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Abstract
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The increasing penetration of electric vehicles in distribution systems has given
rise to numerous predicaments, such as voltage drops, high losses, and insecure
operational states. These issues are exacerbated even more by uncertain production
of the solar distributed generation units, electricity price, and unpredictability that is
driven by behavioral patterns of electric vehicle (EV) owners. The researchers have
illustrated that the peak demand of EVs (when they are charged uncoordinatedly)
will co-occur with conventional residential peak demand, which can put a greatdeal of tension on the distribution systems. To account for these issues this book
chapter investigated the coordinated scheduling of smart residential EV parking lots
(SEVPL) in electric distribution systems, and solar distributed generation (SDG).
The vehicles have been congregated into fleets, utilizing the K-means clustering,
then the stochastic programming (SP) method was deployed to deal with indefinite
parameters such as their arrival time, departure time, and daily traveled miles.
Furthermore, to integrate a risk measure into the problem, robust optimization
method (RO) has been deployed in parallel to SP for the purpose of dealing with
uncertain parameters that do not follow any specific probability distribution, such as
solar energy production and energy price.
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