End-user energy behaviour is thus influenced by a broad range of both behavioural and situational factors. Behavioural factors include ‘rational’ factors (like financial gains), non-monetary motivators (like beliefs, values, habits, and routines), social influences (like norms and leadership), and personal capabilities (like knowledge, skills, and financial means). Situational factors, amongst others include institutional factors (laws, and regulations), culture, infrastructure and social networks that may equally influence energy behaviour. This implies that a nuanced view on end-user behaviour is required, taking both behavioural and situational factors into account.
Recent literature particularly highlights energy-related practices as key to understanding and influencing smart energy behaviour. Practices are said to reside at the ‘interface’ of individual behaviours and social structure, as these behaviours are the product of, and also reinforce, social structure. According to practice theory, energy is not used consciously or rationally, but rather as the ‘by-product’ of practices like cooking, washing, showering, working, commuting, watching TV, socialising, and travelling. Such practices are often driven by routines and socially shaped expectations. Smart grid programs would benefit from a thorough understanding of the energy related practices of their target groups.
End-users differ on the practices they adhere to, and on the extent to which the situational and behavioural factors mentioned above influence their energy related behaviour. Strategies for involving end-users should thus depart from a thorough understanding of the target group, for example by applying a segmentation approach. Current segmentation models can roughly be divided into models based on general values, preferences and opinions (‘population segmentation models’) and models that are tailored to specific (smart grid) products and programs and/or regions (‘target group segmentation models’). They classify end-users generally on the basis of socio-demographic criteria (age, household, income and education level), behavioural factors (preferences, beliefs, values, norms) and more recently also on the basis of energy-related behavioural characteristics.
To actively engage with end-users, a number of further principles for communication and engagement apply. These are reflected in key (social) marketing models like the 4P’s marketing mix (product, price, promotion, place), the AIDA model (attention interest, desire, action), Cialdini’s principles of influence (reciprocity, commitment, social proof, liking, authority, scarcity), and Defra’s 4E model (enable, encourage, engage, exemplify). A mix of solutions is generally recommended to ‘serve’ different user types. In addition, communications theory emphasises that an effective communication strategy needs to consider the following key components: the sender (make clear who is communicating), the target group (to whom is communications addressed?), the aim (make explicit why one is communicating), the message (content and form need to be adapted to the target group), the timing (when should the message be delivered?) and the communication channels (which ones are used by the target group?).
These findings are largely consistent with, and complementary to, the findings from empirical literature. Different types of incentive based programs are described to engage with end-users in demand response. These may be ‘classical’ or ‘market-oriented’, comprising monetary and/or non-monetary incentives, and which could be operated on a capacity and/or use oriented mode. End-user questionnaires reveal that financial benefits, reliability, comfort, and the level of control over controllable appliances are some of the key factors taken into account when deciding to enrol in such programs.
Alternatively, dynamic pricing schemes may be used. Various tariff structures may be offered for which different levels of peak clipping and reduction of the energy bill have been reported. To better compare the different tariffs structures, we identified several key attributes, including the rationale of the scheme, the number of time blocks used, the price update frequency, duration of peak periods, rates and rebates offered, the price spread, the price components that are made dynamic, and whether automated or manual control is applied. Further key lessons include the need for a variety of tailored interventions to address different user segments, and the need for convincing feedback mechanisms and communication and engagement strategies to make dynamic pricing ‘work’.
Feedback on energy consumption forms a key component of an end-user interaction scheme. Regarding feedback channels and devices, various options can be used. Most experience has been gained with in-home displays, but also others channels like websites, ambient displays, informative billing, and smartphone apps are equally promising and rapidly developing. Considering the influence of the feedback channel (and its design) on energy use behaviour, a suite of factors play a role. As a general finding, mixed feedback channels are considered best suited to address a heterogeneous end-user population. Concerning feedback content, different types of information can be delivered to the end-user, including current and expected usage rates, bill predictions, historical comparison, differentiation by appliance, unusual usage alerts, social feedback (comparison with others) etc. It tends to be difficult to assess which type ‘works best’ with partially contradictory empirical results. Nonetheless, direct feedback (e.g. real-time and historic usage) tends to be somewhat more effective than indirect feedback (e.g. processed via billing), and also social feedback appears relatively effective. Other general recommendations include linking feedback directly to advice on actions and ensuring that feedback is interactive and sufficiently disaggregated.
Regarding communication and engagement, training to end-users and installers, innovative customer service and support (e.g. using social media), appropriate communication channels, face-to-face interaction and the need for continuous information are highlighted to generate long-term end-user interest and involvement.
Regarding data privacy concerns, the literature stresses three important points: data minimization, transparency, and end-user empowerment (adequate information and permission requests). In addition, appropriate technical measures need to be taken to ensure data security.
Regarding energy markets, the literature describes new market structures and services that can be developed in an unbundled market and in a smart grid framework. Although largely uncharted territory, the concept of aggregation has emerged as a key contributor to these new energy markets. Aggregators enable the participation of small end-consumers into power markets which would not be accessible for them otherwise. They typically take an intermediary role between end-users and other market players on a multi-sided platform. They commercialize the aggregated load flexibility from the end-users to the other market players. This aggregated flexibility can provide a number of services to the different market players, like offering reserve capacity (for TSOs), load-flow optimization at local level (for DSOs), portfolio of consumers management (for Balancing Responsible Parties and retailers), and energy usage monitoring and optimization (for end-users). Such innovative business models currently remain largely untested (partly due to uncertainties under the current regulatory framework), but they will most probably become increasingly important over the coming years. Important will be to further our understanding of end-user preferences in this context, for example, regarding what their offered flexibility is used for (e.g. balancing of the local network, balancing energy consumption and micro-generation in their own home, or balancing the general, ‘anonymous’ energy market) or regarding the actors taking up the role of the aggregator.
Recent developments in the telecommunication and mobile phone industry provide a number of additional relevant lessons learned. These include thinking about new business models (e.g. tying arrangements) and thinking serious about usability (e.g. simple, self-learning devices), design (devices that fit into every household) and marketing (e.g. emphasising lower energy costs and more comfort, and creating ‘cool’ lifestyles around products that fulfil the need for distinction). Furthermore, example projects in the field of energy monitoring and management of offices show how automated systems can be developed that reduce energy consumption, while minimizing the need for behavioural change on behalf of the end-user.
The findings from theoretical and empirical literature have in this report been integrated into an overview of reported enablers and barriers for engaging end-users in smart energy behaviour. Enablers and barriers are found to fall under the following key categories: comfort, control, environment, finance, knowledge & information, security, and social process. Interestingly, for most categories, both enablers and barriers can be identified. Further, we have classified the various recommendations from literature into a set of key success factors supported both by empirical findings and established theoretical insight. In the initial phase of end-user engagement (‘activation phase’), the following factors appear particularly important:
- Provide added value: This corresponds broadly with providing clear added value on the various categories of enablers, while relieving barriers as much as possible. This includes, for example, applying attractive financial incentives, ensure comfort gains rather than losses, providing new information services, ensuring data privacy and security, and include possibilities to overrule automatic procedures while offering new forms of end-user control.
- Understand end-users: Different target groups may be susceptible to very different enablers and barriers. The challenge is thus to understand which ones are of particular relevance, and to base engagement strategies on that.
- Educate end-users: Relieving possible knowledge & information barriers will involve some form of education as programs need to take into account consumer (non-)ability to deal with new technology.
- Create commitment & appeal: This involves taking full advantage of social processes as important enablers. This includes ensuring trust in the whole smart grid process, involving end-users at early project stages, involving role models, believable customer testimonials, and dealing with possible free-rider effects. Creating commitment & appeal also requires effective marketing and outreach to create a ‘desire’ for new products, for example by emphasising key benefits and creating new lifestyles around products.
In the following ‘continuation phase’, key factors to consider are:
- Effective feed-back, pricing & communication: A lot is known about which factors need to be considered when designing effective feedback (system communication) and pricing schemes. Regarding project communication, it is particularly important to ensure a continuous information flow to maintain high engagement levels. Moreover, it is considered promising to link dynamic pricing, convincing feedback mechanisms and communication strategies to achieve an optimal response.
- Variety of intervention methods: Although understanding the end-user is key, there are limitations on the extent to which ‘tailor made solutions’ can be offered, especially for a heterogeneous target group. Several studies therefore also stress the need for adopting a variety of intervention methods and techniques to serve different user types.
- Ease of use: User-friendly, intuitive designs are important to minimize effort needed for operating new devices and schemes (i.e. to minimize knowledge & information barriers perceived by end-users). Ease of use also includes adequate and pro-active support and service.
- Social comparison: It is generally considered stimulating to allow end-users to compare their (new) energy behaviours to peers. Besides setting individual energy-saving targets, this thus involves comparing those targets (and their fulfilment) to others.
- Reflection & learning: Smart grid innovations can be considered ‘complex’, involving many connections to other domains and scale levels and significant uncertainties on technical, social and other dimensions. Reflection and learning is therefore needed throughout the process. This may include eliciting and evaluating end-users‘ expectations, incorporating monitoring and evaluation cycles, letting initiatives explicitly be part of a wider programme with clear objectives, and creating arenas in which end-users, suppliers, designers and other actors collaborate and co-create knowledge in the further development of the smart grid.
Despite the wealth of current knowledge, various challenges remain for further research on end-user engagement in smart grid projects. The S3C project identifies 9 key challenges that will be addressed in its further research:
1. Understanding the target group(s): Which instruments or approaches contribute to achieving better understanding of the enablers and barriers of target groups and the type of end-user interaction scheme best suited to them?
2. Products & services: How / in what way can innovative products and services provide clear added value to end-users, while contributing to fostering smart energy behaviour?