1. Modelling the impact of integrated EV charging and domestic heating strategies on future energy demands
Nick Kelly, Jon Hand,
Aizaz Samuel
ESRU, University of Strathclyde, Glasgow

2. Overview UK policy & domestic electrification aims role of simulation
high temporal resolution
solar
modelling demand
hot water
building model
EV charging algorithm
simulations
demand control strategies
results
conclusions
future work

3. Domestic Electrification
UK has a target of 80% GHG emission reductions by 2050 (relative to 1990 levels)
electrification of domestic heating and associated vehicle use seen as a means to achieve this
coupled with decarbonisation of the UK electricity supply
however, serious implications for electricity network
increase in electricity use and peak demands

4. Domestic Electrification

5. Impacts of Electrification
greatly increased electrical energy use
increased peak demand
possible power quality problems
failure of key electrical network components e.g. transformers
need for network reinforcement at all levels

6. Aim of Work
assess the potential impact of electrification of heating and vehicle loads on a future high-efficiency UK dwelling and investigate mitigating strategies

7. Role of Simulation integrated building simulation powerful tool for
predicting building thermal performance
by extension heat-driven electrical demands (e.g. heat pumps)
heat-driven generation (micro-CHP)
generation from solar technologies such as PV
effects of changes in buildings and climate
can be used to generate “realistic” electrical demand data for buildings
and assess impacts on end users

8. Higher Temporal Resolution
BS grounded in modelling of building thermal performance
tools often run at hourly resolution
however this is not suitable for assessment of peak electrical demands
… and interaction of a dwelling with LV network
requires higher temporal resolution boundary conditions
solar (PV output)
hot water demand
domestic electrical loads

9. Higher Temporal Resolution
electrical demand

10. High Resolution Solar Data
ESP-r’s default climate database holds hourly data
for sub-hourly time steps interpolation is used
tool developed to generate sub hourly data from hourly datasets (1st order Markov model) [1]
transition probabilities need to be calibrated using measured high resolution data
temporal definition database (TDF) can hold sub-hourly data (either real or generated)
[1] McCracken D Synthetic High Resolution Solar Data, MSc Thesis, University of Strathclyde, Glasgow.

11. Electrical Demand
tool developed by Richardson [2] adapted to pre-simulate annual domestic electrical demand at 1-min resolution
… and corresponding thermal gains
pre-simulated data held in TDF
used as a boundary condition by ESP-r’s electrical systems solver
[2] Richardson I, Thompson M, Infield D, Clifford C Domestic electricity use: A high-resolution energy demand model. Eng. and Build. 42(10) pp

12. Hot Water Draws
work of Jordan and Vagen [3] (IEA SHC Annex 26) used to generate new stochastic hot water draw component in ESP-r
determines if hot water draw takes place ,draw flow rate and duration at each simulation time step (at up to 1-min resolution)
model also used as basis of draw profiles generated in IEA ECBCS Annex 42
[3] Jordan U and Vajen K, DHWCALC: Program to Generate Domestic Hot Water Draws with Statistical Means for User Defined Conditions, Proc. ISES Solar World Congress, Orlando, US.

13. ‘Zero-Energy’ Building Model
future UK family dwelling used as basis for modelling ~ 90m2
insulated to Passivhaus standard
8kWp PV installation on monopitch roof
A++ appliances and LED lighting data used to generate electrical demands
PV output covers electrical demand
… but not EV demand

14. Zero-Energy Building Model
space and water heating from buffered (500L tank) ASHP
MVHR with heating coil in supply
4m2 roof mounted solar collectors
200L SDHW tank
200L grey water heat recovery tank

15. EV Charging Algorithm
probabilistic EV charging algorithm developed for ESP-r
calculates real power draw or injection from EV battery
vehicle presence calculation
vehicle state (charging/idle/absent)
trip distance estimation
SOC tracking
multiple charging strategies
uses data from UK transport survey to determine trip probability

16. EV Charging Algorithm

17. Simulations
model used to predict the total electrical demand of the dwelling accounting for:
heat pump (space & water heating loads)
appliance and lighting demands (pre-calculated)
EV demands
PV generation
performance simulated Jan/Feb UK climate: “worst case”
1-min time resolution

18. Simulations
used in assessing the impacts of different ASHP and EV operating strategies
Base Case – no EV, no Heat Pump
The house is assumed to be heated using biomass and there is no EV.
Case 1 – unrestricted slow charging
Both heating system operation and vehicle charging are uncontrolled. The vehicle is slow charged (3.3kW – up to 6.5 hrs) when it returns from trips and heat is supplied when required.
Case 2 – Unrestricted fast charging
Both heating system operation and vehicle charging are uncontrolled. The vehicle is fast charged (6.6kW – up to 3hrs) when it returns from trips and heat is supplied when required.
Case 3 – load sensitive vehicle battery slow- charging
The vehicle battery is only slow charged at full power if the dwelling and vehicle demand would be less than 7.5 kW.
Case 4 – load sensitive vehicle battery fast- charging
The vehicle battery is only fast charged at full power when the overall dwelling and vehicle demand would be less than 7.5 kW.
Case 5 – off-peak heating and unrestricted slow charging
The heating buffer tank (figure 5) is charged during off peak periods (11 pm – 7am), slow vehicle charging is unrestricted.
Case 6 – off peak heating and unrestricted fast charging
The heating buffer tank (figure 5) is charged during off peak periods (11 pm – 7am), fast vehicle charging is unrestricted.
Case 7 – off peak slow charging and heat load shifting
Both slow vehicle charging and heating system buffer tank charging are shifted to off peak periods (11 pm – 7am).
Case 8 – off-peak fast battery charging and heat load shifting
Both fast vehicle charging and heating system buffer tank charging are shifted to off peak periods (11 pm – 7am).

19. Simulations Load restricted fast EV charge
Load restricted slow EV charge
Off peak heat pump, fast EV charge
Unrestricted fast EV charge
Off peak heat pump and off-peak fast EV charge
Off peak heat pump and off-peak slow EV charge
Off peak heat pump, slow EV charge
Unrestricted slow EV charge
Base case

20. Distance travelled (km) Maximum charge time (mins)
Results
electrical
Scenario
Base Case 1 2 3 4 5 6 7 8
Elec. demand (kWh)
387.8
1106.1
1136.9
1081.2
1074.6
1137.6
1133.3
1124.1
1144.2
EV demand (kWh) -
395.8
426.4
379.8
365.0
443.4
408.8
425.9
Appl. demand (kWh)
463.7
ASHP demand (kWh)
279.8
273.9
271.6
269.7
269.1
PV output (kWh)
223.7
Elec. export (kWh)
139.3
112.9
116.1
110.1
118.5
106.3
116.3
128.2
Self-consumption (kWh)
84.4
110.8
107.6
113.6
105.2
117.4
107.4
95.5
BOP and losses kWh
160.4
144.0
134.7
149.9
131.0
156.6
133.9
113.1
Max P demand W
8019.2
8251.6
7960.6
9088.0
Max P export W
2239.8 EV
Scenario
Base Case 1 2 3 4 5 6 7 8
EV demand (kWh) - 
395.8
426.4
379.8
365.0
443.4
408.8
425.9
Distance travelled (km)  -
2388.4
2588.7
2292.4
2219.6
2673.5
2547.2
2620.3
Return trips (-) -
107.0
112.0
111.0
109.0
103.0
105.0
Maximum charge time (mins)
328.0
172.0
368.0
190.0
348.0
1156.0
998.0
Mean SOC (%)
97.1
98.3
96.8
99.0
96.0
74.1
78.2
Heat pump
Scenario
Base Case 1 2 3 4 5 6 7 8
ASHP demand (kWh) -
279.8
273.9
271.6
269.7
269.1
ASHP heat output (kWhrs)
858.5
841.3
839.3
832.1
833.8
832.3
Mean air temp. occupied hours (oC)
21.4
21.3
21.2
% of time air temp < 18oC
0.17
0.1
0.2
3.9
4.1
Mean hot water temp. hours (oC)
53.8
54.0
54.1
53.3

21. Key Results - Electrical
electrical energy use

22. Key Results - Electrical
peak electrical demand

23. Key Results - EV
vehicle trips

24. Key Results - EV
mean SOC

25. Key Results - ASHP
low indoor air temperatures

26. Conclusions
building simulation used to assess impact of domestic electrification
ASHP, EV, PV
model of zero carbon UK dwelling developed
ESP-r tool adapted to use higher resolution boundary data
adapted to generate high resolution solar data, appliance demand/thermal gains and hot water draws
algorithm developed to mimic effect of EV charging on household power demand
demand peak mitigation strategies tested

27. Conclusions
addition of EV and ASHP more than doubles annual electrical energy use
peak demand doubles with unrestricted EV & ASHP use
load sensitive EV charging successful in reducing demand for both fast and slow charging (but still 60% higher than base case)
load shifting ASHP to off peak only effective coupled with slow charging
load shifting both ASHP and EV to off peak counter productive

28. Conclusions
EV availability not significantly affected by charging restrictions or rate
Off peak EV charging reduced mean SOC and possibly number of trips
Off peak heating resulted in deterioration in ASHP performance

29. Further Work extension to transition and summer periods
more specific EV use modelled e.g. “commuter”
modelling additional ASHP operating strategies and ASHP/EV combinations
ASHP modulation (algorithm adaptation)
modelling of populations of buildings and householders
modelling in different building types
modelling with future climates

30. Acknowledgements
this work was undertaken as part of the UKRC Energy Programme - Grand Challenge in Energy Systems: Top and Tail Transformation

31. Thank you!