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Power system operation with high penetration of electric vehicles and renewable energy

Alvarez Guerrero, Jose David (2022) Power system operation with high penetration of electric vehicles and renewable energy. Doctoral thesis, Northern Arizona University.

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This dissertation document explores the potential impacts of integrating electric vehicles (EVs) and variable renewable energies (VRE) on power system operation. EV penetration scenarios of the light-duty vehicle fleet of 10%, 20%, and 30% are considered in the RTS-GMLC test system. RTS-GMLC is an updated version of the IEEE RTS-96 test system, allowing for modern inter-hour or real-time market modeling; it has a generation fleet with characteristics comparable to current power systems used in the western United States, and VRE penetration equal to 34% of annual energy consumption. The impacts of EVs are investigated during the annual peak in the summer and during four weeks of the year in which high VRE combined with low loads lead to overgeneration. Uncoordinated and coordinated EV charging scenarios are considered. In the uncoordinated scenario, charging is undertaken at the convenience of the EV owners, and this is modeled using data from the Idaho National Laboratory's EV Project. To create an uncoordinated charging load profile, the parameters of importance are the number of vehicles, charger type, battery capacity, availability for charging, and battery beginning and ending states of charge. Beta distributions were found to be the most appropriate distribution for statistically modeling the initial and final state of charge (SoC) of vehicles in an EV fleet. A Monte Carlo technique was implemented by sampling the charging parameters of importance to create an uncoordinated charging load time series. Coordinated charging is modeled as a controllable load using an "aggregator" model, wherein EV charging is scheduled to minimize operating costs while meeting the daily charging requirements subject to EV availability and charging constraints. To capture the impacts, the production cost model (PCM) Power System Optimizer (PSO), created by Polaris, is employed. The model uses load and VRE forecasts as part of a multiple-planning cycle rolling horizon approach to commit realistic generating units based on their start-up characteristics, while preserving the flexibility of units with short start-up times and EV charging. At each EV penetration level, the uncoordinated charging costs were higher than the coordinated ones. At 10% EV penetration, the uncoordinated charging cost per vehicle was four times higher than with coordinated charging during a low load week in February. During a high-VRE, low-load week in April, with uncoordinated EV charging at 30% penetration (3% energy penetration), the peak load increased as much as 27%. Using coordinated EV charging, the EV load shifts to hours with low prices, coincident with either low load, high VRE, or both. Furthermore, coordinated charging reduces the curtailment of PV by as much as nine times during the winter operation season, and the curtailment of wind generation by more than half during the summer operation season, compared to the scenarios with no EVs and uncoordinated EV charging. Using multiple planning cycles, load and VRE forecasts, and a “look ahead” period during scheduling and dispatching, units were judged to be important in creating and utilizing the flexibility of coordinated EV charging.

Item Type: Thesis (Doctoral)
Publisher’s Statement: © Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords: curtailment; EV charging; LMP; PCM; Peak load; VRE
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
NAU Depositing Author Academic Status: Student
Department/Unit: Graduate College > Theses and Dissertations
College of the Environment, Forestry, and Natural Sciences > School of Earth Sciences and Environmental Sustainability
Date Deposited: 13 Jul 2022 16:56
Last Modified: 13 Jul 2022 16:56
URI: https://openknowledge.nau.edu/id/eprint/5840

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