The assumptions about the roles of users differed throughout the literature depending on the key topic addressed. The topics were in turn often related to the specificities of the pilot project. The most important knowledge about each user role is presented herein by discussing the dominant assumptions and relating them to the key findings in recent empirical research.
3.1. Demand Shifting
The literature on the topic of flexibility in relation to smart grids ties in with the older debates on demand-side management. The key challenge is to match supply and demand better in order to accommodate the distributed energy sources and reduce CO2 emissions. Users are expected to respond to information about the availability of electricity or financial incentives, such as dynamic pricing, by shifting the energy load to preferred moments or a combination thereof. They tend to be addressed as isolated individuals making their own, autonomous decisions. The implicit assumption underlying information services is that new knowledge and awareness might motivate users to shift their energy use for environmental or financial reasons.
However, there were several examples of “disappointing” responses in the investigated projects. In the Jouw Energie Moment (Your Energy Moment) project, for instance, householders had requested for “smarting” their electrical heat pumps, but only four out of 38 actually switched it on [
11].
Since 2014, several good social scientific studies have been conducted on demand shifting. Most of them related to what is called the “practice turn” in social sciences. The idea is that genuine interest in what moves people in their homes to consume energy helps to understand to what extent and under what conditions they may shift their energy consumption to other moments of the day.
A first insight is that domestic practices are pinned in time and place, related to relationships within the households, social conventions, and time structures of the activities of members of the households [
12,
13]. As a consequence, some practices are more prone to active time shifting than others. In general, cleaning practices (dishwashing, washing, and tumble drying) were found to be most suitable for demand-side response [
14,
15]. Practices implied in ambiance regulation, leisure, cooking, and eating, are less easy to change. Solitary tasks are easier to change than collective ones, such as family dining as well as practices that depend on the structure of activities outside the home.
Secondly, several authors confirmed that households have to “learn” to adapt their demand, i.e., change their practices and start seeing options for doing so [
8,
12,
16]. In two projects investigated by Hansen [
8], the knowledge about household consumption, electricity markets, prices, and electricity system loads had increased. There were, however, also accounts of what could be called “unlearning” the practices geared toward demand shifting. Kessels [
16], for instance, showed that there was a “response fatigue” in one of the investigated projects in case of manual feedback and control.
Thirdly, flexibility interventions were seen to influence relationships among household members and to even have undesirable effects. Skjølsvold et al. [
17] emphasized internal household dynamics between men and women. Drawing on two Norwegian smart grid demonstration projects, the provided feedback was found to “trigger” learning amongst eager men while alienating or excluding women.
Three key issues in relation to demand shifting were addressed in the literature: (1) automation and control, (2) financial incentives, and (3) feedback and communication.
(1) A key issue in demand shifting is the level of automation and control. Several authors emphasized that automation defines demand shifting more than economic incentives [
8]. While earlier studies raised concerns about a loss of control, more recent studies provided another image [
18,
19]. In general, remote or automatic control seems to be acceptable to residents, that is, under conditions. It depends on what equipment is controlled, the information and security of smart techniques provided, and whether they have the ability to override the external control [
8,
19]. Results of a representative survey indicated that a direct load control tariff was more acceptable than the time-of-use tariffs presented [
18]. The load control tariff gave residents a better sense of control over comfort, timing of activities, and spending as well as ease of use. The majority of respondents were inclined to accept direct load control if they would have the option to override it. Another study on actual projects showed similar results. The households involved did not mind having their heating devices (heat pumps and electric heating) remotely controlled, but they did not appreciate automatic charging of electric vehicles and automated control of other devices, such as freezers, fridges, and pumps [
8].
Interestingly, even in the case of full automation, residents may show active engagement, for instance, by connecting extra devices to the system [
8]. In general, however, active engagement by members of households is expected to involve only the anticipated responses to informational and financial incentives.
(2) Flexible network tariffs are based on the assumption that energy consumption is financially motivated. In an important study based on a meta-review of literature and an empirical validation in 32 European projects with user engagement, the following was concluded [
16]:
Time of use tariffs have more potential than dynamic tariffs (real-time [ricing), while the latter is more relevant in the case of local energy production.
In order to work effectively, the dynamic tariff
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should be simple to understand for the end users,
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should have timely notifications of price changes, and
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should have a considerable effect on their energy bill.
If the tariff is more complex, the burden for the consumer could be eased by introducing automated control.
Whether dynamic tariffs will be accepted is most likely dependent on whether users consider them to be fair. A recent study [
19] found that transport or capacity charges are considered fairer than peak pricing and much more than a flat rate. What is considered fair depends on noneconomic justification, guarantee that basic needs will be fulfilled, predictability, and being sure that peak use is not only affordable for rich people.
Several studies indicated that there is seldom a linear relationship between financial incentives and energy consumption. This is explained by the intermediating role of domestic practices, as mentioned above. It also relates to the fact that considerations other than financial ones influence energy consumption. Saving energy, an interest in new techniques, and a green image are among the other reasons for becoming flexible [
5,
19]. If households are primarily motivated by a desire to become self-sufficient and autonomous, this could even create a tension with demand-side flexibility schemes, which tend to create new types of grid dependency (whether it be flexibility contracts, automated demand response, or market-based dynamic energy pricing incentives). In such cases, users may prefer to stick to established forms of “green engagement” with energy, such as green energy contracts and applying energy-efficient light bulbs [
14].
(3) A recent study confirmed findings from earlier studies regarding the effectiveness of feedback provided [
8]. Simple information and visualization methods, such as a light signal (green/red) and emails or text messages that integrate information about demand and its relationship with supply, tend to have a positive influence (see also [
3]). Users evaluate them positively, understand the information, and tend to change their consumption patterns based on the input.
More complex interfaces provided ambiguous results. Web-based tools were used by less people: approximately 25% in 11 Danish experiments [
8]. Moreover, the satisfaction with the applicability of websites and advanced in-home boxes varied. Kendel [
20] showed that if people are willing to visit a portal, more advanced information per appliance could slightly increase the effectiveness in flexibility.
It tends to be forgotten, however, that people’s awareness of patterns of energy consumption is not only provided by direct, individual feedback. Naus [
12] nicely showed how, in addition to individual learning, community interaction in the form of workshops and informal encounters with neighbors contributes to effective demand shifting.
In conclusion, it can be stated that the insights about demand shifting have considerably increased in the past years due to a large number of studies. It has been shown that there is certainly some room for triggering active responses from users, even though the relationship between incentive and energy consumption is mediated by practices and motives other than that intended with the measure. The findings of the studies provide some clear rules of thumb. However, whether and how measures for automation, financial incentives, and feedback mechanisms are effective depends ultimately on the specific features of the local energy system and the local setting, such as the relationships between residents and the project managers, conventions embedded in culture, weather conditions, etc. This was well illustrated in the comparative study by Bulkeley [
21], which showed the outcomes of different settings and interventions. The first group of users had photovoltaic (PV) panels and a display showing their domestic consumption. This group was stimulated to do financial calculations of the revenues of possessing PV. The second group had PV and a device showing the moments of available electricity. The users were stimulated to change their routines of washing and dishwashing (modestly), among other things, by a timer on the washing machine. They were also engaged in energy management by checking weather forecasts. The third group was provided with PV and a water tank that automatically absorbs excess generation. This group started shifting their showering practices from the morning to the daytime or evening—a response that is rather surprising in the light of the social practices studies mentioned above.
3.2. Energy Saving
Smart grid systems are expected to stimulate residential users to save electricity. This expectation stems from two integrated smart grid developments. First, the diffusion of distributed renewable energy may trigger a motivation to save energy. Here, the mere possession of renewable energy technologies is assumed to trigger further behavioral change. A literature review reported that little is known about this effect [
22]. While the installation of a PV system stimulates many households to reduce their overall electricity consumption according to themselves, the rare reports on actual consumption show otherwise, partly due to rebound effects.
Secondly, smart meters are applied to provide residential users with regular information about their electricity consumption who hitherto lacked such insight. The related assumption is that energy consumption data, supplemented with, for instance, the related financial incentives, or a comparison with other households will stimulate a reduction in electricity consumption. The data provided with smart meters, however, are seldom the preferred and recommended real-time data.
For groups of users, energy saving seem to be quite important. Smale et al. [
14], for instance, reported that not all discussants in the focus groups were convinced of the primacy of the time-shifting problem over other sustainability issues. Hansen and Borup [
8] concluded on the basis of a comparison of 11 Danish experiments that other motivations are an interest in new energy technologies, being more environmentally friendly, and reducing the overall energy consumption.
Few studies have investigated energy saving in smart grid pilot projects. One of the exceptions is the Danish eFlex project. The study showed an overall reduction in electricity consumptions in response to the feedback from home energy management systems: “the ‘control’ household group, [with only the management system] … had on average saved approximately 10% on their kilowatt hour consumption during the project period March 2011 to February 2012” [
15].
While energy saving is of importance for specific user groups, it has received too little attention to arrive at conclusions about the relationship between smart grids and energy saving. A complicating factor is that the term “energy saving” has become ambiguous with the introduction of smart grids. Some researchers use it for demand shifting rather than reducing the overall energy consumption (see, for instance, [
20]). Moreover, due to the electrification trend accompanying the smart grid development, just assessing increases or decreases in kilowatt hour consumption has little meaning. The meaning of, and aspirations regarding energy saving hence needs reviewing in the context of smart grids.
3.3. Co-Design
Many social scientists and some engineers critique the technological and economic rationality in the design of smart grid technologies and projects. Hansen et al. [
8] (p. 260), for instance, stated: “Our analysis shows that the projects employ a technology driven approach to household users with a focus on testing ready-made technologies rather than on improving technologies by including consumers”. For this reason, several scholars presuppose that if the smart grid technologies are co-designed, they will better address the users’ needs [
3,
7]. There were some examples where future users were addressed with images or prototypes of potential products. However, we could not find any study on future users purposively and structurally being involved in the preparation phase of pilot projects.
That does not mean that users play no role at all in design processes. In the literature, we found several examples of projects in which the applied techniques or their applications were changed because of users’ wishes and responses [
15,
19]. Hansen and Hauge [
19] showed how, in a project in which 20 households were to be equipped with the same air/water heat pump, it was installed in only seven households. After a negotiation process, the other households instead received a hybrid air/water heat pump with a gas boiler installed, sunwells, or a geothermal heat pump.
It can be concluded that co-design is rare and has so far yielded little insights in the preferences of users. The documentation on interactions between users and project managers in the course of actual projects provides evidence that, at times, feedback from users does influence choices made in these projects. Allowing room for such forms of co-design during the use phase of projects may instigate a countervailing power against the misconceptions of leading engineers.
In general, it seems useful to think of co-design not at the level of products and services only but at the level of projects as well. In this way, socio-technological aspects regarding the local energy system and stakeholder relationships will be part of the negotiations rather than merely the interaction between residents and technology.
3.4. Co-Provision
Where smart grids projects involve renewable energy, users are often regarded as co-providers. Instead of the passive consumer receiving energy, he/she is also producing and supplying energy. Such co-provision is regarded as an active role of users because their conduct “influences the grid and community, by reducing risks of load and voltage problems enabling more households to use PV” [
21] (p. 17). Such perceptions of co-provision are merely technical; just the possession of renewable energy makes residents a provider. They are connected to the preoccupation with the flexibility dimension of smart grids. The related literature has been discussed above in the section “demand shifting”.
A more radical perception is that of active, responsible citizens in the renewable energy transition process, where the relationships between the energy sector and users change drastically if users indeed take and get the responsibilities and power of being an energy provider. Transition advocates as well as several social scientists regard local energy initiatives (LEIs) to have the potential to transform the energy sector from fossil-fuel-based to renewable-energy-based by changing the relationships between citizens and institutional stakeholders. The related literature about LEIs forms a niche in the social scientific literature; smart grids are only touched upon as an issue. This may well mirror the priorities of the cooperatives.
A radical form of LEIs is the independent, local electricity systems. In microgrids or virtual power plants, prosumers may use, produce, and trade electricity without the interference of a central authority. The Brooklyn Microgrid is an example of an experiment in which energy is traded between prosumers and consumers. The grid operator has access to consumer data and manages energy use, loads, and demand response at negotiated rates. Quantitatively, the energy cooperatives do not yet contribute substantially. In the Netherlands, for instance, just 2% of the solar power installed was collectively owned in 2017 [
23]. In this country, two cooperative energy suppliers organize 107 local energy initiatives, aiming to close the energy cycle. A study by Arentsen and Bellekom [
24] illustrated that local energy associations often combine localized with centralized features, which is why the authors concluded that (p. 1) “local electricity initiatives can be considered a seedbed of innovation but with no potential to develop dominance in the electricity supply”. Instead, the authors expected that the local initiatives will develop as niches inside the dominant electricity system, challenging its centrality and ever-increasing scale, and will add to the hybridization of its products and services. A dissimilar conclusion was drawn by Blanchet [
25]. He studied the role of two LEIs in what he called the “remunicipalization” of Berlin’s electricity grid and concluded that the (potential) impact of local initiatives on energy systems and their governance has been underestimated.
Unfortunately, there are few documented experiences with a truly active role of users as co-providers, probably due to demotivating policy. The findings are ambiguous regarding the potential of LEIs to change the energy system. Experiences in citizen-led initiatives may well provide very important insights on co-provision in addition to the industry- and government-led pilot projects that outnumber them.