End user perceptions toward smart grid technology: Acceptance, adoption, risks, and trust

Although smart grid technology has been extensively accepted, social factors influence the performance of smart grid systems. This smart technology will enable the automated monitoring and control of the power delivery system, increase the capacity of the power delivery system, and enhance the performance and connectivity of end users. However, the perceptions of end users are a key factor for adoption of this technology. When end users do not fully accept the smart grid, the operation of the smart grid is not satisfactory. Most literature has concentrated on the technological aspects of smart grids; a technological solution may be defined as one that requires a change only in the developed technology, demanding little or no change in human values or ideas of morality about the usage of electrical energy. However, the solutions to the problems of implementing smart grid technology are not to be found only in technological aspects. This paper presents experimental scenarios that use signal detection theory (SDT), a well-known tool in psychology research, to capture the perceptions of end users about smart grid technology. If the perceptions of end users are positive, the performance of the smart grid is improved. End user criteria can be analyzed using SDT. In addition, fuzzy logic type 2 is suggested as a way to increase the descriptive power of fuzzy signal detection theory. To obtain end user perceptions, several experimental scenarios were created using a didactic smart grid system designed by Delorenzo Group Italy. End users were faced with real situations that enabled determination of their perceptions about the smart grid technology. Experimental results of end users׳ perceptions of smart grid technology are shown using SDT, fuzzy detection theory, and fuzzy detection theory type 2. The results show that end users have a conservative criterion because they are not entirely confident in the intelligent technology provided by the smart grid; this conservative criterion limits smart grid performance.

[1]  P. Karthik,et al.  Development of renewable energy based microgrid project implementations for residential consumers in India: Scope, challenges and possibilities , 2015 .

[2]  Martin Anda,et al.  Smart metering for residential energy efficiency: The use of community based social marketing for behavioural change and smart grid introduction , 2014 .

[3]  Stephanie M. Stern.  Smart-Grid: Technology and the Psychology of Environmental Behavior Change , 2010 .

[4]  Jess Marcum,et al.  A statistical theory of target detection by pulsed radar , 1948, IRE Trans. Inf. Theory.

[5]  B. Sovacool.  What Are We Doing Here? Analyzing Fifteen Years of Energy Scholarship and Proposing a Social Science Research Agenda , 2014 .

[6]  Raja Parasuraman,et al.  Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations , 2003, Ergonomics.

[7]  Johan Vinterbäck,et al.  Consumer behavior in renewable electricity: Can branding in accordance with identity signaling increase demand for renewable electricity and strengthen supplier brands? , 2015 .

[8]  M. Slattery,et al.  Public attitudes of wind energy in Texas: Local communities in close proximity to wind farms and their effect on decision-making , 2010 .

[9]  Charles R. Warren,et al.  Does community ownership affect public attitudes to wind energy? A case study from south-west Scotland , 2010 .

[10]  G. Assefa,et al.  Social sustainability and social acceptance in technology assessment: A case study of energy technologies , 2007 .

[11]  M. Wolsink.  The research agenda on social acceptance of distributed generation in smart grids: Renewable as common pool resources , 2012 .

[12]  John Thøgersen,et al.  The importance of framing for consumer acceptance of the Smart Grid: A comparative study of Denmark, Norway and Switzerland , 2014 .

[13]  W. W. Peterson,et al.  The theory of signal detectability , 1954, Trans. IRE Prof. Group Inf. Theory.

[14]  Sarah C. Darby,et al.  Smart metering: what potential for householder engagement? , 2010 .

[15]  C. Clastres.  Smart grids: Another step towards competition, energy security and climate change objectives , 2011 .

[16]  Raja Parasuraman,et al.  Fuzzy Signal Detection Theory: Basic Postulates and Formulas for Analyzing Human and Machine Performance , 2000, Hum. Factors.

[17]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[18]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[19]  Woei Wan Tan,et al.  Towards an efficient type-reduction method for interval type-2 fuzzy logic systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[20]  J. G. Snodgrass,et al.  Pragmatics of measuring recognition memory: applications to dementia and amnesia. , 1988, Journal of experimental psychology. General.

[21]  Lalit Mohan Saini,et al.  Performance analysis of smart metering for smart grid: An overview , 2015 .

[22]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[23]  Nadeem Javaid,et al.  A review of wireless communications for smart grid , 2015 .

[24]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[25]  Chan-Kook Park,et al.  A study of factors enhancing smart grid consumer engagement , 2014 .

[26]  N. Macmillan,et al.  Response bias : characteristics of detection theory, threshold theory, and nonparametric indexes , 1990 .

[27]  Garrett Hardin,et al.  Extensions of "The Tragedy of the Commons" , 1998, Science.

[28]  L. Steg,et al.  Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework , 2012 .

[29]  Ijeoma Onyeji,et al.  Consumer engagement: An insight from smart grid projects in Europe , 2013 .

[30]  H. Son,et al.  Cross-country review of smart grid adoption in residential buildings , 2015 .

[31]  Tom Rodden,et al.  Smart grids, smart users? The role of the user in demand side management , 2014 .

[32]  María M. Moreno-Fernández,et al.  ROC analysis in olive oil tasting: A Signal Detection Theory approach to tasting tasks , 2010 .

[33]  J. Dada.  Towards understanding the benefits and challenges of Smart/Micro-Grid for electricity supply system in Nigeria , 2014 .

[34]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[35]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[36]  Kaamran Raahemifar,et al.  A survey on Advanced Metering Infrastructure , 2014 .

[37]  Rolf Wüstenhagen,et al.  Social acceptance of renewable energy innovation: An introduction to the concept , 2007 .