1 Challenges of implementing Smart Grids inIndia 2.1.1 Policy and Regulation Thecurrent policies and regulatory frameworks are meant to address the currentscenario. However, with the introduction of smart meters, the current policiesand framework must evolve to encourage and develop incentives for investment insmart grid technology. These frameworks will have to include the interests ofthe consumer, the utility and other key stakeholders to ensure that theconsumers benefit from the smart grid technology at the least cost. Futurepolicy will have to look to encourage private investment by allowing risksharing among consumers and shareholders so that the risks and rewards arebalanced with least aggregate cost to the consumer (Kaushal, 2011).

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In England and Wales itwas found that political support for smart water metering was lacking due tolimited legislation and guidance (Pericli & Jenkins, 2015).2.1.2 Technology standards The integration of the hardware system andthe management of high volumes of data will be the challenge. As the smart gridimplementation will involve multiple software providers, there will bedifferent data formats and complex data models (Kaushal, 2011).

Technology standards refer to technical specifications whichdetermine how a technology operates or interfaces with other technologies. Evenif these standards are available, the lack of enforcement of these standardscan 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 oncompliance instead of comprehensive security, the lack of sufficient inbuiltsecurity features, the lack of a proper forum for disseminating knowledge, andthe lack of metrics for performance evaluation (Wilshusen and Trimble, 2012).

2.1.3 Lack of Awareness Even though smart metering is on the nationalagenda with the India Smart Grid Forum being constituted to conduct research inthe area and spread awareness, the level of awareness of citizens to smartmeters and smart grids is low. Strategies for communicating the benefits toconsumers should be made so that they are clear about the future prospects ofsmart grids (Kaushal, 2011).  Communication and raisingawareness is key to enlisting the participation of consumers to optimize theimpact of smart water meters.

The data from smart meters regarding the consumers’consumption should be presented in a way that is understandable for a laymanand this is another challenge that utilities will have to overcome. Poorcommunication can be a deterrent to active participation of the end user (Pericli & Jenkins, 2015).2.1.4 Current metering practicesAnother challenge is the currentconfiguration of water meters. Many water meters are located underground or ininaccessible locations. Another possible challenge that could occur is thelimited battery life of smart water meters which can be a negative variable inthe cost-benefit analysis of smart meters. The battery life of a smart watermeter can significantly affect the storage and transmission range of data whichcould hamper the data presented to the consumer (Pericli & Jenkins, 2015).

2.1.5 SkillDevelopment Withthe implementation of smart meters, there will be a demand for a new skill setto fulfil the current inadequacies in technology skills. Skills in terms ofanalytics, data management and decision support will become instrumental forevery utility to facilitate the transition to smart metering. Capacity buildingfor managerial roles in the new scenario with support from the private sectorwill become crucial(Kaushal, 2011).

2.2 Factors Affecting the Adoption of technology by stakeholders2.2.1Social Acceptance of Smart metersSocialAcceptance of technology has been described in literature as competinginterests between different stakeholders during the development and deploymentof the technology. Social Acceptance is said to comprise of three dimensionsnamely; socio-political acceptance, market acceptance and community acceptance.

These dimensions enable policy makers to develop and formulate strategies forthe acceptance of technology by various stakeholder groups and ensure that thetechnology supports the values or the interest of each stakeholder group tofacilitate technology adoption (Kizhakenath, 2016). Figure 4 Social Acceptance of SmartMetersThethree dimensions of Social Acceptability area) Socio-Political Acceptance – This isthe broadest and most general level of acceptance and it includes acceptabilityacross various stakeholder groups i.e. the end users, the utility and smartmeter vendors. For institutionalization of smart grid technology it isimportant to incorporate the values or interest of different stakeholder groupsto facilitate the adoption of the technology. In relation to socio-politicalstakeholders the vales that have been identified are as follows.Privacy– in the case of smart metering technology, the utility has access to theconsumer’s data at any time which is in contrast to the current situation wherethe utility has access to the meter readings only in the presence of theconsumer.

Privacy has been an issue in the mandatory smart meter roll-out inthe Netherlands which had to make the roll-out voluntary on account of privacyconcerns by consumer organizations (Ligtvoet et al., 2015). Reliability– This refers to the ability of the technology to carry out its functionswithout the need for monitoring and controlling(Ligtvoet et al., 2015)Compatibility– This refers to the uniformity of technology standards across the country tofacilitate the institutionalization and adoption of smart metering technology.Trust– The level of trust that the public has towards the policies promoting smartmetering should be evaluated. In India, public support for the Smart CitiesMission and the trust they place in the mission will determine the adoption ofsmart metering technology.CostEffectiveness – Cost Effectiveness is a value which is shared by both theutility, smart vendors and the end user.

Therefore the policy should promotecost-effectiveness across the stakeholder groups. This is particularlyimportant in developing countries.EnvironmentalSustainability – One of the major drivers of the mandatory smart meter roll-outin the Netherlands was environmental sustainability. In the Indian context too,environmental issues especially with respect to water are on the nationalagenda which has been a driver for the policy promoting smart grid technology.Control– For households an important value is the degree of control they would be ableto exercise with respect to their consumption.

This is particularly relevant tosmart grid technology.b)     Household Acceptance – According to Sauter and Watson (2007), to achievethe market up-take of smart metering technology, a positive public and privateattitude is required. As the success of the smart metering technology is basedon the feedback that the end user receives on his consumption and thesubsequent change in behaviour that is required on the part of the consumer toreduce his consumption, it can be said that the technology requires the active participationof households rather than the passive consent or acceptance that is relevantfor the conventional water and energy infrastructure (Sauter and Watson, 2007).Therefore for household acceptance the values for not only passive   consentbut also active participation and involvement become crucial. 2.2.2 Theories of Technology Acceptancea)     TechnologyAcceptance Model (TAM)(Davis, 1989)explored the question of why people acceptsome technologies and reject others in his research.

Based on his research itwas observed that there are primarily two determinants for predictingtechnology acceptance and subsequent usage, they are: 1) PerceivedUsefulness – “the degree to which a person believes that using aparticular system would enhance his or her job performance” 2) Perceived Easeof Use – “the degree to which a person believes that using a particularsystem would be free of effort.”Theseare two determinants which can be used to make an assessment of technologyacceptance prior to the launch of a new technology or innovation.  Figure 5 Technology AcceptanceModelb)    Theory ofReasoned Action (TRA) It states that anindividual’s decision to behave in a particular way is influenced by hisattitude and the beliefs of people who are important to him. However, thetheory has limitation with respect to cases where the individual has novolitional control or the ability to decide whether or not he wants to performthe behaviour. Figure 6 Theory of Reasoned Action c)     The Theory ofPlanned Behavior Theory ofPlanned behaviour builds on the Theory of Reasoned Action. It includes beliefsregarding the possession of the required resources and opportunities forperforming a particular behaviour. It includes a person’s belief that he isable to perform a behaviour successfully and the level of perceived difficultycalled perceived behavioural control.

It is an extension of TRA (Ajzen, 1991).  Figure 7 Theory of Plannedbehaviourd)    The UnifiedTheory of Acceptance and Use of Technology Thetheory is based on a compilation of eight prominent technology acceptancetheories and models; each consists of a different set of technology acceptancedeterminants, and originates from different research area such as: informationsystems, sociology, and psychology. The theories comprising the UTAUT modelare: 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 SocialCognitive Theory – SCT (Venkatesh, Morris, Davis, & Davis, 2003).

Here gender, age, experience andvoluntariness of use are known to moderate the effect of performanceexpectancy, effort expectancy, social influence and facilitating conditions onuse behavior. Facilitating conditions directly impact use behavior. Figure 8 The Unified Theory ofAcceptance and use of Technologye)     Framework forSmart Meter AcceptanceThisresearch model was developed based on the Unified theory of Acceptance and Useof Technology and adapted for smart meters (Alabdulkarim, Lukszo, & Fens, 2012)Performance Expectancy – It is referredto as the degree to which a person believes using a particular technology willincrease his/her job performance.Effort Expectancy – It isreferred to as the degree of effort that is required to use a particulartechnologySocial Influence – It is thedegree to which a person thinks people who are important to him should use aparticular technology.Trialibility – The degree towhich a technology can be experimented with on a limited basis.

Observability – The degree towhich the performance of a technology is visible to others.Compatibility – The degree towhich a technology addresses the needs of potential adopters and its alignmentwith existing values and past experiences of potential adopters.Perceived Organization Image – The extent ofawareness that a consumer has to the agency that implements or governs atechnology.Technology Awareness – The extent towhich a consumer knows about smart meters.

Mass media – the extent to which a technology is mentionedin media outlets.Effective Feedback – It refers tothe feedback that a consumer would get on his consumption and howunderstandable it is to consumers.Perceived Financial Costs – Consumersreluctance 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 wishto remain in control of their consumption habits and seek assurance that theywill remain in control even after implementation of the technology. If theconsumer perceives that the utility has the control to shut off supply to theirresidence with the technology then the technology will not be welcome by theconsumer. Perceived Health Risks – This isassociated with the adverse health effects perceived by consumers due to thepresence of smart meters in their premises. As radio transmissions used insmart meters for communication have been related to health risks, consumers’perception of smart meters as a health hazard may cause reluctance to acceptsmart meters in their premises.

Perceived Information Security and PrivacyRisks – This refers to the worries of the consumer regarding unlawful access topersonal information by unauthorized or ilicit third parties for commercialgain or usage of their personal information as a means of exploitation.International experiences of smart metering have shown that this is a majorarea of concern for consumers and has been the reason for backlash againstsmart meters in many countries. Figure 9 Framework for SmartMetering Systems by (Alabdulkarim et al., 2012)  2.3 Case Studies of Smart Meter AcceptanceTable 1 Case studies on smartmeter Acceptance Summary Methodology Conclusion Limitations Reference 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 comfort 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. (Stragier, Hauttekeete, & Marez, 2010) The study aimed to investigate the evaluation of smart meters by  potential customers in Germany 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, 2012) 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., 2012)


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