system researchers bus. The electric consumption behavior of the three sectors without confidentiality concerns. As many power system studies vary, so load composition ratios will distinguish and identify require load information, it is important to have bus level their load contribution at the bus level. Modeling the time- variation modeled in these synthetic test cases. This paper presents a methodology for bus level static load modeling in dependent behavior of the three load sectors is the next step in synthetic electric grid test cases. A realistic load composition the creation of realistic load profile curves for the synthetic ratio for residential, commercial and industrial sectors is electric grid. One of multiple template load curves in each RCI assigned to each bus according to the real electric grid data. sector is statistically assigned to each bus. According to the Multiple normalized template load curves for each load type are load composition ratio and template curves of each sector, a created and assigned statically to each bus. The load composition yearly load curve for each bus is obtained and scaled to match ratio and load templates are then combined and scaled to a footprint electric grid to obtain the bus level load curve for the the peak value in synthetic network base case to determine the whole year. -
Influence of Data Sampling Frequency on Household Consumption Load
sensors Article Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain J. C. Hernandez 1 , F. Sanchez-Sutil 2 , A. Cano-Ortega 2,* and C. R. Baier 3 1 Center for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaén, 23071 Jaén, Spain; [email protected] 2 Department of Electrical Engineering, University of Jaen, 23071 Jaen, Spain; [email protected] 3 Department of Electrical Engineering, University of Talca, Camino los Niches Km. 1, Curicó 3344158, Chile; [email protected] * Correspondence: [email protected]; Tel.: +34-953-212343 Received: 13 September 2020; Accepted: 20 October 2020; Published: 23 October 2020 Abstract: Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. -
Grid Integration of Large-Capacity Renewable Energy Sources and Use of Large-Capacity Electrical Energy Storage
grid stability – power integration – battery – energy-generation – ywheel – decarbonization – energy security – wind – solar – variability – unpredictability – dependency – photovoltaic – UHVDC – control – pumped storage - exibility – assessment – planning – storage technologies – generation forecast – operational enhancement - demand response – modeling – electrical energy storage – alternating current – contingency analysis – direct current – induction generator – transmission network – energy eciency – heat recovery – electrotechnology – analysis – strategy – electricity market – renewables – energy access – grid operator – grid infrastructure – synthesis – impact – coordination® – energy market operators – transmission planning body – challenges – standards – hydrogen – policy-makers – regulators – industry – research community – energy access – grid architecture – zero-fuel-cost – energy cost – interconnection – power uctuations – black-out – geopolitics – distribution – utility – SCADA – generation portfolio – grid operation – hydro – variable generation – steam – solar concentrating power – PV – intermittency – location – dependent – grid owner – wind speed – supply and demand – voltage support – forecast – weather – dispatchable load – nuclear – transmission capacity – sunlight – load management – voltage control – frequency regulation – spinning reserve – black-start capacity – Smart Grid – remote locations – uncertainty – deterministic – complexity – micro-grid – power output – power conversion – steam turbine – thermal energy