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Challenges of the future Danish energy scenario with 50 % wind integration and how electric vehicles adaptive charging can help to mitigate.

Sanchez Garcia, Adrian
Master thesis
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URI
http://hdl.handle.net/11250/2368179
Date
2015
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  • Institutt for elkraftteknikk [1628]
Abstract
Denmark aims for a 50% wind integration in 2020. In the current scenario, where

wind represents around 34% of domestic consumption, mothballing of conventional

power plants and heavy dependency in interconnection lines is already the trend.

One of the strategies from the Transmission System Operator is to integrate a big

volume of flexible loads to adapt to wind production and mitigate some of its draw

backs. EVs, even when a significant increase is expected in the following years, will

not represent a volume of consumption that can really impact the load curve by

2020 and this type of response will rely in the short term in other flexible loads like

Heat Pumps.

Due to its configuration and advanced technology, EVs can participate to other

services vital to the correct operation of the Electric Power System as it is provision

of frequency reserves to maintain balance between consumption and generation.

This work presents a solution using the adaptive charging capabilities of an EV

to get the best respond in both the day-ahead market and the regulation market.

The adaptive scheme will achieve: lower price for purchased electricity in the dayahead

market, with higher levels of wind energy penetration, and the possibility to

participate to the frequency regulation market and get revenues. All this features

are gained without affecting the normal operation of the vehicle. Two different

configurations for the battery of the EV are compared in this work: unidirectional

and bidirectional.

A fleet of 400 EVs have been modeled based on statistical survey data for EVs

users driving profiles in weekdays and weekends. This fleet is managed by the figure

of an aggregator who purchases electricity in the day-ahead market and bid on

the frequency regulation market. The reference charging profile is a non-controlled

consumption scheme of plug-and-charge. This reference model is compared first

with a basic adaptive models based on weight coefficients varying according to the

State of Charge of the battery and the level of wind penetration. Later on, the

adaptive model is optimized, following the same indicators, seeking to maximize

wind penetration while bidding to frequency regulation market the most number

of times. The optimization algorithms used are Gradient Search, Genetic and

Differential Evolution. The decision factor for the adaptive charging strategies is the

forecast wind penetration signal with is the coefficient between the level of forecast

wind production and the level of forecast consumption. The idea behind using

this signal is that it will yield typically lower cost of electricity and high net wind

penetration. Allowing high net wind penetration will reduce the presence of energy

from other generation facilities and thus the CO2 content in the battery charge.

Results show that the owner of an EV with bi-directional capabilities and Genetic

Algorithm can reduce the final expenses on the EV by 20% in one year. If GSA is

used instead, 36% more wind energy will be integrated in the vehicle. In addition, because

currently upward regulation is provided by coal and gas fired units, 60% of the

current emissions by providing this service could be cut with a GSA charging scheme.
Publisher
NTNU

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