Abstract: Wind integration studies are an important tool for understanding the effects of increasing wind power deployment on grid reliability and system costs. This paper provides a detailed review of the statistical methods and results from 12 large-scale regional wind integration studies. In particular, we focus our review on the modeling methods and conclusions associated with estimating short-term balancing reserves (regulation and load-following). Several important observations proceed from this review. First, we found that many of the studies either explicitly or implicitly assume that wind power step-change data follow exponential probability distributions, such as the Gaussian distribution. To understand the importance of this issue we compared empirical wind power data to Gaussian data. The results illustrate that the Gaussian assumption significantly underestimates the frequency of very large changes in wind power, and thus may lead to an underestimation of undesirable reliability effects and of operating costs. Secondly, most of these studies make extensive use of wind speed data generated from mesoscale numerical weather prediction (NWP) models. We compared the wind speed data from NWP models with empirical data and found that the NWP data have substantially less power spectral energy, a measure of variability, at higher frequencies relative to the empirical wind data. To the extent that this difference results in reduced high-frequency variability in the simulated wind power plants, studies using this approach could underestimate the need for fast ramping balancing resources. On the other hand, the magnitude of this potential underestimation is uncertain, largely because the methods used for estimating balancing reserve requirements depend on a number of heuristics, several of which are discussed in this review. Finally, we compared the power systems modeling methods used in the studies and suggest potential areas where research and development can reduce uncertainty in future wind integration studies.
Abstract: This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25 kVA overhead distribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging.
Abstract: Switching from liquid fuels to electricity in the transportation and heating sectors can result in greenhouse gas emissions reductions. These reductions are maximized when electricity-sector carbon emissions are constrained through policy measures. We use a linear optimization, generation expansion/dispatch model to evaluate the impact of increased electricity demand for plug-in electric vehicle charging on the generating portfolio, overall generating fuel mix, and the costs of electricity generation. We apply this model to the PJM Interconnect and ISO-New England Regional Transmission Organization service areas assuming a CO2 pricing scheme that is applied to the electricity sector but does not directly regulate emissions from other sectors. We find that a shift from coal toward natural gas and wind generation is sufficient to achieve a 50% reduction in electricity-sector CO2 emissions while supporting vehicle charging for 25% of the vehicle fleet. The price impacts of these shifts are sensitive to demand side price responsiveness and the capital costs of new wind construction.
Abstract: As the number of electric vehicles (EVs) increase we must consider not only how this fuel switch may affect electrical power infrastructure but also mobility. Specifically, the suitability and charging requirements of these vehicles may differ in rural areas, where the electrical grid may be less robust and miles driven higher. Although other studies have examined issues of regional power requirements of EVs, none have done so in conjunction with the spatial considerations of travel demand. We use three datasets to forecast the future spatial distribution of EVs, as well as these vehicles’ ability to meet current daily travel demand: the National Household Travel Survey (NHTS), geocoded Vermont vehicle fleet data, and an E911 geocoded dataset of every building statewide. We consider spatial patterns in daily travel and home-based tours to identify optimal EV charging locations, as well as any area-types that are unsuited for widespread electric vehicle adoption. We found that hybrid vehicles were more likely to be near other hybrids than conventional vehicles were. This suggestion of clustering of current hybrid vehicles, in both urban and rural areas, suggests that the distribution of future EVs may also cluster in rural areas. Our analysis suggests that between 69 and 84% of the state’s vehicles could be replaced by a 40-mile range EV, depending on the availability of workplace charging. Problematic areas for EV adoption may be suburban areas, where both residential density is high (and potential clustering of hybrids), as well as miles driven. Our results suggest EVs are viable for rural mobility demand but require special consideration for power supply and vehicle charging infrastructure.
Abstract: A model estimates the short-run effect of plug-in hybrid electric vehicle (PHEV) charging on electricity costs, given a cap on carbon dioxide (CO2) emissions that covers only the electricity sector. In the short run, cap-and-trade systems that cover the electricity sector increase the marginal cost of electricity production. The magnitude of the increase in cost depends on several factors, including the stringency of the cap in relation to the demand for electricity. The use of PHEVs, which also has the potential to decrease net greenhouse gas emissions, would increase demand for electricity and thus would increase the upward pressure on marginal costs. The model described examines this effect for the New England electricity market, which as of January 2009 operates under the Regional Greenhouse Gas Initiative, a cap-and-trade system for CO2. The model uses linear optimization to dispatch power plants to minimize fuel costs given inelastic electric demand and constraints on nitrogen oxide and CO2 emissions. The model is used to estimate costs for three fleet penetration levels (1%, 5%, and 10%) and three charging scenarios (evening charging, nighttime charging, and twice-a-day charging). The results indicate that PHEV charging demand increases the marginal cost of CO2 emissions as well as the average and marginal fuel costs for electricity generation. At all penetration levels the cost increases were minimized in the nighttime-charging scenario.