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Analysis of
Decentralised,
Distributed Decision-
Making for
Optimising Domestic
Electric Car Charging
This examines patterns of electricity
demand, private vehicle usage and electric
car charging to determine the feasibility an
approach to optimising electricity usage
generation and minimise impact on the
electricity grid using distributed decision
making
Alan McSweeney
April 2021
http://ie.linkedin.com/in/alanmcsweeney
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 2
Contents
Introduction ........................................................................................................................................................5
What Is The Potential Problem? ..........................................................................................................................5
Electric Vehicle Market Share And Growth Patterns.............................................................................................7
Electric Vehicles In Ireland ..............................................................................................................................7
Electric Vehicles In Norway ...........................................................................................................................13
Pattern of Electricity Usage In Ireland...............................................................................................................18
Patterns of Vehicle Usage In Ireland...................................................................................................................20
Potential Impact Of Electric Vehicles Charging On Electricity Demand ..............................................................22
Approaches To EV Charging To Avoid Increased Peaks In Electricity Demand...................................................26
Introduction ..................................................................................................................................................26
Electric Car Charging Simulations ..................................................................................................................28
Simulation Set 1 Results.................................................................................................................................29
Utilisation Profile 1 Simulation Results ......................................................................................................30
Utilisation Profile 2 Simulation Results ......................................................................................................31
Utilisation Profile 3 Simulation Results ......................................................................................................32
Utilisation Profile 4 Simulation Results ......................................................................................................33
Utilisation Profile 5 Simulation Results ......................................................................................................34
Utilisation Profile 6 Simulation Results ......................................................................................................35
Simulation Set 2 Results.................................................................................................................................36
Utilisation Profile 1 Simulation Results ......................................................................................................37
Utilisation Profile 2 Simulation Results ......................................................................................................38
Utilisation Profile 3 Simulation Results ......................................................................................................39
Utilisation Profile 4 Simulation Results ......................................................................................................40
Utilisation Profile 5 Simulation Results ......................................................................................................41
Utilisation Profile 6 Simulation Results ......................................................................................................42
Simulation Set 3 Results.................................................................................................................................43
Summary .......................................................................................................................................................44
Conclusions ........................................................................................................................................................44
Limitations On Domestic Electric Car Charging ..............................................................................................45
Approaches To Implementing An Electric Car Charging Infrastructure ...........................................................46
Assumptions Contained In This Analysis ........................................................................................................47
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 3
List of Figures
Figure 1 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage ..................................6
Figure 2 – Notional Electricity Generation Capacity Available For Electric Car Charging......................................7
Figure 3 – Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2019 .............8
Figure 4 – Cumulative Number Of All and Electric New Private Cars Registered from 2010 To 2020......................9
Figure 5 – Number Of New Private Vehicles By Fuel Type 2010 To 2020 ............................................................10
Figure 6 – New Private Vehicles By Fuel Type By Month Jan 2012 To Feb 2021.................................................11
Figure 7 – Norway Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2020
..........................................................................................................................................................................14
Figure 8 – Comparison of Numbers Of Private Vehicles As A Proportion Of Population For Ireland And Norway 15
Figure 9 – Change In The Number Of Private Cars And Electric Cars In Norway From 2008 To 2020 ..................16
Figure 10 – Patterns of Weekday Electricity Usage In Ireland in 2019 By Time Of Day.......................................18
Figure 11 – Daily MWh Electricity Consumption From July 2013 To December 2020..........................................19
Figure 12 – Electricity Usage For Tuesday 11 February 2020 ..............................................................................20
Figure 13 – Comparison Of Traffic In 2019 And 2020 ..........................................................................................20
Figure 14 – Traffic Data Sampling Locations ......................................................................................................21
Figure 15 – Traffic Data At Point N01 South of M50 Junction 02 Santry, Whitehall ...........................................21
Figure 16 – Traffic Data At Point M50 Between Junction 07 N04/M50 and Junction 09 N07/M50 Red Cow,
Palmerstown, Co. Dublin....................................................................................................................................22
Figure 17 – Traffic Data At Point M11 Between M50/M11 and Bray North Junction, Bray, Co. Dublin ...............22
Figure 18 – Annual Electricity Usage For 2019 Overlaid With Traffic Usage For 2019.........................................23
Figure 19 – Assumed Percentage Of Electric Cars Charging By Weekday Interval ...............................................23
Figure 20 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage...............................26
Figure 21 – Start And End Intervals Of Electric Car Charging.............................................................................27
Figure 22 – Profile of Electric Car Battery Capacity Used In Analysis .................................................................28
Figure 23 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
42.67%...............................................................................................................................................................30
Figure 24 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 42.67%..........................................................................................................................................................30
Figure 25 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
40.36%...............................................................................................................................................................31
Figure 26 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 40.36%..........................................................................................................................................................31
Figure 27 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
37.71%...............................................................................................................................................................32
Figure 28 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 37.71%..........................................................................................................................................................32
Figure 29 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
34.14%...............................................................................................................................................................33
Figure 30 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 34.14%..........................................................................................................................................................33
Figure 31 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
30.45%...............................................................................................................................................................34
Figure 32 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 30.45%..........................................................................................................................................................34
Figure 33 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of
25.83%...............................................................................................................................................................35
Figure 34 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement
Of 25.83%..........................................................................................................................................................35
Figure 35 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
83.41%...............................................................................................................................................................37
Figure 36 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 83.41%..........................................................................................................................................................37
Figure 37 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
82.25%...............................................................................................................................................................38
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 4
Figure 38 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 82.25%..........................................................................................................................................................38
Figure 39 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
81.04%...............................................................................................................................................................39
Figure 40 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 81.04%..........................................................................................................................................................39
Figure 41 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
79.98%...............................................................................................................................................................40
Figure 42 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 79.98%..........................................................................................................................................................40
Figure 43 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
78.87%...............................................................................................................................................................41
Figure 44 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 78.87%..........................................................................................................................................................41
Figure 45 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
77.57%...............................................................................................................................................................42
Figure 46 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 77.57%..........................................................................................................................................................42
Figure 47 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of
77.57% Where The Target Completion Time Is Moved To 07:30.........................................................................43
Figure 48 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement
Of 77.57% Where The Target Completion Time Is Moved To 07:30 ....................................................................44
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 5
Introduction
This document looks at the potential impact that large numbers of electric vehicles could have on electricity
demand, electricity generation capacity and on the electricity transmission and distribution grid in Ireland. It
combines data from a number of sources – electricity usage patterns, vehicle usage patterns, electric vehicle current
and possible future electric car market share – to assess the potential impact of electric vehicles.
It then analyses a possible approach to electric vehicle charging where the domestic charging unit has some degree
of intelligence in deciding when to start vehicle charging to minimise electricity usage impact and optimise
electricity generation usage.
There are many different types of electric vehicles such as plug-in hybrid electric vehicles (PHEVs), range-
extended electric vehicles (REEVs), battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs) and hybrid
electric vehicles (HEVs).
This analysis is limited to battery electric vehicles (BEVs).
In all cases, charts showing data for daily intervals start at 17:00. The purpose of this is to highlight the beginning
of peak electricity usage and the beginning of the end of peak traffic usage.
What Is The Potential Problem?
The potential problem to be addressed is that if large numbers of electric cars are plugged-in and charging starts
immediately when the drivers of those cars arrive home, the impact on demand for electricity will be substantial.
The following chart summarises the potential impact based on 300,000, 200,000, 100,000 and 50,000 electric cars. It
assumes that all domestic car chargers operate at 7.4 kWh.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
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Figure 1 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage
In this example, for the 300,000 electric car scenario, electricity usage is 19.20% higher than the daily peak. This
represents an additional 800 MW of electricity generation capacity. In the 200,000 car scenario it is 10.66% higher.
In the 100,000 car scenario it is 4.04% higher and in the 50,000 car scenario it is 1.71%.
Patterns of electric vehicle market share and growth are analysed on page 7.
One way in which this problem could be addressed to have a centrally managed electric car charging telecommand
infrastructure collecting and processing real-time or near real-time data and where charging is controlled and
scheduled based on available electricity,
The objective of this analysis is to determine if a simpler, less expensive decentralised and autonomous approach to
controlling electric car charging can achieve the same end.
The following chart shows the potentially available electricity, assuming that the total amount of electricity does
not exceed the maximum rate of electricity generation, which is 5,033 MW for the selected single day used for this
analysis. This notional available electricity is the inverse of electricity usage, assuming that the peak rate of
generation can be maintained.
The concept is that amount of electricity allocated to electric car charging should never exceed this notional
amount of available electricity generation capacity.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 7
Figure 2 – Notional Electricity Generation Capacity Available For Electric Car Charging
There are more details on page 18 on patterns of electricity usage in Ireland.
There are more details on page 20 on patterns of private car usage in Ireland.
Electric Vehicle Market Share And Growth Patterns
Electric vehicles will only have a substantial impact on electricity demand and usage in Ireland if their numbers
increase from their present very low values.
This section summarises the current state of electric vehicle usage in Ireland. It then compares the number and rate
of take-up of electric vehicles in Norway, a country with a similar population size to Ireland and where electric car
usage increased very rapidly from a very low base in a very short interval.
This provides a context for the analysis of electric car impact on patterns of electricity usage.
Electric Vehicles In Ireland
Three factors that will affect electric car penetration are the overall population size and the proportion of the
population that buy cars and the overall number of private cars in use.
The following chart shows the growth in the population of Ireland from the years 1982 to 20191 (on the right hand
vertical axis) and the growth in the total number of vehicles and number of private cars registered2 (on the left
hand vertical axis).
1 Population data is taken from https://data.cso.ie/table/PEA01.
2 Vehicle registration data is taken from https://data.cso.ie/table/TEA11.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 8
Figure 3 – Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2019
The information contained in this chart is shown in the following table. The number of private vehicles as a
proportion of the population increased from 20.37% in 1982 to 44.19% in 2019. This reflects a number of
changes such as:
• Increased wealth
• Increased car usage due to commuting as the distance between where people live and work increases
• Changing profile of the population with more people of car-buying age
• Increased number of multi-car families
• Parents buying cars for their children
Year Population All Vehicles Private Cars Private Cars as
a Percentage of
All Vehicles
Private Vehicles
as a Percentage
of Population
1982 3,480,000 882,140 709,000 80.37% 20.37%
1983 3,504,000 897,381 718,555 80.07% 20.51%
1984 3,529,000 906,109 711,098 78.48% 20.15%
1985 3,540,000 914,758 709,546 77.57% 20.04%
1986 3,540,600 922,484 711,087 77.08% 20.08%
1987 3,546,500 959,753 736,595 76.75% 20.77%
1988 3,530,700 981,296 749,459 76.37% 21.23%
1989 3,509,500 1,019,560 773,396 75.86% 22.04%
1990 3,505,800 1,054,259 796,408 75.54% 22.72%
1991 3,525,700 1,105,545 836,583 75.67% 23.73%
1992 3,554,500 1,126,473 858,498 76.21% 24.15%
1993 3,574,100 1,151,238 891,027 77.40% 24.93%
1994 3,585,900 1,202,273 939,022 78.10% 26.19%
1995 3,601,300 1,262,503 990,384 78.45% 27.50%
1996 3,626,100 1,338,616 1,057,383 78.99% 29.16%
1997 3,664,300 1,432,330 1,134,429 79.20% 30.96%
1998 3,703,100 1,510,853 1,196,901 79.22% 32.32%
1999 3,741,600 1,608,156 1,269,245 78.93% 33.92%
2000 3,789,500 1,682,221 1,319,250 78.42% 34.81%
2001 3,847,200 1,769,684 1,384,704 78.25% 35.99%
2002 3,917,200 1,850,046 1,447,908 78.26% 36.96%
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 9
Year Population All Vehicles Private Cars Private Cars as
a Percentage of
All Vehicles
Private Vehicles
as a Percentage
of Population
2003 3,979,900 1,937,429 1,507,106 77.79% 37.87%
2004 4,045,200 2,036,307 1,582,833 77.73% 39.13%
2005 4,133,800 2,138,680 1,662,157 77.72% 40.21%
2006 4,232,900 2,296,393 1,778,861 77.46% 42.02%
2007 4,375,800 2,441,564 1,882,901 77.12% 43.03%
2008 4,485,100 2,497,568 1,924,281 77.05% 42.90%
2009 4,533,400 2,467,660 1,902,429 77.09% 41.96%
2010 4,554,800 2,416,387 1,872,715 77.50% 41.12%
2011 4,574,900 2,425,156 1,887,810 77.84% 41.26%
2012 4,593,700 2,403,223 1,882,550 78.33% 40.98%
2013 4,614,700 2,482,557 1,910,165 76.94% 41.39%
2014 4,645,400 2,515,322 1,943,868 77.28% 41.85%
2015 4,687,800 2,570,294 1,985,130 77.23% 42.35%
2016 4,739,600 2,624,958 2,026,977 77.22% 42.77%
2017 4,792,500 2,675,879 2,066,112 77.21% 43.11%
2018 4,857,000 2,717,722 2,106,369 77.50% 43.37%
2019 4,921,500 2,805,839 2,174,779 77.51% 44.19%
The following chart shows the numbers and fuel types of new private vehicles registered since 20103. The left axis
refers to private cars. The right axis refers to electric cars.
Figure 4 – Cumulative Number Of All and Electric New Private Cars Registered from 2010 To 2020
The information contained in the chart is shown in the following table.
Year Electric Cars Hybrid Cars All Other Fuel
Types
Total Electric Cars %
Of Total
2010 23 1,210 122,777 124,010 0.02%
2011 52 735 127,294 128,081 0.04%
3 New vehicle registration data is taken from https://data.cso.ie/table/TEA27.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 10
Year Electric Cars Hybrid Cars All Other Fuel
Types
Total Electric Cars %
Of Total
2012 165 805 113,755 114,725 0.14%
2013 67 1,010 120,033 121,110 0.06%
2014 273 1,356 143,595 145,224 0.19%
2015 571 2,040 165,716 168,327 0.34%
2016 608 4,160 207,301 212,069 0.29%
2017 1,092 7,392 211,069 219,553 0.50%
2018 1,922 13,223 205,468 220,613 0.87%
2019 4,054 19,083 199,063 222,200 1.82%
2020 4,368 20,741 137,741 162,850 2.68%
Total 13,195 71,755 1,753,812 1,838,762 0.72%
Electric cars represent a very small proportion of new cars registered. 2020 is a poor year for any data analysis. In
2020, the number of private cars registered was 162,850, -26.71% fewer than in 2019. The number of new electric
cars registered in 2020 was 4,368, 7.75% higher than the number in 2019. So, in the context of a reduction in new
private cars in 2020 there is a small increase in the number of electric cars. However, the number of electric cars is
still very small. It cannot currently have any significant impact on electricity demand.
The following chart shows the numbers of new vehicles registered by year.
Figure 5 – Number Of New Private Vehicles By Fuel Type 2010 To 2020
The following chart shows monthly new private vehicle registration numbers by fuel type from January 2015 to
February 20214. The left-hand axis refers to the numbers of non-electric vehicles. The right-hand axis refers to the
numbers of electric vehicles. I have added a simple linear trend line showing the rate of growth of electric vehicles.
4 New monthly vehicle registration data is taken from https://data.cso.ie/table/TEM12.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 11
Figure 6 – New Private Vehicles By Fuel Type By Month Jan 2012 To Feb 2021
The information contained in the chart is shown in the following table.
Month All Fuel Types Fossil and Other
Fuels
Electric Hybrid Electric Vehicle
Percentage
Jan 2015 20,105 19,723 68 314 0.34%
Feb 2015 15,384 15,126 72 186 0.47%
Mar 2015 17,054 16,834 56 164 0.33%
Apr 2015 13,166 12,972 72 122 0.55%
May 2015 9,027 8,901 36 90 0.40%
Jun 2015 3,924 3,870 13 41 0.33%
Jul 2015 21,290 20,921 61 308 0.29%
Aug 2015 8,572 8,391 49 132 0.57%
Sep 2015 5,924 5,790 26 108 0.44%
Oct 2015 3,943 3,873 11 59 0.28%
Nov 2015 1,874 1,837 9 28 0.48%
Dec 2015 847 834 3 10 0.35%
Jan 2016 27,106 26,576 32 498 0.12%
Feb 2016 21,173 20,736 92 345 0.43%
Mar 2016 20,096 19,668 81 347 0.40%
Apr 2016 14,847 14,493 51 303 0.34%
May 2016 10,125 9,964 23 138 0.23%
Jun 2016 4,143 4,068 8 67 0.19%
Jul 2016 22,462 21,889 38 535 0.17%
Aug 2016 9,781 9,523 32 226 0.33%
Sep 2016 5,842 5,698 11 133 0.19%
Oct 2016 3,831 3,750 13 68 0.34%
Nov 2016 1,846 1,758 11 77 0.60%
Dec 2016 679 655 0 24 0.00%
Jan 2017 26,668 25,538 107 1,023 0.40%
Feb 2017 16,905 16,191 89 625 0.53%
Mar 2017 17,180 16,611 49 520 0.29%
Apr 2017 13,427 13,003 80 344 0.60%
May 2017 9,581 9,274 34 273 0.35%
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 12
Month All Fuel Types Fossil and Other
Fuels
Electric Hybrid Electric Vehicle
Percentage
Jun 2017 3,585 3,474 12 99 0.33%
Jul 2017 21,316 20,317 78 921 0.37%
Aug 2017 8,105 7,708 65 332 0.80%
Sep 2017 4,828 4,583 50 195 1.04%
Oct 2017 3,255 3,076 35 144 1.08%
Nov 2017 1,594 1,523 19 52 1.19%
Dec 2017 601 585 5 11 0.83%
Jan 2018 25,813 23,844 77 1,892 0.30%
Feb 2018 16,501 15,489 78 934 0.47%
Mar 2018 16,088 15,236 74 778 0.46%
Apr 2018 11,557 10,811 153 593 1.32%
May 2018 9,362 8,705 108 549 1.15%
Jun 2018 3,716 3,523 34 159 0.91%
Jul 2018 20,743 18,848 292 1,603 1.41%
Aug 2018 7,681 7,070 193 418 2.51%
Sep 2018 4,397 4,092 119 186 2.71%
Oct 2018 2,874 2,691 58 125 2.02%
Nov 2018 1,647 1,533 25 89 1.52%
Dec 2018 778 748 11 19 1.41%
Jan 2019 22,279 19,696 607 1,976 2.72%
Feb 2019 14,178 12,423 375 1,380 2.64%
Mar 2019 14,404 12,826 321 1,257 2.23%
Apr 2019 13,794 12,124 307 1,363 2.23%
May 2019 9,126 7,942 257 927 2.82%
Jun 2019 3,858 3,478 76 304 1.97%
Jul 2019 18,741 16,023 512 2,206 2.73%
Aug 2019 7,202 6,138 303 761 4.21%
Sep 2019 4,104 3,612 146 346 3.56%
Oct 2019 3,214 2,777 215 222 6.69%
Nov 2019 1,676 1,364 194 118 11.58%
Dec 2019 729 559 130 40 17.83%
Jan 2020 20,665 16,357 579 3,729 2.80%
Feb 2020 13,263 10,879 439 1,945 3.31%
Mar 2020 10,239 8,644 477 1,118 4.66%
Apr 2020 1,338 1,106 100 132 7.47%
May 2020 1,490 1,135 147 208 9.87%
Jun 2020 2,189 1,880 98 211 4.48%
Jul 2020 15,329 12,269 520 2,540 3.39%
Aug 2020 7,360 5,846 389 1,125 5.29%
Sep 2020 5,747 4,288 596 863 10.37%
Oct 2020 4,189 3,275 399 515 9.52%
Nov 2020 1,468 1,171 111 186 7.56%
Dec 2020 1,032 865 85 82 8.24%
Jan 2021 16,948 11,367 739 4,842 4.36%
Feb 2021 11,672 8,115 570 2,987 4.88%
Total 737,477 678,482 11,405 47,590
As shown before, the number of electric cars is growing but from a very low base.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
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Electric Vehicles In Norway
I have included details on electric car usage in Norway to demonstrate how its share of the overall private vehicle
market can increase very substantially and in a very short interval. Norway has achieved this through a wide range
of financial and other incentives, including:
• No Value Added Tax on electric car purchases
• No import duties and taxes on electric car purchases
• No annual road or other vehicle taxes
• Electric cars are charged road tolls at 50% of the rate of other private cars
• Electric cars are charged for parking at 50% of the rate of other private cars
• Electric cars can be driven in bus lanes
These incentives can be classified along the following lines:
Initial Incentives Ongoing Incentives
Financial Incentives No VAT
No taxes
No road tax
Non-Financial Incentives Use bus lanes Reduced tolls
Reduced parking charges
The net effect of the purchase-related financial incentives is to make electric cars cheaper to buy than their fossil
fuel equivalent. The ongoing financial and non-financial incentives make electric cars an attractive option in
Norway.
The population of Norway is similar to Ireland. The country is over five times the size of Ireland. Country size will
be a factor in overall private vehicle usage.
This section provides a context to what can happen with electric car usage in a country similar to Ireland should
the same set of incentive be implemented.
The following chart shows the population of Norway5 and the number of private vehicles6 for the years 1982 to
2020. This parallels the information contained in Figure 3 on page 8.
5 Norway population data is taken from https://www.ssb.no/en/statbank/table/06913/.
6 Norway vehicle data is taken from https://www.ssb.no/en/statbank/table/01960/.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
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Figure 7 – Norway Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2020
The information contained in this chart is shown in the following table.
Year Population Private Cars Private Vehicles
as a Percentage
of Population
1982 4,122,511 1,337,884 32.45%
1983 4,134,353 1,383,367 33.46%
1984 4,145,845 1,429,710 34.49%
1985 4,159,187 1,513,954 36.40%
1986 4,175,521 1,592,195 38.13%
1987 4,198,289 1,623,137 38.66%
1988 4,220,686 1,621,955 38.43%
1989 4,233,116 1,612,674 38.10%
1990 4,249,830 1,613,037 37.96%
1991 4,273,634 1,614,623 37.78%
1992 4,299,167 1,619,438 37.67%
1993 4,324,815 1,633,088 37.76%
1994 4,348,410 1,653,678 38.03%
1995 4,369,957 1,684,664 38.55%
1996 4,392,714 1,661,247 37.82%
1997 4,417,599 1,758,001 39.80%
1998 4,445,329 1,786,404 40.19%
1999 4,478,497 1,813,642 40.50%
2000 4,503,436 1,851,929 41.12%
2001 4,524,066 1,872,862 41.40%
2002 4,552,252 1,899,767 41.73%
2003 4,577,457 1,933,660 42.24%
2004 4,606,363 1,977,922 42.94%
2005 4,640,219 2,028,909 43.72%
2006 4,681,134 2,084,193 44.52%
2007 4,737,171 2,154,837 45.49%
2008 4,799,252 2,197,193 45.78%
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 15
Year Population Private Cars Private Vehicles
as a Percentage
of Population
2009 4,858,199 2,244,039 46.19%
2010 4,920,305 2,308,548 46.92%
2011 4,985,870 2,376,426 47.66%
2012 5,051,275 2,442,964 48.36%
2013 5,109,056 2,500,265 48.94%
2014 5,165,802 2,555,443 49.47%
2015 5,213,985 2,610,352 50.06%
2016 5,258,317 2,662,910 50.64%
2017 5,295,619 2,719,395 51.35%
2018 5,328,212 2,751,948 51.65%
2019 5,367,580 2,801,208 52.19%
2020 5,391,369 2,810,475 52.13%
In Ireland in in 2019 the number of private vehicles as a percentage of the population was 44.19%. The same figure
for Norway was 52.19%. This implies that private car numbers in Ireland have the potential to grow.
The following chart shows the number of private cars as a percentage of the population for both Ireland
and Norway for the years 1982 to 2020. At the time of writing, private vehicle information is not
available for Ireland for 2020.
Figure 8 – Comparison of Numbers Of Private Vehicles As A Proportion Of Population For Ireland And Norway
Norway shows a consistent growth in the number of private cars as a proportion of the population.
Norway started at a much higher percentage in 1982 than Ireland. Ireland shows a rapid growth from
1982 to 2007. Thereafter the number dropped, probably due to the global and local economic recession.
There are many reasons for the differences in private car ownership between countries, including:
• Economic
• Social and cultural
• Size of country
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
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• Extent and quality of public transportation network
• Demographic – this includes factors such as the age profile of the population, the population density
and the proportion of the population that lives in centralised urban areas and the proportion of the
population that consists of migrants who may not be able to afford to or want to buy a car
Analysing these factors is outside the scope of this document. However, this shows that there is still
potential for growth in the number of private vehicles in Ireland. If Ireland reaches the same population
proportion of private cars as Norway, the number of private cars could increase by 430,000.
The following chart shows the growth in the number of electric cars in Norway for the years 2008 to
20207.
Figure 9 – Change In The Number Of Private Cars And Electric Cars In Norway From 2008 To 2020
The information contained in this chart is shown in the following table.
Year Total Private
Cars
Electric Cars Electric as
Proportion of
All Private
Vehicles
Annual Rate of
Increase of
Electric Cars
Annual Rate of
Increase of All
Private Cars
2008 2,196,107 1,693 0.08%
2009 2,242,948 1,776 0.08% 4.90% 2.13%
2010 2,307,485 2,068 0.09% 16.44% 2.88%
2011 2,375,365 3,909 0.16% 89.02% 2.94%
2012 2,441,859 8,031 0.33% 105.45% 2.80%
2013 2,499,191 17,770 0.71% 121.27% 2.35%
2014 2,554,361 38,652 1.51% 117.51% 2.21%
2015 2,609,263 69,134 2.65% 78.86% 2.15%
2016 2,661,806 97,532 3.66% 41.08% 2.01%
2017 2,718,281 138,983 5.11% 42.50% 2.12%
2018 2,750,856 195,351 7.10% 40.56% 1.20%
7 Norway private vehicle fuel type data is taken from https://www.ssb.no/en/statbank/table/07849.
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Year Total Private
Cars
Electric Cars Electric as
Proportion of
All Private
Vehicles
Annual Rate of
Increase of
Electric Cars
Annual Rate of
Increase of All
Private Cars
2019 2,800,090 260,692 9.31% 33.45% 1.79%
2020 2,809,358 340,002 12.10% 30.42% 0.33%
This illustrates a very rapid rate of growth in the number of electric cars. It is interesting to note that while the
population of Norway increased by 12.34% from 2008 to 2020, the total number of private cars increased by
27.92% in the same interval. One possible explanation for this might be the growth in the number of multi-car
families. Additionally, this trend, if it exists, might be driving the increase in the number of electric cars where the
additional car is electric and is bought because of the convenience associated with its many incentives. This is
unvalidated speculation. But it demonstrates what could happen in Ireland. It is a scenario that should be planned
for. In terms of the total number of cars and total number of electric cars, Ireland in 2016 is at roughly the same
position as Norway in 2008. This is not to imply that Ireland is lagging eight years behind Norway in terms of
electric car market share or that Ireland will definitely experience the same level of growth in electric cars as
Norway.
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Pattern of Electricity Usage In Ireland
The following shows the patterns of the rate of electricity usage in megawatts (MW) for the weekdays of 2019 by
time of day8.
Figure 10 – Patterns of Weekday Electricity Usage In Ireland in 2019 By Time Of Day
This shows a consist pattern of usage with only the volume changing throughout the year. In general electricity
usage peaks at around 18:00 and drops thereafter.
The following chart shows the daily MWh totals (previous charts have shown usage in terms of megawatts being
generated while this shows the number of megawatt hours consumed) by day from July 2013 to December 2020. I
have added a simply linear trend line to this. This chart shows a clear seasonal pattern of electricity usage with
periods of high usage occurring in winter and periods of low usage in summer.
8 Electricity usage data is taken from http://smartgriddashboard.eirgrid.com/.
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Figure 11 – Daily MWh Electricity Consumption From July 2013 To December 2020
Notwithstanding changes in patterns of electricity usage due to COVID-19 restrictions the average rate of
electricity usage has increased by around 16% over this interval.
For the purposes of analysing electricity usage and the impact of electric cars, I have selected the date Tuesday 11
February 2020 for the single day to use. This represents the day when the largest recent use of electricity occurred
before COVID 19 restrictions were implemented that have affected subsequent electricity usage patterns. This is
used as the reference day for subsequent determination of potentially available electricity generation capacity and
for analysing the approach to optimising both electric car recharging and electricity generation capacity. The
selected date is purely illustrative.
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Figure 12 – Electricity Usage For Tuesday 11 February 2020
Patterns of Vehicle Usage In Ireland
This section looks at private vehicle traffic data9. I have taken traffic data for cars for 2019 at the points shown in
the diagram below. Clearly this is only a small subset of all the available traffic data. The intention is to sample
traffic data to identify patterns of usage and, by assumption, patterns of home charging (the assumption is that if
an electric car is not being driven in the evening, it is plugged in to a home charger). Traffic patterns for 2020 have
be affected by COVID-19 restrictions.
2019 2020
Figure 13 – Comparison Of Traffic In 2019 And 2020
9 Traffic data is taken from https://www.nratrafficdata.ie/.
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Figure 14 – Traffic Data Sampling Locations
The following charts show the weekday car-only traffic data for these three sampling points. I have omitted days
where there are any gaps in data. In all cases, the pattern of usage is very similar: a peak between 17:00 and 18:00,
a gradual drop to low levels of traffic at 01:00 and a rapid increase after 05:00 to a morning peak at around 08:00
and with fairly consistent levels of traffic throughout the day until the evening peak again.
Figure 15 – Traffic Data At Point N01 South of M50 Junction 02 Santry, Whitehall
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Figure 16 – Traffic Data At Point M50 Between Junction 07 N04/M50 and Junction 09 N07/M50 Red Cow, Palmerstown, Co.
Dublin
Figure 17 – Traffic Data At Point M11 Between M50/M11 and Bray North Junction, Bray, Co. Dublin
I have used these patterns of traffic usage to estimate the rate at which electric cars are not being driven and are
therefore plugged in.
Potential Impact Of Electric Vehicles Charging On Electricity Demand
The following chart overlays the electric usage for weekdays in 2019 with weekday private car traffic patterns for
2019.
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Figure 18 – Annual Electricity Usage For 2019 Overlaid With Traffic Usage For 2019
The following chart shows the estimated and assumed percentage of electric cars that might be charging at any
interval during a weekday.
Figure 19 – Assumed Percentage Of Electric Cars Charging By Weekday Interval
The information contained in this chart is shown in the following table.
Interval % Cars Not On The Road
17:00 2.73%
17:15 6.21%
17:30 9.69%
17:45 13.17%
18:00 16.66%
18:15 22.74%
18:30 28.83%
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Interval % Cars Not On The Road
18:45 34.92%
19:00 41.00%
19:15 45.28%
19:30 49.56%
19:45 53.84%
20:00 58.12%
20:15 60.82%
20:30 63.53%
20:45 66.24%
21:00 68.95%
21:15 70.09%
21:30 71.23%
21:45 72.37%
22:00 73.52%
22:15 76.82%
22:30 80.13%
22:45 83.43%
23:00 86.74%
23:15 87.95%
23:30 89.17%
23:45 90.38%
00:00 91.59%
00:15 92.58%
00:30 93.56%
00:45 94.54%
01:00 95.52%
01:15 96.11%
01:30 96.70%
01:45 97.28%
02:00 97.87%
02:15 97.56%
02:30 97.25%
02:45 96.94%
03:00 96.62%
03:15 95.39%
03:30 94.15%
03:45 92.91%
04:00 91.67%
04:15 89.41%
04:30 87.15%
04:45 84.89%
05:00 82.63%
05:15 73.16%
05:30 63.70%
05:45 54.24%
06:00 44.78%
06:15 37.94%
06:30 31.11%
06:45 24.27%
07:00 17.44%
07:15 18.40%
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Interval % Cars Not On The Road
07:30 19.36%
07:45 20.32%
08:00 21.28%
08:15 23.68%
08:30 26.08%
08:45 28.48%
09:00 30.89%
09:15 31.79%
09:30 32.69%
09:45 33.60%
10:00 34.50%
10:15 34.43%
10:30 34.36%
10:45 34.29%
11:00 34.21%
11:15 33.55%
11:30 32.89%
11:45 32.23%
12:00 31.56%
12:15 30.88%
12:30 30.20%
12:45 29.52%
13:00 28.85%
13:15 28.95%
13:30 29.05%
13:45 29.16%
14:00 29.26%
14:15 26.35%
14:30 23.44%
14:45 20.53%
15:00 17.62%
15:15 13.22%
15:30 8.81%
15:45 4.41%
16:00 0.00%
16:15 0.68%
16:30 1.36%
16:45 2.05%
This assumed percentage is then used to describe the potential impact of different numbers of electric cars being
plugged-in according to this schedule. The following chart is a replica of Figure 1 on page 6. It uses this car
charging profile to estimate the impact of uncontrolled domestic car charging. The electricity usage profile for the
single day Tuesday 11 February 2020 is used as a baseline.
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Figure 20 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage
Approaches To EV Charging To Avoid Increased Peaks In Electricity Demand
Introduction
This analysis consists of running simulations of various charging scenarios using randomly generated values. Each
simulation consisted of running the calculations for 450,000 electric cars
The following calculations were performed:
1. A probability density function has already been calculated using the electricity availability profile shown in
Figure 21 on page 27.
2. A currently battery capacity level for the electric car is randomly calculated using a uniform distribution
between a usage profile level and the maximum battery capacity for the car profile – the car profiles used are
listed in Figure 22 on page 28.
This analysis looks at an approach to domestic electric car charging where the charging unit applies the following
rules:
1. The target battery capacity is calculated to bring the car to 100% charge.
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2. The time required is calculated and it is assumed there is no need for a pre-charge battery warm-up.
3. Charging cannot start before 18:30 to ensure that the peak electricity usage interval has passed. The electricity
availability profile probability density function is truncated before this time.
4. Charging should end before 05:30 to ensure that the car is charged before the start of peak traffic. The
electricity availability profile probability density function is truncated after this time.
5. The last time interval when charging can start so that the car is fully charged before the charging end interval
of 05:30 is calculated.
6. A charging start interval is randomly generated using the previously calculated available electricity
probability density function between the start charging interval of 18:30 and the last time interval when
charging can start.
7. Car charging capacities are calculated as integers.
These rules are applied independently for each car charge.
The following is an example:
1. A car has a 100 kWh battery capacity. The randomly generated available battery charge is 58 kWh, so a
charge of 42 kWh is required to bring the car to a full charge.
2. Based on a charging capacity of 7.4 kWh, the charge time is roughly 5 hours 45 minutes.
3. A random charge start time is generated between 18:30 and 23:45 (end charge time of 05:30 less charge time of
5 hours 45 minutes). In this example, the assigned charge start time is 21:45.
The following chart shows the start and stop charging times used in the analysis.
Figure 21 – Start And End Intervals Of Electric Car Charging
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The notional amount of electricity capacity available in the target charging interval is 15,319 MWh.
The following chart shows the profile of electric car battery capacities used in the analysis.
Figure 22 – Profile of Electric Car Battery Capacity Used In Analysis
Electric Car Charging Simulations
Different sets of simulations have been run multiple times. Each simulation set corresponds to an electric car usage
profile from high to low for populations of 300,000 and 450,000 electric cars. The amount of battery usage governs
the amount of residual battery capacity available and therefore the amount of charge required and the time to
deliver it.
The following table summarises the total amount of charge required for a population of 450,000 cars for ten sample
runs for six utilisation profiles, representing 27 million individual electric car simulations. This is only a subset of
the total number of simulations run but there is little variation between them. The total battery capacity of the
sample population of 450,000 cars is 29,021 MWh based on the profile of car battery capacity listed in Figure 22
above.
Simulation Run Total MWh Charge Required
Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6
1 12,236 12,218 10,362 10,048 8,640 7,290
2 12,496 12,130 10,929 9,983 8,952 7,571
3 12,508 11,504 10,957 9,953 8,740 7,570
4 12,324 11,563 11,072 10,219 9,024 7,484
5 12,618 11,716 11,028 9,957 9,062 7,320
6 11,934 11,708 11,020 9,719 8,809 7,604
7 12,630 11,513 10,932 9,562 8,772 7,703
8 12,168 11,638 11,159 9,784 8,728 7,406
9 12,728 11,404 11,069 10,005 8,806 7,452
10 12,197 11,727 10,920 9,848 8,829 7,497
Average MWh Required 12,384 11,712 10,945 9,908 8,836 7,290
Percentage to Total Battery
Capacity Requiring Charge
42.67% 40.36% 37.71% 34.14% 30.45% 25.83%
Average Utilisation of Available
Charging Capacity
80.84% 76.45% 71.44% 64.67% 57.68% 48.94%
More details on these simulations are listed on page 29.
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The following table summarises for a second set of simulations the total amount of charge required for a population
of 300,000 cars for ten sample runs for six utilisation profiles, representing 18 million individual electric car
simulations. The simulations listed here have high levels of battery charging requirements. The total battery
capacity of the sample population of 300,000 cars is 19,347 MWh based on the profile of car battery capacity listed
in Figure 22 on page 28.
Simulation Run Total MWh Charge Required
Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6
1 16,145 15,891 15,658 15,460 15,227 15,023
2 16,116 15,867 15,687 15,531 15,317 15,004
3 16,124 15,908 15,688 15,463 15,259 15,030
4 16,115 15,988 15,638 15,454 15,266 15,015
5 16,126 15,869 15,725 15,443 15,250 14,959
6 16,181 15,924 15,680 15,475 15,316 15,092
7 16,158 15,911 15,625 15,467 15,237 14,962
8 16,138 15,920 15,701 15,492 15,228 14,993
9 16,153 15,901 15,686 15,483 15,234 15,041
10 16,117 15,953 15,693 15,480 15,251 14,960
Average MWh Required 16,137 15,913 15,678 15,475 15,259 15,008
Percentage to Total Battery
Capacity Requiring Charge
83.41% 82.25% 81.04% 79.98% 78.87% 77.57%
Average Utilisation of Available
Charging Capacity
105.34% 103.88% 102.34% 101.01% 99.60% 97.97%
More details on these simulations are listed on page 36.
Simulation Set 1 Results
This section contains more details on the six simulations contained in simulation set 1. There are two charts for
each simulation:
1. Electricity allocation profile for car charging
2. Impact on overall electricity allocation
In all cases, these simulations show that, at the simulated levels of charge required across the population of cars,
electric car charging can be handled without any additional impact on overall electricity utilisation and that all
cars are fully charged before the target end time of 05:30.
These results show the individual car charging units can deliver the required charge without the need for any
centralised scheduling and allocation facility by applying the simple rules listed on page 26.
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Utilisation Profile 1 Simulation Results
Figure 23 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 42.67%
Figure 24 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 42.67%
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Utilisation Profile 2 Simulation Results
Figure 25 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 40.36%
Figure 26 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 40.36%
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Utilisation Profile 3 Simulation Results
Figure 27 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 37.71%
Figure 28 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 37.71%
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Utilisation Profile 4 Simulation Results
Figure 29 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 34.14%
Figure 30 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 34.14%
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Utilisation Profile 5 Simulation Results
Figure 31 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 30.45%
Figure 32 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 30.45%
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Utilisation Profile 6 Simulation Results
Figure 33 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 25.83%
Figure 34 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 25.83%
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Simulation Set 2 Results
This section contains more details on the six simulations contained in simulation set 2. As before, there are two
charts for each simulation:
1. Electricity allocation profile for car charging
2. Impact on overall electricity allocation
In all cases, these simulations show that, at the simulated high levels of charge required across the population of
cars, electric car charging cannot be handled without any additional impact on overall electricity utilisation and
that most but not all cars are fully charged before the target end time of 05:30. This is not surprising given that for
four of the six simulations, the required charge is greater than the available electricity over the charging interval.
These results are to be expected given that some of the sample cars have a battery capacity of 100 kWh. Using a
standard domestic charger operating at 7.4kWh it would take 13.5 hours to completely charge the car. If charging
starts at 18:30, it cannot complete before 10:00 on the following day. As the battery capacity of electric cars
increases, the viability of domestic chargers as the sole source of charging capacity will be diminished for high
levels of charging requirements.
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Utilisation Profile 1 Simulation Results
Figure 35 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 83.41%
Figure 36 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 83.41%
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Utilisation Profile 2 Simulation Results
Figure 37 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 82.25%
Figure 38 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 82.25%
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Utilisation Profile 3 Simulation Results
Figure 39 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 81.04%
Figure 40 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 81.04%
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Utilisation Profile 4 Simulation Results
Figure 41 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 79.98%
Figure 42 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 79.98%
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Utilisation Profile 5 Simulation Results
Figure 43 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 78.87%
Figure 44 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 78.87%
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Utilisation Profile 6 Simulation Results
Figure 45 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57%
Figure 46 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57%
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Simulation Set 3 Results
This shows the results of a single set of simulations run for 300,000 cars where the target completion time is moved
out from 05:30 to 07:30. It repeats the simulation contained in Utilisation Profile 6 Simulation Results in
Simulation Set 2 on page 42
This shows that when the target time is pushed out, the impact of electric car charging on overall electricity usage
is minimised as the peak is flattened and the charging curve moved out to a later interval. In this simulation, the
charging has no impact on peak electricity demand.
Simulation Run Total MWh Charge
Required
Profile 1
1 14,971
2 14,988
3 14,946
4 14,969
5 14,999
6 14,981
7 15,003
8 15,064
9 15,028
10 15,090
Average MWh Required 15,004
Percentage to Total Battery
Capacity Requiring Charge
77.55%
Average Utilisation of Available
Charging Capacity
97.94%
Figure 47 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57% Where The
Target Completion Time Is Moved To 07:30
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Figure 48 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57%
Where The Target Completion Time Is Moved To 07:30
Summary
The results of the simulations are not surprising. All it really shows is that the distribution of randomly values
generated based on a probability distribution function will always mirror that distribution. So, if charging start
times are randomly allocated based on a probability density function generated from notionally available
electricity then the times assigned will match that distribution.
The analyses show that even for large numbers of electric cars there should not be any need to create any
additional electricity generation capacity to cater for their charging requirements.
This does not need any complex centrally managed car charging co-ordination and allocation infrastructure. The
autonomous and independent application of a simple set of rules can ensure the success of this charging approach
even for large numbers of electric cars requiring high levels of charging.
The simulations could be extended to include some variability in the target charge completion time. However, this
will would add to the core result which is that the independent application of a simple set of rules in itself provides
for the effective management of electric car charging.
Conclusions
The number of electric cars in Ireland is currently very low and so there is no need to be concerned about any
impact on electricity usage patterns and generation requirements.
The experience from Norway is that, given the right set of incentives, electric car usage can increase very
substantially and very rapidly.
A second less obvious experience from Norway is that the total number of private cars continues to increase at a
rate far higher than population growth. As described on page 17, that while the population of Norway increased by
12.34% from 2008 to 2020, the total number of private cars increased by 27.92% in the same interval. One possible
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explanation for this might be the growth in the number of multi-car families. Additionally, this trend, if it exists,
might be driving the increase in the number of electric cars where the additional car is electric and is bought
because of the convenience associated with its many incentives. This is unvalidated speculation.
A decentralised approach to car charging time allocation will work for large numbers of cars without any
disruption on the usability of electric cars.
This would require co-ordination between the providers of domestic car charging facilities to implement the
charging time allocation approach described here. The set of rules would have to be defined and agreed centrally.
The simple approach described in this document could be extended to include features in the charging unit such as:
• Notification of the car owner of the expected car start and end time.
• An emergency option to provide for immediate rather than deferred charging.
• The inclusion of some individual unit user-specific additional information collection and decision-making
regarding factors such as the desired charging end time and the target end charge capacity.
• If charging starts outside the managed interval and if the charging ends before the managed interval, it can
proceed as normal.
The analysis conducted here applies to Ireland. Other countries will show similar patterns of car and electricity
usage and so the conclusions can be applied more generally.
Limitations On Domestic Electric Car Charging
There are limitations to the capacity of domestic car chargers. In Ireland every connection to the electricity
network – house, apartment, retail or industrial unit – has a designated Maximum Import Capacity (MIC). The
same approach is used in most other countries. Different terms are used but their meanings are the same. MIC is
the maximum electricity demand level that can be catered for by the electric network connection. MIC values are
quoted in kVA (kilovolt-amps). kVA is a measure of the total amount of electric power in use in a system. In a
system that operates at 100% efficiency kW = kVA. However electrical systems are never 100% efficient. The
conversion between kW and kVA uses a Power Factor which is less than one. 1 kVA x Power Factor of 0.95 = 0.95
kW.
Domestic connections in Ireland have MIC values of 12kVA, 16kVA, 20kVA or 29kVA. The lower values of 12kVA
and 16kVA apply to apartments and small houses. So, a 7.4 kWh charging unit represents a high proportion of the
total capacity of a domestic connection. It is not realistic to have a separate electric car charging unit operating at
7.4 kWh for an apartment. The MIC of a property can be increased but at a cost that can be significant.
So, there are limitations to where domestic charging units can be installed. The following table lists the number of
private households by type of property taken from the 2016 census10.
Type of Private Accommodation Number
Detached house 715,133
Semi- detached house 471,948
Terraced house 284,569
Flat or apartment in a purpose- built block 172,096
Flat or apartment in a converted house or commercial building 28,783
10 Property data is taken from https://data.cso.ie/table/E1001.
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This refers the accommodation occupied by households and does not include details such as vacant and holiday
properties. The following table contains information on types of property taken from the 2016 census11.
Unfortunately, this information does not contain specific property type data.
Type of Private Accommodation Number
Total housing stock 2,003,645
Occupied by usual resident(s) of the household 1,697,665
Occupied by visitors only 9,788
Unoccupied - residents temporarily absent 50,732
Unoccupied - vacant house 140,120
Unoccupied - vacant flat 43,192
The following data shows similar property data for Norway from 202112.
Type of Private Accommodation 2016 2017 2018 2019 2020 2021
Detached house 1,258,298 1,265,093 1,271,158 1,276,690 1,281,004 1,284,892
House with 2 dwellings 225,132 227,798 230,328 232,948 235,467 237,789
Row house, linked house and
house with 3 dwellings or more 292,926 297,409 302,720 307,910 311,648 315,499
Multi-dwelling building 585,679 598,020 610,742 628,145 643,631 655,847
Residence for communities 53,227 55,580 60,458 61,912 63,309 68,479
Other building 70,091 71,689 72,326 73,550 74,981 75,015
Total 2,485,353 2,515,589 2,547,732 2,581,155 2,610,040 2,637,521
Presumably the Norwegian classification House with 2 dwellings is equivalent to Semi- detached house and
Row house, linked house and house with 3 dwellings or more is roughly equivalent to Terraced house.
In 2016, the latest year for which data exists for all categories, the population of Norway was 5,258,317 and the
number of private properties was 2,485,353. The ratio of properties to population was 47.27%. For Ireland in 2016,
the population was 4,739,600 and the number of private properties was 2,003,645, giving a ratio of 42.27%. The
differences in Norway could be due to factors such as higher ownership of second/holiday properties and
demographic reasons such as smaller family units due to higher divorce rates.
Car charging units cannot be installed in all domestic property types and so some other form of car charging
infrastructure will always be needed. There are limitations to the applicability of domestic charging infrastructure,
irrespective of whether it is managed or unmanaged.
Approaches To Implementing An Electric Car Charging Infrastructure
This analysis looks at the viability of optimising domestic car charging using a decentralised approach to allocation
of car charging times.
There are a number of approaches to implementing an electric car charging infrastructure. The following table
summarises these. These approaches are not mutually exclusive and can be mixed.
Options Description
Unmanaged domestic charging In the short term, this is the easiest approach to implementing a charging
11 Property type data is taken from https://data.cso.ie/table/E1068.
12 Norway property data is taken from https://www.ssb.no/en/statbank/table/06265/.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 47
Options Description
infrastructure. It requires no large investment and no centralised
decision-making. However, as the number of electric cars grows,
unmanaged domestic charging has the potential to be disruptive to
electricity generation patterns.
This is the worst and least sustainable option for electric car charging
infrastructure if the number of electric cars is expected to grow beyond a
small number.
Centrally managed domestic charging A centrally managed domestic charging infrastructure is expensive to
design, implement, operate, support and maintain. It has few advantages
over other forms of domestic charging other than adding cost and
complexity.
Locally managed domestic charging As described in this document, this would appear to be a realistic and
sustainable option in the context of domestic charging. It adds no real
cost to a basic domestic charging infrastructure other than possibly
retrofitting existing units. This could be a valid option to allow any
decision on and investment in centrally located public charging
infrastructure.
Distributed public charging Public car charging infrastructure can operate at much higher rates –
such as 43, 50, 120 and 150 kWh – than domestic units. These units can
be installed individually in a distributed pattern. This may be less than
optimal as drivers may have to travel some distance to find an available
unit. Single unts may be subject to longer queueing times than clusters of
multiple units.
Centrally located public charging Multi-bay car charging units, similar to existing service stations, could
offer shorter queueing times than individual units. They could be located
for convenience near other facilities and be managed and offer other
services. However, this option is the most expensive in the short-term and
requires centralised decision-making and ahead-of-the-curve up-front
investment.
These options exclude charging facilities installed in public or private car parks such as office and apartment
buildings.
Assumptions Contained In This Analysis
The following table lists the assumptions contained in this analysis and their potential impact on the validity of its
conclusions.
Year Potential
Impact
Notes
All domestic car chargers operate at 7.4
kW.
Low Most domestic chargers operate at 7.4 kW.
The requirement for a pre-charging
battery warm-up cycle when the
battery is not at optimum charging
temperature has been omitted from any
charging time calculations.
Low When the external temperature is low, a pre-charging
warm-up interval is required. The assumption is that
electric cars are plugged-in directly the arrive home and so
will not need to be warmed-up.
Norway is comparable to Ireland. Low The use of Norway as an example of how the number of
electric cars can increase very rapidly over a short interval
when the right set of incentives is put in place gives rise to
one scenario.
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 48
Year Potential
Impact
Notes
Additional electricity generation can be
scheduled for off peak times to provide
capacity for electric car charging
Medium A core assumption of this analysis is that notional
electricity availability profile (the inverse of the electricity
usage and demand profile) described in Figure 21 on page 27
is valid. Currently, there is reduced generation during this
interval. It is assumed that electricity generation can be
easily turn on without significant cost during this interval.
Generators may schedule maintenance and other activities
during this interval that could longer occur.
The selection of a single day of Tuesday
11 February 2020
Low A single day was selected for convenience. Any capacity
planning analysis needs to use as a baseline a maximum or
near to maximum level of current utilisation.
However, during periods of lower electricity utilisation such
as during the summer, the effect would be to increase
electricity usage.
The selection of three traffic sampling
intervals is sufficient.
Medium The selected traffic sampling intervals are on or near
motorways in the Dublin area. This introduces bias in
favour of car travel to/from/within Dublin. It may skew
journeys in favour of longer-distance commuting. While it is
reasonable to assume that private car traffic on these roads
are all ultimately driven home, the selected sampling points
are all in Dublin. Traffic at these points will all be some
distance away from their ultimate destinations.
The patterns of traffic usage at the three sampling points all
show the same patterns of use.
The pattern of existing private car
usage will be replicated for electric cars
Low It may be that electric cars will be used for shorter journeys.
The pattern of car usage is directly
related to the time when an electric car
will be plugged in to a domestic charger
Low It is reasonable to assume that as soon as an electric car
arrives at its home destination it will be immediately
plugged-in and will remain plugged-in overnight. A small
number may be used for additional journeys.
All electric cars must complete charging
by 05:30 so they will be available for
use
Medium This assumption affects the overall allocation of charging to
cars. When the required overall battery charge capacity is
high then this causes the peak electricity requirement to be
pushed up If the end time can be extended then as shown in
the Simulation Set 3 Results on page 43 then the peak
electricity utilisation is lowered.
The distribution of electric car battery
capacities listed in Figure 22 on page 28
reflects reality.
Medium There is currently no accurate information on the battery
capacities of electric cars.
The charge required for and thus the
implied distance travelled by electric
cars used in simulations is valid.
Medium Table 1.5: Average journey profile by mode of travel, 2013,
2014 and 2016 of the National Travel Survey 201613 lists
the average car journey was 16.3 km. Based on this, the
high levels of battery charge contained in Simulation Set 2
may be significantly overestimated.
13 https://www.cso.ie/en/releasesandpublications/ep/p-nts/nts2016/keyf/
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 49
Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car
Charging
Page 50
For more information, please contact:
http://ie.linkedin.com/in/alanmcsweeney

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Analysis of Decentralised, Distributed Decision-Making For Optimising Domestic Electric Car Charging

  • 1. Analysis of Decentralised, Distributed Decision- Making for Optimising Domestic Electric Car Charging This examines patterns of electricity demand, private vehicle usage and electric car charging to determine the feasibility an approach to optimising electricity usage generation and minimise impact on the electricity grid using distributed decision making Alan McSweeney April 2021 http://ie.linkedin.com/in/alanmcsweeney
  • 2. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 2 Contents Introduction ........................................................................................................................................................5 What Is The Potential Problem? ..........................................................................................................................5 Electric Vehicle Market Share And Growth Patterns.............................................................................................7 Electric Vehicles In Ireland ..............................................................................................................................7 Electric Vehicles In Norway ...........................................................................................................................13 Pattern of Electricity Usage In Ireland...............................................................................................................18 Patterns of Vehicle Usage In Ireland...................................................................................................................20 Potential Impact Of Electric Vehicles Charging On Electricity Demand ..............................................................22 Approaches To EV Charging To Avoid Increased Peaks In Electricity Demand...................................................26 Introduction ..................................................................................................................................................26 Electric Car Charging Simulations ..................................................................................................................28 Simulation Set 1 Results.................................................................................................................................29 Utilisation Profile 1 Simulation Results ......................................................................................................30 Utilisation Profile 2 Simulation Results ......................................................................................................31 Utilisation Profile 3 Simulation Results ......................................................................................................32 Utilisation Profile 4 Simulation Results ......................................................................................................33 Utilisation Profile 5 Simulation Results ......................................................................................................34 Utilisation Profile 6 Simulation Results ......................................................................................................35 Simulation Set 2 Results.................................................................................................................................36 Utilisation Profile 1 Simulation Results ......................................................................................................37 Utilisation Profile 2 Simulation Results ......................................................................................................38 Utilisation Profile 3 Simulation Results ......................................................................................................39 Utilisation Profile 4 Simulation Results ......................................................................................................40 Utilisation Profile 5 Simulation Results ......................................................................................................41 Utilisation Profile 6 Simulation Results ......................................................................................................42 Simulation Set 3 Results.................................................................................................................................43 Summary .......................................................................................................................................................44 Conclusions ........................................................................................................................................................44 Limitations On Domestic Electric Car Charging ..............................................................................................45 Approaches To Implementing An Electric Car Charging Infrastructure ...........................................................46 Assumptions Contained In This Analysis ........................................................................................................47
  • 3. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 3 List of Figures Figure 1 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage ..................................6 Figure 2 – Notional Electricity Generation Capacity Available For Electric Car Charging......................................7 Figure 3 – Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2019 .............8 Figure 4 – Cumulative Number Of All and Electric New Private Cars Registered from 2010 To 2020......................9 Figure 5 – Number Of New Private Vehicles By Fuel Type 2010 To 2020 ............................................................10 Figure 6 – New Private Vehicles By Fuel Type By Month Jan 2012 To Feb 2021.................................................11 Figure 7 – Norway Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2020 ..........................................................................................................................................................................14 Figure 8 – Comparison of Numbers Of Private Vehicles As A Proportion Of Population For Ireland And Norway 15 Figure 9 – Change In The Number Of Private Cars And Electric Cars In Norway From 2008 To 2020 ..................16 Figure 10 – Patterns of Weekday Electricity Usage In Ireland in 2019 By Time Of Day.......................................18 Figure 11 – Daily MWh Electricity Consumption From July 2013 To December 2020..........................................19 Figure 12 – Electricity Usage For Tuesday 11 February 2020 ..............................................................................20 Figure 13 – Comparison Of Traffic In 2019 And 2020 ..........................................................................................20 Figure 14 – Traffic Data Sampling Locations ......................................................................................................21 Figure 15 – Traffic Data At Point N01 South of M50 Junction 02 Santry, Whitehall ...........................................21 Figure 16 – Traffic Data At Point M50 Between Junction 07 N04/M50 and Junction 09 N07/M50 Red Cow, Palmerstown, Co. Dublin....................................................................................................................................22 Figure 17 – Traffic Data At Point M11 Between M50/M11 and Bray North Junction, Bray, Co. Dublin ...............22 Figure 18 – Annual Electricity Usage For 2019 Overlaid With Traffic Usage For 2019.........................................23 Figure 19 – Assumed Percentage Of Electric Cars Charging By Weekday Interval ...............................................23 Figure 20 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage...............................26 Figure 21 – Start And End Intervals Of Electric Car Charging.............................................................................27 Figure 22 – Profile of Electric Car Battery Capacity Used In Analysis .................................................................28 Figure 23 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 42.67%...............................................................................................................................................................30 Figure 24 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 42.67%..........................................................................................................................................................30 Figure 25 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 40.36%...............................................................................................................................................................31 Figure 26 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 40.36%..........................................................................................................................................................31 Figure 27 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 37.71%...............................................................................................................................................................32 Figure 28 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 37.71%..........................................................................................................................................................32 Figure 29 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 34.14%...............................................................................................................................................................33 Figure 30 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 34.14%..........................................................................................................................................................33 Figure 31 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 30.45%...............................................................................................................................................................34 Figure 32 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 30.45%..........................................................................................................................................................34 Figure 33 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 25.83%...............................................................................................................................................................35 Figure 34 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 25.83%..........................................................................................................................................................35 Figure 35 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 83.41%...............................................................................................................................................................37 Figure 36 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 83.41%..........................................................................................................................................................37 Figure 37 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 82.25%...............................................................................................................................................................38
  • 4. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 4 Figure 38 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 82.25%..........................................................................................................................................................38 Figure 39 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 81.04%...............................................................................................................................................................39 Figure 40 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 81.04%..........................................................................................................................................................39 Figure 41 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 79.98%...............................................................................................................................................................40 Figure 42 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 79.98%..........................................................................................................................................................40 Figure 43 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 78.87%...............................................................................................................................................................41 Figure 44 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 78.87%..........................................................................................................................................................41 Figure 45 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57%...............................................................................................................................................................42 Figure 46 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57%..........................................................................................................................................................42 Figure 47 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57% Where The Target Completion Time Is Moved To 07:30.........................................................................43 Figure 48 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57% Where The Target Completion Time Is Moved To 07:30 ....................................................................44
  • 5. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 5 Introduction This document looks at the potential impact that large numbers of electric vehicles could have on electricity demand, electricity generation capacity and on the electricity transmission and distribution grid in Ireland. It combines data from a number of sources – electricity usage patterns, vehicle usage patterns, electric vehicle current and possible future electric car market share – to assess the potential impact of electric vehicles. It then analyses a possible approach to electric vehicle charging where the domestic charging unit has some degree of intelligence in deciding when to start vehicle charging to minimise electricity usage impact and optimise electricity generation usage. There are many different types of electric vehicles such as plug-in hybrid electric vehicles (PHEVs), range- extended electric vehicles (REEVs), battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs) and hybrid electric vehicles (HEVs). This analysis is limited to battery electric vehicles (BEVs). In all cases, charts showing data for daily intervals start at 17:00. The purpose of this is to highlight the beginning of peak electricity usage and the beginning of the end of peak traffic usage. What Is The Potential Problem? The potential problem to be addressed is that if large numbers of electric cars are plugged-in and charging starts immediately when the drivers of those cars arrive home, the impact on demand for electricity will be substantial. The following chart summarises the potential impact based on 300,000, 200,000, 100,000 and 50,000 electric cars. It assumes that all domestic car chargers operate at 7.4 kWh.
  • 6. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 6 Figure 1 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage In this example, for the 300,000 electric car scenario, electricity usage is 19.20% higher than the daily peak. This represents an additional 800 MW of electricity generation capacity. In the 200,000 car scenario it is 10.66% higher. In the 100,000 car scenario it is 4.04% higher and in the 50,000 car scenario it is 1.71%. Patterns of electric vehicle market share and growth are analysed on page 7. One way in which this problem could be addressed to have a centrally managed electric car charging telecommand infrastructure collecting and processing real-time or near real-time data and where charging is controlled and scheduled based on available electricity, The objective of this analysis is to determine if a simpler, less expensive decentralised and autonomous approach to controlling electric car charging can achieve the same end. The following chart shows the potentially available electricity, assuming that the total amount of electricity does not exceed the maximum rate of electricity generation, which is 5,033 MW for the selected single day used for this analysis. This notional available electricity is the inverse of electricity usage, assuming that the peak rate of generation can be maintained. The concept is that amount of electricity allocated to electric car charging should never exceed this notional amount of available electricity generation capacity.
  • 7. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 7 Figure 2 – Notional Electricity Generation Capacity Available For Electric Car Charging There are more details on page 18 on patterns of electricity usage in Ireland. There are more details on page 20 on patterns of private car usage in Ireland. Electric Vehicle Market Share And Growth Patterns Electric vehicles will only have a substantial impact on electricity demand and usage in Ireland if their numbers increase from their present very low values. This section summarises the current state of electric vehicle usage in Ireland. It then compares the number and rate of take-up of electric vehicles in Norway, a country with a similar population size to Ireland and where electric car usage increased very rapidly from a very low base in a very short interval. This provides a context for the analysis of electric car impact on patterns of electricity usage. Electric Vehicles In Ireland Three factors that will affect electric car penetration are the overall population size and the proportion of the population that buy cars and the overall number of private cars in use. The following chart shows the growth in the population of Ireland from the years 1982 to 20191 (on the right hand vertical axis) and the growth in the total number of vehicles and number of private cars registered2 (on the left hand vertical axis). 1 Population data is taken from https://data.cso.ie/table/PEA01. 2 Vehicle registration data is taken from https://data.cso.ie/table/TEA11.
  • 8. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 8 Figure 3 – Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2019 The information contained in this chart is shown in the following table. The number of private vehicles as a proportion of the population increased from 20.37% in 1982 to 44.19% in 2019. This reflects a number of changes such as: • Increased wealth • Increased car usage due to commuting as the distance between where people live and work increases • Changing profile of the population with more people of car-buying age • Increased number of multi-car families • Parents buying cars for their children Year Population All Vehicles Private Cars Private Cars as a Percentage of All Vehicles Private Vehicles as a Percentage of Population 1982 3,480,000 882,140 709,000 80.37% 20.37% 1983 3,504,000 897,381 718,555 80.07% 20.51% 1984 3,529,000 906,109 711,098 78.48% 20.15% 1985 3,540,000 914,758 709,546 77.57% 20.04% 1986 3,540,600 922,484 711,087 77.08% 20.08% 1987 3,546,500 959,753 736,595 76.75% 20.77% 1988 3,530,700 981,296 749,459 76.37% 21.23% 1989 3,509,500 1,019,560 773,396 75.86% 22.04% 1990 3,505,800 1,054,259 796,408 75.54% 22.72% 1991 3,525,700 1,105,545 836,583 75.67% 23.73% 1992 3,554,500 1,126,473 858,498 76.21% 24.15% 1993 3,574,100 1,151,238 891,027 77.40% 24.93% 1994 3,585,900 1,202,273 939,022 78.10% 26.19% 1995 3,601,300 1,262,503 990,384 78.45% 27.50% 1996 3,626,100 1,338,616 1,057,383 78.99% 29.16% 1997 3,664,300 1,432,330 1,134,429 79.20% 30.96% 1998 3,703,100 1,510,853 1,196,901 79.22% 32.32% 1999 3,741,600 1,608,156 1,269,245 78.93% 33.92% 2000 3,789,500 1,682,221 1,319,250 78.42% 34.81% 2001 3,847,200 1,769,684 1,384,704 78.25% 35.99% 2002 3,917,200 1,850,046 1,447,908 78.26% 36.96%
  • 9. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 9 Year Population All Vehicles Private Cars Private Cars as a Percentage of All Vehicles Private Vehicles as a Percentage of Population 2003 3,979,900 1,937,429 1,507,106 77.79% 37.87% 2004 4,045,200 2,036,307 1,582,833 77.73% 39.13% 2005 4,133,800 2,138,680 1,662,157 77.72% 40.21% 2006 4,232,900 2,296,393 1,778,861 77.46% 42.02% 2007 4,375,800 2,441,564 1,882,901 77.12% 43.03% 2008 4,485,100 2,497,568 1,924,281 77.05% 42.90% 2009 4,533,400 2,467,660 1,902,429 77.09% 41.96% 2010 4,554,800 2,416,387 1,872,715 77.50% 41.12% 2011 4,574,900 2,425,156 1,887,810 77.84% 41.26% 2012 4,593,700 2,403,223 1,882,550 78.33% 40.98% 2013 4,614,700 2,482,557 1,910,165 76.94% 41.39% 2014 4,645,400 2,515,322 1,943,868 77.28% 41.85% 2015 4,687,800 2,570,294 1,985,130 77.23% 42.35% 2016 4,739,600 2,624,958 2,026,977 77.22% 42.77% 2017 4,792,500 2,675,879 2,066,112 77.21% 43.11% 2018 4,857,000 2,717,722 2,106,369 77.50% 43.37% 2019 4,921,500 2,805,839 2,174,779 77.51% 44.19% The following chart shows the numbers and fuel types of new private vehicles registered since 20103. The left axis refers to private cars. The right axis refers to electric cars. Figure 4 – Cumulative Number Of All and Electric New Private Cars Registered from 2010 To 2020 The information contained in the chart is shown in the following table. Year Electric Cars Hybrid Cars All Other Fuel Types Total Electric Cars % Of Total 2010 23 1,210 122,777 124,010 0.02% 2011 52 735 127,294 128,081 0.04% 3 New vehicle registration data is taken from https://data.cso.ie/table/TEA27.
  • 10. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 10 Year Electric Cars Hybrid Cars All Other Fuel Types Total Electric Cars % Of Total 2012 165 805 113,755 114,725 0.14% 2013 67 1,010 120,033 121,110 0.06% 2014 273 1,356 143,595 145,224 0.19% 2015 571 2,040 165,716 168,327 0.34% 2016 608 4,160 207,301 212,069 0.29% 2017 1,092 7,392 211,069 219,553 0.50% 2018 1,922 13,223 205,468 220,613 0.87% 2019 4,054 19,083 199,063 222,200 1.82% 2020 4,368 20,741 137,741 162,850 2.68% Total 13,195 71,755 1,753,812 1,838,762 0.72% Electric cars represent a very small proportion of new cars registered. 2020 is a poor year for any data analysis. In 2020, the number of private cars registered was 162,850, -26.71% fewer than in 2019. The number of new electric cars registered in 2020 was 4,368, 7.75% higher than the number in 2019. So, in the context of a reduction in new private cars in 2020 there is a small increase in the number of electric cars. However, the number of electric cars is still very small. It cannot currently have any significant impact on electricity demand. The following chart shows the numbers of new vehicles registered by year. Figure 5 – Number Of New Private Vehicles By Fuel Type 2010 To 2020 The following chart shows monthly new private vehicle registration numbers by fuel type from January 2015 to February 20214. The left-hand axis refers to the numbers of non-electric vehicles. The right-hand axis refers to the numbers of electric vehicles. I have added a simple linear trend line showing the rate of growth of electric vehicles. 4 New monthly vehicle registration data is taken from https://data.cso.ie/table/TEM12.
  • 11. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 11 Figure 6 – New Private Vehicles By Fuel Type By Month Jan 2012 To Feb 2021 The information contained in the chart is shown in the following table. Month All Fuel Types Fossil and Other Fuels Electric Hybrid Electric Vehicle Percentage Jan 2015 20,105 19,723 68 314 0.34% Feb 2015 15,384 15,126 72 186 0.47% Mar 2015 17,054 16,834 56 164 0.33% Apr 2015 13,166 12,972 72 122 0.55% May 2015 9,027 8,901 36 90 0.40% Jun 2015 3,924 3,870 13 41 0.33% Jul 2015 21,290 20,921 61 308 0.29% Aug 2015 8,572 8,391 49 132 0.57% Sep 2015 5,924 5,790 26 108 0.44% Oct 2015 3,943 3,873 11 59 0.28% Nov 2015 1,874 1,837 9 28 0.48% Dec 2015 847 834 3 10 0.35% Jan 2016 27,106 26,576 32 498 0.12% Feb 2016 21,173 20,736 92 345 0.43% Mar 2016 20,096 19,668 81 347 0.40% Apr 2016 14,847 14,493 51 303 0.34% May 2016 10,125 9,964 23 138 0.23% Jun 2016 4,143 4,068 8 67 0.19% Jul 2016 22,462 21,889 38 535 0.17% Aug 2016 9,781 9,523 32 226 0.33% Sep 2016 5,842 5,698 11 133 0.19% Oct 2016 3,831 3,750 13 68 0.34% Nov 2016 1,846 1,758 11 77 0.60% Dec 2016 679 655 0 24 0.00% Jan 2017 26,668 25,538 107 1,023 0.40% Feb 2017 16,905 16,191 89 625 0.53% Mar 2017 17,180 16,611 49 520 0.29% Apr 2017 13,427 13,003 80 344 0.60% May 2017 9,581 9,274 34 273 0.35%
  • 12. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 12 Month All Fuel Types Fossil and Other Fuels Electric Hybrid Electric Vehicle Percentage Jun 2017 3,585 3,474 12 99 0.33% Jul 2017 21,316 20,317 78 921 0.37% Aug 2017 8,105 7,708 65 332 0.80% Sep 2017 4,828 4,583 50 195 1.04% Oct 2017 3,255 3,076 35 144 1.08% Nov 2017 1,594 1,523 19 52 1.19% Dec 2017 601 585 5 11 0.83% Jan 2018 25,813 23,844 77 1,892 0.30% Feb 2018 16,501 15,489 78 934 0.47% Mar 2018 16,088 15,236 74 778 0.46% Apr 2018 11,557 10,811 153 593 1.32% May 2018 9,362 8,705 108 549 1.15% Jun 2018 3,716 3,523 34 159 0.91% Jul 2018 20,743 18,848 292 1,603 1.41% Aug 2018 7,681 7,070 193 418 2.51% Sep 2018 4,397 4,092 119 186 2.71% Oct 2018 2,874 2,691 58 125 2.02% Nov 2018 1,647 1,533 25 89 1.52% Dec 2018 778 748 11 19 1.41% Jan 2019 22,279 19,696 607 1,976 2.72% Feb 2019 14,178 12,423 375 1,380 2.64% Mar 2019 14,404 12,826 321 1,257 2.23% Apr 2019 13,794 12,124 307 1,363 2.23% May 2019 9,126 7,942 257 927 2.82% Jun 2019 3,858 3,478 76 304 1.97% Jul 2019 18,741 16,023 512 2,206 2.73% Aug 2019 7,202 6,138 303 761 4.21% Sep 2019 4,104 3,612 146 346 3.56% Oct 2019 3,214 2,777 215 222 6.69% Nov 2019 1,676 1,364 194 118 11.58% Dec 2019 729 559 130 40 17.83% Jan 2020 20,665 16,357 579 3,729 2.80% Feb 2020 13,263 10,879 439 1,945 3.31% Mar 2020 10,239 8,644 477 1,118 4.66% Apr 2020 1,338 1,106 100 132 7.47% May 2020 1,490 1,135 147 208 9.87% Jun 2020 2,189 1,880 98 211 4.48% Jul 2020 15,329 12,269 520 2,540 3.39% Aug 2020 7,360 5,846 389 1,125 5.29% Sep 2020 5,747 4,288 596 863 10.37% Oct 2020 4,189 3,275 399 515 9.52% Nov 2020 1,468 1,171 111 186 7.56% Dec 2020 1,032 865 85 82 8.24% Jan 2021 16,948 11,367 739 4,842 4.36% Feb 2021 11,672 8,115 570 2,987 4.88% Total 737,477 678,482 11,405 47,590 As shown before, the number of electric cars is growing but from a very low base.
  • 13. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 13 Electric Vehicles In Norway I have included details on electric car usage in Norway to demonstrate how its share of the overall private vehicle market can increase very substantially and in a very short interval. Norway has achieved this through a wide range of financial and other incentives, including: • No Value Added Tax on electric car purchases • No import duties and taxes on electric car purchases • No annual road or other vehicle taxes • Electric cars are charged road tolls at 50% of the rate of other private cars • Electric cars are charged for parking at 50% of the rate of other private cars • Electric cars can be driven in bus lanes These incentives can be classified along the following lines: Initial Incentives Ongoing Incentives Financial Incentives No VAT No taxes No road tax Non-Financial Incentives Use bus lanes Reduced tolls Reduced parking charges The net effect of the purchase-related financial incentives is to make electric cars cheaper to buy than their fossil fuel equivalent. The ongoing financial and non-financial incentives make electric cars an attractive option in Norway. The population of Norway is similar to Ireland. The country is over five times the size of Ireland. Country size will be a factor in overall private vehicle usage. This section provides a context to what can happen with electric car usage in a country similar to Ireland should the same set of incentive be implemented. The following chart shows the population of Norway5 and the number of private vehicles6 for the years 1982 to 2020. This parallels the information contained in Figure 3 on page 8. 5 Norway population data is taken from https://www.ssb.no/en/statbank/table/06913/. 6 Norway vehicle data is taken from https://www.ssb.no/en/statbank/table/01960/.
  • 14. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 14 Figure 7 – Norway Population Change And Change In All Vehicles And Private Vehicles Registered 1982 To 2020 The information contained in this chart is shown in the following table. Year Population Private Cars Private Vehicles as a Percentage of Population 1982 4,122,511 1,337,884 32.45% 1983 4,134,353 1,383,367 33.46% 1984 4,145,845 1,429,710 34.49% 1985 4,159,187 1,513,954 36.40% 1986 4,175,521 1,592,195 38.13% 1987 4,198,289 1,623,137 38.66% 1988 4,220,686 1,621,955 38.43% 1989 4,233,116 1,612,674 38.10% 1990 4,249,830 1,613,037 37.96% 1991 4,273,634 1,614,623 37.78% 1992 4,299,167 1,619,438 37.67% 1993 4,324,815 1,633,088 37.76% 1994 4,348,410 1,653,678 38.03% 1995 4,369,957 1,684,664 38.55% 1996 4,392,714 1,661,247 37.82% 1997 4,417,599 1,758,001 39.80% 1998 4,445,329 1,786,404 40.19% 1999 4,478,497 1,813,642 40.50% 2000 4,503,436 1,851,929 41.12% 2001 4,524,066 1,872,862 41.40% 2002 4,552,252 1,899,767 41.73% 2003 4,577,457 1,933,660 42.24% 2004 4,606,363 1,977,922 42.94% 2005 4,640,219 2,028,909 43.72% 2006 4,681,134 2,084,193 44.52% 2007 4,737,171 2,154,837 45.49% 2008 4,799,252 2,197,193 45.78%
  • 15. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 15 Year Population Private Cars Private Vehicles as a Percentage of Population 2009 4,858,199 2,244,039 46.19% 2010 4,920,305 2,308,548 46.92% 2011 4,985,870 2,376,426 47.66% 2012 5,051,275 2,442,964 48.36% 2013 5,109,056 2,500,265 48.94% 2014 5,165,802 2,555,443 49.47% 2015 5,213,985 2,610,352 50.06% 2016 5,258,317 2,662,910 50.64% 2017 5,295,619 2,719,395 51.35% 2018 5,328,212 2,751,948 51.65% 2019 5,367,580 2,801,208 52.19% 2020 5,391,369 2,810,475 52.13% In Ireland in in 2019 the number of private vehicles as a percentage of the population was 44.19%. The same figure for Norway was 52.19%. This implies that private car numbers in Ireland have the potential to grow. The following chart shows the number of private cars as a percentage of the population for both Ireland and Norway for the years 1982 to 2020. At the time of writing, private vehicle information is not available for Ireland for 2020. Figure 8 – Comparison of Numbers Of Private Vehicles As A Proportion Of Population For Ireland And Norway Norway shows a consistent growth in the number of private cars as a proportion of the population. Norway started at a much higher percentage in 1982 than Ireland. Ireland shows a rapid growth from 1982 to 2007. Thereafter the number dropped, probably due to the global and local economic recession. There are many reasons for the differences in private car ownership between countries, including: • Economic • Social and cultural • Size of country
  • 16. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 16 • Extent and quality of public transportation network • Demographic – this includes factors such as the age profile of the population, the population density and the proportion of the population that lives in centralised urban areas and the proportion of the population that consists of migrants who may not be able to afford to or want to buy a car Analysing these factors is outside the scope of this document. However, this shows that there is still potential for growth in the number of private vehicles in Ireland. If Ireland reaches the same population proportion of private cars as Norway, the number of private cars could increase by 430,000. The following chart shows the growth in the number of electric cars in Norway for the years 2008 to 20207. Figure 9 – Change In The Number Of Private Cars And Electric Cars In Norway From 2008 To 2020 The information contained in this chart is shown in the following table. Year Total Private Cars Electric Cars Electric as Proportion of All Private Vehicles Annual Rate of Increase of Electric Cars Annual Rate of Increase of All Private Cars 2008 2,196,107 1,693 0.08% 2009 2,242,948 1,776 0.08% 4.90% 2.13% 2010 2,307,485 2,068 0.09% 16.44% 2.88% 2011 2,375,365 3,909 0.16% 89.02% 2.94% 2012 2,441,859 8,031 0.33% 105.45% 2.80% 2013 2,499,191 17,770 0.71% 121.27% 2.35% 2014 2,554,361 38,652 1.51% 117.51% 2.21% 2015 2,609,263 69,134 2.65% 78.86% 2.15% 2016 2,661,806 97,532 3.66% 41.08% 2.01% 2017 2,718,281 138,983 5.11% 42.50% 2.12% 2018 2,750,856 195,351 7.10% 40.56% 1.20% 7 Norway private vehicle fuel type data is taken from https://www.ssb.no/en/statbank/table/07849.
  • 17. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 17 Year Total Private Cars Electric Cars Electric as Proportion of All Private Vehicles Annual Rate of Increase of Electric Cars Annual Rate of Increase of All Private Cars 2019 2,800,090 260,692 9.31% 33.45% 1.79% 2020 2,809,358 340,002 12.10% 30.42% 0.33% This illustrates a very rapid rate of growth in the number of electric cars. It is interesting to note that while the population of Norway increased by 12.34% from 2008 to 2020, the total number of private cars increased by 27.92% in the same interval. One possible explanation for this might be the growth in the number of multi-car families. Additionally, this trend, if it exists, might be driving the increase in the number of electric cars where the additional car is electric and is bought because of the convenience associated with its many incentives. This is unvalidated speculation. But it demonstrates what could happen in Ireland. It is a scenario that should be planned for. In terms of the total number of cars and total number of electric cars, Ireland in 2016 is at roughly the same position as Norway in 2008. This is not to imply that Ireland is lagging eight years behind Norway in terms of electric car market share or that Ireland will definitely experience the same level of growth in electric cars as Norway.
  • 18. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 18 Pattern of Electricity Usage In Ireland The following shows the patterns of the rate of electricity usage in megawatts (MW) for the weekdays of 2019 by time of day8. Figure 10 – Patterns of Weekday Electricity Usage In Ireland in 2019 By Time Of Day This shows a consist pattern of usage with only the volume changing throughout the year. In general electricity usage peaks at around 18:00 and drops thereafter. The following chart shows the daily MWh totals (previous charts have shown usage in terms of megawatts being generated while this shows the number of megawatt hours consumed) by day from July 2013 to December 2020. I have added a simply linear trend line to this. This chart shows a clear seasonal pattern of electricity usage with periods of high usage occurring in winter and periods of low usage in summer. 8 Electricity usage data is taken from http://smartgriddashboard.eirgrid.com/.
  • 19. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 19 Figure 11 – Daily MWh Electricity Consumption From July 2013 To December 2020 Notwithstanding changes in patterns of electricity usage due to COVID-19 restrictions the average rate of electricity usage has increased by around 16% over this interval. For the purposes of analysing electricity usage and the impact of electric cars, I have selected the date Tuesday 11 February 2020 for the single day to use. This represents the day when the largest recent use of electricity occurred before COVID 19 restrictions were implemented that have affected subsequent electricity usage patterns. This is used as the reference day for subsequent determination of potentially available electricity generation capacity and for analysing the approach to optimising both electric car recharging and electricity generation capacity. The selected date is purely illustrative.
  • 20. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 20 Figure 12 – Electricity Usage For Tuesday 11 February 2020 Patterns of Vehicle Usage In Ireland This section looks at private vehicle traffic data9. I have taken traffic data for cars for 2019 at the points shown in the diagram below. Clearly this is only a small subset of all the available traffic data. The intention is to sample traffic data to identify patterns of usage and, by assumption, patterns of home charging (the assumption is that if an electric car is not being driven in the evening, it is plugged in to a home charger). Traffic patterns for 2020 have be affected by COVID-19 restrictions. 2019 2020 Figure 13 – Comparison Of Traffic In 2019 And 2020 9 Traffic data is taken from https://www.nratrafficdata.ie/.
  • 21. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 21 Figure 14 – Traffic Data Sampling Locations The following charts show the weekday car-only traffic data for these three sampling points. I have omitted days where there are any gaps in data. In all cases, the pattern of usage is very similar: a peak between 17:00 and 18:00, a gradual drop to low levels of traffic at 01:00 and a rapid increase after 05:00 to a morning peak at around 08:00 and with fairly consistent levels of traffic throughout the day until the evening peak again. Figure 15 – Traffic Data At Point N01 South of M50 Junction 02 Santry, Whitehall
  • 22. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 22 Figure 16 – Traffic Data At Point M50 Between Junction 07 N04/M50 and Junction 09 N07/M50 Red Cow, Palmerstown, Co. Dublin Figure 17 – Traffic Data At Point M11 Between M50/M11 and Bray North Junction, Bray, Co. Dublin I have used these patterns of traffic usage to estimate the rate at which electric cars are not being driven and are therefore plugged in. Potential Impact Of Electric Vehicles Charging On Electricity Demand The following chart overlays the electric usage for weekdays in 2019 with weekday private car traffic patterns for 2019.
  • 23. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 23 Figure 18 – Annual Electricity Usage For 2019 Overlaid With Traffic Usage For 2019 The following chart shows the estimated and assumed percentage of electric cars that might be charging at any interval during a weekday. Figure 19 – Assumed Percentage Of Electric Cars Charging By Weekday Interval The information contained in this chart is shown in the following table. Interval % Cars Not On The Road 17:00 2.73% 17:15 6.21% 17:30 9.69% 17:45 13.17% 18:00 16.66% 18:15 22.74% 18:30 28.83%
  • 24. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 24 Interval % Cars Not On The Road 18:45 34.92% 19:00 41.00% 19:15 45.28% 19:30 49.56% 19:45 53.84% 20:00 58.12% 20:15 60.82% 20:30 63.53% 20:45 66.24% 21:00 68.95% 21:15 70.09% 21:30 71.23% 21:45 72.37% 22:00 73.52% 22:15 76.82% 22:30 80.13% 22:45 83.43% 23:00 86.74% 23:15 87.95% 23:30 89.17% 23:45 90.38% 00:00 91.59% 00:15 92.58% 00:30 93.56% 00:45 94.54% 01:00 95.52% 01:15 96.11% 01:30 96.70% 01:45 97.28% 02:00 97.87% 02:15 97.56% 02:30 97.25% 02:45 96.94% 03:00 96.62% 03:15 95.39% 03:30 94.15% 03:45 92.91% 04:00 91.67% 04:15 89.41% 04:30 87.15% 04:45 84.89% 05:00 82.63% 05:15 73.16% 05:30 63.70% 05:45 54.24% 06:00 44.78% 06:15 37.94% 06:30 31.11% 06:45 24.27% 07:00 17.44% 07:15 18.40%
  • 25. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 25 Interval % Cars Not On The Road 07:30 19.36% 07:45 20.32% 08:00 21.28% 08:15 23.68% 08:30 26.08% 08:45 28.48% 09:00 30.89% 09:15 31.79% 09:30 32.69% 09:45 33.60% 10:00 34.50% 10:15 34.43% 10:30 34.36% 10:45 34.29% 11:00 34.21% 11:15 33.55% 11:30 32.89% 11:45 32.23% 12:00 31.56% 12:15 30.88% 12:30 30.20% 12:45 29.52% 13:00 28.85% 13:15 28.95% 13:30 29.05% 13:45 29.16% 14:00 29.26% 14:15 26.35% 14:30 23.44% 14:45 20.53% 15:00 17.62% 15:15 13.22% 15:30 8.81% 15:45 4.41% 16:00 0.00% 16:15 0.68% 16:30 1.36% 16:45 2.05% This assumed percentage is then used to describe the potential impact of different numbers of electric cars being plugged-in according to this schedule. The following chart is a replica of Figure 1 on page 6. It uses this car charging profile to estimate the impact of uncontrolled domestic car charging. The electricity usage profile for the single day Tuesday 11 February 2020 is used as a baseline.
  • 26. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 26 Figure 20 – Impact Of Uncontrolled Electric Car Charging On Patterns Of Electricity Usage Approaches To EV Charging To Avoid Increased Peaks In Electricity Demand Introduction This analysis consists of running simulations of various charging scenarios using randomly generated values. Each simulation consisted of running the calculations for 450,000 electric cars The following calculations were performed: 1. A probability density function has already been calculated using the electricity availability profile shown in Figure 21 on page 27. 2. A currently battery capacity level for the electric car is randomly calculated using a uniform distribution between a usage profile level and the maximum battery capacity for the car profile – the car profiles used are listed in Figure 22 on page 28. This analysis looks at an approach to domestic electric car charging where the charging unit applies the following rules: 1. The target battery capacity is calculated to bring the car to 100% charge.
  • 27. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 27 2. The time required is calculated and it is assumed there is no need for a pre-charge battery warm-up. 3. Charging cannot start before 18:30 to ensure that the peak electricity usage interval has passed. The electricity availability profile probability density function is truncated before this time. 4. Charging should end before 05:30 to ensure that the car is charged before the start of peak traffic. The electricity availability profile probability density function is truncated after this time. 5. The last time interval when charging can start so that the car is fully charged before the charging end interval of 05:30 is calculated. 6. A charging start interval is randomly generated using the previously calculated available electricity probability density function between the start charging interval of 18:30 and the last time interval when charging can start. 7. Car charging capacities are calculated as integers. These rules are applied independently for each car charge. The following is an example: 1. A car has a 100 kWh battery capacity. The randomly generated available battery charge is 58 kWh, so a charge of 42 kWh is required to bring the car to a full charge. 2. Based on a charging capacity of 7.4 kWh, the charge time is roughly 5 hours 45 minutes. 3. A random charge start time is generated between 18:30 and 23:45 (end charge time of 05:30 less charge time of 5 hours 45 minutes). In this example, the assigned charge start time is 21:45. The following chart shows the start and stop charging times used in the analysis. Figure 21 – Start And End Intervals Of Electric Car Charging
  • 28. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 28 The notional amount of electricity capacity available in the target charging interval is 15,319 MWh. The following chart shows the profile of electric car battery capacities used in the analysis. Figure 22 – Profile of Electric Car Battery Capacity Used In Analysis Electric Car Charging Simulations Different sets of simulations have been run multiple times. Each simulation set corresponds to an electric car usage profile from high to low for populations of 300,000 and 450,000 electric cars. The amount of battery usage governs the amount of residual battery capacity available and therefore the amount of charge required and the time to deliver it. The following table summarises the total amount of charge required for a population of 450,000 cars for ten sample runs for six utilisation profiles, representing 27 million individual electric car simulations. This is only a subset of the total number of simulations run but there is little variation between them. The total battery capacity of the sample population of 450,000 cars is 29,021 MWh based on the profile of car battery capacity listed in Figure 22 above. Simulation Run Total MWh Charge Required Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 1 12,236 12,218 10,362 10,048 8,640 7,290 2 12,496 12,130 10,929 9,983 8,952 7,571 3 12,508 11,504 10,957 9,953 8,740 7,570 4 12,324 11,563 11,072 10,219 9,024 7,484 5 12,618 11,716 11,028 9,957 9,062 7,320 6 11,934 11,708 11,020 9,719 8,809 7,604 7 12,630 11,513 10,932 9,562 8,772 7,703 8 12,168 11,638 11,159 9,784 8,728 7,406 9 12,728 11,404 11,069 10,005 8,806 7,452 10 12,197 11,727 10,920 9,848 8,829 7,497 Average MWh Required 12,384 11,712 10,945 9,908 8,836 7,290 Percentage to Total Battery Capacity Requiring Charge 42.67% 40.36% 37.71% 34.14% 30.45% 25.83% Average Utilisation of Available Charging Capacity 80.84% 76.45% 71.44% 64.67% 57.68% 48.94% More details on these simulations are listed on page 29.
  • 29. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 29 The following table summarises for a second set of simulations the total amount of charge required for a population of 300,000 cars for ten sample runs for six utilisation profiles, representing 18 million individual electric car simulations. The simulations listed here have high levels of battery charging requirements. The total battery capacity of the sample population of 300,000 cars is 19,347 MWh based on the profile of car battery capacity listed in Figure 22 on page 28. Simulation Run Total MWh Charge Required Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 1 16,145 15,891 15,658 15,460 15,227 15,023 2 16,116 15,867 15,687 15,531 15,317 15,004 3 16,124 15,908 15,688 15,463 15,259 15,030 4 16,115 15,988 15,638 15,454 15,266 15,015 5 16,126 15,869 15,725 15,443 15,250 14,959 6 16,181 15,924 15,680 15,475 15,316 15,092 7 16,158 15,911 15,625 15,467 15,237 14,962 8 16,138 15,920 15,701 15,492 15,228 14,993 9 16,153 15,901 15,686 15,483 15,234 15,041 10 16,117 15,953 15,693 15,480 15,251 14,960 Average MWh Required 16,137 15,913 15,678 15,475 15,259 15,008 Percentage to Total Battery Capacity Requiring Charge 83.41% 82.25% 81.04% 79.98% 78.87% 77.57% Average Utilisation of Available Charging Capacity 105.34% 103.88% 102.34% 101.01% 99.60% 97.97% More details on these simulations are listed on page 36. Simulation Set 1 Results This section contains more details on the six simulations contained in simulation set 1. There are two charts for each simulation: 1. Electricity allocation profile for car charging 2. Impact on overall electricity allocation In all cases, these simulations show that, at the simulated levels of charge required across the population of cars, electric car charging can be handled without any additional impact on overall electricity utilisation and that all cars are fully charged before the target end time of 05:30. These results show the individual car charging units can deliver the required charge without the need for any centralised scheduling and allocation facility by applying the simple rules listed on page 26.
  • 30. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 30 Utilisation Profile 1 Simulation Results Figure 23 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 42.67% Figure 24 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 42.67%
  • 31. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 31 Utilisation Profile 2 Simulation Results Figure 25 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 40.36% Figure 26 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 40.36%
  • 32. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 32 Utilisation Profile 3 Simulation Results Figure 27 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 37.71% Figure 28 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 37.71%
  • 33. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 33 Utilisation Profile 4 Simulation Results Figure 29 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 34.14% Figure 30 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 34.14%
  • 34. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 34 Utilisation Profile 5 Simulation Results Figure 31 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 30.45% Figure 32 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 30.45%
  • 35. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 35 Utilisation Profile 6 Simulation Results Figure 33 – Car Charging Electricity Allocation For 450,000 Cars With An Average Battery Requirement Of 25.83% Figure 34 – Overall Electricity Usage For Car Charging For 450,000 Cars With An Average Battery Requirement Of 25.83%
  • 36. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 36 Simulation Set 2 Results This section contains more details on the six simulations contained in simulation set 2. As before, there are two charts for each simulation: 1. Electricity allocation profile for car charging 2. Impact on overall electricity allocation In all cases, these simulations show that, at the simulated high levels of charge required across the population of cars, electric car charging cannot be handled without any additional impact on overall electricity utilisation and that most but not all cars are fully charged before the target end time of 05:30. This is not surprising given that for four of the six simulations, the required charge is greater than the available electricity over the charging interval. These results are to be expected given that some of the sample cars have a battery capacity of 100 kWh. Using a standard domestic charger operating at 7.4kWh it would take 13.5 hours to completely charge the car. If charging starts at 18:30, it cannot complete before 10:00 on the following day. As the battery capacity of electric cars increases, the viability of domestic chargers as the sole source of charging capacity will be diminished for high levels of charging requirements.
  • 37. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 37 Utilisation Profile 1 Simulation Results Figure 35 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 83.41% Figure 36 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 83.41%
  • 38. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 38 Utilisation Profile 2 Simulation Results Figure 37 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 82.25% Figure 38 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 82.25%
  • 39. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 39 Utilisation Profile 3 Simulation Results Figure 39 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 81.04% Figure 40 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 81.04%
  • 40. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 40 Utilisation Profile 4 Simulation Results Figure 41 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 79.98% Figure 42 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 79.98%
  • 41. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 41 Utilisation Profile 5 Simulation Results Figure 43 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 78.87% Figure 44 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 78.87%
  • 42. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 42 Utilisation Profile 6 Simulation Results Figure 45 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57% Figure 46 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57%
  • 43. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 43 Simulation Set 3 Results This shows the results of a single set of simulations run for 300,000 cars where the target completion time is moved out from 05:30 to 07:30. It repeats the simulation contained in Utilisation Profile 6 Simulation Results in Simulation Set 2 on page 42 This shows that when the target time is pushed out, the impact of electric car charging on overall electricity usage is minimised as the peak is flattened and the charging curve moved out to a later interval. In this simulation, the charging has no impact on peak electricity demand. Simulation Run Total MWh Charge Required Profile 1 1 14,971 2 14,988 3 14,946 4 14,969 5 14,999 6 14,981 7 15,003 8 15,064 9 15,028 10 15,090 Average MWh Required 15,004 Percentage to Total Battery Capacity Requiring Charge 77.55% Average Utilisation of Available Charging Capacity 97.94% Figure 47 – Car Charging Electricity Allocation For 300,000 Cars With An Average Battery Requirement Of 77.57% Where The Target Completion Time Is Moved To 07:30
  • 44. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 44 Figure 48 – Overall Electricity Usage For Car Charging For 300,000 Cars With An Average Battery Requirement Of 77.57% Where The Target Completion Time Is Moved To 07:30 Summary The results of the simulations are not surprising. All it really shows is that the distribution of randomly values generated based on a probability distribution function will always mirror that distribution. So, if charging start times are randomly allocated based on a probability density function generated from notionally available electricity then the times assigned will match that distribution. The analyses show that even for large numbers of electric cars there should not be any need to create any additional electricity generation capacity to cater for their charging requirements. This does not need any complex centrally managed car charging co-ordination and allocation infrastructure. The autonomous and independent application of a simple set of rules can ensure the success of this charging approach even for large numbers of electric cars requiring high levels of charging. The simulations could be extended to include some variability in the target charge completion time. However, this will would add to the core result which is that the independent application of a simple set of rules in itself provides for the effective management of electric car charging. Conclusions The number of electric cars in Ireland is currently very low and so there is no need to be concerned about any impact on electricity usage patterns and generation requirements. The experience from Norway is that, given the right set of incentives, electric car usage can increase very substantially and very rapidly. A second less obvious experience from Norway is that the total number of private cars continues to increase at a rate far higher than population growth. As described on page 17, that while the population of Norway increased by 12.34% from 2008 to 2020, the total number of private cars increased by 27.92% in the same interval. One possible
  • 45. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 45 explanation for this might be the growth in the number of multi-car families. Additionally, this trend, if it exists, might be driving the increase in the number of electric cars where the additional car is electric and is bought because of the convenience associated with its many incentives. This is unvalidated speculation. A decentralised approach to car charging time allocation will work for large numbers of cars without any disruption on the usability of electric cars. This would require co-ordination between the providers of domestic car charging facilities to implement the charging time allocation approach described here. The set of rules would have to be defined and agreed centrally. The simple approach described in this document could be extended to include features in the charging unit such as: • Notification of the car owner of the expected car start and end time. • An emergency option to provide for immediate rather than deferred charging. • The inclusion of some individual unit user-specific additional information collection and decision-making regarding factors such as the desired charging end time and the target end charge capacity. • If charging starts outside the managed interval and if the charging ends before the managed interval, it can proceed as normal. The analysis conducted here applies to Ireland. Other countries will show similar patterns of car and electricity usage and so the conclusions can be applied more generally. Limitations On Domestic Electric Car Charging There are limitations to the capacity of domestic car chargers. In Ireland every connection to the electricity network – house, apartment, retail or industrial unit – has a designated Maximum Import Capacity (MIC). The same approach is used in most other countries. Different terms are used but their meanings are the same. MIC is the maximum electricity demand level that can be catered for by the electric network connection. MIC values are quoted in kVA (kilovolt-amps). kVA is a measure of the total amount of electric power in use in a system. In a system that operates at 100% efficiency kW = kVA. However electrical systems are never 100% efficient. The conversion between kW and kVA uses a Power Factor which is less than one. 1 kVA x Power Factor of 0.95 = 0.95 kW. Domestic connections in Ireland have MIC values of 12kVA, 16kVA, 20kVA or 29kVA. The lower values of 12kVA and 16kVA apply to apartments and small houses. So, a 7.4 kWh charging unit represents a high proportion of the total capacity of a domestic connection. It is not realistic to have a separate electric car charging unit operating at 7.4 kWh for an apartment. The MIC of a property can be increased but at a cost that can be significant. So, there are limitations to where domestic charging units can be installed. The following table lists the number of private households by type of property taken from the 2016 census10. Type of Private Accommodation Number Detached house 715,133 Semi- detached house 471,948 Terraced house 284,569 Flat or apartment in a purpose- built block 172,096 Flat or apartment in a converted house or commercial building 28,783 10 Property data is taken from https://data.cso.ie/table/E1001.
  • 46. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 46 This refers the accommodation occupied by households and does not include details such as vacant and holiday properties. The following table contains information on types of property taken from the 2016 census11. Unfortunately, this information does not contain specific property type data. Type of Private Accommodation Number Total housing stock 2,003,645 Occupied by usual resident(s) of the household 1,697,665 Occupied by visitors only 9,788 Unoccupied - residents temporarily absent 50,732 Unoccupied - vacant house 140,120 Unoccupied - vacant flat 43,192 The following data shows similar property data for Norway from 202112. Type of Private Accommodation 2016 2017 2018 2019 2020 2021 Detached house 1,258,298 1,265,093 1,271,158 1,276,690 1,281,004 1,284,892 House with 2 dwellings 225,132 227,798 230,328 232,948 235,467 237,789 Row house, linked house and house with 3 dwellings or more 292,926 297,409 302,720 307,910 311,648 315,499 Multi-dwelling building 585,679 598,020 610,742 628,145 643,631 655,847 Residence for communities 53,227 55,580 60,458 61,912 63,309 68,479 Other building 70,091 71,689 72,326 73,550 74,981 75,015 Total 2,485,353 2,515,589 2,547,732 2,581,155 2,610,040 2,637,521 Presumably the Norwegian classification House with 2 dwellings is equivalent to Semi- detached house and Row house, linked house and house with 3 dwellings or more is roughly equivalent to Terraced house. In 2016, the latest year for which data exists for all categories, the population of Norway was 5,258,317 and the number of private properties was 2,485,353. The ratio of properties to population was 47.27%. For Ireland in 2016, the population was 4,739,600 and the number of private properties was 2,003,645, giving a ratio of 42.27%. The differences in Norway could be due to factors such as higher ownership of second/holiday properties and demographic reasons such as smaller family units due to higher divorce rates. Car charging units cannot be installed in all domestic property types and so some other form of car charging infrastructure will always be needed. There are limitations to the applicability of domestic charging infrastructure, irrespective of whether it is managed or unmanaged. Approaches To Implementing An Electric Car Charging Infrastructure This analysis looks at the viability of optimising domestic car charging using a decentralised approach to allocation of car charging times. There are a number of approaches to implementing an electric car charging infrastructure. The following table summarises these. These approaches are not mutually exclusive and can be mixed. Options Description Unmanaged domestic charging In the short term, this is the easiest approach to implementing a charging 11 Property type data is taken from https://data.cso.ie/table/E1068. 12 Norway property data is taken from https://www.ssb.no/en/statbank/table/06265/.
  • 47. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 47 Options Description infrastructure. It requires no large investment and no centralised decision-making. However, as the number of electric cars grows, unmanaged domestic charging has the potential to be disruptive to electricity generation patterns. This is the worst and least sustainable option for electric car charging infrastructure if the number of electric cars is expected to grow beyond a small number. Centrally managed domestic charging A centrally managed domestic charging infrastructure is expensive to design, implement, operate, support and maintain. It has few advantages over other forms of domestic charging other than adding cost and complexity. Locally managed domestic charging As described in this document, this would appear to be a realistic and sustainable option in the context of domestic charging. It adds no real cost to a basic domestic charging infrastructure other than possibly retrofitting existing units. This could be a valid option to allow any decision on and investment in centrally located public charging infrastructure. Distributed public charging Public car charging infrastructure can operate at much higher rates – such as 43, 50, 120 and 150 kWh – than domestic units. These units can be installed individually in a distributed pattern. This may be less than optimal as drivers may have to travel some distance to find an available unit. Single unts may be subject to longer queueing times than clusters of multiple units. Centrally located public charging Multi-bay car charging units, similar to existing service stations, could offer shorter queueing times than individual units. They could be located for convenience near other facilities and be managed and offer other services. However, this option is the most expensive in the short-term and requires centralised decision-making and ahead-of-the-curve up-front investment. These options exclude charging facilities installed in public or private car parks such as office and apartment buildings. Assumptions Contained In This Analysis The following table lists the assumptions contained in this analysis and their potential impact on the validity of its conclusions. Year Potential Impact Notes All domestic car chargers operate at 7.4 kW. Low Most domestic chargers operate at 7.4 kW. The requirement for a pre-charging battery warm-up cycle when the battery is not at optimum charging temperature has been omitted from any charging time calculations. Low When the external temperature is low, a pre-charging warm-up interval is required. The assumption is that electric cars are plugged-in directly the arrive home and so will not need to be warmed-up. Norway is comparable to Ireland. Low The use of Norway as an example of how the number of electric cars can increase very rapidly over a short interval when the right set of incentives is put in place gives rise to one scenario.
  • 48. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 48 Year Potential Impact Notes Additional electricity generation can be scheduled for off peak times to provide capacity for electric car charging Medium A core assumption of this analysis is that notional electricity availability profile (the inverse of the electricity usage and demand profile) described in Figure 21 on page 27 is valid. Currently, there is reduced generation during this interval. It is assumed that electricity generation can be easily turn on without significant cost during this interval. Generators may schedule maintenance and other activities during this interval that could longer occur. The selection of a single day of Tuesday 11 February 2020 Low A single day was selected for convenience. Any capacity planning analysis needs to use as a baseline a maximum or near to maximum level of current utilisation. However, during periods of lower electricity utilisation such as during the summer, the effect would be to increase electricity usage. The selection of three traffic sampling intervals is sufficient. Medium The selected traffic sampling intervals are on or near motorways in the Dublin area. This introduces bias in favour of car travel to/from/within Dublin. It may skew journeys in favour of longer-distance commuting. While it is reasonable to assume that private car traffic on these roads are all ultimately driven home, the selected sampling points are all in Dublin. Traffic at these points will all be some distance away from their ultimate destinations. The patterns of traffic usage at the three sampling points all show the same patterns of use. The pattern of existing private car usage will be replicated for electric cars Low It may be that electric cars will be used for shorter journeys. The pattern of car usage is directly related to the time when an electric car will be plugged in to a domestic charger Low It is reasonable to assume that as soon as an electric car arrives at its home destination it will be immediately plugged-in and will remain plugged-in overnight. A small number may be used for additional journeys. All electric cars must complete charging by 05:30 so they will be available for use Medium This assumption affects the overall allocation of charging to cars. When the required overall battery charge capacity is high then this causes the peak electricity requirement to be pushed up If the end time can be extended then as shown in the Simulation Set 3 Results on page 43 then the peak electricity utilisation is lowered. The distribution of electric car battery capacities listed in Figure 22 on page 28 reflects reality. Medium There is currently no accurate information on the battery capacities of electric cars. The charge required for and thus the implied distance travelled by electric cars used in simulations is valid. Medium Table 1.5: Average journey profile by mode of travel, 2013, 2014 and 2016 of the National Travel Survey 201613 lists the average car journey was 16.3 km. Based on this, the high levels of battery charge contained in Simulation Set 2 may be significantly overestimated. 13 https://www.cso.ie/en/releasesandpublications/ep/p-nts/nts2016/keyf/
  • 49. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 49
  • 50. Analysis of Decentralised, Distributed Decision-Making for Optimising Domestic Electric Car Charging Page 50 For more information, please contact: http://ie.linkedin.com/in/alanmcsweeney