2.1 standards The integration of the hardware system and

2.1 Challenges of implementing Smart Grids in

2.1.1 Policy and Regulation

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current policies and regulatory frameworks are meant to address the current
scenario. However, with the introduction of smart meters, the current policies
and framework must evolve to encourage and develop incentives for investment in
smart grid technology. These frameworks will have to include the interests of
the consumer, the utility and other key stakeholders to ensure that the
consumers benefit from the smart grid technology at the least cost. Future
policy will have to look to encourage private investment by allowing risk
sharing among consumers and shareholders so that the risks and rewards are
balanced with least aggregate cost to the consumer (Kaushal, 2011). In England and Wales it
was found that political support for smart water metering was lacking due to
limited legislation and guidance (Pericli & Jenkins, 2015).

2.1.2 Technology standards

The integration of the hardware system and
the management of high volumes of data will be the challenge. As the smart grid
implementation will involve multiple software providers, there will be
different data formats and complex data models (Kaushal, 2011). Technology standards refer to technical specifications which
determine how a technology operates or interfaces with other technologies. Even
if these standards are available, the lack of enforcement of these standards
can act as a barrier to smart grid adoption (Guo, Bond, & Narayanan, 2015). With respect to cyber security the challenges that will be faced are;
Lack of coordinated effort, jurisdictional issues, a misplaced focus on
compliance instead of comprehensive security, the lack of sufficient inbuilt
security features, the lack of a proper forum for disseminating knowledge, and
the lack of metrics for performance evaluation (Wilshusen and Trimble, 2012).

2.1.3 Lack of Awareness

Even though smart metering is on the national
agenda with the India Smart Grid Forum being constituted to conduct research in
the area and spread awareness, the level of awareness of citizens to smart
meters and smart grids is low. Strategies for communicating the benefits to
consumers should be made so that they are clear about the future prospects of
smart grids (Kaushal, 2011).  Communication and raising
awareness is key to enlisting the participation of consumers to optimize the
impact of smart water meters. The data from smart meters regarding the consumers’
consumption should be presented in a way that is understandable for a layman
and this is another challenge that utilities will have to overcome. Poor
communication can be a deterrent to active participation of the end user (Pericli & Jenkins, 2015).

2.1.4 Current metering practices

Another challenge is the current
configuration of water meters. Many water meters are located underground or in
inaccessible locations. Another possible challenge that could occur is the
limited battery life of smart water meters which can be a negative variable in
the cost-benefit analysis of smart meters. The battery life of a smart water
meter can significantly affect the storage and transmission range of data which
could hamper the data presented to the consumer (Pericli & Jenkins, 2015).

2.1.5 Skill

the implementation of smart meters, there will be a demand for a new skill set
to fulfil the current inadequacies in technology skills. Skills in terms of
analytics, data management and decision support will become instrumental for
every utility to facilitate the transition to smart metering. Capacity building
for managerial roles in the new scenario with support from the private sector
will become crucial(Kaushal, 2011).

2.2 Factors Affecting the Adoption of technology by stakeholders

Social Acceptance of Smart meters

Acceptance of technology has been described in literature as competing
interests between different stakeholders during the development and deployment
of the technology. Social Acceptance is said to comprise of three dimensions
namely; socio-political acceptance, market acceptance and community acceptance.
These dimensions enable policy makers to develop and formulate strategies for
the acceptance of technology by various stakeholder groups and ensure that the
technology supports the values or the interest of each stakeholder group to
facilitate technology adoption (Kizhakenath, 2016).

Figure 4 Social Acceptance of Smart

three dimensions of Social Acceptability are

a) Socio-Political Acceptance – This is
the broadest and most general level of acceptance and it includes acceptability
across various stakeholder groups i.e. the end users, the utility and smart
meter vendors. For institutionalization of smart grid technology it is
important to incorporate the values or interest of different stakeholder groups
to facilitate the adoption of the technology. In relation to socio-political
stakeholders the vales that have been identified are as follows.

– in the case of smart metering technology, the utility has access to the
consumer’s data at any time which is in contrast to the current situation where
the utility has access to the meter readings only in the presence of the
consumer. Privacy has been an issue in the mandatory smart meter roll-out in
the Netherlands which had to make the roll-out voluntary on account of privacy
concerns by consumer organizations (Ligtvoet et al., 2015).

– This refers to the ability of the technology to carry out its functions
without the need for monitoring and controlling(Ligtvoet et al., 2015)

– This refers to the uniformity of technology standards across the country to
facilitate the institutionalization and adoption of smart metering technology.

– The level of trust that the public has towards the policies promoting smart
metering should be evaluated. In India, public support for the Smart Cities
Mission and the trust they place in the mission will determine the adoption of
smart metering technology.

Effectiveness – Cost Effectiveness is a value which is shared by both the
utility, smart vendors and the end user. Therefore the policy should promote
cost-effectiveness across the stakeholder groups. This is particularly
important in developing countries.

Sustainability – One of the major drivers of the mandatory smart meter roll-out
in the Netherlands was environmental sustainability. In the Indian context too,
environmental issues especially with respect to water are on the national
agenda which has been a driver for the policy promoting smart grid technology.

– For households an important value is the degree of control they would be able
to exercise with respect to their consumption. This is particularly relevant to
smart grid technology.

Household Acceptance – According to Sauter and Watson (2007), to achieve
the market up-take of smart metering technology, a positive public and private
attitude is required. As the success of the smart metering technology is based
on the feedback that the end user receives on his consumption and the
subsequent change in behaviour that is required on the part of the consumer to
reduce his consumption, it can be said that the technology requires the active participation
of households rather than the passive consent or acceptance that is relevant
for the conventional water and energy infrastructure (Sauter and Watson, 2007).
Therefore for household acceptance the values for not only passive   consent
but also active participation and involvement become crucial.


2.2.2 Theories of Technology Acceptance

Acceptance Model (TAM)

(Davis, 1989)explored the question of why people accept
some technologies and reject others in his research. Based on his research it
was observed that there are primarily two determinants for predicting
technology acceptance and subsequent usage, they are: 1) Perceived
Usefulness – “the degree to which a person believes that using a
particular system would enhance his or her job performance” 2) Perceived Ease
of Use – “the degree to which a person believes that using a particularsy
stem would be free of effort.”

are two determinants which can be used to make an assessment of technology
acceptance prior to the launch of a new technology or innovation.


Figure 5 Technology Acceptance

Theory of
Reasoned Action (TRA)

 It states that an
individual’s decision to behave in a particular way is influenced by his
attitude and the beliefs of people who are important to him. However, the
theory has limitation with respect to cases where the individual has no
volitional control or the ability to decide whether or not he wants to perform
the behaviour.

Figure 6 Theory of Reasoned Action


The Theory of
Planned Behavior

Theory of
Planned behaviour builds on the Theory of Reasoned Action. It includes beliefs
regarding the possession of the required resources and opportunities for
performing a particular behaviour. It includes a person’s belief that he is
able to perform a behaviour successfully and the level of perceived difficulty
called perceived behavioural control. It is an extension of TRA (Ajzen, 1991).


Figure 7 Theory of Planned

The Unified
Theory of Acceptance and Use of Technology

theory is based on a compilation of eight prominent technology acceptance
theories and models; each consists of a different set of technology acceptance
determinants, and originates from different research area such as: information
systems, sociology, and psychology. The theories comprising the UTAUT model
are: Theory of Reasoned Action- TRA, Technology Acceptance Model- TAM,
Motivational Model- MM, Theory of Planned Behavior- TPB, Combined TAM and TPB
(C-TAM-TPB), Model of PC Utilization- MPCU, Diffusion of Innovation– DOI, and Social
Cognitive Theory – SCT (Venkatesh, Morris, Davis, & Davis, 2003). Here gender, age, experience and
voluntariness of use are known to moderate the effect of performance
expectancy, effort expectancy, social influence and facilitating conditions on
use behavior. Facilitating conditions directly impact use behavior.

Figure 8 The Unified Theory of
Acceptance and use of Technology

Framework for
Smart Meter Acceptance

research model was developed based on the Unified theory of Acceptance and Use
of Technology and adapted for smart meters (Alabdulkarim, Lukszo, & Fens, 2012)

Performance Expectancy – It is referred
to as the degree to which a person believes using a particular technology will
increase his/her job performance.

Effort Expectancy – It is
referred to as the degree of effort that is required to use a particular

Social Influence – It is the
degree to which a person thinks people who are important to him should use a
particular technology.

Trialibility – The degree to
which a technology can be experimented with on a limited basis.

Observability – The degree to
which the performance of a technology is visible to others.

Compatibility – The degree to
which a technology addresses the needs of potential adopters and its alignment
with existing values and past experiences of potential adopters.

Perceived Organization Image – The extent of
awareness that a consumer has to the agency that implements or governs a

Technology Awareness – The extent to
which a consumer knows about smart meters.

Mass media – the extent to which a technology is mentioned
in media outlets.

Effective Feedback – It refers to
the feedback that a consumer would get on his consumption and how
understandable it is to consumers.

Perceived Financial Costs – Consumers
reluctance to adopt smart meters can be attributed to cost related concerns.
Financial costs are said to negatively affect smart meter acceptance.

Perceived Loss of Control – Consumers wish
to remain in control of their consumption habits and seek assurance that they
will remain in control even after implementation of the technology. If the
consumer perceives that the utility has the control to shut off supply to their
residence with the technology then the technology will not be welcome by the

Perceived Health Risks – This is
associated with the adverse health effects perceived by consumers due to the
presence of smart meters in their premises. As radio transmissions used in
smart meters for communication have been related to health risks, consumers’
perception of smart meters as a health hazard may cause reluctance to accept
smart meters in their premises.

Perceived Information Security and Privacy
Risks – This refers to the worries of the consumer regarding unlawful access to
personal information by unauthorized or ilicit third parties for commercial
gain or usage of their personal information as a means of exploitation.
International experiences of smart metering have shown that this is a major
area of concern for consumers and has been the reason for backlash against
smart meters in many countries.

Figure 9 Framework for Smart
Metering Systems by (Alabdulkarim et al., 2012)



2.3 Case Studies of Smart Meter Acceptance

Table 1 Case studies on smart
meter Acceptance






The study aimed to study the perceptions of
households towards usage of smart appliances and the factors affecting
acceptance of technology prior to launch of the innovation in Finland.

a sample of 500 was taken from different types of
households with the help pf computer assisted personal interviews. TAM was
used as a framework with 3 additional components of safety, control and

PeOU and PU had a significant impact on attitude
formation. PeOU had more impact on acceptance. A positive attitude has an
effect on intention to use smart appliances.

The study did not look into the role of cultural
differences on perception.
The role of safety, comfort and control was not
clearly defined.

Hauttekeete, & Marez, 2010)

The study aimed to investigate the evaluation of
smart meters by  potential customers in

An online survey of 351 participants was conducted.
In order to provide participants with basic information on smart meters
visual presentations were used. TAM was used. 

Subjective control was found to be a reliable
predictor of attitude towards use. Attitude was found to mediate the effect
of PU, PeOU and subjective control.

The study did not measure the actual usage of smart
meters and did not account for cultural differences.
Another limitation of the study was that attitude
was a single construct.
As it was online it included participants already
familiar with technology

(Kranz, Gallenkamp,
& Picot, 2010)

The study aimed to investigate the acceptance of
smart meters by households in 3 different countries.

An online survey was conducted using TAM and NAM as
a framework. The questionnaire included a short text on smart meters to
inform participants.

The study concluded that besides the rational
reasons for  adoption like PeOU &
PU, personal norms also played a significant role in determining acceptance

As the study was conducted in the early stages of
introduction, it did not take into account factors like actual usage.
As it was online it included participants already
familiar with technology

(Toft, Schuitema,
& Thøgersen, 2014)

The study aimed to evaluate the impact of attitude
and its antecedents environmental concerns, normative beliefs and perceived
behavioral control affect intention to use SMT.

An online survey was conducted for students. The
research model incorporated environmental concerns, attitude, perceived
behavioral control and normative beliefs. 
Sample of 284 students.

The study found that PU was the strongest indicator
of attitude. PeOU, energy price consciousness 
were also found to impact attitude. Normative beliefs were identified
as an important adoption driver.

The survey included online survey of students which
indicates a selection bias.
Focused more on intention to use than actual usage.

(Kranz & Picot,

To study the perception of potential consumers on
smart meters.

The interview procedure followed a two stage mental
model of open ended telephonic interviews followed by structured survey.

Most of the participants had a positive view of
smart meters but had misconceptions relating to their functions. 20 of the 22
participants confused smart meters with in-home displays. Unrealistic
expectations from smart meters can lead to consumer disappointment.


(Krishnamurti et al.,

Ajzen, I. (1991). The theory of planned behavior. Orgnizational
Behavior and Human Decision Processes, 50, 179–211.

Alabdulkarim, A., Lukszo, Z., & Fens, T. W. (2012).
Acceptance of Privacy-Sensitive Infrastructure Systems: A Case of Smart
Metering in The Netherlands, (June), 399–404.

Blom, A., Cox, P., & Raczka, K. (2010). Developing a
Policy Position on Smart Water Metering, (1).

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of
Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3),
319. https://doi.org/10.2307/249008

Dromacque, C., Xu, S., & Baynes, S. (2013). Case study on
innovative smart billing for household consumers.

Guo, C., Bond, C., & Narayanan, A. (2015). The Adoption
of New Smart-Grid Technologies.

Gupta, A., Mishra, S., Bokde, N., & Kulat, K. (2016).
Need of Smart Water Systems In India, 11(4), 2216–2223.

Kaushal, R. (2011). Challenges of implementing smart grids in
India, 1–10.

Kizhakenath, A. (2016). Social acceptance of smart meters, Master

10.  Kranz, J.,
Gallenkamp, J., & Picot, A. (2010). Power Control To the People?? Private
Consumers ‘ Acceptance of Smart Meters. European Conference on Information
Systems, (October), 96.

11.  Kranz, J.,
& Picot, A. (2012). Is it money or the environment? An empirical analysis
of factors influencing consumers’ intention to adopt the smart metering
technology. 18th Americas Conference on Information Systems 2012, AMCIS 2012,
1, 669–676.

12.  Krishnamurti,
T., Schwartz, D., Davis, A., Fischhoff, B., de Bruin, W. B., Lave, L., &
Wang, J. (2012). Preparing for smart grid technologies: A behavioral decision
research approach to understanding consumer expectations about smart meters. Energy
Policy, 41, 790–797. https://doi.org/10.1016/j.enpol.2011.11.047

13.  Ligtvoet,
A., Van De Kaa, G., Fens, T., Van Beers, C., Herder, P., & Van Den Hoven,
J. (2015). Policy Practice and Digital Science. Policy Practice and
Digital Science: Integrating Complex Systems, Social Simulation and Public
Administration in Policy Research.

14.  Martyusheva,
O. (2014). Smart Water Grid, 1–80.

15.  Oracle.
(2010). Testing the water: Smart Metering for Water Utilities, (January), 1–44.

16.  Pericli,
A., & Jenkins, J. O. (2015). Smart Meters and Domestic Water Usage FR /
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17.  Public
Utilities Board Singapore. (2016). Managing the water distribution network
with a Smart Water Grid (Vol. 1). https://doi.org/10.1186/s40713-016-0004-4

18.  Sastry, G.
S. (2006). Working paper 176.

19.  Stragier,
J., Hauttekeete, L., & Marez, L. De. (2010). Introducing smart grids in
residential contexts?: Consumers â€TM Perception of Smart Household
Appliances Introducing Smart Grids in Residential Contexts?: Consumers â€TM
Perception of Smart Household Appliances, (October 2015), 1–8.

20.  Sumpena,
A. (2016). What is Smart City?

21.  Toft, M.
B., Schuitema, G., & Thøgersen, J. (2014). Responsible technology
acceptance?: Model development and application to consumer acceptance of Smart
Grid technology, 134, 392–400.

22.  Venkatesh,
V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of
Information Technology: Toward a Unified View. Source: MIS Quarterly, 27(3),
425–478. https://doi.org/10.2307/30036540

23.  Yesudas,
R., & Clarke, R. (2015). Measures to Improve Public Acceptance of Smart
Metering System, (August).

24.  https://www.thebalance.com/pros-and-cons-of-smart-meters-1182648

25.  //economictimes.indiatimes.com/articleshow/60099818.cms?utm_source=contentofinterest=text=cppst

26.  https://seekingalpha.com/article/4059641-much-competition-smart-meter-industry