Abstract
In the past decade, plug-in (hybrid) electric vehicles (PHEVs) have been widely proposed as a viable alternative to internal combustion vehicles to reduce fossil fuel emissions and dependence on petroleum. Off-peak vehicle charging is frequently proposed to reduce the stress on the electric power grid by shaping the load curve. Time of use (TOU) rates have been recommended to incentivize PHEV owners to shift their charging patterns. Many utilities are not currently equipped to provide real-time use rates to their customers, but can provide two or three staggered rate levels. To date, an analysis of the optimal number of levels and rate-duration of TOU rates for a given consumer demographic versus utility generation mix has not been performed. In this paper, we propose to use the U.S. National Household Travel Survey (NHTS) database as a basis to analyze typical PHEV energy requirements. We use Monte Carlo methods to model the uncertainty inherent in battery state-of-charge and trip duration. We conclude the paper with an analysis of a different TOU rate schedule proposed by a mix of U.S. utilities. We introduce a centralized scheduling strategy for PHEV charging using a genetic algorithm to accommodate the size and complexity of the optimization.
Highlights
Increasing fuel prices, diminishing fossil fuel reserves, rising greenhouse gas emissions and political unrest have made plug-in vehicles an attractive alternative to traditional internal combustion engine vehicles (ICEV)
The growth of plug-in electric vehicles (PHEVs) as a clean, safe and economical transportation option to ICEVs can be promoted by extending driving range, improving battery health and life, increasing electric grid reliability and promoting acceptance of plug-in (hybrid) electric vehicles (PHEVs) by the consumer
The degree of penetration of PHEVs as a transportation option depends on a variety of factors, including charging technology, communication security, advanced metering infrastructure (AMI), incentives to customers, electricity pricing structures and standardization
Summary
Increasing fuel prices, diminishing fossil fuel reserves, rising greenhouse gas emissions and political unrest have made plug-in (hybrid) vehicles an attractive alternative to traditional internal combustion engine vehicles (ICEV). The wide-spread adoption of electric vehicles will have many multi-faceted socio-economic impacts Among these are increased system load, leading to stressed distribution systems and insufficient generation, power quality and reliability problems, degrading battery health, scheduling of vehicles as a potential power source in an ancillary market, costs incurred versus revenues earned by end user in offering such services, along-with the dependence on variable consumer behavior have been considered as hurdles to PHEV implementation [1,2]. We interpret this concern as the desire to have a fully-charged battery at the beginning of the daily commute This consumer desire for daily full charge must be balanced against the desire on the part of the utility to shape its load curve and avoid a load spike due to concurrent vehicle charging. The critical contribution is the selection of the most appropriate fitness function to optimally shape the load curve
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