Moral Values as Factors for Social Acceptance of Smart Grid Technologies

Smart grid technologies are considered an important enabler in the transition to more sustainable energy systems because they support the integration of rising shares of volatile renewable energy sources into electricity networks. To implement them in a large scale, broad acceptance in societies is crucial. However, a growing body of research has revealed societal concerns with these technologies. To achieve sustainable energy systems, such concerns should be taken into account in the development of smart grid technologies. In this paper, we show that many concerns are related to moral values such as privacy, justice, or trust. We explore the effect of moral values on the acceptance of smart grid technologies. The results of our systematic literature review indicate that moral values can be both driving forces and barriers for smart grid acceptance. We propose that future research striving to understand the role of moral values as factors for social acceptance can benefit from an interdisciplinary approach bridging literature in ethics of technology with technology acceptance models.

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Towards sustainable energy systems: integrating renewable energy sources is the key for rural area power supply

2005 International Power Engineering Conference ◽

10.1109/ipec.2005.206887 ◽

2005 ◽

Cited By ~ 5

Author(s):

P.K. Katti ◽

M.K. Khedkar

Keyword(s):

Renewable Energy ◽

Rural Area ◽

Power Supply ◽

Sustainable Energy ◽

Renewable Energy Sources ◽

Energy Systems ◽

Energy Sources ◽

Sustainable Energy Systems

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Future energy services: empowering local communities and citizens

foresight ◽

10.1108/fs-08-2013-0035 ◽

2015 ◽

Vol 17 (4) ◽

pp. 349-364 ◽

Cited By ~ 11

Author(s):

Kirsi Hyytinen ◽

Marja Toivonen

Keyword(s):

Smart Grids ◽

Management Practices ◽

Technological Development ◽

Innovation Management ◽

Sustainable Energy ◽

Renewable Energy Sources ◽

Energy Systems ◽

Content Type ◽

Hybrid Solutions ◽

Sustainable Energy Systems

Purpose – The purpose of this paper is to examine the future prospects of innovative services linked to sustainable energy systems. Design/methodology/approach – Service perspective is examined in the context of socio-technical transition and linked to the bottom-up and top-down social processes that foster sustainability. The foresight method applied is trend analysis. Findings – Two groups of trends were identified: the trends driven by technological development and the trends focussing on societal, managerial and consumer issues. The former consists of renewable energy sources, hybrid solutions, smart grids and smart energy markets. The latter involves distributed energy production, demand response, optimisation of sustainability and the role of energy as an opportunity and as service. The study reveals that energy is increasingly understood as a comprehensive and tailor-made service solution for communities and individual households. Consumers will enter the energy market as active participants; it raises the need for many types of services. Research limitations/implications – Deepening of understanding is required in several topics of this study, and more formal methods of foresight are needed to test the generalisability of its qualitative results. Practical implications – More effective policy measures are needed for fostering new services and social and system innovations in the area of sustainable energy. Innovation management practices should be developed in these areas. Originality/value – The paper aims to narrow the research gap linked to foresight in services by examining services in the area of sustainable energy systems – one of the “grand challenges” today.

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Machine Learning for Sustainable Energy Systems

Annual Review of Environment and Resources ◽

10.1146/annurev-environ-020220-061831 ◽

2021 ◽

Vol 46 (1) ◽

Author(s):

Priya L. Donti ◽

J. Zico Kolter

Keyword(s):

Machine Learning ◽

Sustainable Energy ◽

Energy Systems ◽

Future Research ◽

Annual Review ◽

Publication Date ◽

Research Directions ◽

Sustainable Energy Systems ◽

Future Research Directions ◽

And Storage

In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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