Benchmarking customer priorities and trust in the energy sector. Appendix to Final report.

C9 ResearchProject

Benchmarking customer priorities and trust in the energy sector

Appendix to Final Report

Final report

RACE for Change

Research Theme C9: Incorporating end users in whole-ofsystem design

ISBN: 978-1-922746-52-8

Industry Report

Benchmarking customer priorities and trust in the energy sector

May 2024

Citations

Russell-Bennett, R., Gordon, R., Letheren, K., McAndrew, R., Mathmann, F., Van Hummel, A., and Bowring, N. (2023).

Benchmarking Customer Priorities and Trust: Report Queensland University of Technology, Australia

Prepared for RACE for 2030 CRC.

Project partners

Acknowledgements

Project team QUT

• Prof. Rebekah Russell-Bennett

• Prof. Ross Gordon

• Dr. Kate Letheren

• Dr. Ryan McAndrew

• Assoc. Prof. Frank Mathmann

• Ms. Aleksandra van Hummel

• Mrs. Natalie Bowring

We would like to thank all the stakeholders that contributed their time to this report. Our Industry Reference Group consisted of Ausgrid, Synergy, Western Power, St Vincent de Paul, WA Expert Consumers Advisory Panel, DEPW (QLD), EnergyOS, Ergon Energy, Essential Energy, NSW DCCEEW, Essential Energy and AER. Whilst their input is very much appreciated, any views expressed here are the responsibility of the authors alone

Acknowledgement of Country

The authors of this report would like to respectfully acknowledge the Traditional Owners of the ancestral lands throughout Australia and their connection to land, sea and community. We recognise their continuing connection to the land, waters, and culture and pay our respects to them, their cultures and to their Elders past, present, and emerging.

What is RACE for 2030?

Reliable, Affordable Clean Energy for 2030 (RACE for 2030) is an innovative cooperative research centre for energy and carbon transition. We were funded with $68.5 million of Commonwealth funds and commitments of $280 million of cash and in-kind contributions from our partners. Our aim is to deliver $3.8 billion of cumulative energy productivity benefits and 20 megatons of cumulative carbon emission savings by 2030. racefor2030.com.au

Disclaimer

The authors have used all due care and skill to ensure the material is accurate as at the date of this report. The authors do not accept any responsibility for any loss that may arise by anyone relying upon its contents.

INTRODUCTION

The aim of this report is to present the findings of an online survey examining Electric Vehicle (EV) preferences held by 1,029 Australian consumers. During a co-creative survey design session with key representatives from Ausgrid and WesternPower, seven questions pertaining to consumer EV preferences were written and embedded within the national online survey being conducted as part of the larger project The seven EV questions asked about:

• Ownership status

• Ownership length

• Charger type preference

• Charging time preference

• Charging location preference

• Willingness to change charging times for a reward

• Information trust for charging

Additional questions relating to respondent demographics and psychographics are also included in the preceding analysis, as these offer an additional lens through which to examine and understand the findings Analysis methods used were primarily descriptive, focusing on identifying the means, scores or percentages that people selected. Where possible statistical testing was conducted to identify potential differences between jurisdictions, for instance, using t-tests or one-way analysis of variance (ANOVA) with post hoc Tukey Tests for continuous variables, chi-square (χ2) testing for categorical variables.

SUMMARY OF ELECTRIC VEHICLE PREFERENCES FINDINGS

Electric Vehicle Question 1 – Ownership Status

• Only a small percentage of respondents said they already own an EV (3.3%). Many people have no intention of purchasing an EV (29%).

• When aggregating the Yes responses and the No responses most people either had an EV or wanted one in the future (56%), with 36% saying No to EVs Eight percent of people were unsure

• It should be noted that the rest of the analysis examined results from the group of respondents who either had an EV or wanted one; the Yes group.

Electric Vehicle Question 2 – Ownership Length

• Of those who own – or plan to own – an EV, the majority intend to drive their EVs for as long as they can (51.2%). A further 21.1% planned to sell within the next five years, and another 12.4% within 1-3 years.

Electric Vehicle Question 3 – Charger Type

• Respondents preferred to have a dedicated home charger for faster charging, with some also desiring the ability to use their EV to power their homes during a power outage.

• The preferences of respondents for charger type are (from most to least selected):

1. A dedicated home charger for faster charging (41%)

2. A dedicated home charger for faster charging and ability for my EV to power my house in case of an outage (vehicle to grid) (39.8%)

3. A normal power point (slow connection) (10.7%)

4. I would prefer to use or need to reply on a public charger (8.5%)

Electric Vehicle Question 4 – Charging Times

• The highest rated charging time was “Overnight 10pm to 6am” with a median preference of 82%.

• The next most highly rated charging time was “Late evening 9pm to midnight” (74%).

• Finally, the lowest rated charging time was “Daytime 10am to 4pm” (30.5%).

Electric Vehicle Question 5 – Charging Locations

• Sixty-percent (60%) of respondents said they wanted at home charging, followed by charging at work (20%), then a charger close to home (12%), followed by no preference (7%), and don’t know (1%)

Electric Vehicle Question 6 – Reward for

Shifting Charging Times

• Seventy-one percent (71%) said they would be willing to shift their charging time to overnight, 18% would shift to midday, 11% indicated they would not shift at all.

Electric Vehicle Question 7 – Information Trust

for Charging

• Respondents trust car dealers the most when it comes to EV charging information (34.39% selected this option), followed by their electricity retailer (25.05%), comparison websites (20.81%), electricity grid provider (18.81%) and other – open response (1.96%).

• Other trusted sources were YouTube, Vehicle manufacturer, solar installed, podcasts, internet searchers, government regulators, friends with similar vehicles and online forums.

TRUST AND DISTRUST IN THE ENERGY SECTOR

Trust and distrust scores were both measured in the survey, with results provided in Figure 1 The standard deviations (spread of the data) are shown with whiskers. The scores are both very similar and no statistically significant difference is found between trust and distrust

Trust and Distrust was measured using several items (questions) which were averaged to create a construct. The Trust questions included those about:

• Competence (expert, experienced, knowledgeable),

• Responsibility (green, ecologically worthwhile, environmentally responsible, sustainable),

• Openness (service oriented, approachable, accessible, customer oriented), and

• Authenticity (trustworthy, honest, reliable, sincere).

The Distrust questions included those about:

• Malevolence,

• Incompetence, and

• Deceit

Figure 1 Trust and Distrust Scores with Standard Deviations

Figure 2 with the standard deviation. No statistically significant differences were found between jurisdictions on trust and distrust scores.

Trust and Distrust in the energy sector and its correlation with EV ownership and flex charging

An analysis of the current trust and distrust levels revealed no statistically significant differences between level of trust/distrust and EV preference. Which means levels of trust and distrust between EV preferences are equal.

Interestingly, while the Distrust items were not statistically different, the Trust scores were (p = .007), with shift to midday significantly higher than shift to overnight. This suggests, that the shift to midday switching is more likely to be associated with higher levels of trust.

Percentage of the responses

I wouldn't shift at all 11%

Shift to midday 18%

Shift to overnight 71%

Shift to midday Shift to overnight I wouldn't shift at all

A cross tabulation comparison of trust and distrust scores was undertaken with ‘who do you trust for information about how to charge your EV’ . Electricity grid provider had the highest score for trust and lowest score for distrust.

Figure 5 Percentage of responses to time shifting

The highest response category for Who do you trust for information about how to charge your EV was ‘Car dealer’ at 33%, followed by ‘Electricity retailer’ at 28%, ‘Comparison websites’ at 24%, and lastly the electricity grid provider at 15%. It does appear though that the higher consumer trust score did not correlate with most trusted source of information on EV charging

Percetage of responses

ELECTRIC VEHICLE PERCEPTIONS

This section of the report presents the findings of the seven co-created Electric Vehicle (EV) questions embedded as part of the larger national survey. All questions have their own sub-section, as follows:

• Electric Vehicle Question 1 - Ownership status

• Electric Vehicle Question 2 - Ownership length

• Electric Vehicle Question 3 - Charger type preference

• Electric Vehicle Question 4 - Charging time preference

• Electric Vehicle Question 5 - Charging location preference

• Electric Vehicle Question 6 - Willingness to change charging times for a reward

• Electric Vehicle Question 7 - Information trust for charging

Each EV question was examined against various demographic factors to determine if statistically significant differences existed. Specifically, jurisdictions, age, solar PV ownership, gender, dwelling type, and income levels. The overall results are shown in the table below, with non-significant results represented by n.s. and significant results by Sig. The Sig. value was set at the threshold 0.05, meaning any value below this number was significant. Where statistical testing could not be undertaken a “-“ symbol is used. This occurred when participants could select multiple items and thus heterogeneity of variance could not be established, a fundamental assumption within statistical significance testing. Where the box is blank testing was not conducted. Not all corresponding graphs are shown, this was done to save space.

Table 1 Summary Table of Results

EV Question #

1. Would you consider purchasing an electric vehicle (EV)?

2. How long do you plan to own/lease the EV model you currently own (or the one you are considering owning in the future)?

3. What type of charger do you have or would you consider buying?

4. Whether you already own an EV or you might own one in the future, can you tell us when you are most likely to charge it on a scale from 0-100?

5. Where do you, or would you consider most convenient to charge your EV?”

6. If you were rewarded for shifting your charging time to a certain time, which of these slots would you choose?

7. Who do you trust for information about how to charge your EV?

For question 1, age was significant, this meant that the younger the person the more likely they owned or wanted to own an EV, those that said no were more likely to be older. Income was also significant which meant that higher incomes were associated with owning or wanting an EV in the future

For question 4, age was significant, meaning that all relationships except for one were significantly related to age. Interestingly results showed that the higher age became the less likely a person was to select a higher value for their charging time. Late afternoon 4pm-9pm had the strongest relationship. Daytime 10am to 4pm had the weakest. And Overnight 10pm to 6am was not significant. Solar PV was also significant, this means For Solar Ownership and desire for charging times, only Daytime 10am to 4pm was significantly different between solar Ownership types.

Electric Vehicle Question 1 – Ownership Status

Respondents were also asked about their current or intended EV ownership status with the question “Would you consider purchasing an electric vehicle (EV)?”. Only a small percentage of respondents said they already own an EV (3.3%), though if all responses indicating positive intentions are amalgamated, a majority of the sample intends to purchase an EV at some point in the next 1 to 5 years (52.4%). Table 2 provides an overview of the data, with a visual representation of the frequency of different responses provided in Figure 8 and the results of amalgamating yes and no responses in

Table 2 Electric Vehicle Ownership Status

Yes, but not within the next 5 years No

Yes, within the next 3-5 years

Yes, within the next 1-2 years

Don’t know

No, I prefer hybrid cars

Yes, I already own an EV

No, I prefer combustion engine cars

Frequency of Response

Figure 8 Electric Vehicle Ownership Status

Figure 9 Overall Responses to Electric Vehicle Ownership

Breakdown by Jurisdiction (States/Territories)

Responses to the EV ownership status question were also analysed by state and territory (see Error! Reference source not found.). No statistically significant differences between jurisdictions were found, though it should be noted that the small NT and ACT sample size negated the ability to run statistical tests.

Breakdown by age

Notably, age was significantly related to EV ownership (p = .006), specifically the younger the person the more likely they owned or wanted to own an EV, those that said no were more likely to be older.

Figure 10 Ownership by Age

Breakdown by Solar power system installed

Results for the EV ownership and solar power system installed showed that those who said no to solar also mostly said no to EV, unless it was ‘no, I prefer hybrid cars’ where considering purchasing solar was the majority.

Gender and EV

Gender was equally split for those that said no for an EV.

12 Gender and EV selection

Figure 11 Solar power system installed and EV ownership type

Figure

Dwelling type and EV

Figure 13 Dwelling type and EV Ownership

Income level and EV Ownership

Higher incomes are associated with owning of wanting an EV in the future.

Figure 14 Income level and EV Ownership

Electric Vehicle Question 2 – Ownership Length

People who said yes to the previous question (Would you consider purchasing an electric vehicle (EV)?) (56% of this sample, or 574 people), were asked a follow-up question: How long do you plan to own/lease the EV model you currently own (or the one you are considering owning in the future)?

Results indicated that most people intend to drive their EV for as long as possible (51.2%) or to sell within 5 years (21.1%). Results are summarised in Table 3

Table 3 Electric Vehicle Ownership Length

Most likely going to sell within the next 1-3 years

likely going to sell within the next 5 years

Will try and drive my EV for as long as I can

Will replace my EV at the end of the lease period

Other (please provide comment)

Total

People who selected the ‘other’ option were provided with the opportunity for comment. The following comments were selected to give nuance:

“Driving for as long as I can then converting the remaining battery into home storage”

“End of life”

“It depends how fast technology advances”

“It will depend on performance and reliability”

Breakdown by Jurisdiction (States/Territories)

No statistically significant differences were observed between states/territories, though it should be noted that some jurisdictions such as the NT had too few people to allow for statistical tests to be conducted. The majority of people in each state/territory indicated an intention to drive their EV for as long as they could (see Error! Reference source not found.). Queensland and The Northern Territory had the highest rates of this response, at 62.4% and 100% respectively (though noting the low sample size for the Northern Territory).

For age and EV ownership length no statistically significant differences were found (p = .173). For EV ownership length and gender type, males made up the large proportion of choices, except for “I will try and drive my EV for as long as I can” where females were the majority.

Electric Vehicle Question 3 – Charger Type

Respondents were also asked about the type of charger they would use for their EV Specifically, “What type of charger do you have or would you consider buying?” The majority of people indicated that they preferred either a home charger (41%) or a home charger that would also allow the EV to provide residential power (39.8%), with far fewer people opting for a normal power point or a public chargers (see Figure 15).

Figure 15 Charger Type Selection

A dedicated home charger for faster charging (7kw or charge time in hrs)

A dedicated home charger for faster charging and ability for my EV to power my house in case of an outage (vehicle to grid)

No charger, I would use a normal power point (slow connection)

No charger, I would prefer to use or need to rely on a public charger

Breakdown by Jurisdiction (States/Territories)

The percentage of people in this sample indicating a preference for different charger types is indicated in Figure 16

Figure 16 Charger Type by State/Territory percentage

A DEDICATED HOME CHARGER FOR FASTER CHARGING (7KW OR CHARGE TIME IN HRS)

A DEDICATED HOME CHARGER FOR FASTER CHARGING AND ABILITY FOR MY EV TO POWER MY HOUSE IN CASE OF AN OUTAGE (VEHICLE TO GRID)

NO CHARGER, I WOULD USE A NORMAL POWER POINT (SLOW CONNECTION)

NO CHARGER, I WOULD PREFER TO USE OR NEED TO RELY ON A PUBLIC CHARGER

Excluding the ACT and the NT, results for dedicated charger were mixed. SA and TAS both indicated a higher preference for Vehicle-to-Grid (V2G), than for a simple home charger. NSW, VIC and QLD had mixed results although these states remained mostly even for dedicated chargers.

Figure

A dedicated home charger for faster charging (7kw or charge time in hrs)

A dedicated home charger for faster charging and ability for my EV to power my house in case of an outage (vehicle to grid)

No charger, I would use a normal power point (slow connection)

No charger, I would prefer to use or need to rely on a public charger

Electric Vehicle Question 4 – Charging Times

Respondents were asked about their preference for specific charging times. In particular, “Whether you already own an EV or you might own one in the future, can you tell us when you are most likely to charge it on a scale from 0-100?” The highest rated time frame was “Overnight 10pm to 6am” with a median preference of 82%. This was followed by “Late evening 9pm to midnight” with a median preference of 74%. The lowest rates time frame was “Daytime 10am to 4pm” with a median preference of 30.5%. Results are summarised in Table 4 It should be noted that this question is only asking the time of day they would likely charge (without considerations for cost or incentive to charge at a particular time of day) i.e. assumed convenience charging behaviour.

These results are represented visually in Figure 20, and clearly demonstrate a preference for evening/night charging – potentially highlighting a desire to charge vehicles after work to ensure enough charge for the next day. Hence, like most electricity use, EV charging demand will be heavily dependent on the schedules of consumer households.

Table 4 Score for Charging Time Preference

Breakdown by Jurisdiction (States/Territories)

State and territory break downs showed similar results with overnight charging 10pm to 6am being the most popular (see Error! Reference source not found.) A one-way ANOVA was performed on each time period by state/territory and no significant differences were found.

No significant differences were found for Dwelling type and Charging times

For Solar Ownership and desire for charging times, only Daytime 10am to 4pm was significantly different between solar Ownership types. Figure 21 Solar power and charging time

At home

At a charger close to home

Don’t know

At work

preference would charge anywhere

Electric Vehicle Question 5 – Charging Locations

Respondents were also asked for their preferred charging locations with the question, “Where do you, or would you consider most convenient to charge your EV?” Sixty-percent indicated a preference for at-home charging, followed by charging at work (20%), then a charger close to home (12%), followed by no preference (7%), and don’t know at (1%) Results are visually summarised in Figure 23

Breakdown by Jurisdiction (States/Territories)

States and territories revealed few differences, with most favouring at-home charging followed by work charging (see Figure 24).

Figure 23 Charging Locations

Figure 24 Charging Locations percentage by State/Territory

Age and charging location

Since these various charging location options were tick boxes, people could select multiple options of any combination, as such statistical testing cannot be undertaken with a continuous variable such as age. However, results show that younger people did not know where they wanted to charge and that those that were older either wanted to charge at home or had no preference.

Figure 25 Age and charging location

Figure 26 Solar Power and charging location

27 Dwelling Type and Charging location

Figure

Electric Vehicle Question 6 – Reward for Shifting Charging Times

Respondents were asked “If you were rewarded for shifting your charging time to a certain time, which of these slots would you choose?” It should be noted that no actual reward was given as an example. Seventy-one percent indicated they would shift to overnight, with 18% being willing to shift to midday, and 11% being unwilling to shift at all It should be noted that the strong preference to ‘shift’ to overnight charging aligns very closely with respondents existing stated preference for overnight charging, hence, this result may not be a true indication of a willingness to shift but rather a willingness to retain their first preference for overnight charging (see Figure 29).

I wouldn't shift at all 11%

Shift to overnight 71%

Shift to midday 18%

Figure 29 Shifting Charging Times

Breakdown by Jurisdiction (States/Territories)

No statistically significant differences were found for this question, with every state and territory selecting ‘shift to overnight’ as their majority option (see Error! Reference source not found.). Age was not statistically significant. Further, rewarding and Solar power showed no major differences.

Standalone house

Semi-detached

Townhouse/Villa Apartment Mobile hose, park home, or trailer home

Figure 30 Rewarding and Household Dwelling Type

Electric Vehicle Question 7 – Information Trust for Charging

The last EV question asked respondents “Who do you trust for information about how to charge your EV?” Respondents trust car dealers the most when it comes to EV charging information (34.39% selected this option), followed by their electricity retailer (25.05%), comparison websites (20.81%), electricity grid provider (18.81%) and other – open response (1.96%). The results are presented visually in Figure 31, followed by a listing of verbatim comments from participants who selected the ‘other – open response’ option.

Other included comments:

“YouTube films”

“YouTube”

“Vehicle manufacturer” “car maker”

“Solar Installer”

“Online / Podcasts etc”

“Internet search for user guides and reviews

“Government regulators

“Government Agency

“Friends with similar vehicles

“Best advice has been from online forums (whirlpool specifically)”

Figure 31 Preference for Trust regarding Charging

Breakdown by Jurisdiction (States/Territories)

The preferences for different EV charging information sources are visually represented in Figure 32.

Figure 32 Percentage of Information on Charging Trust Preference by State/Territory

Figure 33 Information on Charging Trust Preference and Age

Information charging trust preference and age were the same except for those that selected Other, they were much older than the mean ages for the other trusted sources.

Dwelling type and charging trust preferences were very similar across the sources. Because participants could select multiple options statistical testing was unable to be conducted, however results showed similar levels of dwelling type across source.

CONCLUSION

This report has presented the findings of an online survey examining Electric Vehicle (EV) preferences held by Australian consumers with questions co-created by key representatives from Ausgrid and WesternPower Questions about demographics and psychographics were also included. The key findings about Australian consumer EV preferences indicate that consumers do/would like to own an EV for the long-term, prefer an at-home fast-charger that operates overnight, and tend to trust car dealers and electricity retailers most when it comes to information about EV charging. Overall, it is clear that consumers are open to owning EVs and are already considering the practicalities of this mode of transport.

Limitations and Future Research

Due to the nature of cross-sectional surveys causal claims cannot be made. As such associations and correlations are reported rather than causal links. Future research should use multiple longitudinal surveys to track changes and causal influences over time.

Future research should also consider asking about off-street parking / charging access which will likely influence EV and charging preferences.

Future research could consider preferences on charging times and speeds for instance, 7kw to 22kw home charging speeds or 7kw to 350kw public charging speeds versus any available.

Additional research could include variables such as lifestyle stage, parking conditions (e.g. for apartments are there adequate parks per EV), density of charging stations in local area, and possibly personality traits.

Appendix 1 RESPONDENT DEMOGRAPHICS

This section outlines the demographics of survey respondents from the area of operation for the two partners for this report (New South Wales for Ausgrid and Western Australia for WesternPower). The remainder of this report provides findings from the overall sample followed by comparisons between jurisdictions. The overall sampling was representative of the national population of Australia according to state and territory populations. In total 1,029 clean responses were obtained (see below).

Table 5 State/Territory Sampling

*It should be noted that the very small sample sizes in NT and ACT mean results are unlikely to generalise to a greater population and caution should be used in their interpretation.

Demographics: Sex

Sex was also recorded for respondents. Overall, the sample was equally split between male and female respondents, however at the state/territory level some differences emerge. For example, more females than males responded to the survey in WA, TAS and the ACT, while the opposite is true of VIC and SA (see below).

Figure 34 Sex by State/Territory

Demographics: Age

The mean (average) age in the sample was 37.6 years, with the median being 33 years. The standard deviation for age was 15.7, meaning that most of the sample was within 15.7 years of the average age. A range of ages was captured in this study, with the youngest person in the sample being 18 years of age, and the oldest 86 years.

The average age by state and territory is provided below. A one-way analysis of variance was conducted and showed no statistically significant differences (p = .241), meaning that the average age was not significantly different between jurisdictions. Tasmania had the oldest mean age at 40 years and the Northern Territory had the youngest at 27 years, though results for these jurisdictions should be interpreted with caution given the relatively lower sample numbers captured.

Figure 35 Average Age by State/Territory

Demographics: Housing Type

Five categories of housing type were collected as part of this study. Specifically: standalone house, semi-detached, townhouse/villa, apartment, or mobile/park/trailer home. Most people lived in standalone houses (67%), followed by apartments (20%). This is visually represented in below.

The state/territory breakdown showed similar results to the overall ratings, except for ACT with greater than usual numbers of apartments, as per Error! Reference source not found.

37 Housing Type by State/Territory

Figure 36 Housing Type

Figure

Demographics: Income

Income was measured as household gross income (before taxes). Household income ranged from less than $20,000 to $150,000+. The two most common income categories were $100,000 to $149,999 (20.1% of the sample) and 150,000+ (12.3% of the sample). Results are provided in Figure 38

Results for most common household income level were similar when analysing across states/territories, and no statistically significant differences were found (see above).

Figure 39 Income Categories by State/Territory

Prefer not to answer

Figure 38 Income levels

Demographics: Education

Educational attainment demographic questions revealed that most of the sample have completed undergraduate university study (30.2%), TAFE/technical college (25.4%) or grade 12 (17.9%), though attainment varied from below grade 10 (2.3%) to PhD studies (1.5%). See Figure 40

Results by state/territory align with those of the overall sample (see below), with no significant differences.

Grade 12

Grade 11

Grade 10

Below grade 10

Figure 40 Educational Attainment

Figure 41 Educational Attainment by State/Territory

Demographics: Solar Power

Respondents were also asked “Do you have a solar power system installed?” The vast majority said no (60%), with just under a third of the sample indicating they did have solar power (31%) and a further 9% considering purchasing a solar power system in the future (below).

The graph below shows the jurisdictional breakdown had similar results to the overall sample, with the majority of people indicating they did not own a solar power system currently.

Figure 42 Solar Power Ownership