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Munkhammar PhD Thesis Defence Presentation 27/3 2015

Title: Distributed Photovoltaics, Household Electricity Use and Electric Vehicle Charging : Mathematical Modeling and Case Studies (Link to thesis: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243159)
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Joakim Munkhammar

on 8 February 2016

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Transcript of Munkhammar PhD Thesis Defence Presentation 27/3 2015

On the use and production of power
What separates us today
from the past?
We have access to power!
The question is how to obtain reliable and
sustainable power supply.

And how to use it.
Renewable energy and it's use
Most renewable energy on Earth is
energy from the sun:

Photovoltaics is direct
How to harness it?
It varies over the day, over season and with
clouds.
Need to quantify the use, for example:

- Household electricity use
- Electric vehicle charging
Aims of the thesis
Develop mathematical models for solar irradiance and photovoltaic (PV) power production
(Papers I-II)
Develop a mathematical model for household electricity use
(Paper III)
.
Develop mathematical models for electric vehicle (EV) charging
(Papers IV-V)
.
Develop, investigate and apply mathematical models regarding the combined system of PV power production, household electricity use and EV charging to study grid interaction and self-consumption
(Papers VI-VIII)
.
Investigate and analyze grid interaction and self-consumption from meter data on PV power production and EV charging
(Paper IX)
.
Joakim Munkhammar
Built Environment Energy Systems Group
Department of Engineering Sciences
Uppsala University
Distributed Photovoltaics,
Household Electricity Use and
Electric Vehicle Charging

Mathematical Modeling and Case Studies
Methodology
Mathematical modeling and data analysis of:
Solar irradiance and photovoltaic power production.

Household electricity use.
Electric vehicle charging.
(And the combination of all above)
Modeling solar irradiance and PV power
Bimodal Distribution model
Convolution model
Household electricity use
Distribution model
Household electricity use
Widén Markov-chain model (Used in this thesis, not developed)
Electric vehicle charging
Distribution model: charging anywhere
Markov-chain model: home-charging
Combination models
Markov chain
Distribution
Case studies
Westminster, London, UK
Solelia Solar-charging stations
Some general results
PV power production, Household electricity use, EV charging and their combination can be accurately modeled with probability distributions.
Bimodal distribution model
Convolution model
A bimodal distribution is
fit to the clear-sky index
of solar irradiance.
In turn this is used to
simulate PV power
production.
Paper I
Assumption that the
clear-sky index could
be modeled as a convolution
of stochastic variables for
direct and diffuse irradiance.
Resulted in a generalized
Ångström equation relating
irradiance to number of hours
of bright sunshine:
Assumption: Household electricity use can be modeled as a probability distribution.

By inspection: A Weibull distribution
Modeled for different time-bins:
Each hour of the day
Each day of the week
Each season
Paper III
Paper II
It was developed by Joakim Widén in:
Widén et al., Energy and Buildings 2009
Widén&Wäckelgård, Applied Energy 2010
Markov-chain EV home-charging
model
Based on the Widén Markov chain model for household electricity use.
Paper IV
Collaboration with Pia Grahn (KTH), Joakim Widén (UU), Lennart Söder (KTH) and Karin Alvehag (KTH). (IEEE Trans. Power Systems 28, 2013)
Assumption: Certain percentage when the Markov chain model
enters the state away: the EV is taken out driving. When back
home: plugged in and charged.
EV Distribution model
Observation: Charging or not
might be modeled via a
Bernoulli distribution.
Charges whenever stops.
Distribution model: Do that
based on actual driving
patterns.
Collaboration with Jesper Rydén (UU), Joakim Widén (UU) and Pia Grahn (KTH). (in proceedings of IEVC 2014)
Paper V
PV - EV - Household model
(Markov-chain)
Paper VII
PV power production was provided
from a simple linear model.

Household electricity use from
the Widén Model.

EV Charging from the
home-charging model.
Collaboration with Pia Grahn (KTH) and Joakim Widén (UU). (Solar Energy 97, 2013)
PV - Household - EV:
Probability distribution model
Paper VIII
It characterized the
variability of all
components combined.
Westminster study
Collaboration with Justin Bishop, Juan José Sarralde, Wei Tian and Ruchi Choudhary (All at Cambridge). (Energy and Buildings 86, 2014)
Paper VI
Study of the potential for PV and
EV in the City of Westminster,
London, UK.
METEONORM + Widén's PV model
Widén Markov-chain model
Home-charging EVs
Solelia solar charging station data
Paper IX
Collaboration with Joakim Widén (UU) and Per Wickman (Solelia). (In proceedings of SIW 2014)
Data analysis of:
PV power production
EV charging
Diurnal average plot for each solar charging station,
PV=Dashed, EV=Solid.
A full coverage of PV in Westminster will during no occasion cause net production.
EV charging can be modeled as an extension to the Widén Markov-chain model.
Data from EV charging reveals no unison in patterns.
Collaboration with Jesper Rydén (UU) and Joakim Widén (UU). (Applied Energy 142, 2015)
Collaboration with Jesper Ryden (UU), Joakim Widen (UU) and David LIngfors (UU). (Manuscript)
Collaboration with Joakim Widén (UU). (Manuscript)
Collaboration with Jesper Rydén (UU) and Joakim Widén (UU). (Applied Energy 135, 2014)
Viktor Vasnetsov (Wikimedia commons)
It is based on the probability of
going from one activity to another.

And electricity use associated
with each activity.
Total EV charging is about the same as the total PV power production over nearly a year.
The mismatch between load and production is less pronounced for aggregates.
Full transcript