TY - JOUR
T1 - Proceeding with caution
T2 - Drivers and obstacles to electric utility adoption of smart grids in the United States
AU - Zheng, You
AU - Stanton, Jeffrey
AU - Ramnarine-Rieks, Angela
AU - Dedrick, Jason
N1 - Funding Information:
This work was supported by a grant from the U.S. National Science Foundation ( SES-1231192 ).
Funding Information:
Finally, good relationships between utilities and regulators appear to facilitate adoption. While regulators must not be captured by the utilities they regulate, they can do more to understand emerging technologies and work with utilities and other institutions to develop strategies for smart grid adoption that benefit utilities, customers, and other stakeholders. For instance, the National Association of Regulatory Utility Commissioners (NARUC) has a Center for Partnerships & Innovation, funded by the U.S. Department of Energy and NIST, that conducts and disseminates research on emerging challenges and innovations in conjunction with experts from academia, industry and national labs. Results are offered to utilities through reports, workshops and training resources, and state utility commissions are connected with experts on a variety of topics [73] . The Electric Power Research Institute (EPRI) serves a similar research and knowledge dissemination function for utility companies. EPRI has advisory council members appointed by NARUC, which shows some interaction between utilities and regulators at this level [74] . At the state level, California has used over $560 million in funds to develop and deploy new grid technologies in a program overseen by the CPUC and administered by the California Energy Commission and the three large IOUs in the state [75] . Such programs can accelerate commercialization and adoption of valuable innovations.
Funding Information:
In the U.S., smart grid deployment has been supported by federal funding. Early deployment was supported by the Energy Independence and Security Act of 2007, which required the Department of Energy (DoE) to establish a federal Smart Grid Task Force and provided a small amount of funding for smart grid technology R&D and demonstration projects [15] . Between 2009 and 2012 about $4.5 billion in funds became available under the American Recovery and Reinvestment Act (ARRA) for smart grid demonstration and technology deployment projects [5,16] . ARRA funding has been credited with a significant increase in R&D and deployment of smart grid technologies [6] . However, after reaching a peak of $5.1 billion in distribution-level smart grid spending in 2011, U.S. utility investment fell to just $2.8 billion in 2014, before rebounding to $3.4 billion in 2015 and 2016, suggesting that ARRA may have accelerated previously planned investments rather than augmenting overall expenditures [12] .
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - Smart grid adoption by U.S. electric utilities promises improved reliability and resilience for an aging electric grid, while enabling integration of renewable energy sources and decarbonization of the electricity sector. Factors influencing adoption are unclear, however. Starting with the technology, organizational and environmental (TOE) model, we examined factors influencing smart grid adoption using a mixed methods approach that included interviews and a survey of utility representatives. We found that utilities were primarily motivated by internal goals such as cost reduction and improved operational performance, and less by external factors such as customer demand or pressure to accommodate renewables. They were inhibited by the perception that technologies are still immature and by a lack of funds to invest. Larger utilities, IOUs, and more technologically opportunistic firms had higher levels of adoption. Structural topic modeling of interviews identified findings not captured in the TOE framework, such as the importance of relationships between utilities and regulators, and the overlapping roles of cooperative member/customers. These interactions are better explained within the institutional logics perspective, which can enrich understanding of smart grid adoption beyond TOE. Policy recommendations are offered to facilitate adoption through regulatory changes, financial support to utilities, and support of standardization.
AB - Smart grid adoption by U.S. electric utilities promises improved reliability and resilience for an aging electric grid, while enabling integration of renewable energy sources and decarbonization of the electricity sector. Factors influencing adoption are unclear, however. Starting with the technology, organizational and environmental (TOE) model, we examined factors influencing smart grid adoption using a mixed methods approach that included interviews and a survey of utility representatives. We found that utilities were primarily motivated by internal goals such as cost reduction and improved operational performance, and less by external factors such as customer demand or pressure to accommodate renewables. They were inhibited by the perception that technologies are still immature and by a lack of funds to invest. Larger utilities, IOUs, and more technologically opportunistic firms had higher levels of adoption. Structural topic modeling of interviews identified findings not captured in the TOE framework, such as the importance of relationships between utilities and regulators, and the overlapping roles of cooperative member/customers. These interactions are better explained within the institutional logics perspective, which can enrich understanding of smart grid adoption beyond TOE. Policy recommendations are offered to facilitate adoption through regulatory changes, financial support to utilities, and support of standardization.
KW - Innovation adoption
KW - Institutional logics
KW - Mixed methods
KW - Regulatory environment
KW - Structural topic modeling
KW - TOE framework
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U2 - 10.1016/j.erss.2022.102839
DO - 10.1016/j.erss.2022.102839
M3 - Article
AN - SCOPUS:85140236709
SN - 2214-6296
VL - 93
JO - Energy Research and Social Science
JF - Energy Research and Social Science
M1 - 102839
ER -