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The team also wishes to acknowledge Dennis Stiles and Rob Pratt for their management support; Jason Fuller, Andrew Fisher, Laurentiu Marinovici for their technical assistance running GridLAB-D simulations; Dave Winiarski for his technical insights and guidance; and Marye Hefty who assisted in the project planning and analysis of industry comments. v

Research for this report involved collaborations among several firms and major contributors, including Pacific Northwest National Laboratory (PNNL), Power Costs, Inc. (PCI), Clean Power Research (CPR), Alstom, and Duke Energy. PNNL and PCI performed the generation impact analysis with photovoltaic (PV) data simulated by CPR. Duke Energy conducted the transmission simulations, and Alstom modeled the distribution effects. PNNL verified all simulation results, performed the analyses, and compiled information from the collaborators into this report. Shuai Lu coordinated the study efforts at PNNL, and compiled the report with help from Mike Warwick. Shuai Lu also led the generation impact analysis and authored the generation section of the report, with significant contributions from Da Meng regarding ESIOS development, Ruisheng Diao and Chunlian Jin regarding reserve requirements, Forrest Chassin and Tony Nguyen regarding simulations using ESIOS and cost analyses, and Yu Zhang for graphic analyses of variability. Nader Samaan led the transmission analysis with contributions from Bharat Vyakaranam, and wrote the transmission study. Jason Fuller led the distribution analysis and wrote the distribution report. Mark Osborn and Marcelo Elizondo provided valuable comments to the draft report. The PNNL team was guided by Landis Kannberg. Buck Feng and Nate Finucane from PCI performed GenTrader simulations and contributed to the methodology development for the generation study. Ben Norris and Skip Dise of CPR were primarily responsible for providing PV data. Ethan Boardman from Alstom worked with Duke Energy to develop the solution approach for distribution modeling and championed the project internal to Alstom. Jesse Gantz from Alstom was responsible for the distribution project deliverables, performed the model enhancements, simulations runs, and validation of data for the distribution study. The study was not possible without the cooperation and individual contributions of many engineers and analysts from various departments at Duke Energy. They provided data and valuable insights throughout the study as well as a critical review of this report. In addition, the authors would like to acknowledge comments and suggestions received on the draft report from the review panel coordinated by Aidan Tuohy from the Electric Power Research Institute. The panel includes the following colleagues:

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Printed in the United States of America Available to DOE and DOE contractors from the Office of Scientific and Technical Information, P.O. Box 62, Oak Ridge, TN 37831-0062; ph: (865) 576-8401 fax: (865) 576-5728 email: reports@adonis.osti.gov Available to the public from the National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Rd., Springfield, VA 22161 ph: (800) 553-6847 fax: (703) 605-6900 email: orders@ntis.fedworld.gov online ordering: http://www.ntis.gov/ordering.htmThis document was printed on recycled paper. Executive SummaryDistributed, generation, demand response, distributed storage, smart appliances, electric vehicles and renewable energy resources are expected to play a key part in the transformation of the American power system. Control, coordination and compensation of these smart grid assets are inherently interlinked. Advanced control strategies to warrant large-scale penetration of distributed smart grid assets do not currently exist. While many of the smart grid technologies proposed involve assets being deployed at the distribution level, most of the significant benefits accrue at the transmission level. The development of advanced smart grid simulation tools, such as GridLAB-D, has led to a dramatic improvement in the models of smart grid assets available for design and evaluation of smart grid technology. However, one of the main challenges to quantifying the benefits of smart grid assets at the transmission level is the lack of tools and framework for integrating transmission and distribution technologies into a single simulation environment. Furthermore, given the size and complexity of the distribution system, it is crucial to be able to represent the behavior of distributed smart grid assets using reduced-order controllable models and to analyze their impacts on the bulk power system in terms of stability and reliability.The objectives of the project were to: Develop a simulation environment for integrating transmission and distribution control,  Construct reduced-order controllable models for smart grid assets at the distribution level,  Design and validate closed-loop control strategies for distributed smart grid assets, and  Demonstrate impact of integrating thousands of smart grid assets under closed-loop control demand response strategies on the transmission system.More specifically, GridLAB-D, a distribution system tool, and PowerWorld, a transmission planning tool, are integrated into a single simulation environment. The integrated environment allows the load flow interactions between the bulk power system and end-use loads to be explicitly modeled. Power system interactions are modeled down to time intervals as short as 1-second.Another practical issue is that the size and complexity of typical distribution systems makes direct integration with transmission models computationally intractable. Hence, the focus of the next main task is to develop reduced-order controllable models for some of the smart grid assets. In particular, HVAC units, which ...

Printed in the United Introduction and MotivationThis analysis provides detailed distribution-level insights into the leveraging potential of distributed rooftop photovoltaic (PV) technologies and electric vehicle (EV) charging. Either of the two technologies by themselves -at some high penetrations -may cause some voltage control challenges or overloading problems, respectively. But when combined, there could be synergistic effects, at least intuitively, whereby one technology mitigates the negative impacts of the other. High penetration of EV charging may overload existing distribution system components, most prominently the secondary transformer. If PV technology is installed at residential premises or anywhere downstream of the secondary transformer, it will provide another electricity source, thus relieving the loading on the transformers. Another synergetic or mitigating effect could be envisioned when high PV penetration reverses the power flow upward in the distribution system (from the homes upstream into the distribution system). Protection schemes may then no longer function as designed and voltage violations (exceeding the voltage upper limit of the American National Standards Institute (ANSI) voltage range) may occur. In this particular situation, EV charging could consume the generated energy from the PV, such that the reversal of power flow can be reduced or alleviated. Given these potential mutual synergistic behaviors of PV and EV technologies, this project attempted to quantify the benefits of combining the two technologies.Furthermore, of interest was how advanced EV control strategies may influence the outcome of the synergy between EV charging and distributed PV installations. Particularly, California utility companies with high penetration of distributed PV technology, who have experienced voltage control problems, are interested in how intelligent EV charging could support or affect the voltage control challenges. Methodology and ScopeThe analysis explored a small parameter space of different penetration levels of electric vehicles and distributed solar generation and the impacts these penetrations have on a distribution system. The investigation was performed by means of a power flow simulation of an Institute of Electrical and Electronics Engineers (IEEE) test feeder. To keep this study generic, a readily available generic IEEE feeder was utilized, rather than a specific feeder of a specific distribution company. The IEEE 123-node radial distribution feeder was used in this study [IEEE 1991]. This feeder represents a small distribution feeder with several regulators to maintain system operating voltage. The IEEE 123-node feeder is defined for a constant load. The original load conditions were replaced with explicit thermal and load representations of residences and commercial buildings, and EV charging behavior that is defined by EV owners' driving patterns. The feeder was populated with 1251 residential homes and a few light commercial buildings. Building characteristics and weather dat...