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

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

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

Domestic Electrification

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

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

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

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

Higher Temporal Resolution electrical demand

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 2011. Synthetic High Resolution Solar Data, MSc Thesis, University of Strathclyde, Glasgow.

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 2010. Domestic electricity use: A high-resolution energy demand model. Eng. and Build. 42(10) pp 1878-1887.

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, 2005. DHWCALC: Program to Generate Domestic Hot Water Draws with Statistical Means for User Defined Conditions, Proc. ISES Solar World Congress, Orlando, US.

‘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

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

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

EV Charging Algorithm

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

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).

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

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 5116.52 10083.6 12222.1 8019.2 8251.6 7960.6 11327.5 9088.0 11571.9 Max P export W 2287.16 2239.83 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

Key Results - Electrical electrical energy use

Key Results - Electrical peak electrical demand

Key Results - EV vehicle trips

Key Results - EV mean SOC

Key Results - ASHP low indoor air temperatures

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

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

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

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

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

Thank you!