Distributed Generation in State Renewable Portfolio Standards
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A Simple and Fast Algorithm for Estimating the Capacity Credit of Solar and Storage
Electricity Markets & Policy Energy Analysis & Environmental Impacts Division Lawrence Berkeley National Laboratory A Simple and Fast Algorithm for Estimating the Capacity Credit of Solar and Storage Andrew D. Mills and Pía Rodriguez July 2020 This is a pre-print version of an article published in Energy. DOI: https://doi.org/10.1016/j.energy.2020.118587 This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technology Office under Lawrence Berkeley National Laboratory Contract No. DE-AC02-05CH11231. DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or The Regents of the University of California. Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer. -
Wind Energy Technology Data Update: 2020 Edition
Wind Energy Technology Data Update: 2020 Edition Ryan Wiser1, Mark Bolinger1, Ben Hoen, Dev Millstein, Joe Rand, Galen Barbose, Naïm Darghouth, Will Gorman, Seongeun Jeong, Andrew Mills, Ben Paulos Lawrence Berkeley National Laboratory 1 Corresponding authors August 2020 This work was funded by the U.S. Department of Energy’s Wind Energy Technologies Office, under Contract No. DE-AC02-05CH11231. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or The Regents of the University of California. Photo source: National Renewable Energy Laboratory ENERGY T ECHNOLOGIES AREA ENERGY ANALYSISAND ENVIRONMENTAL I MPACTS DIVISION ELECTRICITY M ARKETS & POLICY Disclaimer This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or The Regents of the University of California. -
Renewable Energy Generator Output – Sample Calculations
Net Metering Program Highlights Net metering will be available to new customers until the size of the program reaches 1% of the electric provider’s previous year system peak in MW. The 1% will be measured against the total of the generator nameplate capacities for all participating customer’s generators and is split into the following tiers: (1) 0.5% for ≤ 20 kW True Net Metering This tier will include almost all residential customers. • Billing is based on net usage. • Customer receives the full retail rate for all excess kWh. • Utility shall use the customer’s existing meter if it is capable of reverse registration (spinning backwards) or install an upgraded meter at no additional cost to the net metering customer. • Utilities with fewer than 1,000,000 customers shall charge net metering customers at cost for an upgraded meter if the customer’s existing meter is not capable of reverse registration (spinning backwards). • A generator meter shall be provided at cost, if requested by the customer. (The generator meter is for the customer’s benefit. Utilities are not obligated to read a customer’s generator meter.) • No interconnection costs (beyond the combined $100 interconnection/net metering application fees), study fees, testing or inspection charges. • Net metering credits carry forward indefinitely. (2) 0.25% for >20 kW up to 150 kW Modified Net Metering • Customers pay full retail rate for electricity deliveries from the utility and receive the generation portion of the retail rate or a wholesale rate for deliveries to the grid. • No charge for the engineering review. • Customers pay all interconnection costs, distribution study fees and any network upgrade costs. -
Distributed Wind Competitiveness Improvement Project
Distributed Wind Competitiveness Improvement Project The Competitiveness Improvement Project (CIP) is a periodic solicitation through the U.S. Department of Energy (DOE) and its National Renewable Energy Laboratory (NREL). Manufacturers of small and medium wind turbines are awarded cost-shared subcontracts via a competitive process to optimize their designs, develop advanced manufacturing processes, and perform turbine testing. The goals of the CIP are to make wind energy cost competitive with other distributed generation technology and increase the number of wind turbine Photo from Northern Power Systems, NREL 36193 Increased Energy Reduced Hardware Production Costs Performance & Safety CIP component innovations and CIP manufacturing process CIP awardee Primus system optimization awardee innovation awardee Pika Energy Windpower of Lakewood, Colorado, achieved Bergey Windpower of Norman, of Westbrook, Maine, reduced Oklahoma, achieved a 110% blade costs by approximately for safety, function, performance, and durability— energy production increase for the 90% by developing an innovative to international standards on two of their turbine Excel 15 turbine over the Excel 10 tooling and cooling strategy to models. Northern Power Systems also recently turbine by increasing blade length produce blades using injection- and improving blade aerodynamics molded plastic. Two additional CIP awardees are currently and system controls. Through six funding cycles, DOE and NREL have awarded 28 subcontracts to 15 companies, totaling $6.3 million of investment -
Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications from the Department of Electrical & Computer Engineering, Department of Electrical and Computer Engineering 2013 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected] Sohrab Asgarpoor University of Nebraska-Lincoln, [email protected] Wei Qiao University of Nebraska-Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/electricalengineeringfacpub Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons Kahrobaee, Salman; Asgarpoor, Sohrab; and Qiao, Wei, "Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households" (2013). Faculty Publications from the Department of Electrical and Computer Engineering. 280. http://digitalcommons.unl.edu/electricalengineeringfacpub/280 This Article is brought to you for free and open access by the Electrical & Computer Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications from the Department of Electrical and Computer Engineering by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 4, DECEMBER 2013 1791 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee, Student Member, IEEE, Sohrab Asgarpoor, Senior Member, IEEE,and Wei Qiao, Senior Member, IEEE Abstract—In the near future, a smart grid will accommodate DI Decision interval in h. customers who are prepared to invest in generation-battery sys- tems and employ energy management systems in order to cut down Time-step of the simulation in h. on their electricity bills. -
Electric Transmission and Distribution Equipment Use
Electric Transmission and Distribution Equipment Use Final Rule: Mandatory Reporting of Greenhouse Gases (40 CFR 98, Subpart DD) Under the final Mandatory Reporting Rule for Additional Sources of Fluorinated Greenhouse Gases (GHGs), owners and operators of electric power system facilities with a total nameplate capacity that exceeds 17,820 lbs (7,838 kg) of sulfur hexafluoride (SF6) and/or perfluorocarbons (PFCs) must report emissions of SF6 and/or PFCs from the use of electrical transmission and distribution equipment. Owners or operators must collect emissions data, calculate GHG emissions, and follow the specified procedures for quality assurance, missing data, recordkeeping, and reporting. How Is This Source Category Defined? The electrical transmission and distribution equipment use source category consists of all electric transmission and distribution equipment and servicing inventory insulated with or containing SF6 or PFCs used within an electric power system. This equipment includes but is not limited to gas-insulated substations; circuit breakers; switchgear, including closed-pressure and hermatically sealed-pressure switchgear; gas-insulated lines containing SF6 or PFCs; gas containers such as pressurized cylinders; gas carts; electric power transformers; and other containers of SF6 or PFC. For the purposes of this subpart, facility is defined as the electric power system, comprising all electric transmission and distribution equipment insulated with or containing SF6 or PFCs that is linked through electric power transmission -
Demand Side Management Effects on Substation Transformer Capacity
applied sciences Article Demand Side Management Effects on Substation Transformer Capacity Limits Kerry D. McBee, Jacquelyn Chong and Prasanth Rudraraju * Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA * Correspondence: [email protected] Received: 24 June 2019; Accepted: 7 August 2019; Published: 9 August 2019 Featured Application: The research provides insight into the effects that high penetrations of photovoltaic (PV) systems, energy storage (ES) applications, and electric vehicle (EV) charging systems will have on substation transformer capacity ratings. The manuscript identifies how these applications change the operational conditions of a transformer to the point where capacity ratings (normal and emergency) should be adjusted to account for the additional internal heating in the windings and oil. The research also identifies how harmonic distortion induced by PV, ES, and EV charging equipment affects capacity ratings. The results of the analysis are utilized to describe mitigating approaches for existing units and new units being designed. The approaches are applied to the design attributes of a 50 MVA SPX Waukesha transformer. Abstract: In high penetrations, demand side management (DMS) applications augment a substation power transformer’s load profile, which can ultimately affect the unit’s capacity limits. Energy storage (ES) applications reduce the evening peaking demand, while time-of-use rates incentivize end-users to charge electric vehicles overnight. The daily load profile is further augmented by high penetrations of photovoltaic (PV) systems, which reduce the midday demand. The resulting load profile exhibits a more flattened characteristic when compared to the historical cyclic profile. Although the initial impact of PV and ES applications may reduce a unit’s peak demand, long-term system planning and emergency conditions may require operation near or above the nameplate rating. -
Microgen & DG Report June 2020
Small Distribution-Connected Generation Resources in Alberta Within Alberta, electric distribution systems are operated by utility companies and are the portion of the electric system that operates at 25 kilovolts or less. These distribution systems are used to deliver electricity from the Alberta interconnected electric system to an end-use customer. A Distributed Energy Resource is any resource that is connected to and can supply energy to an electric distribution system. This includes distribution-connected generation resources and energy storage. This report focuses on distribution-connected generation resources, which are typically connected to a distribution system at customer locations. Distribution-connected generation, if it qualifies, can be designated as micro-generation. For the purposes of this report, resources that are not designated as micro-generation will be referred to simply as distributed generation. This report has a section containing a brief description of each class of generation. In addition, there is data on the number of sites and total installed capacity broken out by energy source. Micro-Generation Distributed generation may qualify as micro-generation under the Micro-generation Regulation. To qualify as micro-generation, a generating unit must use renewable or alternative energy sources, be intended to offset consumer load, and have a nameplate capacity that does not exceed 5,000 kW. Electricity may be generated from solar, wind, hydro, fuel cells or biomass. Other sources may be permitted if they have current EcoLogo certification or their greenhouse gas intensity is lower then the regulated limit. Micro-generators producing electricity in excess of on-site load receive credits for what they feed to the electricity grid. -
Distributed Generation: a Brighter Future?
DISTRIBUTED GENERATION: A BRIGHTER FUTURE? Sponsored by DISTRIBUTED GENERATION A BRIGHTER FUTURE? CONTENTS 2 About this report 3 Executive summary 5 Part one: The shift to decentralised power production 9 Part two: Early movers 14 Part three: Looking ahead 17 Conclusion © The Economist Intelligence Unit Limited 2018 1 DISTRIBUTED GENERATION A BRIGHTER FUTURE? ABOUT THIS REPORT Distributed generation: a brighter future? is an Economist Intelligence Unit report, sponsored by E.ON. In this paper, The Economist Intelligence Unit examines the growth in distributed generation, as an increasing number of UK businesses are meeting a greater proportion of their energy needs through electricity generated themselves, on-site. This report seeks to examine the impact that distributed generation could have on UK businesses. To do so, we surveyed 450 senior executives with familiarity of their companies’ energy strategies in April-May 2018. The survey focused exclusively on executives from energy-intensive industries: manufacturing, transport and logistics, hospitality and retail. Half of the respondents are either members of their companies’ boards or hold C-level positions; the remainder are other senior managers and executives. All are from UK businesses with annual revenue exceeding £100m. The Economist Intelligence Unit supplemented the survey results with in-depth interviews with executives and industry experts. We would like to thank all survey respondents, as well as the following executives (listed alphabetically by company), for their time and insights: l Richard Carter, head of finance and sustainability, Adnams l Ilesh Patel, partner—energy and resources, Baringa l Simon Virley, partner and UK head of power and natural resources, KPMG l Caroline Hill, head of sustainability and public affairs, Landsec l Tom Byrne, sustainability manager, Landsec l Stuart Ravens, principal research analyst, Navigant Research l James Pitcher, director of sustainability, Whitbread This paper was written by Jessica Twentyman and edited by Jeremy Kingsley. -
SDRC 4 Learning Generated from the Openlv Project Trials by Method 1
SDRC 4 Learning Generated from the OpenLV Project trials by Method 1. SDRC 4 LEARNING GENERATED FROM THE OPENLV PROJECT TRIALS Report Title : SDRC 4 - Learning Generated from the OpenLV Project trials by all Method 1.. Report Status : Final Draft for Review Project Ref : WPD/EN/NIC/02 - OpenLV Date : 31.01.20 Document Control Name Date Prepared by: T. Butler, K. Platt, P. 24.01.20 Morris & R. Burns Reviewed by: D. Russell & S Rossi 24.01.20 Ashton Recommended by: D. Roberts & J. Berry 27.01.20 Approved (WPD): A. Sleightholm WPD 31.01.20 Resources and External Affairs Director Revision History Date Issue Status 24.01.20 3 Draft for Review 31.01.20 3.1 For Issue Page 2 of 76 SDRC 4 LEARNING GENERATED FROM THE OPENLV PROJECT TRIALS Contents Executive Summary ....................................................................................................................... 7 Key Findings .................................................................................................................................. 8 1 Introduction ........................................................................................................................... 9 1.1 Project Background...................................................................................................... 9 1.1.1 Method 1: Network Capacity Uplift .................................................................. 9 1.1.2 Method 2: Community Engagement ................................................................. 10 1.1.3 Method 3: OpenLV Extensibility ....................................................................... -
Grid Energy Storage
Grid Energy Storage U.S. Department of Energy December 2013 Acknowledgements We would like to acknowledge the members of the core team dedicated to developing this report on grid energy storage: Imre Gyuk (OE), Mark Johnson (ARPA-E), John Vetrano (Office of Science), Kevin Lynn (EERE), William Parks (OE), Rachna Handa (OE), Landis Kannberg (PNNL), Sean Hearne & Karen Waldrip (SNL), Ralph Braccio (Booz Allen Hamilton). Table of Contents Acknowledgements ....................................................................................................................................... 1 Executive Summary ....................................................................................................................................... 4 1.0 Introduction .......................................................................................................................................... 7 2.0 State of Energy Storage in US and Abroad .......................................................................................... 11 3.0 Grid Scale Energy Storage Applications .............................................................................................. 20 4.0 Summary of Key Barriers ..................................................................................................................... 30 5.0Energy Storage Strategic Goals .......................................................................................................... 32 6.0 Implementation of its Goals ............................................................................................................... -
2015 Renewable Energy Data Book Acknowledgments
2015 Renewable Energy Data Book Acknowledgments This report was produced by Philipp Beiter and Tian Tian, edited by Mike Meshek, and designed by Alfred Hicks of the U.S. Department of Energy's National Renewable Energy Laboratory (NREL). We greatly appreciate the input, review, and support of Assistant Secretary David Friedman, Ookie Ma, Steve Capanna, Paul Basore, Hoyt Battey, Charlie Gay, Susan Hamm, Ian Hamos, Fred Joseck, Benjamin King, Tien Nguyen, Sunita Satyapal, Paul Spitsen, Rich Tusing, Timothy Welch, and Jeff Winick of the U.S. Department of Energy (DOE), as well as Chad Augustine, Jerry Davis, Judi Deitchel, David Feldman, Ran Fu, Bryan Hannegan, Caley Johnson, Henry Johnston, Eric Lantz, Al LiVecchi, Jeff Logan, David Mooney, Robin Newmark, Gian Porro, and Paul Schwabe of NREL. Notes Capacity data are reported in watts of alternating current (AC) unless indicated otherwise. The primary data represented and synthesized in the 2015 Renewable Energy Data Book come from the publicly available data sources identified on page 122. Front page inset photos (left to right): iStock/754519; iStock/4393369; iStock/354309; iStock/2101722; iStock/2574180; iStock/5080552; Leslie Eudy, NREL 17854; iStock/11265066 Page 2: iStock/721000; page 6: iStock/5751076; page 17: photo from Invenergy LLC, NREL 14369; page 41: iStock/750178; page 53: iStock/ 754519; page 63: iStock/4393369; page 71: iStock/354309; page 77: iStock/2101722; page 83: iStock/2574180; page 87: iStock/5080552; page 91: photo by Leslie Eudy, NREL 17854; page 99: iStock/11265066; page 109: iStock/330791; page 119: iStock/3459287 Key Findings • The overall U.S. energy consumption decreased to 97.7 quadrillion British thermal units (Btu) in 2015—a 0.6% decline from 2014.